采用改进GD-TLS的进动目标参数估计方法

    A Parameter Estimation Method for Precession Targets Through Improved GD-TLS

    • 摘要: 针对现有的进动目标参数估计方法存在的进动参数估计不准确、冲击噪声建模不全面的问题,文中提出了一种采用同步压缩小波变换(SWT)与改进梯度下降总体最小二乘法(GD-TLS)的进动目标参数估计方法。首先,针对雷达视线角约为90°的情况,分析了进动目标在平动与非平动时的微多普勒信号特征,采用SWT对进动频率进行预估计,避免了短时傅里叶变换时频曲线交叠的影响;其次,利用改进的奇异值比谱方法,通过范围搜索对进动频率进行精准估计;最后,通过微多普勒推导,在代价函数中引入指数函数抑制冲击噪声干扰,对GD-TLS算法进行改进,提升其余进动参数估计的抗冲击噪声性能。实验结果表明,该算法在无噪声干扰下估计误差可降至0.4 %,在存在冲击噪声的低信噪比(10 dB)情况下,估计误差仍控制在5.2 % 以内,优于传统的最小均方、递归最小二乘和鲁棒最小均方对数等方法,验证了所提方法对冲击噪声的鲁棒性。

       

      Abstract: To address the limitations of existing algorithms of precession target parameter estimation, including inaccurate precession parameter estimation and incomplete modeling of impulsive noise, a precession target parameter estimation method employing synchronized wavelet transform (SWT) and an improved gradient-descent total least-squares (GD-TLS) algorithm is proposed in the paper. First, for the scenario where the radar line-of-sight angle is approximately 90°, the micro-Doppler signal characteristics of precession targets under both translational and non-translational motion are analyzed, and SWT is adopted to pre-estimate the precession frequency, avoiding the influence of time-frequency curve overlapping in short-time Fourier transform. Next, the improved singular value ratio spectrum method is utilized to accurately estimate the precession frequency through traversal search. Finally, the GD-TLS algorithm is optimized based on micro-Doppler derivation: an exponential function is introduced into the cost function to suppress impulsive noise, which enhances the algorithm′s immunity to impulsive noise during precession parameter estimation. Experimental results show that the proposed algorithm achieves an estimation error of 0.4 % under noise-free conditions, and still maintain an estimation error within 5.2 % under low signal-to-noise ratio (10 dB) with impulsive noise, outperforming traditional methods including the least mean square, recursive least squares, and robust least mean logarithmic square, which verifies the strong impulsive noise robustness of the proposed method.

       

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