Terrain Aided Navigation(TAN)technology has become increasingly important due to its effectiveness in environments where Global Positioning System(GPS)is unavailable.In recent years,TAN systems have been extensively r...Terrain Aided Navigation(TAN)technology has become increasingly important due to its effectiveness in environments where Global Positioning System(GPS)is unavailable.In recent years,TAN systems have been extensively researched for both aerial and underwater navigation applications.However,many TAN systems that rely on recursive Unmanned Aerial Vehicle(UAV)position estimation methods,such as Extended Kalman Filters(EKF),often face challenges with divergence and instability,particularly in highly non-linear systems.To address these issues,this paper proposes and investigates a hybrid two-stage TAN positioning system for UAVs that utilizes Particle Filter.To enhance the system’s robustness against uncertainties caused by noise and to estimate additional system states,a Fuzzy Particle Filter(FPF)is employed in the first stage.This approach introduces a novel terrain composite feature that enables a fuzzy expert system to analyze terrain non-linearities and dynamically adjust the number of particles in real-time.This design allows the UAV to be efficiently localized in GPS-denied environments while also reducing the computational complexity of the particle filter in real-time applications.In the second stage,an Error State Kalman Filter(ESKF)is implemented to estimate the UAV’s altitude.The ESKF is chosen over the conventional EKF method because it is more suitable for non-linear systems.Simulation results demonstrate that the proposed fuzzy-based terrain composite method achieves high positional accuracy while reducing computational time and memory usage.展开更多
An altemative algorithm for mitigating GPS multipath was presented by integrating unscented Kalman filter （UKF） and wavelet transform with particle filter. Within consideration of particle degeneracy, UKF was taken ...An altemative algorithm for mitigating GPS multipath was presented by integrating unscented Kalman filter （UKF） and wavelet transform with particle filter. Within consideration of particle degeneracy, UKF was taken for drawing particle. To remove the noise from raw data and data processing error, adaptive wavelet filtering with threshold was adopted while data preprocessing and drawing particle. Three algorithms, named EKF-PF, UKF-PF and WM-UKF-PF, were performed for comparison. The proposed WM-UKF-PF algorithm gives better error minimization, and significantly improves performance of multipath mitigation in terms of SNR and coefficient even though it has computation complexity. It is of significance for high-accuracy positioning and non-stationary deformation analysis.展开更多
In this paper, an adaptive estimation algorithm is proposed for non-linear dynamic systems with unknown static parameters based on combination of particle filtering and Simultaneous Perturbation Stochastic Approxi- ma...In this paper, an adaptive estimation algorithm is proposed for non-linear dynamic systems with unknown static parameters based on combination of particle filtering and Simultaneous Perturbation Stochastic Approxi- mation （SPSA） technique. The estimations of parameters are obtained by maximum-likelihood estimation and sampling within particle filtering framework, and the SPSA is used for stochastic optimization and to approximate the gradient of the cost function. The proposed algorithm achieves combined estimation of dynamic state and static parameters of nonlinear systems. Simulation result demonstrates the feasibilitv and efficiency of the proposed algorithm展开更多
This paper introduces throughput-efficient wireless system based on an extension to binary phasemodulations,named extended binary phase shift keying(EBPSK),and the corresponding analysis ofpower spectra,especially the...This paper introduces throughput-efficient wireless system based on an extension to binary phasemodulations,named extended binary phase shift keying(EBPSK),and the corresponding analysis ofpower spectra,especially the extension to channel capacity are given.Importantly,a novel sequential es-timation and detection approach for this EBPSK system is proposed.The basic idea is to design a proba-bilistic approximation method for the computation of the maximum a posterior distribution via particle fil-tering method(PF).Subsequently,a new important function in PF is presented,so that the performanceof the detector has a great improvement.Finally,computer simulation illustrates that EBPSK system hasvery high transmission rate,and also the good performance of the proposed PF detector is demonstrated.展开更多
A novel statistical method based on particle filtering is presented for multiple vehicle acoustic signals separation problem in wireless sensor network. The particle filtering method is able to deal with non-Gaussian ...A novel statistical method based on particle filtering is presented for multiple vehicle acoustic signals separation problem in wireless sensor network. The particle filtering method is able to deal with non-Gaussian and nonlinear models and non-stationary sources. Using some instantaneously mixed observations of several real-world vehicle acoustic signals, the proposed statistical method is compared with a conventional non-stationary Blind Source Separation algorithm and attractive simulation results are achieved. Moreover, considering the natural convenience to transmit particles between sensor nodes, the algorithm based on particle filtering is believed to have potential to enable the task of multiple vehicles recognition collaboratively performed by sensor nodes in distributed wireless sensor network.展开更多
This paper improves the resampling step of particle filtering（PF） based on a broad interactive genetic algorithm to resolve particle degeneration and particle shortage.For target tracking in image processing,this pa...This paper improves the resampling step of particle filtering（PF） based on a broad interactive genetic algorithm to resolve particle degeneration and particle shortage.For target tracking in image processing,this paper uses the information coming from the particles of the previous fame image and new observation data to self-adaptively determine the selecting range of particles in current fame image.The improved selecting operator with jam gene is used to ensure the diversity of particles in mathematics,and the absolute arithmetical crossing operator whose feasible solution space being close about crossing operation,and non-uniform mutation operator is used to capture all kinds of mutation in this paper.The result of simulating experiment shows that the algorithm of this paper has better iterative estimating capability than extended Kalman filtering（EKF）,PF,regularized partide filtering（RPF）,and genetic algorithm（GA）-PF.展开更多
