基于到达时间差与频率差量测无偏转换的辐射源跟踪算法

doi:10.12305/j.issn.1001-506X.2026.03.02

摘要/Abstract

摘要：

由于单通道接收机量测与辐射源状态之间的非线性关系，利用到达时间差（time difference of arrival, TDOA）和到达频率差（frequency difference of arrival, FDOA）量测进行远距离跟踪变得具有挑战性，导致状态估计在二阶矩上产生偏差。为解决该问题，提出基于量测转换的算法。利用二阶泰勒展开逼近非线性观测方程，进而将TDOA/FDOA量测转换到辐射源状态空间。同时，在二阶泰勒逼近的程度上实现量测协方差转换，尽可能减少转换过程中带来的偏差。仿真结果表明，所提算法相对于一阶泰勒展开近似的量测转换以及无迹卡尔曼滤波器具有更高的辐射源跟踪精度，且计算量与无迹卡尔曼滤波器相近。因此，所提方法具有广阔的应用前景。

关键词: 到达时间差, 到达频率差, 无偏量测转换卡尔曼滤波, 二阶泰勒展开

Abstract:

Due to the nonlinear relationship between single channel receiver measurements and radiation source states, it becomes challenging to use time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements for long-distance tracking, resulting in bias in state estimation on the second-order moment. To address this issue, a measurement conversion-based algorithm is proposed. It employs second-order Taylor expansion to approximate nonlinear observation equations, transforming TDOA/FDOA measurements into the emitter state space. Simultaneously, measurement covariance conversion is achieved to the extent permitted by second-order Taylor approximation, aiming to reduce the bias incurred during the conversion process. The simulation results show that the proposed algorithm has higher radiation source tracking accuracy compared to the first-order Taylor expansion approximation measurement conversion and the unscented Kalman filter, and the computational complexity is similar to that of the unscented Kalman filter. Therefore, the proposed algorithm has broad application prospects.

Key words: time difference of arrival (TDOA), frequency difference of arrival (FDOA), unbiased conversion measurement Kalman filter (UCMKF), second-order Taylor expansion

中图分类号:

TN 953

谢欣辰, 高林, 魏平, 李万春. 基于到达时间差与频率差量测无偏转换的辐射源跟踪算法[J]. 系统工程与电子技术, 2026, 48(3): 751-757.

Xinchen XIE, Lin GAO, Ping WEI, Wanchun LI. Radiation source tracking algorithm based on unbiased measurement conversion of TDOA/FDOA[J]. Systems Engineering and Electronics, 2026, 48(3): 751-757.

图/表 5

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量测场景	UKF	UCM1	UCM2
1	1.84	0.86	1.93
2	1.88	0.89	1.97
3	1.94	0.91	2.02

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