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为了提高杂波条件下的空中机动目标跟踪精度,提出了一个改进的交互多模型概率数据关联算法。该算法将交互多模型、去偏转换测量和概率数据关联算法相结合,利用交互多模型算法模型集合间不同模型的相互切换来估计跟踪目标的状态;利用去偏转换测量算法对转换测量误差进行去偏补偿,从而减小观测数据坐标变换引起的误差;利用概率数据关联算法处理数据关联和测量的不确定性。通过将本文的算法和基于扩展卡尔曼滤波的概率数据关联算法进行对比分析和验证,实验结果表明本文提出的算法可以提高机动目标的跟踪精度,且跟踪精度相对基于扩展卡尔曼滤波的概率数据关联算法减少26.38%的位置误差。
In order to improve tracking accuracy of airborne maneuvering targets under clutter, an improved algorithm for correlating probability data with multiple models is proposed. The algorithm combines interactive multi-model, de-skew measurement and probabilistic data association algorithm to estimate the state of the tracking target by using the mutual switch between different models of the interaction model set. The algorithm of de-skewing measurement is used to measure the conversion error To offset the compensation, thereby reducing the error caused by the coordinate transformation of the observed data; the use of probability data association algorithm to deal with the data association and measurement uncertainty. Experimental results show that the algorithm proposed in this paper can improve the tracking accuracy of maneuvering targets and the tracking accuracy is better than that of the extended Kalman filter based on probability data association algorithm The algorithm reduces the position error by 26.38%.