In light of degradation of particle filtering and robust weakness in the utilization of single feature tracking,this paper presents a kernel particle filtering tracking method based on multi-feature integration.In thi...In light of degradation of particle filtering and robust weakness in the utilization of single feature tracking,this paper presents a kernel particle filtering tracking method based on multi-feature integration.In this paper,a new weight upgrading method is given out during kernel particle filtering at first,and then robust tracking is realized by integrating color and texture features under the framework of kernel particle filtering.Space histogram and integral histogram is adopted to calculate color and texture features respectively.These two calculation methods effectively overcome their own defectiveness,and meanwhile,improve the real timing for particle filtering.This algorithm has also improved sampling effectiveness,resolved redundant calculation for particle filtering and degradation of particles.Finally,the experiment for target tracking is realized by using the method under complicated background and shelter.Experiment results show that the method can reliably and accurately track target and deal with target sheltering situation properly.展开更多
A particle filtering based AutoRegressive (AR) channel prediction model is presented for cognitive radio systems. Firstly, this paper introduces the particle filtering and the system model. Secondly, the AR model of o...A particle filtering based AutoRegressive (AR) channel prediction model is presented for cognitive radio systems. Firstly, this paper introduces the particle filtering and the system model. Secondly, the AR model of order p is used to approximate the flat Rayleigh fading channels; its stability is discussed, and an algorithm for solving the AR model parameters is also given. Finally, an AR channel prediction model based on particle filtering and second-order AR model is presented. Simulation results show that the performance of the proposed AR channel prediction model based on particle filtering is better than that of Kalman filtering.展开更多
Traditional chaotic pulse position modulation(CPPM)system has many drawbacks.It introduces delay into the feedback loop,which will lead to divergence of chaotic map easily.The wrong decision of data will cause error p...Traditional chaotic pulse position modulation(CPPM)system has many drawbacks.It introduces delay into the feedback loop,which will lead to divergence of chaotic map easily.The wrong decision of data will cause error propagation.Mismatch of parameters and synchronization error between the receiver and transmitter will arouse high bit error rate.To solve these problems,a demodulation algorithm of CPPM based on particle filtering is proposed.According to the mathematical model of the system,it tracks the real signal by online separation in demodulation.Simulation results show that the proposed method can track the true signal better than the traditional CPPM scheme.What's more,it has good synchronization robustness,reduced error propagation by wrong decision and low bit error rate.展开更多
Two variants of systematic resampling （S-RS） are proposed to increase the diversity of particles and thereby improve the performance of particle filtering when it is utilized for detection in Bell Laboratories Layer...Two variants of systematic resampling （S-RS） are proposed to increase the diversity of particles and thereby improve the performance of particle filtering when it is utilized for detection in Bell Laboratories Layered Space-Time （BLAST） systems. In the first variant, Markov chain Monte Carlo transition is integrated in the S-RS procedure to increase the diversity of particles with large importance weights. In the second one, all particles are first partitioned into two sets according to their importance weights, and then a double S-RS is introduced to increase the diversity of particles with small importance weights. Simulation results show that both variants can improve the bit error performance efficiently compared with the standard S-P^S with little increased complexity.展开更多
An adaptive object tracking algorithm based on particle filtering and a modified Gradient Vector Flow (GVF) Snake is proposed for tracking moving and deforming objects. The original contours of objects are obtained by...An adaptive object tracking algorithm based on particle filtering and a modified Gradient Vector Flow (GVF) Snake is proposed for tracking moving and deforming objects. The original contours of objects are obtained by using the background differencing method,and the true contours of objects can be converged by means of the powerful searching ability of a modified GVF-Snake. Finally,an Energetic Particle Filtering (EPF) algorithm is obtained by combining particle filtering and a modified GVF-Snake,and by using K-means and the EPF algorithm,multiple objects can be tracked. The proposed tracking tactic for partially occluded objects can effectively improve its anti-occlusion ability. Experiments show that this algorithm can obtain better tracking effect even though the tracked object is occluded.展开更多
To solve the problem of information fusion in the strapdown inertial navigation system(SINS)/celestial navigation system(CNS)/global positioning system(GPS) integrated navigation system described by the nonlinear/non-...To solve the problem of information fusion in the strapdown inertial navigation system(SINS)/celestial navigation system(CNS)/global positioning system(GPS) integrated navigation system described by the nonlinear/non-Gaussian error models,a new algorithm called the federated unscented particle filtering(FUPF) algorithm was introduced.In this algorithm,the unscented particle filter(UPF) served as the local filter,the federated filter was used to fuse outputs of all local filters,and the global filter result was obtained.Because the algorithm was not confined to the assumption of Gaussian noise,it was of great significance to integrated navigation systems described by the non-Gaussian noise.The proposed algorithm was tested in a vehicle's maneuvering trajectory,which included six flight phases:climbing,level flight,left turning,level flight,right turning and level flight.Simulation results are presented to demonstrate the improved performance of the FUPF over conventional federated unscented Kalman filter(FUKF).For instance,the mean of position-error decreases from(0.640×10-6 rad,0.667×10-6 rad,4.25 m) of FUKF to(0.403×10-6 rad,0.251×10-6 rad,1.36 m) of FUPF.In comparison of the FUKF,the FUPF performs more accurate in the SINS/CNS/GPS system described by the nonlinear/non-Gaussian error models.展开更多
