In this paper,a sparse nonuniform rectangular array based on spatially spread electromagnetic vector sensor(SNRASSEMVS)is introduced,and a method for estimating 2D-direction of arrival(DOA)and polarization is devised....In this paper,a sparse nonuniform rectangular array based on spatially spread electromagnetic vector sensor(SNRASSEMVS)is introduced,and a method for estimating 2D-direction of arrival(DOA)and polarization is devised.Firstly,according to the special structure of the sparse nonuniform rectangular array(SNRA),a set of accurate but ambiguous direction-cosine estimates can be obtained.Then the steering vector of spatially spread electromagnetic vector sensor(SSEMVS)can be extracted from the array manifold to obtain the coarse but unambiguous direction-cosine estimates.Finally,the disambiguation approach can be used to get the final accurate estimates of 2DDOA and polarization.Compared with some existing methods,the SNRA configuration extends the spatial aperture and refines the parameters estimation accuracy without adding any redundant antennas,as well as reduces the mutual coupling effect.Moreover,the proposed algorithm resolves multiple sources without the priori knowledge of signal information,suffers no ambiguity in the estimation of the Poynting vector,and pairs the x-axis direction cosine with the y-axis direction cosine automatically.Simulation results are given to verify the effectiveness and superiority of the proposed algorithm.展开更多
The sensor array calibration methods tailored to uniform rectangular array(URA)in the presence of mutual coupling and sensor gain-and-phase errors were addressed.First,the mutual coupling model of the URA was studied,...The sensor array calibration methods tailored to uniform rectangular array(URA)in the presence of mutual coupling and sensor gain-and-phase errors were addressed.First,the mutual coupling model of the URA was studied,and then a set of steering vectors corresponding to distinct locations were numerically computed with the help of several time-disjoint auxiliary sources with known directions.Then,the optimization modeling with respect to the array error matrix(defined by the product of mutual coupling matrix and sensor gain-and-phase errors matrix)was constructed.Two preferable algorithms(called algorithm I and algorithm II)were developed to minimize the cost function.In algorithm I,the array error matrix was regarded as a whole parameter to be estimated,and the exact solution was available.Compared to some existing algorithms with the similar computation framework,algorithm I can make full use of the potentially linear characteristics of URA's error matrix,thus,the calibration precision was obviously enhanced.In algorithm II,the array error matrix was decomposed into two matrix parameters to be optimized.Compared to algorithm I,it can further decrease the number of unknowns and,thereby,yield better estimation accuracy.However,algorithm II was incapable of producing the closed-form solution and the iteration operation was unavoidable.Simulation results validate the excellent performances of the two novel algorithms compared to some existing calibration algorithms.展开更多
An electro-optic tunable rectangular array illuminator in one-dimensional periodically poled LiNbO3 （PPLN） crystal is presented experimentally which result is in good agreement with results from simu- lation. The il...An electro-optic tunable rectangular array illuminator in one-dimensional periodically poled LiNbO3 （PPLN） crystal is presented experimentally which result is in good agreement with results from simu- lation. The illuminator is formed based on the Talbot self-imaging effect by applying an electric field on PPLN. The intensi~.y distribution of rectangular array could be precisely modulated. Compared with other array illuminators, this tunable illuminator uses a lower voltage and could get a more concentrated intensity distribution. The influence of the incident angle to the self-imaging patterns is studied for the first time.展开更多
Due to the shared nature of the wireless medium, the performance of wireless sensor network is often limited by both internal interference and external interference. The internal interference is that simultaneous traf...Due to the shared nature of the wireless medium, the performance of wireless sensor network is often limited by both internal interference and external interference. The internal interference is that simultaneous traffic activity by neighboring nodes in the same network, while the external interference is from wireless transmissions by other types of devices, such as Wi-Fi and Bluetooth nodes. In this paper, we present two channel hopping algorithms for multichannel, single-radio wireless sensor networks. The first algorithm achieves collision-free transmission environment while do not introduce extra control overhead. The second algorithm, in addition to reducing internal interference effects, reduces the external interference effects from Wi-Fi devices. Simulation results show that both of them significantly improve performance in wireless sensor network.展开更多
