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Multisensor data fusion has played a significant role in diverse areas ranging from local robot guidance to global military theatre defense etc.Various multisensor data fusion methods have been extensively investigated by researchers,of which Klaman filtering is one of the most important.Kalman filtering is the best-known recursive least mean-square algorithm to optimally estimate the unknown states of a dynamic system,which has found widespread application in many areas.The scope of the work is restricted to investigate the various data fusion and track fusion techniques based on the Kalman Filter methods,then a new method of state fusion is proposed. Finally the simulation results demonstrate the effectiveness of the introduced method.
Multisensor data fusion has played a significant role in diverse areas ranging from local robot guidance to global military the defense defense etc.Various multisensor data fusion methods have been extensively investigated by researchers, of which Klaman filtering is one of the most important. Kalman filtering is the best-known recursive least mean-square algorithm to optimally estimate the unknown states of a dynamic system, which has found widespread applications in many areas. Scope of the work is restricted to investigate the various data fusion and track fusion techniques based on the Kalman Filter the method, then a new method of state fusion is proposed. Finally the simulation results demonstrate the effectiveness of the introduced method.