基于PHD滤波的相控阵雷达多目标跟踪算法

对于相控阵雷达方向余弦量测,采用扩展卡尔曼概率假设密度(extended Kalman probability hypothesis density, EK PHD)滤波进行多目标跟踪时,存在目标数估计偏高和目标状态估计准确度低的问题。针对上述问题,提出了一种新的多目标跟踪算法——无偏转换量测概率假设密度(unbiased converted measurements PHD, UBCM PHD)滤波算法。该算法采用方向余弦量测下的量测转换方法,保留了更多的量测信息;同时对转换后的量测偏差进行补偿,使量测转换误差的均值、方差准确近似原始量测高斯分布的一、二阶矩。仿真实验表明,所提算法可提高目标数和目标状态估计准确性。

Multi-target tracking based on PHD filter for phased array radar

The extended Kalman probability hypothesis density (EK PHD) filter has a higher bias in the estimation of the number of targets and a lower estimation accuracy of their states by using the direction cosine coordinate measurements for the phased array radar. To solve this problem, a novel multi target tracking algorithm called unbiased converted measurements probability hypothesis density (UBCM PHD) filter algorithm is proposed. The proposed algorithm utilizes the unbiased converted method to remain more information about the direction cosine coordinate measurements. Meanwhile, it compensates the bias caused by the converting direction cosine coordinate to Cartesian coordinate measurements, and the means and variances of the converted errors could accurately approximate the first order and second order moments of Gaussian distribution for original measurements. The simulation results indicate that the proposed algorithm improves the estimation accuracy of both the number of targets and their states.