基于奇异值分解的互相关时延估计优化算法

Cross-correlation delay estimation optimization algorithm based on singular value decomposition

针对多站时差定位系统在低信噪比下无法获得准确的时延估计,进而影响时差定位的精度,提出一种基于奇异值分解和希尔伯特差值的互相关时延估计优化算法。首先对接收信号进行奇异值分解,提高信号的信噪比,然后将处理后的信号作互相关运算,最后通过希尔伯特差值法锐化相关函数的峰值,进一步提高时延估计精度。在相同条件下,模拟分析了不同算法的时延估计精度,结果表明,新的优化算法时延估计精度更高,具有良好的抗噪声性能。

For the multi-station TDOA location in low SNR unable to obtain accurate time delay estimation, which affecting the accuracy of TDOA location. A cross-correlation time delay estimation optimization algorithm based on singular value decomposition and Hilbert difference is proposed. Firstly, the received signal is decomposed by singular value decomposition to improve the signal-to-noise ratio, then the decomposed signal was cross-correlated, and finally the peak of the correlation function was sharpened by the Hilbert difference method to obtain higher delay estimation accuracy. Under the same conditions, the time delay estimation accuracy of different algorithms was simulated and analyzed. The results show that the new optimization algorithm has higher delay estimation accuracy. It has good anti-noise performance.