Wireless sensor indoor positioning based on an improved particle filter algorithm

Abstract

Positioning by wireless sensor network is one of its main functions and has been widely used in many fields. However, when signal propagation is hindered, serious errors, non-line-of-sight errors, occur. In order to solve this problem, this article proposes an improved particle filter algorithm, which introduces the idea of residual analysis to improve reliability. The algorithm assigns weights to the particles based on the residuals and selects the appropriate particles. In addition, the non-line-of-sight error parameter a is introduced, and the second selection is made according to a, which considers the influence of non-line-of-sight error. The non-line-of-sight error is greatly reduced after two selections. The simulation is performed under several different non-line-of-sight errors, and results show that the algorithm is superior to Kalman filter and particle filter.

Application Research of DV-HOP Wireless Sensor Localization System Based on Geometric Constraint Optimization

“…Then performed secondary selection on particles based on non line of sight error parameters. The improved particle filter algorithm proposed above had better positioning performance compared with the particle filter predicted by Kalman filter in the experiment [11]. G. Envelopeet al proposed an autonomous WSN as the hardware basis for implementing an information physical system.…”

Network Aided Positioning Techniques Based on Rssi and Signal Processing

“…Sensor networks have a "Localization problem" in which it is necessary to know the site of sensor nodes. Sensor networks have broadly been used for environmental sensing, observing encroachment in battlefields, and observing wildlife [15][16][17][18].…”