Most existing studies about passive radar systems are based on the already known illuminator of opportunity(IO)states.However,in practice,the receiver generally has little knowledge about the IO states.Little research...Most existing studies about passive radar systems are based on the already known illuminator of opportunity(IO)states.However,in practice,the receiver generally has little knowledge about the IO states.Little research has studied this problem.This paper analyzes the observability and estimability for passive radar systems with unknown IO states under three typical scenarios.Besides,the directions of high and low estimability with respect to various states are given.Moreover,two types of observations are taken into account.The effects of different observations on both observability and estimability are well analyzed.For the observability test,linear and nonlinear methods are considered,which proves that both tests are applicable to the system.Numerical simulations confirm the correctness of the theoretical analysis.展开更多
Due to electronic jamming transmitted by hostile electronic jamming equipments and unintentional jamming from other illuminating sources in the complex electromagnetic environment,the performance of non-cooperative pa...Due to electronic jamming transmitted by hostile electronic jamming equipments and unintentional jamming from other illuminating sources in the complex electromagnetic environment,the performance of non-cooperative passive detection systems may degrade it significantly.To solve the problem,a receiving frame with multiple channels for signal preprocessing is designed and a theoretical analysis to the received signals in the complex electromagnetic environment is provided.Furthermore,a scheme for jamming removal using independent component analysis is proposed.Simulation results demonstrate the proposed scheme appears as a very appealing solution for removal of jamming and an approximate 10 dB signal to distortion ratio over traditional schemes is obtained.展开更多
In this paper,by utilizing the angle of arrivals(AOAs) and imprecise positions of the sensors,a novel modified Levenberg-Marquardt algorithm to solve the source localization problem is proposed.Conventional source loc...In this paper,by utilizing the angle of arrivals(AOAs) and imprecise positions of the sensors,a novel modified Levenberg-Marquardt algorithm to solve the source localization problem is proposed.Conventional source localization algorithms,like Gauss-Newton algorithm and Conjugate gradient algorithm are subjected to the problems of local minima and good initial guess.This paper presents a new optimization technique to find the descent directions to avoid divergence,and a trust region method is introduced to accelerate the convergence rate.Compared with conventional methods,the new algorithm offers increased stability and is more robust,allowing for stronger non-linearity and wider convergence field to be identified.Simulation results demonstrate that the proposed algorithm improves the typical methods in both speed and robustness,and is able to avoid local minima.展开更多
To solve the problem of multiple moving sources passive location,a novel blind source separation(BSS) algorithm based on the multiset canonical correlation analysis(MCCA) is presented by exploiting the different tempo...To solve the problem of multiple moving sources passive location,a novel blind source separation(BSS) algorithm based on the multiset canonical correlation analysis(MCCA) is presented by exploiting the different temporal structure of uncorrelated source signals first,and then on the basis of this algorithm,a novel multiple moving sources passive location method is proposed using time difference of arrival(TDOA) and frequency difference of arrival(FDOA) measurements.The key technique of this location method is TDOA and FDOA joint estimation,which is based on BSS.By blindly separating mixed signals from multiple moving sources,the multiple sources location problem can be translated to each source location in turn,and the effect of interference and noise can also be removed.The simulation results illustrate that the performance of the MCCA algorithm is very good with relatively light computation burden,and the location algorithm is relatively simple and effective.展开更多
基金This work was supported by the National Natural Science Foundation of China(61803379)the China Postdoctoral Science Foundation(2017M613370,2018T111129).
文摘Most existing studies about passive radar systems are based on the already known illuminator of opportunity(IO)states.However,in practice,the receiver generally has little knowledge about the IO states.Little research has studied this problem.This paper analyzes the observability and estimability for passive radar systems with unknown IO states under three typical scenarios.Besides,the directions of high and low estimability with respect to various states are given.Moreover,two types of observations are taken into account.The effects of different observations on both observability and estimability are well analyzed.For the observability test,linear and nonlinear methods are considered,which proves that both tests are applicable to the system.Numerical simulations confirm the correctness of the theoretical analysis.
基金Supported by the National High Technology Research and Development Programme of China(No.2012AA7014061,2013AA7014061)
文摘Due to electronic jamming transmitted by hostile electronic jamming equipments and unintentional jamming from other illuminating sources in the complex electromagnetic environment,the performance of non-cooperative passive detection systems may degrade it significantly.To solve the problem,a receiving frame with multiple channels for signal preprocessing is designed and a theoretical analysis to the received signals in the complex electromagnetic environment is provided.Furthermore,a scheme for jamming removal using independent component analysis is proposed.Simulation results demonstrate the proposed scheme appears as a very appealing solution for removal of jamming and an approximate 10 dB signal to distortion ratio over traditional schemes is obtained.
基金Supported by the National High Technology Research and Development Programme of China(No.2011AA7014061)
文摘In this paper,by utilizing the angle of arrivals(AOAs) and imprecise positions of the sensors,a novel modified Levenberg-Marquardt algorithm to solve the source localization problem is proposed.Conventional source localization algorithms,like Gauss-Newton algorithm and Conjugate gradient algorithm are subjected to the problems of local minima and good initial guess.This paper presents a new optimization technique to find the descent directions to avoid divergence,and a trust region method is introduced to accelerate the convergence rate.Compared with conventional methods,the new algorithm offers increased stability and is more robust,allowing for stronger non-linearity and wider convergence field to be identified.Simulation results demonstrate that the proposed algorithm improves the typical methods in both speed and robustness,and is able to avoid local minima.
基金Supported by the National High Technology Research and Development Program of China(No.2009AAJ116,2009AAJ208,2010AA7010422)the National Science Foundation for Post-Doctoral Scientists of China(No.20080431379,200902671)the Hubei Natural Science Foundation(No.2009CDB031)
文摘To solve the problem of multiple moving sources passive location,a novel blind source separation(BSS) algorithm based on the multiset canonical correlation analysis(MCCA) is presented by exploiting the different temporal structure of uncorrelated source signals first,and then on the basis of this algorithm,a novel multiple moving sources passive location method is proposed using time difference of arrival(TDOA) and frequency difference of arrival(FDOA) measurements.The key technique of this location method is TDOA and FDOA joint estimation,which is based on BSS.By blindly separating mixed signals from multiple moving sources,the multiple sources location problem can be translated to each source location in turn,and the effect of interference and noise can also be removed.The simulation results illustrate that the performance of the MCCA algorithm is very good with relatively light computation burden,and the location algorithm is relatively simple and effective.