In this paper,we propose improved approaches for two-dimensional(2 D) direction-of-arrival(DOA) estimation for a uniform rectangular array(URA).Unlike the conventional eigenstructure-based estimation approaches such a...In this paper,we propose improved approaches for two-dimensional(2 D) direction-of-arrival(DOA) estimation for a uniform rectangular array(URA).Unlike the conventional eigenstructure-based estimation approaches such as Multiple Signals Classification(MUSIC) and Estimation of Signal Parameters via Rotational Invariance Technique(ESPRIT),the proposed approaches estimate signal and noise subspaces with Nystr?m approximation,which only need to calculate two sub-matrices of the whole sample covariance matrix and avoid the need to directly calculate the eigenvalue decomposition(EVD) of the sample covariance matrix.Hence,the proposed approaches can improve the computational efficiency greatly for large-scale URAs.Numerical results verify the reliability and efficiency of the proposed approaches.展开更多
We propose a novel method for joint two-dimensional (2D) direction-of-arrival (DOA) and channel estimation with data detection for uniform rectangular arrays (URAs) for the massive multiple-input multiple-output (MIMO...We propose a novel method for joint two-dimensional (2D) direction-of-arrival (DOA) and channel estimation with data detection for uniform rectangular arrays (URAs) for the massive multiple-input multiple-output (MIMO) systems. The conventional DOA estimation algorithms usually assume that the channel impulse responses are known exactly. However, the large number of antennas in a massive MIMO system can lead to a challenge in estimating accurate corresponding channel impulse responses. In contrast, a joint DOA and channel estimation scheme is proposed, which first estimates the channel impulse responses for the links between the transmitters and antenna elements using training sequences. After that, the DOAs of the waves are estimated based on a unitary ESPRIT algorithm using previous channel impulse response estimates instead of accurate channel impulse responses and then, the enhanced channel impulse response estimates can be obtained. The proposed estimator enjoys closedform expressions, and thus it bypasses the search and pairing processes. In addition, a low-complexity approach toward data detection is presented by reducing the dimension of the inversion matrix in massive MIMO systems.Different cases for the proposed method are analyzed by changing the number of antennas. Experimental results demonstrate the validity of the proposed method.展开更多
基金This work was supported by the innovation project of Science and Technology Commission of the Central Military Commission。
文摘In this paper,a sparse nonuniform rectangular array based on spatially spread electromagnetic vector sensor(SNRASSEMVS)is introduced,and a method for estimating 2D-direction of arrival(DOA)and polarization is devised.Firstly,according to the special structure of the sparse nonuniform rectangular array(SNRA),a set of accurate but ambiguous direction-cosine estimates can be obtained.Then the steering vector of spatially spread electromagnetic vector sensor(SSEMVS)can be extracted from the array manifold to obtain the coarse but unambiguous direction-cosine estimates.Finally,the disambiguation approach can be used to get the final accurate estimates of 2DDOA and polarization.Compared with some existing methods,the SNRA configuration extends the spatial aperture and refines the parameters estimation accuracy without adding any redundant antennas,as well as reduces the mutual coupling effect.Moreover,the proposed algorithm resolves multiple sources without the priori knowledge of signal information,suffers no ambiguity in the estimation of the Poynting vector,and pairs the x-axis direction cosine with the y-axis direction cosine automatically.Simulation results are given to verify the effectiveness and superiority of the proposed algorithm.
基金Project(61201381)supported by the National Natural Science Foundation of ChinaProject(YP12JJ202057)supported by the Future Development Foundation of Zhengzhou Information Science and Technology College,China
文摘The sensor array calibration methods tailored to uniform rectangular array(URA)in the presence of mutual coupling and sensor gain-and-phase errors were addressed.First,the mutual coupling model of the URA was studied,and then a set of steering vectors corresponding to distinct locations were numerically computed with the help of several time-disjoint auxiliary sources with known directions.Then,the optimization modeling with respect to the array error matrix(defined by the product of mutual coupling matrix and sensor gain-and-phase errors matrix)was constructed.Two preferable algorithms(called algorithm I and algorithm II)were developed to minimize the cost function.In algorithm I,the array error matrix was regarded as a whole parameter to be estimated,and the exact solution was available.Compared to some existing algorithms with the similar computation framework,algorithm I can make full use of the potentially linear characteristics of URA's error matrix,thus,the calibration precision was obviously enhanced.In algorithm II,the array error matrix was decomposed into two matrix parameters to be optimized.Compared to algorithm I,it can further decrease the number of unknowns and,thereby,yield better estimation accuracy.However,algorithm II was incapable of producing the closed-form solution and the iteration operation was unavoidable.Simulation results validate the excellent performances of the two novel algorithms compared to some existing calibration algorithms.
