无源定位观测站站址误差自校正算法

Self-Calibration Algorithm for Receiver Position Error in Passive Source Localization

无源定位中观测站的站址误差会对定位精度产生不良影响。本文提出了一种基于观测站站址误差自校正的定位算法,推导了高斯噪声模型下定位误差的克拉美罗下界(CRLB),通过泰勒级数展开建立了关于站址误差的线性方程并得到了误差的线性最小均方误差(LMMSE)估计,改善了观测站的位置,使用Chan算法得到了定位结果,仿真验证了算法的定位精度在高斯噪声模型下能够达到CRLB。

Receiver position error is known to degrade significantly the passive source localization accuracy. A new algorithm based on self-calibration of receiver position error is proposed. The Cramer-Rao Lower Bound of source location estimate under Ganssian noise model is derived. Using Taylor series expansion, a linear equation is established and the LMMSE estimator of receiver position error is obtained. The receiver position is improved and the estimate of the source location is achieved by Chan location algorithm. Simulation results indicate that the proposed solution reaches an accuracy close to the CRLB under Gaussian noise.