| The multi-sensor information fusion technology is an interdisciplinary study. In recent years, as the development of the micro-electronics, the network communicate technology and the control technology, which made the target tracking technology of the information fusion is widely used in the military affairs and civil aspect. The multi-sensor information fusion technology includes target tracking technology of the data correlation and the state fusion estimation.Based on the improved of the fusion structure and the algorithm already existed, this paper mainly studies the problem of the multi-sensor state fusion estimation under the premise that the data calibration and the data association are completed. Firstly, based on the EFRLS filter, the track fusion algorithm of multi-sensor dynamic system without the knowledge of noise covariance in multi-target tracking is presented. Secondly,on the account of the difference sampling rates between the sensor platform and the track fusion center, the MAP fusion algorithm of the asynchronous multi-sensor system with feedback and without feedback is presented; Then on the account of degrade of the performance because of the environment factor and the unspecified statistical characteristics of the system noise, the weighting fusion algorithm which based on the adaptive fading Kalman filter weighted by different value probability from different sensor is given. Lastly, the distributed IMM fusion algorithm of multi-platform in case of the known correlated noise covariance when the target maneuver is unknown is presented. Furthermore, this paper gives the numerical simulation and analysis for all algorithms. The simulation shows that all of the algorithms are feasibility and validity, and forecasts the further research.Keywords: without the knowledge of the noise covariance, asynchronous multi-sensor, maximum posterior estimation(MAP), integrated navigation, adaptive fading filter, target maneuver, distributed interacting multiple mode... |
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