基于t分布混合模型的抗差关联算法  

Anti-bias Track Association Algorithm Based on t-distribution Mixture Model

作　　者：李保珠[1] 董云龙[1] 李秀友[1] 关键[1] 

机构地区：[1]海军航空工程学院信息融合技术研究所,烟台264001

出　　处：《电子与信息学报》2017年第7期1774-1778,共5页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(61471382;61401495;61501487;61531020);山东省自然科学基金(2015ZRA06052)~~

摘　　要：针对传感器系统误差和观测目标不完全一致的情况下目标航迹关联中鲁棒性问题,该文提出一种基于t分布混合模型的抗差关联算法。将航迹关联问题转化为图像匹配中的非刚性点集匹配问题,针对非共同观测目标影响关联性能的问题,将非共同观测目标的航迹视为图像匹配中的异常点,建立了对异常点具有更好鲁棒性的重拖尾t分布混合模型,利用期望最大化(EM)算法求解t分布混合模型的闭合解,在求解中为了确保航迹点间的运动一致性(CPD),加入Tikhonov正则项。最后通过实验仿真验证,所提算法在系统误差和观测目标不完全一致情况下的鲁棒性和有效性。In order to solve the problem of robust track-to-track association in the presence of sensor biases and non-identical observation, an anti-bias track association algorithm based on t-distribution mixture model is proposed. The robust track-to-track association problem is turned into the non-rigid point matching problem. The tracks of non-common are regarded as outliers in the point matching for the effects of the track-to-track association caused by non-identical observation. The heavy-tailed t-distribution mixture model is established with better robustness to outliers. The closed-form solution of t-distribution mixture model is solved by Expectation Maximization （EM） algorithm. The conditional expectation function is added a regular item of point set, so that the points have a feature of Coherent Point Drift （CPD）. Finally, the effectiveness of the proposed algorithm is verified by simulation experiments at the presence of sensor biases and missed detections.

关 键 词：航迹关联 系统误差 t分布混合模型 期望最大化算法 运动一致性 

分 类 号：TN957[电子电信—信号与信息处理]