稳定分布噪声下基于粒子滤波的双站伪多普勒定位方法

Dual-station pseudo-Doppler localization method based on particle filtering with stable distribution noise

传统的伪多普勒测向算法在高信噪比和高斯噪声环境下能较为精确地计算出到达方位角,但对于稳定分布噪声的顽健性较差。针对以上不足,提出了一种基于粒子滤波的双站伪多普勒定位方法。用粒子滤波对2个接收机的来波方位角进行联合估计,并通过非线性映射得到信源位置坐标估计,实现了方位角计算与双站定位的集成。仿真实验表明,当稳定分布参数α为1.4(中等脉冲程度)时,所提方法在低信噪比下的顽健性要显著优于传统方法,在高信噪比时估计精度与传统方法相当;当信噪比为10 d B时,所提方法在α<1.9的情况下定位精度远高于传统方法。

Traditional pseudo-Doppler bearing estimation algorithm could accurately calculate the angle of arrival（AOA） with Gaussian noise and high signal to noise ratio（SNR）, but it was less robust with stable distribution noise. To overcome these shortcomings, a dual-station pseudo-Doppler localization method based on the particle filtering was proposed. The method employed particle filtering approach to jointly estimate the AOA of both stations, then applied a non-linear mapping to acquire the source location, forming an integration of AOA calculation and dual-station localization. Simulations demonstrate that when the characteristic exponent of the stable distribution is in a medium degree, for example α =1.4, the proposed method is much more robust than the traditional method in low SNR circumstances, while maintaining the estimation accuracy of the traditional method when SNR is high. When SNR equals 10 d B, the positioning accuracy of the proposed method is much higher than the traditional method with α 1.9.