Key Technologies Research On Multi-target Tracking Of Multi-sensor Data Fusion

With the target tracking environment changing significantly, the tracking system design faces serious challenge of stealth and anti-stealth, ECM and anti-ECM, strong maneuvering, high clutter, low detection probability and high false-alarm ratio. The faced complex application background multi-sensor data fusion technology receives more and more attention, to get more complete and more accurate information of observation object. It is certain that the study of data fusion is the key of winning future war. The multi-sensor multi-target tracking is the bottom key technology of dada fusion. To get better estimate on the state of target motion than single sensor's estimate, the multi-sensor information is synthesized organically. In the background of multi-sensor data fusion, three key technologies are discussed in this thesis focused on multi-sensor multi-target tracking, including maneuvering target state estimation, data association and special situation processing. The main research and innovation contents are as following:Firstly, when fixed Interacting Multiple Model (IMM) is used to track target with great maneuver, it is difficult to remain satisfactory performance. A new EVIM adaptive filtering algorithm based on expected system noise model is proposed. In this approach, a part of the system noise models are adjusted to adaptively match the unknown true mode, and track precision is raised.Secondly, according to the problem of difficult data association in complex environment, two data association algorithms are proposed; both of them improved performance of data association. The first is a modified Fuzzy C-means (FCM) cluster algorithm based data association algorithm. The original FCM based algorithm always makes mistake in data association in dense targets and clutter environment. Based on that, the proposed algorithm includes two parts: coarse and precision correlation: a part of interferences are eliminated during coarse correlation; then the remained sensors data are fuzzy clustered using FCM algorithm, through decomposing the data association problem of multi-sensor multi-target into that of several single sensor multi-target, the latter is of better performance for multi-sensor multi-target data association; finally, the better state estimation of the targets are gained after measure-to-measure fusion; Another proposed algorithm is the S-dimensional(S-D) assignment algorithm based on Particle Swarm Optimization algorithm. The main challenge in associating data from three or more scans of measurements, is that the resulting S-D assignment problem for S≥3 is NP-hard. Therefore, S-D assignment is formulated as a combinational optimal problem, and solved faster by Particle Swarm Optimization (PSO) algorithm. And before S-D algorithm carrying out, the track gate is used to confirm the validation of measurements, which reduces computation enormously. Moreover, the crossover operator and mutation operator are introduced to PSO, and the best solution for data association could be searched faster, because of reducing the search range, through the validated candidate measures are considered in particle swarm initiation, crossover rules and mutation rules.Thirdly, the traditional data association methods always use those direct correlation kinematics information, so the information for association is of little quantity and low quality, and consequently correct ratio of it is low in complex environment. For that, three methods are described in the following for combining the classification information and the kinematic information, to improve data association. The first method: the classification information and the kinematic information are combined, and both used to IPDA (Intergrated Probabilistic Data Association). For the original IPDA algorithm, the intergrated measurements of targets are always confounded in dense measurements environment. Thus the classification information likelihood function, which is defined by class confusion matrix, is used to adjust the kinematic measurement likelihood function in former algorithm. So the new synthetical likelihood function is structured on two kinds of information, and association probability is also modified. In this way, the classification information can aid data association effectively; the second method: Similarly the classification information likelihood function is used to modify graphical models based on track association algorithm. For original algorithm has low association precision in complex environment, the two kinds of information are both applied to rebuilding node compatibility functions and edge compatibility functions. The proposed algorithm improves data association performance in the sensor networks; the third method: the multi-character information and the kinematic information are fused directly, both used to Fuzzy C-means cluster based on data association algorithm. In dense measurements, when the kinematic information is only used to cluster, the association result is always not correct. So that in the proposed algorithm, the calculation of key parameter-distance and subjection functions is based on both kenematic information and multiple features of the target. Furthermore, the effect of features can be adjusted, thus data association is better realized based on the multiple features. The comprehensive information greatly raises the performance of data association.Finally, outlier and out-of-sequence measurement, two possible kinds of uncertain situation in data fusion system, are researched. For outlier, when it arrives in fusion center directly, it would induce large deviation to data fusion. So a method of real-time eliminating outlier multi-sensor data fusion is proposed. In this method, firstly the track of multi-sensor is clustered by FCM, and then the outliers are detected and eliminated, according to the compact degree and the membership. Finally forecasted estimation takes the place of outliers, and is sent to fusion. This method solves outlier problem rapidly and effectively, as well as data association problem; For out-of-sequence measurement (OOSM), traditional filters can hardly deal with it. According to extended Kalman filter (EKF) based OOSM filter has low precision for the nonlinear system, an optimal and a sub-optimal Unscented Kalman filter for out-of-sequence measurements are proposed. The procedure of out-of-sequence measurement updating, that converting multiple steps lag problem to one step lag problem, is deduced again based on UKF. And the method needs little memory burden. Simulation results show that the presented algorithms are more effective in utilizing out-of-sequence measurements and target tracking performance than those in EKF filter for nonlinear system. At last, the treatment of multiple OOSMs, which have the same lags, is researched. A FCM based data association algorithm is proposed to deal with multiple OOSMs. Thus the proposed UKF can update with multiple OOSMs, and raises track precision.