Random History Set-based MSIF Target Identification Method with Feedback
To improve the accuracy of target identification in complex battlefield environments,a distributed MSIF model is proposed and continuous information in the time domain is processed by in-corporating feedback information flow.Based on the classical D-S evidence theory,the Fisher-Yates shuffling method is used to disorder the information ordering in the ubiquitous sensor matrix to generate a random history set for traditional algorithm's drawback of not being able to process high conflict evi-dence,the algorithm is iterated with the increase of number of operations.By improving the algorithm,the bias of recognition results due to the premature appearance of high conflict evidence was corrected.The numerical examples show that the method can both improve information utilization and overcome the influence of information precedence trap with high accuracy and low computational complexity,which can effectively reduce the system burden and is suitable for large data volume processing in MSIF target recognition.
target recognitionevidence fusionMSIFrandom history set
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