According to the effective sampling of particles and the particles impoverishment caused by re-sampling in particle filter,an improved particle filtering algorithm based on observation inversion optimal sampling was p...According to the effective sampling of particles and the particles impoverishment caused by re-sampling in particle filter,an improved particle filtering algorithm based on observation inversion optimal sampling was proposed. Firstly,virtual observations were generated from the latest observation,and two sampling strategies were presented. Then,the previous time particles were sampled by utilizing the function inversion relationship between observation and system state. Finally,the current time particles were generated on the basis of the previous time particles and the system one-step state transition model. By the above method,sampling particles can make full use of the latest observation information and the priori modeling information,so that they further approximate the true state. The theoretical analysis and experimental results show that the new algorithm filtering accuracy and real-time outperform obviously the standard particle filter,the extended Kalman particle filter and the unscented particle filter.展开更多
Dynamic load imposed on the bridge by mov- ing vehicle depends on several bridge-vehicle parameters with various uncertainties. In the present paper, particle filter technique based on conditional probability has been...Dynamic load imposed on the bridge by mov- ing vehicle depends on several bridge-vehicle parameters with various uncertainties. In the present paper, particle filter technique based on conditional probability has been used to identify vehicle mass, suspension stiffness, and damping including tyre parameters from simulated bridge accelerations at different locations. A closed-form expres- sion is derived to generate independent response samples for the idealized bridge-vehicle coupled system consider- ing spatially non-homogeneous pavement unevenness. Thereafter, it is interfaced with the iterative process of particle filtering algorithm. The generated response sam- ples are contaminated by adding artificial noise in order to reflect field condition. The mean acceleration time history is utilized in particle filtering technique. The vehicle- imposed dynamic load is reconstructed with the identified parameters and compared with the simulated results. The present identification technique is examined in the presence of different levels of artificial noise with bridge response simulated at different locations. The effect of vehicle velocity, bridge surface roughness, and choice of prior probability density parameters on the efficiency of the method is discussed.展开更多
Recent advances in computer with geographic information system(GIS) technologies have allowed modelers to develop physics-based models for modeling soil erosion processes in time and space.However, it has been widely ...Recent advances in computer with geographic information system(GIS) technologies have allowed modelers to develop physics-based models for modeling soil erosion processes in time and space.However, it has been widely recognized that the effect of uncertainties on model predictions may be more significant when modelers apply such models for their own modeling purposes.Sources of uncertainty involved in modeling include data, model structural, and parameter uncertainty.To deal with the uncertain parameters of a catchment-scale soil erosion model(CSEM) and assess simulation uncertainties in soil erosion, particle filtering modeling(PF) is introduced in the CSEM.The proposed method, CSEM-PF, estimates parameters of non-linear and non-Gaussian systems, such as a physics-based soil erosion model by assimilating observation data such as discharge and sediment discharge sequences at outlets.PF provides timevarying feasible parameter sets as well as uncertainty bounds of outputs while traditional automatic calibration techniques result in a time-invariant global optimal parameter set.CSEM-PF was applied to a small mountainous catchment of the Yongdamdam in Korea for soil erosion modeling and uncertainty assessment for three historical typhoon events.Finally, the most optimal parameter sets and uncertainty bounds of simulation of both discharge and sediment discharge at each time step of the study events are provided.展开更多
The reasonable measuring of particle weight and effective sampling of particle state are consid- ered as two important aspects to obtain better estimation precision in particle filter. Aiming at the comprehensive trea...The reasonable measuring of particle weight and effective sampling of particle state are consid- ered as two important aspects to obtain better estimation precision in particle filter. Aiming at the comprehensive treatment of above problems, a novel two-stage prediction and update particle filte- ring algorithm based on particle weight optimization in multi-sensor observation is proposed. Firstly, combined with the construction of muhi-senor observation likelihood function and the weight fusion principle, a new particle weight optimization strategy in multi-sensor observation is presented, and the reliability and stability of particle weight are improved by decreasing weight variance. In addi- tion, according to the prediction and update mechanism of particle filter and unscented Kalman fil- ter, a new realization of particle filter with two-stage prediction and update is given. The filter gain containing the latest observation information is used to directly optimize state estimation in the frame- work, which avoids a large calculation amount and the lack of universality in proposal distribution optimization way. The theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.展开更多
