基于累积直方图的PRF抖动信号基准周期估计

Chai Heng, Zhang Ying-bo, Wang Jin-Feng
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引用次数: 0

摘要

针对PRF抖动雷达信号,研究了基于噪声稀疏观测的基准周期估计问题。针对这一问题,Casey和Sadler提出了一种称为MEA (Modified Euclidean algorithm)的算法,该算法可以在无偏差的情况下适应有噪声的TOA序列基准周期的预估计。该算法需要进行连续的微分计算,并将迭代停止时的微分值作为基准周期的最终估计。当观测稀疏程度较高而信噪比较低时,该算法可能会得到无效估计。针对PRF抖动信号的特点,利用TOA序列各阶的微分累积对其基准周期进行预估计。这样既减小了累积误差,又充分利用了大量抖动点所带来的信息增益,从而在低信噪比和低周期噪声比下实现对基准周期的有效估计。通过对导航雷达抖动信号的实际测试,验证了所提算法的工程可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Estimation of Benchmark Period for PRF Jitter Signal Based on Cumulative Histogram
Aiming at signal of PRF jitter radar, the estimation of benchmark period from sparse observation with noise is researched in this paper. For that problem, Casey and Sadler proposed one algorithm called MEA (Modified Euclidean Algorithm), which can adapt to the pre-estimation of benchmark period of TOA sequence with noise when there is no deviation. That algorithm needs to conduct continuous differential calculation and employs the differential value when iteration stops as final estimation of benchmark period. When the observation sparse level is high and SNR is low, that algorithm may obtain ineffective estimation. Aiming at the characteristics of PRF jitter signal, this paper utilizes differential accumulation of each order of TOA sequence to conduct pre-estimation to its benchmark period. That both decreases the cumulative error and makes full use of information gain caused by large amount of jitter points, so as to achieve effective estimation to benchmark period under low SNR and period-noise ratio. Practical tests are conducted to jitter signal of navigation radar to validate the engineering feasibilty of the proposed algorithm.
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