Registration And Passive Localization Method For Multi-static Passive Radar

Active radar needs to actively emit electromagnetic signals,which are vulnerable to the threat of electromagnetic interference and anti-radiation missiles.Target emitter radar relies on the radiation signals of enemy targets and can not detect silent targets.The characteristics of the passive radar not actively transmitting signals make it have excellent concealment and strong survivability.At the same time,the problem of strong nonlinear observation equations and measurement information deviation of passive source radars makes the traditional positioning algorithm unsuitable for passive positioning systems.In order to solve this problem,this thesis focuses on the follows issues:1.The system biases would seriously degrade the location precision for the multi-static passive radar system.A joint registration and passive localization algorithm based on Constrained Total Least Squares(CTLS)using Time Difference Of Arrival(TDOA)measurements is developed to address the multi-static radar localization problem under the influence of system biases.Firstly,the nonlinear TDOA measurement equations are linearized by introducing auxiliary variables.Considering the statistical correlation properties of the noise matrix in the pseudo-linear equations,a joint biases registration and passive localization problem is formulated as a CTLS problem and the Newton's method is applied to solving the CTLS problem.Moreover a dependent least squares algorithm is designed to improve the target position estimation using the relationship between auxiliary variables and target position.An iterative post-estimate procedure is exploited to further enhance the estimation accuracy of the system biases.Finally,the theoretical error of the proposed algorithm is derived.Simulations demonstrate that the proposed algorithm can effectively estimate the system biases and target position.2.The system biases would seriously degrade the location precision for the multi-static passive radar system.There are two joint registration and passive localization algorithm based on Weighted Least Square(WLS)and CTLS using Direction Of Arrival(DOA)and TDOA measurements is developed to address the multi-static radar localization problem under the influence of system biases.Firstly,the nonlinear DOA and TDOA measurement equations are linearized by introducing auxiliary variables.Considering the statistical correlation properties of the noise matrix in the pseudo-linear equations,a joint biases registration and passive localization problem is formulated as a CTLS problem and the Newton's method is applied to solving the CTLS problem.Moreover a dependent least squares algorithm is designed to improve the target position estimation using the relationship between auxiliary variables and target position.An iterative post-estimate procedure is exploited to further enhance the estimation accuracy of the system biases.Finally,the theoretical error of the proposed algorithm is derived.Simulations demonstrate that the proposed algorithm can effectively estimate the system biases and target position.3.The system biases would seriously degrade the location precision for the multi-static passive radar system.A joint registration and passive localization algorithm based on WLS using Frequency Different of Arrival(FDOA)and TDOA measurements is developed to address the multi-static radar localization problem under the influence of system biases.Firstly,the nonlinear FDOA and TDOA measurement equations are linearized by introducing auxiliary variables.Considering the statistical correlation properties of the noise matrix in the pseudo-linear equations,a joint biases registration and passive localization problem is formulated as a WLS problem and the Newton's method is applied to solving the WLS problem.Moreover a dependent least squares algorithm is designed to improve the target position estimation using the relationship between auxiliary variables and target position.An iterative post-estimate procedure is exploited to further enhance the estimation accuracy of the system biases.Finally,the theoretical error of the proposed algorithm is derived.Simulations demonstrate that the proposed algorithm can effectively estimate the system biases and target position.