引用本文: 李晶, 赵拥军, 李冬海. 基于DOA和TDOA的单站外辐射源目标跟踪IPKF方法[J]. 武汉大学学报 ( 信息科学版), 2017, 42(2): 229-235.
LI Jing, ZHAO Yongjun, LI Donghai. Single-Observer Passive Tracking Based on DOA and TDOA Using Iterative Pseudo-Liner Kalman Filter[J]. Geomatics and Information Science of Wuhan University, 2017, 42(2): 229-235.
 Citation: LI Jing, ZHAO Yongjun, LI Donghai. Single-Observer Passive Tracking Based on DOA and TDOA Using Iterative Pseudo-Liner Kalman Filter[J]. Geomatics and Information Science of Wuhan University, 2017, 42(2): 229-235.

## Single-Observer Passive Tracking Based on DOA and TDOA Using Iterative Pseudo-Liner Kalman Filter

• 摘要: 随着电子战、信息战在现代军事领域的地位日趋重要，基于外辐射源的定位跟踪方法成为现代雷达领域的研究热点。针对通过单站接收多外辐射源信号获取角度（direction of arrival，DOA）和时差（time difference of arrival，TDOA）信息对运动目标跟踪的问题，首先推导角度和时差的伪线性观测方程，在通过最小二乘（least squares，LS）算法获取初值的条件下，利用传统的卡尔曼滤波算法实现目标的跟踪，该方法称为伪线性卡尔曼滤波（pseudo-liner Kalman filter，PKF）算法。进一步分析观测方程，提出了利用迭代的IPKF（iterative PKF）目标跟踪算法，并推导其克拉美罗下界（Cramer-Rao lower bound，CRLB）。仿真实验分析说明，该IPKF算法的跟踪精度、收敛速度和稳定性均优于传统的扩展卡尔曼滤波（extended Kalman filter，EKF）算法，且迭代次数越多，性能越好，观测误差越小，跟踪误差越接近CRLB。

Abstract: Given the growing importance of electronic and information warfare in modern military field, localization and tracking methods based on passive coherent radar have become a research hotspot. This paper studies the problems in passive tracking based on DOA and TDOA, obtained by single-observer receiving signals from multiple passive transmitters. First of all, we deduce the pseudo-liner observation equations for DOA and TDOA. A traditional Kalman filter is applied to track target, on the condition that the least squares (LS) algorithm is used to get the initial value, a pseudo-liner Kalman filter (PKF). Furthermore, after analyzing the observation equations, an Iterative PKF (IPKF) algorithm is used to track a target. We also deduce the CRLB of the proposed algorithm. Simulations show that the tracking precision, the rate of convergence, and the tracking stability of proposed algorithm are higher than extended Kalman filter (EKF). The higher the number of iterations, the better the performance and the tracking errors will be closer to CRLB with smaller observation error.

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