Channel compensation multipath mitigation technique for Kalman‐Based Least Mean Square based on Kalman estimation. (10th December 2020)

• Record Type: Journal Article
• Authors: Li, Dengao; Wang, Xiaoyu; Zhao, Jumin; Cheng, Junbing
• Is Part Of: Concurrency and computation. Volume 33:Number 9(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-12-10
• Subjects: adaptive multipath channel delay estimation block -- GNSS -- KBLMS -- LOS signal estimation block -- multipath mitigation; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.6133
• Languages: English
• ISSNs: 1532-0626

Abstract

Abstract: In the Global Navigation Satellite System, the traditional adaptive equalization algorithm re‐captures and tracks poor performance under the multipath channel for multipath errors caused by occlusion or reflection of buildings. A channel compensation multipath mitigation technique based on Kalman‐Based Least Mean Square based (KBLMS) on Kalman estimation. First, in the tracking loop of the receiver, a KBLMS‐based delay estimation module is designed to compensate for multipath distortion on the received signal. The coefficients of the filter are adaptively adjusted using the feedback signal. Second, a line of sight best estimate block is designed to generate a control error signal in the feedback loop for adaptively updating the filter coefficients. Finally, the performance of the proposed algorithm is verified by analyzing the performance of KBLMS, Recursive Least Squares (RLSs) and Least Mean Squares (LMS) algorithms through measured data and experimental simulation. The results show that KBLMS can converge quickly in multipath channels compared with RLS and LMS, and the code tracking error and carrier tracking error are reduced by 0.1chip, 0.2 cycle and final residual error is reduced by 0.015 compared with the RLS algorithm, which is 0.035 lower than the LMS algorithm, which shows that the KBLMS algorithm can make more accurate estimation.