针对双传感器杂波环境中单目标的跟踪问题,提出了一种红外/毫米波角度观测关联及基于观测对最近邻的双模融合跟踪算法。算法根据对真实目标的观测与对杂波的虚假观测联合分布模型的不同,筛选出当前时刻的嫌疑观测对,剔除掉大部分杂波;然后对各嫌疑观测对进行融合,从中选择距离预测位置最近的观测作为当前时刻的观测,并用EKF对目标进行跟踪。算法关联逻辑简明、运算量小,适合工程应用,可应用在中等杂波密度环境,仿真结果验证了算法的有效性。

Focused on a single target tracking in clutter with two sensors, a dual-mode fusion tracking algorithm based on nearest neighbor extended Kalman filter within IR/MMW angle observations association was presented. After figuring out the suspected pairs of observations (SPO) according to their spatial distribution models of observations originated from the target and clutter, most clutter was eliminated. The remained SPO were fused to single observations in terms of observation noise variance. The fused observation which is nearest to the prediction was used to update the target track, which utilized an extended Kalman filter. The algorithm can be applied to track a single target in mediate clutter density for its simplified association logic and low computation load, which is suitable for engineering implementation. The simulation results reveal the validity of the algorithm.