An improved particle filtering（IPF） is presented to perform maneuvering target tracking in dense clutter.The proposed filter uses several efficient variance reduction methods to combat particle degeneracy,low mode p...An improved particle filtering（IPF） is presented to perform maneuvering target tracking in dense clutter.The proposed filter uses several efficient variance reduction methods to combat particle degeneracy,low mode prior probabilities and measure-ment-origin uncertainty.Within the framework of a hybrid state estimation,each particle samples a discrete mode from its poste-rior distribution and the continuous state variables are approximated by a multivariate Gaussian mixture that is updated by an unscented Kalman filtering（UKF）.The uncertainty of measurement origin is solved by Monte Carlo probabilistic data associa-tion method where the distribution of interest is approximated by particle filtering and UKF.Correct data association and precise behavior mode detection are successfully achieved by the proposed method in the environment with heavy clutter and very low mode prior probability.The performance of the proposed filter is examined and compared by Monte Carlo simulation over typical target scenario for various clutter densities.The simulation results show the effectiveness of the proposed filter.展开更多
Target tracking is one of the most important applications of wireless sensor networks. Optimized computation and energy dissipation are critical requirements to save the limited resource of sensor nodes. A new robust ...Target tracking is one of the most important applications of wireless sensor networks. Optimized computation and energy dissipation are critical requirements to save the limited resource of sensor nodes. A new robust and energy-efficient collaborative target tracking framework is proposed in this article. After a target is detected, only one active cluster is responsible for the tracking task at each time step. The tracking algorithm is distributed by passing the sensing and computation operations from one cluster to another. An event-driven cluster reforming scheme is also proposed for balancing energy consumption among nodes. Observations from three cluster members are chosen and a new class of particle filter termed cost-reference particle filter （CRPF） is introduced to estimate the target motion at the cluster head. This CRPF method is quite robust for wireless sensor network tracking applications because it drops the strong assumptions of knowing the probability distributions of the system process and observation noises. In simulation experiments, the performance of the proposed collaborative target tracking algorithm is evaluated by the metrics of tracking precision and network energy consumption.展开更多
The first automatic algorithm was designed to estimate the pulse pressure variation (PPVPPV) from arterial blood pressure (ABP) signals under spontaneous breathing conditions. While currently there are a few publicly ...The first automatic algorithm was designed to estimate the pulse pressure variation (PPVPPV) from arterial blood pressure (ABP) signals under spontaneous breathing conditions. While currently there are a few publicly available algorithms to automatically estimate PPVPPV accurately and reliably in mechani-cally ventilated subjects, at the moment there is no automatic algorithm for estimating PPVPPV on sponta-neously breathing subjects. The algorithm utilizes our recently developed sequential Monte Carlo method (SMCM), which is called a maximum a-posteriori adaptive marginalized particle filter (MAM-PF). The performance assessment results of the proposed algorithm on real ABP signals from spontaneously breath-ing subjects were reported.展开更多
Resampling is a critical procedure that is of both theoretical and practical significance for efficient implementation of the particle filter. To gain an insight of the resampling process and the filter, this paper co...Resampling is a critical procedure that is of both theoretical and practical significance for efficient implementation of the particle filter. To gain an insight of the resampling process and the filter, this paper contributes in three further respects as a sequel to the tutorial （Li et al., 2015）. First, identical distribution （ID） is established as a general principle for the resampling design, which requires the distribution of particles before and after resampling to be statistically identical. Three consistent met- rics including the （symmetrical） Kullback-Leibler divergence, Kolmogorov-Smimov statistic, and the sampling variance are introduced for assessment of the ID attribute of resampling, and a corresponding, qualitative ID analysis of representative resampling methods is given. Second, a novel resampling scheme that obtains the optimal ID attribute in the sense of minimum sampling variance is proposed. Third, more than a dozen typical resampling methods are compared via simulations in terms of sample size variation, sampling variance, computing speed, and estimation accuracy. These form a more comprehensive under- standing of the algorithm, providing solid guidelines for either selection of existing resampling methods or new implementations展开更多
文摘Terrain Aided Navigation(TAN)technology has become increasingly important due to its effectiveness in environments where Global Positioning System(GPS)is unavailable.In recent years,TAN systems have been extensively researched for both aerial and underwater navigation applications.However,many TAN systems that rely on recursive Unmanned Aerial Vehicle(UAV)position estimation methods,such as Extended Kalman Filters(EKF),often face challenges with divergence and instability,particularly in highly non-linear systems.To address these issues,this paper proposes and investigates a hybrid two-stage TAN positioning system for UAVs that utilizes Particle Filter.To enhance the system’s robustness against uncertainties caused by noise and to estimate additional system states,a Fuzzy Particle Filter(FPF)is employed in the first stage.This approach introduces a novel terrain composite feature that enables a fuzzy expert system to analyze terrain non-linearities and dynamically adjust the number of particles in real-time.This design allows the UAV to be efficiently localized in GPS-denied environments while also reducing the computational complexity of the particle filter in real-time applications.In the second stage,an Error State Kalman Filter(ESKF)is implemented to estimate the UAV’s altitude.The ESKF is chosen over the conventional EKF method because it is more suitable for non-linear systems.Simulation results demonstrate that the proposed fuzzy-based terrain composite method achieves high positional accuracy while reducing computational time and memory usage.
