A Novel Adaptive Kalman Filter Based on Credibility Measure

It is quite often that the theoretic model used in the Kalman filtering may not be sufficiently accurate for practical applications,due to the fact that the covariances of noises are not exactly known.Our previous work reveals that in such scenario the filter calculated mean square errors(FMSE)and the true mean square errors(TMSE)become inconsistent,while FMSE and TMSE are consistent in the Kalman filter with accurate models.This can lead to low credibility of state estimation regardless of using Kalman filters or adaptive Kalman filters.Obviously,it is important to study the inconsistency issue since it is vital to understand the quantitative influence induced by the inaccurate models.Aiming at this,the concept of credibility is adopted to discuss the inconsistency problem in this paper.In order to formulate the degree of the credibility,a trust factor is constructed based on the FMSE and the TMSE.However,the trust factor can not be directly computed since the TMSE cannot be found for practical applications.Based on the definition of trust factor,the estimation of the trust factor is successfully modified to online estimation of the TMSE.More importantly,a necessary and sufficient condition is found,which turns out to be the basis for better design of Kalman filters with high performance.Accordingly,beyond trust factor estimation with Sage-Husa technique(TFE-SHT),three novel trust factor estimation methods,which are directly numerical solving method(TFE-DNS),the particle swarm optimization method(PSO)and expectation maximization-particle swarm optimization method(EM-PSO)are proposed.The analysis and simulation results both show that the proposed TFE-DNS is better than the TFE-SHT for the case of single unknown noise covariance.Meanwhile,the proposed EMPSO performs completely better than the EM and PSO on the estimation of the credibility degree and state when both noise covariances should be estimated online.

Optimization of a Segmented Filter with a New Error Diffusion Approach

The segmented filters, based on spectral cutting, proved their efficiency for the multi-correlation. In this article we propose an optimisation of this cutting according to a new error diffusion method.

A novel differential multiuser detection algorithm for multiuser MIMO-OFDM systems

We propose an efficient low bit error rate(BER) and low complexity multiple-input multiple-output(MIMO) multiuser detection(MUD) method for use with multiuser MIMO orthogonal frequency division multiplexing(OFDM) systems.It is a hybrid method combining a multiuser-interference-cancellation-based decision feedback equalizer using error feedback filter(MIMO MIC DFE-EFF) and a differential algorithm.The proposed method,termed 'MIMO MIC DFE-EFF with a differential algorithm' for short,has a multiuser feedback structure.We describe the schemes of MIMO MIC DFE-EFF and MIMO MIC DFE-EFF with a differential algorithm,and compare their minimum mean square error(MMSE) performance and computational complexity.Simulation results show that a significant performance gain can be achieved by employing the MIMO MIC DFE-EFF detection algorithm in the context of a multiuser MIMO-OFDM system over frequency selective Rayleigh channel.MIMO MIC DFE-EFF with the differential algorithm improves both computational efficiency and BER performance in a multistage structure relative to conventional DFE-EFF,though there is a small reduction in system performance compared with MIMO MIC DFE-EFF without the differential algorithm.