Performance Improvement of an Extended Kalman Filter Using Simplified Indirect Inference Method Fuzzy Logic

간편 간접추론 방식의 퍼지논리에 의한 확장 칼만필터의 성능 향상

  • 채창현 (금오공과대학교 전자공학부)
  • Received : 2016.02.26
  • Accepted : 2016.03.29
  • Published : 2016.04.30


In order to improve the performance of an extended Kalman filter, a simplified indirect inference method (SIIM) fuzzy logic system (FLS) is proposed. The proposed FLS is composed of two fuzzy input variables, four fuzzy rules and one fuzzy output. Two normalized fuzzy input variables are the variance between the trace of a prior and a posterior covariance matrix, and the residual error of a Kalman algorithm. One fuzzy output variable is the weighting factor to adjust for the Kalman gain. There is no need to decide the number and the membership function of input variables, because we employ the normalized monotone increasing/decreasing function. The single parameter to be determined is the magnitude of a universe of discourse in the output variable. The structure of the proposed FLS is simple and easy to apply to various nonlinear state estimation problems. The simulation results show that the proposed FLS has strong adaptability to estimate the states of the incoming/outgoing moving objects, and outperforms the conventional extended Kalman filter algorithm by providing solutions that are more accurate.


Supported by : 금오공과대학교


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