基金Project(51174206)supported by the National Natural Science Foundation of ChinaProject(2013AA12A201)supported by the National Hi-tech Research and Development Program of China+1 种基金Project(2012ZDP08)supported by the Fundamental Research Funds for the Central Universities,ChinaProject(SZBF2011-6-B35)supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD),China
文摘An altemative algorithm for mitigating GPS multipath was presented by integrating unscented Kalman filter （UKF） and wavelet transform with particle filter. Within consideration of particle degeneracy, UKF was taken for drawing particle. To remove the noise from raw data and data processing error, adaptive wavelet filtering with threshold was adopted while data preprocessing and drawing particle. Three algorithms, named EKF-PF, UKF-PF and WM-UKF-PF, were performed for comparison. The proposed WM-UKF-PF algorithm gives better error minimization, and significantly improves performance of multipath mitigation in terms of SNR and coefficient even though it has computation complexity. It is of significance for high-accuracy positioning and non-stationary deformation analysis.
基金the National Natural Science Foundation of China (No. 60404011)
文摘In this paper, an adaptive estimation algorithm is proposed for non-linear dynamic systems with unknown static parameters based on combination of particle filtering and Simultaneous Perturbation Stochastic Approxi- mation （SPSA） technique. The estimations of parameters are obtained by maximum-likelihood estimation and sampling within particle filtering framework, and the SPSA is used for stochastic optimization and to approximate the gradient of the cost function. The proposed algorithm achieves combined estimation of dynamic state and static parameters of nonlinear systems. Simulation result demonstrates the feasibilitv and efficiency of the proposed algorithm
基金Supported by the National Natural Science Foundation of China (No. 60872075)China Postdoctoral Science Foundation (No. 20080441015)
文摘This paper introduces throughput-efficient wireless system based on an extension to binary phasemodulations,named extended binary phase shift keying(EBPSK),and the corresponding analysis ofpower spectra,especially the extension to channel capacity are given.Importantly,a novel sequential es-timation and detection approach for this EBPSK system is proposed.The basic idea is to design a proba-bilistic approximation method for the computation of the maximum a posterior distribution via particle fil-tering method(PF).Subsequently,a new important function in PF is presented,so that the performanceof the detector has a great improvement.Finally,computer simulation illustrates that EBPSK system hasvery high transmission rate,and also the good performance of the proposed PF detector is demonstrated.
基金the National "863" High Technology Development Program (2006AA01Z216)the MajorResearch Program of the Science and Technology Commission of Shanghai Municipality of China (054SGA1001).
文摘A novel statistical method based on particle filtering is presented for multiple vehicle acoustic signals separation problem in wireless sensor network. The particle filtering method is able to deal with non-Gaussian and nonlinear models and non-stationary sources. Using some instantaneously mixed observations of several real-world vehicle acoustic signals, the proposed statistical method is compared with a conventional non-stationary Blind Source Separation algorithm and attractive simulation results are achieved. Moreover, considering the natural convenience to transmit particles between sensor nodes, the algorithm based on particle filtering is believed to have potential to enable the task of multiple vehicles recognition collaboratively performed by sensor nodes in distributed wireless sensor network.
基金supported by the National Natural Science Foundation of China(61302145)
文摘This paper improves the resampling step of particle filtering（PF） based on a broad interactive genetic algorithm to resolve particle degeneration and particle shortage.For target tracking in image processing,this paper uses the information coming from the particles of the previous fame image and new observation data to self-adaptively determine the selecting range of particles in current fame image.The improved selecting operator with jam gene is used to ensure the diversity of particles in mathematics,and the absolute arithmetical crossing operator whose feasible solution space being close about crossing operation,and non-uniform mutation operator is used to capture all kinds of mutation in this paper.The result of simulating experiment shows that the algorithm of this paper has better iterative estimating capability than extended Kalman filtering（EKF）,PF,regularized partide filtering（RPF）,and genetic algorithm（GA）-PF.
