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
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.
