| The target tracking technology based on information fusion theory refers to the process of real-time estimating the states of the target quantity, position, velocity, identity, etc., based on the observations from the multiple sensors, such as radar, infrared, sonar, etc. The target tracking technology has been applied broadly and successfully to the military and civilian applications, including the national defense, navigation, guidance, detection, localization, transportation, manufacturing, finance, medicine, etc. The target tracking environments become more complex recently and the traditional target tracking technologies are challenged seriously by handling high level of uncertainties from environment, such as dense clutters, big observation errors, low SNR, high maneuvers, etc. Most traditional target tracking technologies adopt the single direction and open loop fusion modes and are weak in accumulating, mining and reusing the information. In consequence, the fusion performance of the traditional target tracking technologies will degenerate in complex environments. To this end, this thesis studies the information feedback fusion framework, methods and algorithms for the target tracking problems systematically, and the main results are as follows,1) The traditional information fusion methods have the drawbacks of lacking of the information feedback mechanism. To this end, we first propose the concepts of the spacial information fusion plane and the temporal information fusing space, then construct a general information feedback fusion framework.2) For the traditional variable structure multiple model methods have the drawbacks of lacking of the posterior information feedback mechanism, we propose the information feedback fusion minimum entropy variable structure multiple-model method (MEVSMM) and the sub-optimal algorithm. The proposed method takes the posterior Shannon Entropy as the optimization criterion for the model sequence set, and accomplishes the MSA process using the information feedback fusion mechanism. Compared to some traditional MM methods, the proposed method achieves more robust and accurate estimating results.3) The traditional MSE based algorithms will degenerate in some complex observation error situations. To this end, we set up the GE measure to compensate the ill estimation problem deduced by the SE measure. Further, we propose the information feedback fusion minimum geometrical entropy multiple-model method (MGEMM) and two sub-optimal algorithms. Compared to some traditional MSE methods, the proposed method achieves more robust and accurate results when the prior observation error distribution is inconsistent with the real situation.4) The traditional multiple-model fusion methods have the drawbacks of lacking of the mechanism of historical information feedback processing mechanism. To this end, we propose the historical feedback fusion multiple model method (HFMM) and the sub-optimal algorithm to realize the feedback fusing of the historical information. Compared to some traditional MM methods, the proposed method improves the information utilization ratio and achieves more robust and accurate estimating results.5) For the traditional PHD tracker will lose its efficacy when there is little new target prior information, we propose the historical information feedback fusion multiple targets tracker (HIFMTT) and the sub-optimal algorithm. Compared to the traditional PHD methods, no matter the newborn target prior information is available or not, the proposed method can achieve desirable multiple-target tracking results6) The traditional HIFMTT will deteriorate when tracking the stealth targets. To this end, we propose the information prediction feedback fusion multiple targets tracker (IPFMTT) and the sub-optimal algorithm to handle of low observation detection rate of the stealth target. Compared to the traditional HIFMTT methods, the proposed method improves the tracking detection rate significantly with only a slightly higher false alarm rate. |
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