Adaptive Particle Filter for Nonparametric Estimation with Measurement Uncertainty in Wireless Sensor Networks

Abstract

Particle filters (PFs) are widely used for nonlinear signal processing in wireless sensor networks (WSNs). However, the measurement uncertainty makes the WSN observations unreliable to the actual case and also degrades the estimation accuracy of the PFs. In addition to the algorithm design, few works focus on improving the likelihood calculation method, since it can be pre-assumed by a given distribution model. In this paper, we propose a novel PF method, which is based on a new likelihood fusion method for WSNs and can further improve the estimation performance. We firstly use a dynamic Gaussian model to describe the nonparametric features of the measurement uncertainty. Then, we propose a likelihood adaptation method that employs the prior information and a belief factor to reduce the measurement noise. The optimal belief factor is attained by deriving the minimum Kullback–Leibler divergence. The likelihood adaptation method can be integrated into any PFs, and we use our method to develop three versions of adaptive PFs for a target tracking system using wireless sensor network. The simulation and experimental results demonstrate that our likelihood adaptation method has greatly improved the estimation performance of PFs in a high noise environment. In addition, the adaptive PFs are highly adaptable to the environment without imposing computational complexity.

Positioning optimisation based on particle quality prediction in wireless sensor networks

“…The Gaussian mixture model estimates noises and applies the model parameters to calculate the particle weights, thereby avoiding the particle degradation problem that non-Gaussian mixture models may have. The noise is described by the Gaussian mixture model [9]. The motion noise is denoted as…”

“…9 compares the improvements of different optimisations on the PF algorithm. The PF prediction (PFPre) and PF shift (PFShift) are algorithms proposed in the literature [6,9]. It is shown that all the optimised algorithms can improve the performance of PF, but PQP algorithm based on PQP has greater improvement compared to others.…”

“…The Gaussian mixture model estimates noises and applies the model parameters to calculate the particle weights, thereby avoiding the particle degradation problem that non‐Gaussian mixture models may have. The noise is described by the Gaussian mixture model [9]. The motion noise is denoted aswhere is the time interval of code execution, is the noise along the x ‐ and y ‐directions.…”

“…Distinct from the Kalman filter that can only deal with the probability estimation problem of Gaussian distribution, this method can obtain higher accuracy of positioning [3,7,8]. For these reasons, using the PF for positioning has become a hot research topic [9,10]. The main problem of the PF algorithm is the particle degradation.…”

Probability-Guaranteed Distributed Secure Estimation for Nonlinear Systems Over Sensor Networks Under Deception Attacks on Innovations

“…To date, considerable research effort has been devoted to sensor networks from many different perspectives, and the distributed state estimation problem has stirred particular interest, see e.g. [10]- [12], [19], [24], [34], [41] for some recent works. Compared with the traditional single-sensor systems, the sensor networks could collect more information in a comprehensive and complementary way via the cooperation among individual nodes, thereby making the corresponding estimation algorithms more robust, accurate and flexible.…”