基金Sponsored by Natural Science Foundation of Heilongjiang Province of China(Grant No.QC2001C060)the Science and Technology Research Projectsin Office of Education of Heilongjiang province(Grant No.11531307)
文摘In light of degradation of particle filtering and robust weakness in the utilization of single feature tracking,this paper presents a kernel particle filtering tracking method based on multi-feature integration.In this paper,a new weight upgrading method is given out during kernel particle filtering at first,and then robust tracking is realized by integrating color and texture features under the framework of kernel particle filtering.Space histogram and integral histogram is adopted to calculate color and texture features respectively.These two calculation methods effectively overcome their own defectiveness,and meanwhile,improve the real timing for particle filtering.This algorithm has also improved sampling effectiveness,resolved redundant calculation for particle filtering and degradation of particles.Finally,the experiment for target tracking is realized by using the method under complicated background and shelter.Experiment results show that the method can reliably and accurately track target and deal with target sheltering situation properly.
基金Supported by National Natural Science Foundation of China (No. 60972038)The Open Research Fund of Na-tional Mobile Communications Research Laboratory, Southeast University (N200911)+3 种基金The Jiangsu Province Universities Natural Science Research Key Grant Project (No. 07KJA51006)ZTE Communications Co., Ltd. (Shenzhen) Huawei Technology Co., Ltd. (Shenzhen)The Research Fund of Nanjing College of Traffic Voca-tional Technology (JY0903)
文摘A particle filtering based AutoRegressive (AR) channel prediction model is presented for cognitive radio systems. Firstly, this paper introduces the particle filtering and the system model. Secondly, the AR model of order p is used to approximate the flat Rayleigh fading channels; its stability is discussed, and an algorithm for solving the AR model parameters is also given. Finally, an AR channel prediction model based on particle filtering and second-order AR model is presented. Simulation results show that the performance of the proposed AR channel prediction model based on particle filtering is better than that of Kalman filtering.
基金Supported by the National Natural Science Foundation of China(No.41074090)Henan Science and Technology Key Project(No.092102210360)+1 种基金Henan Provincial Department of Education Science ang Technology Key Project(No.13A510330)Doctorate Program of Henan Polytechnic University(No.B2009-27)
文摘Traditional chaotic pulse position modulation(CPPM)system has many drawbacks.It introduces delay into the feedback loop,which will lead to divergence of chaotic map easily.The wrong decision of data will cause error propagation.Mismatch of parameters and synchronization error between the receiver and transmitter will arouse high bit error rate.To solve these problems,a demodulation algorithm of CPPM based on particle filtering is proposed.According to the mathematical model of the system,it tracks the real signal by online separation in demodulation.Simulation results show that the proposed method can track the true signal better than the traditional CPPM scheme.What's more,it has good synchronization robustness,reduced error propagation by wrong decision and low bit error rate.
基金supported by the National Natural Science Foundation of China(6047209860502046U0635003).
文摘Two variants of systematic resampling （S-RS） are proposed to increase the diversity of particles and thereby improve the performance of particle filtering when it is utilized for detection in Bell Laboratories Layered Space-Time （BLAST） systems. In the first variant, Markov chain Monte Carlo transition is integrated in the S-RS procedure to increase the diversity of particles with large importance weights. In the second one, all particles are first partitioned into two sets according to their importance weights, and then a double S-RS is introduced to increase the diversity of particles with small importance weights. Simulation results show that both variants can improve the bit error performance efficiently compared with the standard S-P^S with little increased complexity.
基金Supported by the Significant Term of Science and Technology Research in Ministry of Education (No. 205060)Open Research Fund of National Mobile Communications Research Laboratory,Southeast University (N200911)+2 种基金Significant Basic Research of Jiangsu Province Colleges and Universities Natural Science Projects (07 KJA51006)Research Fund of Nanjing College of Traffic Vocational Technology (JY0903)Huawei Science and Technology Fund
文摘An adaptive object tracking algorithm based on particle filtering and a modified Gradient Vector Flow (GVF) Snake is proposed for tracking moving and deforming objects. The original contours of objects are obtained by using the background differencing method,and the true contours of objects can be converged by means of the powerful searching ability of a modified GVF-Snake. Finally,an Energetic Particle Filtering (EPF) algorithm is obtained by combining particle filtering and a modified GVF-Snake,and by using K-means and the EPF algorithm,multiple objects can be tracked. The proposed tracking tactic for partially occluded objects can effectively improve its anti-occlusion ability. Experiments show that this algorithm can obtain better tracking effect even though the tracked object is occluded.
