Steady-state Performance Analyses for Sliding Window Max-correlation Matching Adaptive Algorithms
This paper presents the sliding exponential window max-correlation matching (SEWMCM) adaptive algorithm and the sliding rectangular window max-correlation matching (SRWMCM) adaptive algorithm for finding the maximum correlation of two different signal vectors. A unified approach to the steady-state excess mean square error (MSE) performance analyses for proposed algorithms is developed, including several general close-form analytical expressions based on the nonstationary system identification model. It is conclusively shown by numerical simulations that the SEWMCM algorithm converges faster than the SRWMCM algorithm, whereas the estimation accuracy and the steady-state performance of the SRWMCM outperform those of the SEWMCM and the conventional exponentially-weighted RLS (EWRLS).
Wei Liu Aiqun Hu
School of Information Science and Engineering Southeast University Nanjing, China
国际会议
上海
英文
1-6
2010-10-20（万方平台首次上网日期，不代表论文的发表时间）