The extended Kalman probability hypothesis density (EK-PHD)filter has a higher bias in the es-timation of the number of targets and a lower estimation accuracy of their states by using the direction cosine co-ordinate measurements for the phased array radar.To solve this problem,a novel multi-target tracking algo-rithm 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 direc-tion cosine coordinate to Cartesian coordinate measurements,and the means and variances of the converted er-rors 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.%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 hy-pothesis 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。
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