基金supported by the National Basic Research Program of China(No.2011CB808101)the National Natural Science Foundation of China(No.61125503 and 61235009)the Foundation for Development of Science and Technology of Shanghai(No.1313JC1408300)
文摘An electro-optic tunable rectangular array illuminator in one-dimensional periodically poled LiNbO3 （PPLN） crystal is presented experimentally which result is in good agreement with results from simu- lation. The illuminator is formed based on the Talbot self-imaging effect by applying an electric field on PPLN. The intensi~.y distribution of rectangular array could be precisely modulated. Compared with other array illuminators, this tunable illuminator uses a lower voltage and could get a more concentrated intensity distribution. The influence of the incident angle to the self-imaging patterns is studied for the first time.
基金supported by the Natural Science Foundation of China(No.61574035)National Natural Science Foundation of China(No.61550110244)
文摘Due to the shared nature of the wireless medium, the performance of wireless sensor network is often limited by both internal interference and external interference. The internal interference is that simultaneous traffic activity by neighboring nodes in the same network, while the external interference is from wireless transmissions by other types of devices, such as Wi-Fi and Bluetooth nodes. In this paper, we present two channel hopping algorithms for multichannel, single-radio wireless sensor networks. The first algorithm achieves collision-free transmission environment while do not introduce extra control overhead. The second algorithm, in addition to reducing internal interference effects, reduces the external interference effects from Wi-Fi devices. Simulation results show that both of them significantly improve performance in wireless sensor network.
基金supported by"the Fundamental Research Funds for the Central Universities No.2017JBM016"
文摘In this paper,we propose improved approaches for two-dimensional(2 D) direction-of-arrival(DOA) estimation for a uniform rectangular array(URA).Unlike the conventional eigenstructure-based estimation approaches such as Multiple Signals Classification(MUSIC) and Estimation of Signal Parameters via Rotational Invariance Technique(ESPRIT),the proposed approaches estimate signal and noise subspaces with Nystr?m approximation,which only need to calculate two sub-matrices of the whole sample covariance matrix and avoid the need to directly calculate the eigenvalue decomposition(EVD) of the sample covariance matrix.Hence,the proposed approaches can improve the computational efficiency greatly for large-scale URAs.Numerical results verify the reliability and efficiency of the proposed approaches.
基金supported by Ericsson and the National Natural Science Foundation of China(No.61371075)
文摘We propose a novel method for joint two-dimensional (2D) direction-of-arrival (DOA) and channel estimation with data detection for uniform rectangular arrays (URAs) for the massive multiple-input multiple-output (MIMO) systems. The conventional DOA estimation algorithms usually assume that the channel impulse responses are known exactly. However, the large number of antennas in a massive MIMO system can lead to a challenge in estimating accurate corresponding channel impulse responses. In contrast, a joint DOA and channel estimation scheme is proposed, which first estimates the channel impulse responses for the links between the transmitters and antenna elements using training sequences. After that, the DOAs of the waves are estimated based on a unitary ESPRIT algorithm using previous channel impulse response estimates instead of accurate channel impulse responses and then, the enhanced channel impulse response estimates can be obtained. The proposed estimator enjoys closedform expressions, and thus it bypasses the search and pairing processes. In addition, a low-complexity approach toward data detection is presented by reducing the dimension of the inversion matrix in massive MIMO systems.Different cases for the proposed method are analyzed by changing the number of antennas. Experimental results demonstrate the validity of the proposed method.
