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相似性测度算法是根据航迹模式完成各局部航迹分配的关联方法,关联效果较好。为减小相似性测度计算过程中不确定因素对航迹关联的不利影响,实现信息融合系统性能的持续改进,基于不确定性分析提出了修正相似性测度关联算法。分析了归一化航迹相似性测度关联中存在的不确定性;综合考虑了传感器虚情、漏情、未检测区域以及参数未确知性等因素对航迹关联的影响,计算出相应的影响因子,并用这些影响因子对航迹的相似性测度进行修正。实验表明,修正的航迹关联算法能获得更低的关联错误率,有效提高系统鲁棒性。
The similarity measure algorithm is based on the track pattern to complete the correlation of each track allocation, the correlation effect is better. In order to reduce the unfavorable influence of the uncertain factors on the trajectory correlation during the calculation of the similarity measure and to achieve continuous improvement of the performance of the information fusion system, a correlative similarity measure correlation algorithm is proposed based on the uncertainty analysis. The uncertainties in the correlation measure of normalized trajectory similarity are analyzed. The impact of the factors such as the sensor’s falsehood, the leak, the undetected region and the parameter’s unascertainedness on the track association is considered, and the corresponding influence is calculated Factor, and use these influence factors to modify the similarity measure of track. Experiments show that the modified trajectory association algorithm can achieve a lower correlation error rate and improve the system robustness effectively.