Encoding-Decoding-Based Distributed Fusion Filtering for Multi-Rate Nonlinear Systems With Sensor Resolutions

Abstract

The paper investigates the distributed fusion filtering problem for time-varying multi-rate nonlinear systems (TVMRNSs) with sensor resolutions based on the encoding-decoding scheme (EDS) over sensor networks, where the iterative method is applied to the transformation of TVMRNSs. In order to enhance signal interference-resistant capability and improve transmission efficiency, the EDS based on dynamic quantization is introduced during the measurement transmission. On the basis of the decoded measurements, a local distributed filter is constructed, where an upper bound on the local filtering error (LFE) covariance is derived and the local filter gains are obtained by minimizing the trace of the upper bound. Subsequently, the fusion filtering algorithm is presented according to the covariance intersection fusion criterion. In addition, a sufficient condition is provided via reasonable assumptions to ensure the uniform boundedness of the upper bound on the LFE covariance. Finally, a moving target tracking practical example is taken to show the superiority of the proposed filtering algorithm and discuss the monotonicity of the mean-square error of the fusion filter with respect to the sensor resolutions and quantization intervals.