Detecting, estimating and correcting multipath biases affecting GNSS signals using a marginalized likelihood ratio-based method

Abstract

Calmettes. Detecting, estimating and correcting multipath biases affecting GNSS signals using a marginalized likelihood ratio-based method. Signal Processing, Elsevier, 2016, vol. 118, pp. 221-234 a b s t r a c tIn urban canyons, non-line-of-sight (NLOS) multipath interferences affect position estimation based on global navigation satellite systems (GNSS). This paper proposes to model the effects of NLOS multipath interferences as mean value jumps contaminating the GNSS pseudo-range measurements. The marginalized likelihood ratio test (MLRT) is then investigated to detect, identify and estimate the corresponding NLOS multipath biases. However, the MLRT test statistics is difficult to compute. In this work, we consider a Monte Carlo integration technique based on bias magnitude sampling. Jensen's inequality allows this Monte Carlo integration to be simplified. The multiple model algorithm is also used to update the prior information for each bias magnitude sample. Some strategies are designed for estimating and correcting the NLOS multipath biases. In order to demonstrate the performance of the MLRT, experiments allowing several localization methods to be compared are performed. Finally, results from a measurement campaign conducted in an urban canyon are presented in order to evaluate the performance of the proposed algorithm in a representative environment.

Simulink‐based teaching design for multipath‐effect elimination utilizing convolution analysis and pade approximation

“…The Pade approximation is one very useful method for modeling time‐delay effects in continuous‐time systems from Laplace S‐transform domain. This approach simulates time delays like transport and computation delays by rational LTI (“linear Time Invariant”) models [1,6]. Normally, the most commonly transform functions of interest to us, and also easily to be implemented, are the rational functions, one of which is characterized by its N ‐order numerator and its N ‐order denominator, both polynomials of Laplace variable s , …”

“…Generalizations of convolution have been employed in a wide range of applications in science and engineering. Pade approximation is one most commonly used approach to estimate time delays by rational LTI (“linear Time Invariant”) models [1,6]. Normally, the most commonly transform functions of interest to us, and also easily to be implemented, are the rational functions.…”

Convolutional Neural Network for Multipath Detection in GNSS Receivers

“…Alternatively, the multipath can be mitigated at the software level of the receiver either in acquisition or in tracking loops. As the GNSS receiver has to track the direct signal contaminated by delayed reflections, several multipath mitigation methods based on the narrow correlator Delay Lock Loop (DLL) were listed in [12]. They include the strobe correlator [13], the early-late-slope technique [14], the double-delta correlator [15] and the multipath intensive delay lock loop [16].…”

“…In the case of Non-Line-of-Sight (NLOS) effect, multipath can be hardly mitigated by these approaches. To overcome these limits, [12] proposes to use a Generalized Likelihood Ratio Test (GLRT) [19] and also a Marginalized Likelihood Ratio Test (MLRT) [20], for fault detection and diagnosis.…”

NLOS Signal Detection Based on Single Orthogonal Dual-Polarized GNSS Antenna

“…Consistency-checking techniques [6] can identify and eliminate NLOS signals when most of the other received signals are LOS signal with minimal multipath interference. The marginalized likelihood ratio test (MLRT) is used to detect, identify, and estimate the corresponding NLOS signal [7]. The NLOS signal can be detected and mitigated by judging measurement of more satellites using Receiver Autonomous Integrity Monitoring (RAIM) [8].…”