Channel Tracking Using Particle Filtering in Unresolvable Multipath Environments

Abstract

We propose a new timing error detector for timing tracking loops inside the Rake receiver in spread spectrum systems. Based on a particle filter, this timing error detector jointly tracks the delays of each path of the frequency-selective channels. Instead of using a conventional channel estimator, we have introduced a joint time delay and channel estimator with almost no additional computational complexity. The proposed scheme avoids the drawback of the classical early-late gate detector which is not able to separate closely spaced paths. Simulation results show that the proposed detectors outperform the conventional early-late gate detector in indoor scenarios

Review on Sparse-Based Multipath Estimation and Mitigation: Intense Solution to Counteract the Effects in Software GPS Receivers

“…In the highly non stationary environment, Researchers also used Kalman Filter, particle filters and multiple differential GPS receivers to remove multipath errors in final positioning [13]. Code multipath is calibrated and estimated using spherical harmonics in static applications, similarly for kinematic applications, the multipath error mitigation is carried out by Mozaviet et al [14] using wavelet transform.…”

Bayesian Time Delay Estimation of GNSS Signals in Dynamic Multipath Environments

“…measurements that channel parameters are time varying but not independent from one time instance to the next; for example, an echo usually experiences a "life-cycle" from its first occurrence, then a more or less gradual change in its delay and phase over time, until it disappears [21]. These measurements also show that the common channel models considered in communication systems [17,18] are not adequately reflecting the properties that are crucial for highresolution signal delay estimation as required in navigation systems.…”

“…In this paper, we concentrate on dynamic estimators applied per each range. The sequential Monte Carlo approach has also been suggested in the communications field for estimation of time-varying channel responses in spread spectrum systems [17,18].…”

“…is calculated in the so-called prediction step by applying the Chapman-Kolmogorov equation: (18) with p(x k | x k−1 ) being the state transition PDF of the Markov process. In the update step, the new posterior PDF for step k is obtained by applying Bayes' rule to p(x k | z k , Z k−1 ) yielding the normalized product of the likelihood p(z k | x k ) and the prior PDF:…”

Particle filtering for UWB radar applications

“…If the number of particles is sufficiently large, the estimates reach optimal Bayesian estimation. Particle filtering is commonly known to be used in localization application and tracking in dynamic scenarios [9]- [11]. Unlike a maximum likelihood estimator, the particle filtering algorithm is very flexible to model.…”