Key Technologies Research On Multi-Sensor Data Fusion For Target Tracking

With the rapid development of target tracking and information fusion technology, people begin to integrated utilize the measurements from multi-sensor to estimate the position and kinetics parameters, and useful information is extract to the greatest extent to track the moving object. How to effective fuse the measurements from multi-sensor and obtain better tracking performance compared to the single sensor are an important research in multi-sensor target tracking domain.Some key technologies researches on multi-sensor data fusion for target tracking are investigated in this paper, and it can provide a reference for the data fusion application to the target tracking domain. Based on the work of predecessors, specific tasks in this paper are as follows:1. Ensemble Kalman filter is introduced to multi-sensor target tracking system where feasibility and validity of ensemble Kalman filter are verified. For the common process noise and the same measurements to all the EnKF, the cross-covariance can not be omitted simply, a new track-to-track fusion algorithm based on ensemble Kalman filter using covariance matrix weighting is proposed. Considering the affection of target tracking performance from correlated tracks and different initial states, a new target tracking algorithm based on block ensemble Kalman filter is proposed, where initial ensemble is produced by block method and covariance matrix weighting is proposed for all the blocks in the target tracking process. An improved ensemble particle filter (EnPF) algorithm combining the advantages of EnKF and PF is proposed. Two separate ensembles are adopted, one ensemble is handled by EnKF first, then the analysis ensemble produced by EnKF and another ensemble are integrated to generate proposal distribution of PF; finally PF is executed based on this proposal distribution. And the improved EnPF combines the advantages of EnKF and PF and solves the deficiency of Gaussian condition of EnKF and high computational cost of PF.2. A new statistical data fusion algorithm based on relation matrix is discussed when the measurements from sensors do not follow Gaussian distribution, the algorithm uses integrated fusion degree of each sensor to build the relation matrix where eclipse curve fuzzy technology is adopted nearby threshold.3. Measurement errors of sensors are composed of constant errors and random errors. The constant errors are decided by sensors themselves, and the random errors are affected by other factors, such as the target distance, disturbance of weather or man-made. Without consideration of disturbance, random errors caused by the distance are the major causes. A variable-weight data fusion algorithm only considering random errors caused by the distance is discussed, the algorithm uses an exponential function to compute variable weight coefficient of active radar.4. To the problem of tracking multi-target, information gain maximization is not sufficient condition but only necessary condition when it is used to distribute the sensor resources, a new method of sensor assignment based on combing target priority with information gain is proposed, and the quantitative method based on distance and velocity affecting the target priority is discussed.