Mobility Tracking in Cellular Networks Using Particle Filtering

Abstract

Mobility tracking based on data from wireless cellular networks is a key challenge that has been recently investigated both from a theoretical and practical point of view. This paper proposes Monte Carlo techniques for mobility tracking in wireless communication networks by means of received signal strength indications. These techniques allow for accurate estimation of Mobile Station's (MS) position and speed. The command process of the MS is represented by a first-order Markov model which can take values from a finite set of acceleration levels. The wide range of acceleration changes is covered by a set of preliminary determined acceleration values. A particle filter and a Rao-Blackwellised particle filter are proposed and their performance is evaluated both over synthetic and real data. A comparison with an Extended Kalman Filter (EKF) is performed with respect to accuracy and computational complexity. With a small number of particles the RBPF gives more accurate results than the PF and the EKF. A posterior Cramér Rao lower bound (PCRLB) is calculated and it is compared with the filters' rootmean-square error performance.

A fast Bayesian model for latent radio signal prediction

“…The problem of tracking the location of a mobile user within a wireless network was considered by Mihaylova et al [9] (see also the references therein). Mihaylova et al also employ approximation techniques for Bayesian estimation, but instead consider a hybrid technique known as Rao-Blackwellised Particle Filtering.…”

Location estimation using RSS measurements with unknown path loss exponents

“…However, in most of the RSS-based location estimation techniques in the literature, it is assumed that the propagation parameters of the measurement channel are accurately known a priori, either through a training period or by assuming a perfect free-space channel condition [18][19][20][21][22][23][24]. Such an approach results in degraded positioning accuracy in many practical application scenarios, including mobile tracking techniques in [25,26]. More specifically, both [25,26] study Monte Carlo-based mobility tracking algorithms under the assumption that the mobile moves in a 2D environment with input as acceleration.…”

“…Such an approach results in degraded positioning accuracy in many practical application scenarios, including mobile tracking techniques in [25,26]. More specifically, both [25,26] study Monte Carlo-based mobility tracking algorithms under the assumption that the mobile moves in a 2D environment with input as acceleration. In [25], RSS data are used to predict mobile position and velocity using particle filtering techniques, but the method relies on known propagation parameters.…”

“…More specifically, both [25,26] study Monte Carlo-based mobility tracking algorithms under the assumption that the mobile moves in a 2D environment with input as acceleration. In [25], RSS data are used to predict mobile position and velocity using particle filtering techniques, but the method relies on known propagation parameters. In [26], the so-called interacting multiple model method has been extended to predict the state of the mobile for indoor applications by considering multiple fixed environment parameters at the expense of an increase in the number of random processes, whereas the technique herein relies on continuous parameter adaptation.…”

Mobility Tracking Based on Autoregressive Models

“…A linear system model of mobility has been applied to real-time mobility tracking via various state estimation methods, such as Kalman filters [18], [19], [20], sequential Monte Carlo filtering [21], particle filters [22], and predictive methods [6]. In this model, the mobility state consists of position, velocity, and acceleration.…”