Abstract
This work addresses the problem of tracking a signal-emitting mobile target in wireless sensor networks (WSNs) with navigated mobile sensors. The sensors are properly equipped to acquire received signal strength (RSS) and angle of arrival (AoA) measurements from the received signal, while the target transmit power is assumed not known. We start by showing how to linearize the highly non-linear measurement model. Then, by employing a Bayesian approach, we combine the linearized observation model with prior knowledge extracted from the state transition model. Based on the maximum a posteriori (MAP) principle and the Kalman filtering (KF) framework, we propose new MAP and KF algorithms, respectively. We also propose a simple and efficient mobile sensor navigation procedure, which allows us to further enhance the estimation accuracy of our algorithms with a reduced number of sensors. Model flaws, which result in imperfect knowledge about the path loss exponent (PLE) and the true mobile sensors’ locations, are taken into consideration. We have carried out an extensive simulation study, and our results confirm the superiority of the proposed algorithms, as well as the effectiveness of the proposed navigation routine.
Highlights
The Kalman filter (KF) may be thought of as a generalized sequential minimum mean square estimator of a signal embedded in noise, where the unknown parameters are allowed to evolve in time according to a given dynamical model [19]
By allowing sensor mobility, such that they are permitted to move in certain directions based on pre-established rules, we can improve the estimation accuracy of the proposed algorithms, but do so with a reduced number of sensors
We have addressed the target tracking problem in wireless sensor networks (WSNs) where sensor mobility was allowed
Summary
The problem of accurate localization of a moving object in real time has motivated a great deal of scientific research recently, owing to a constant growth of the range of enabling devices and technologies and the requirement for seamless solutions in location-based services [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18]. The works in [3,4,7] investigated target tracking problems where the observations were combined with some prior knowledge to enhance the estimation accuracy. In [6], the authors investigated the target tracking problem by employing hybrid, RSS and AoA, measurements. An estimate of the target location was obtained at the other point of the line This is an effective way to tackle the non-linearity of the measurement model, the authors in [6] treated all links as equal, and no mitigation technique was used to deal with potentially negative impact from distant links. The problem of tracking a mobile target by employing hybrid RSS-AoA measurements is considered.
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