基金Project(60535010) supported by the National Nature Science Foundation of China
文摘To solve the problem of information fusion in the strapdown inertial navigation system(SINS)/celestial navigation system(CNS)/global positioning system(GPS) integrated navigation system described by the nonlinear/non-Gaussian error models,a new algorithm called the federated unscented particle filtering(FUPF) algorithm was introduced.In this algorithm,the unscented particle filter(UPF) served as the local filter,the federated filter was used to fuse outputs of all local filters,and the global filter result was obtained.Because the algorithm was not confined to the assumption of Gaussian noise,it was of great significance to integrated navigation systems described by the non-Gaussian noise.The proposed algorithm was tested in a vehicle's maneuvering trajectory,which included six flight phases:climbing,level flight,left turning,level flight,right turning and level flight.Simulation results are presented to demonstrate the improved performance of the FUPF over conventional federated unscented Kalman filter(FUKF).For instance,the mean of position-error decreases from(0.640×10-6 rad,0.667×10-6 rad,4.25 m) of FUKF to(0.403×10-6 rad,0.251×10-6 rad,1.36 m) of FUPF.In comparison of the FUKF,the FUPF performs more accurate in the SINS/CNS/GPS system described by the nonlinear/non-Gaussian error models.
基金Project(60634030) supported by the Key Project of the National Natural Science Foundation of ChinaProject(60702066) supported by the National Natural Science Foundation of China+1 种基金Project (2007ZC53037) supported by Aviation Science Foundation of ChinaProject(CASC0214) supported by the Space-Flight Innovation Foundation of China
文摘According to the effective sampling of particles and the particles impoverishment caused by re-sampling in particle filter,an improved particle filtering algorithm based on observation inversion optimal sampling was proposed. Firstly,virtual observations were generated from the latest observation,and two sampling strategies were presented. Then,the previous time particles were sampled by utilizing the function inversion relationship between observation and system state. Finally,the current time particles were generated on the basis of the previous time particles and the system one-step state transition model. By the above method,sampling particles can make full use of the latest observation information and the priori modeling information,so that they further approximate the true state. The theoretical analysis and experimental results show that the new algorithm filtering accuracy and real-time outperform obviously the standard particle filter,the extended Kalman particle filter and the unscented particle filter.
文摘Dynamic load imposed on the bridge by mov- ing vehicle depends on several bridge-vehicle parameters with various uncertainties. In the present paper, particle filter technique based on conditional probability has been used to identify vehicle mass, suspension stiffness, and damping including tyre parameters from simulated bridge accelerations at different locations. A closed-form expres- sion is derived to generate independent response samples for the idealized bridge-vehicle coupled system consider- ing spatially non-homogeneous pavement unevenness. Thereafter, it is interfaced with the iterative process of particle filtering algorithm. The generated response sam- ples are contaminated by adding artificial noise in order to reflect field condition. The mean acceleration time history is utilized in particle filtering technique. The vehicle- imposed dynamic load is reconstructed with the identified parameters and compared with the simulated results. The present identification technique is examined in the presence of different levels of artificial noise with bridge response simulated at different locations. The effect of vehicle velocity, bridge surface roughness, and choice of prior probability density parameters on the efficiency of the method is discussed.
基金supported by Korea Ministry of Environment(MOE)as"GAIA Program2014000540005"
文摘Recent advances in computer with geographic information system(GIS) technologies have allowed modelers to develop physics-based models for modeling soil erosion processes in time and space.However, it has been widely recognized that the effect of uncertainties on model predictions may be more significant when modelers apply such models for their own modeling purposes.Sources of uncertainty involved in modeling include data, model structural, and parameter uncertainty.To deal with the uncertain parameters of a catchment-scale soil erosion model(CSEM) and assess simulation uncertainties in soil erosion, particle filtering modeling(PF) is introduced in the CSEM.The proposed method, CSEM-PF, estimates parameters of non-linear and non-Gaussian systems, such as a physics-based soil erosion model by assimilating observation data such as discharge and sediment discharge sequences at outlets.PF provides timevarying feasible parameter sets as well as uncertainty bounds of outputs while traditional automatic calibration techniques result in a time-invariant global optimal parameter set.CSEM-PF was applied to a small mountainous catchment of the Yongdamdam in Korea for soil erosion modeling and uncertainty assessment for three historical typhoon events.Finally, the most optimal parameter sets and uncertainty bounds of simulation of both discharge and sediment discharge at each time step of the study events are provided.
