Accurate GNSS Range Estimation in Multipath Environments Using Stochastic-Gradient-Based Adaptive Filtering

Abstract

Multipath propagation is one of the dominant sources in ranging applications. The problem of GNSS multipath mitigation is investigated herein through the development of adaptive multipath channel compensation filters that impose a smaller computational load on the receiver compared to most of the advanced delay estimation techniques. An optimum estimation block is designed to produce the control error signal in the feedback loop used to update the filter coefficients adaptively. Using the proposed method, multipath distortion on the received signal is compensated for by passing signals through a filter whose coefficients are adaptively adjusted employing feedback signals. A set of simulation and real data processing results are presented to compare the performance of the proposed adaptive techniques with some of the conventional DLLs under realistic urban channel conditions. The experimental results are based on live vehicular GPS data collected in dense urban environments; the results demonstrate a reduction of up to 30% in RMSE values of the computed position errors. Copyright © 2016 Institute of Navigation.

Context-Aware Adaptive Multipath Compensation Based on Channel Pattern Recognition for GNSS Receivers

“…For each of these cases, the value of λ is selected based on Table 2. For the suburban pedestrian scenario, it uses WLMS with the corresponding value of μ based on Table 2 (because WLMS is less complex than WRLS (Sokhandan et al, 2015)). Monte-Carlo simulations results are averaged over 8000 ms of data for each value of carrier-to-noise ratio (C/N 0 ).…”

“…The adaptive algorithms applied for multipath compensation and tracking in this paper are selected from the class of stochastic-gradient-based adaptive filters in both time and wavelet domains (Sokhandan et al, 2015). Here, two stochastic gradient approaches, namely the LMS and RLS and their wavelet-domain duals, referred to as WLMS and WRLS, are used to compensate for the effect of multipath channel from the correlation samples and estimate the parameters of the LOS signal.…”

Channel compensation multipath mitigation technique for Kalman‐Based Least Mean Square based on Kalman estimation

“…In the GNSS receiver, after entering the tracking loop through the radio frequency front‐end and baseband signals, the multipath channel output after the baseband signal processing is 17‐19 : where, b q are the data bits, t b is the period, m ( t ) is the signal waveform, and b ( t ) is represented by: where t P is the pseudo code period, N b is the Integer ambiguity, and p ( t ) is the spreading waveform with the chip interval of t c and can be represented as: where c [n] is the PN code, N c is the code length, v ( t − t c ) is the signal waveform.…”