Performance Analysis of Blind Multiuser Detector with Fuzzy Kalman Filter in IR-UWB

With on-going widespread deployment of UWB systems, multi-user detection has received increasing attention. In order to successfully decode the users in a UWB system, the most important issue is to be solved, apart from the multi-path and multiple-access channel. Most existing approaches employ correlators to correlate the received signal with a template signal. This technique appears very powerful, but not so satisfactory in a multi-path and multiple-access channel. A novel multi-user detection algorithm relying on the theory of canonical representation of multi-user receivers and fuzzy Kalman filtering (FKF) is proposed for impulse radio ultra-wideband systems. Dynamical systems of time-hopping BPSK in ultra-wideband are developed separately in the paper. Although the Kalman filtering algorithm is conditioned on perfect knowledge of some system parameters, we have used the fuzzy technique to overcome the difficulties. It is presumed that both, the process noise and the measurement noise, are zero-mean white sequences with their known covariance. If the Kalman filter is based on a complete and perfect model, the innovation should be a zero-mean white noise process, otherwise there is something erroneous with the design and the filter is not performing optimally, thus making the filter to diverge or coverage to a large bound. In practice, it is difficult to know the exact values for covariance. In order to reduce computation, we have to ignore some errors, but sometimes those unmodeled errors will remain significant. The adaptation of the filter uses a fuzzy inference system based on a covariance matching scheme that adjusts the measurement noise covariance matrix online. The basic idea behind this technique is to make the residual consistent with their theoretical covariance. The fuzzy Kalman filter method is proposed in the previous subsection is related to many existing algorithms for adaptive multiuser detector. Now, some performances are given to compare the differences from other prior algorithms. Since the Kalman filter scheme and the RLS one are similar, it is necessary to compare the proposed algorithm with the before RLS algorithms. Theoretical analysis and numerical results show that the new scheme has a better bit error rate performance compared with the conventional detector, and the effectiveness of the novel Kalman filtering algorithm.