基金Supported by the National Natural Science Foundations of China(No.61300214,61170243)the Science and Technology Innovation Team Support Plan of Education Department of Henan Province(No.13IRTSTHN021)+2 种基金the Science and Technology Research Key Project of Education Department of Henan Province(No.13A413066)the Basic and Frontier Technology Research Plan of Henan Province(No.132300410148)the Funding Scheme of Young Key Teacher of Henan Province Universities,and the Key Project of Teaching Reform Research of Henan University(No.HDXJJG2013-07)
文摘The reasonable measuring of particle weight and effective sampling of particle state are consid- ered as two important aspects to obtain better estimation precision in particle filter. Aiming at the comprehensive treatment of above problems, a novel two-stage prediction and update particle filte- ring algorithm based on particle weight optimization in multi-sensor observation is proposed. Firstly, combined with the construction of muhi-senor observation likelihood function and the weight fusion principle, a new particle weight optimization strategy in multi-sensor observation is presented, and the reliability and stability of particle weight are improved by decreasing weight variance. In addi- tion, according to the prediction and update mechanism of particle filter and unscented Kalman fil- ter, a new realization of particle filter with two-stage prediction and update is given. The filter gain containing the latest observation information is used to directly optimize state estimation in the frame- work, which avoids a large calculation amount and the lack of universality in proposal distribution optimization way. The theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.
基金National Natural Science Foundation of China (60975028)National High-tech Research and Development Program (2009AA112203)+1 种基金Fundamental Research Funds for the Central Universities (CHD2009JC037)Natural Science Basic Research Plan in Shaanxi Province (2006F12)
文摘An improved particle filtering（IPF） is presented to perform maneuvering target tracking in dense clutter.The proposed filter uses several efficient variance reduction methods to combat particle degeneracy,low mode prior probabilities and measure-ment-origin uncertainty.Within the framework of a hybrid state estimation,each particle samples a discrete mode from its poste-rior distribution and the continuous state variables are approximated by a multivariate Gaussian mixture that is updated by an unscented Kalman filtering（UKF）.The uncertainty of measurement origin is solved by Monte Carlo probabilistic data associa-tion method where the distribution of interest is approximated by particle filtering and UKF.Correct data association and precise behavior mode detection are successfully achieved by the proposed method in the environment with heavy clutter and very low mode prior probability.The performance of the proposed filter is examined and compared by Monte Carlo simulation over typical target scenario for various clutter densities.The simulation results show the effectiveness of the proposed filter.
基金supported by the Hi-Tech Research and Development Program of China (2006AA01Z216).
文摘Target tracking is one of the most important applications of wireless sensor networks. Optimized computation and energy dissipation are critical requirements to save the limited resource of sensor nodes. A new robust and energy-efficient collaborative target tracking framework is proposed in this article. After a target is detected, only one active cluster is responsible for the tracking task at each time step. The tracking algorithm is distributed by passing the sensing and computation operations from one cluster to another. An event-driven cluster reforming scheme is also proposed for balancing energy consumption among nodes. Observations from three cluster members are chosen and a new class of particle filter termed cost-reference particle filter （CRPF） is introduced to estimate the target motion at the cluster head. This CRPF method is quite robust for wireless sensor network tracking applications because it drops the strong assumptions of knowing the probability distributions of the system process and observation noises. In simulation experiments, the performance of the proposed collaborative target tracking algorithm is evaluated by the metrics of tracking precision and network energy consumption.
文摘The first automatic algorithm was designed to estimate the pulse pressure variation (PPVPPV) from arterial blood pressure (ABP) signals under spontaneous breathing conditions. While currently there are a few publicly available algorithms to automatically estimate PPVPPV accurately and reliably in mechani-cally ventilated subjects, at the moment there is no automatic algorithm for estimating PPVPPV on sponta-neously breathing subjects. The algorithm utilizes our recently developed sequential Monte Carlo method (SMCM), which is called a maximum a-posteriori adaptive marginalized particle filter (MAM-PF). The performance assessment results of the proposed algorithm on real ABP signals from spontaneously breath-ing subjects were reported.
基金Project supported by the National Natural Science Foundation of China(No.51475383)European Commission MSCA-RISE-2014(No.641794)+1 种基金the Excellent Doctorate Foundation of Northwestern Polytechnical Universitythe Postdoctoral Fellowship of the University of Salamanca
文摘Resampling is a critical procedure that is of both theoretical and practical significance for efficient implementation of the particle filter. To gain an insight of the resampling process and the filter, this paper contributes in three further respects as a sequel to the tutorial （Li et al., 2015）. First, identical distribution （ID） is established as a general principle for the resampling design, which requires the distribution of particles before and after resampling to be statistically identical. Three consistent met- rics including the （symmetrical） Kullback-Leibler divergence, Kolmogorov-Smimov statistic, and the sampling variance are introduced for assessment of the ID attribute of resampling, and a corresponding, qualitative ID analysis of representative resampling methods is given. Second, a novel resampling scheme that obtains the optimal ID attribute in the sense of minimum sampling variance is proposed. Third, more than a dozen typical resampling methods are compared via simulations in terms of sample size variation, sampling variance, computing speed, and estimation accuracy. These form a more comprehensive under- standing of the algorithm, providing solid guidelines for either selection of existing resampling methods or new implementations
