Target Localization with Unknown Transmit Power and Path-Loss Exponent Using a Kalman Filter in WSNs

Abstract

We present a novel hybrid localization algorithm for wireless sensor networks in the absence of knowledge regarding the transmit power and path-loss exponent. Transmit power and the path-loss exponent are critical parameters for target localization algorithms in wireless sensor networks, which help extract target position information from the received signal strength. In the absence of information on transmit power and path-loss exponent, it is critical to estimate them for reliable deployment of conventional target localization algorithms. In this paper, we propose a simultaneous estimation of transmit power and path-loss exponent based on Kalman filter. The unknown transmit power and path-loss exponent are estimated using a Kalman filter with the tentatively estimated target position based solely on angle information. Subsequently, the target position is refined using a hybrid method incorporating received signal strength measurements based on the estimated transmit power and path-loss exponent. Our proposed algorithm accurately estimates transmit power and path-loss exponent and yields almost the same target position accuracy as the simulation results confirm, as the hybrid target localization algorithms with known transmit power and path-loss exponent. Simulation results confirm the proposed algorithm achieves 99.7% accuracy of the target localization performance with known transmit power and path-loss exponent, even in the presence of severe received signal strength measurement noise.

Multi-Target Localization Based on Unidentified Multiple RSS/AOA Measurements in Wireless Sensor Networks

“…Let be the known position of the i -th sensor (anchor) node for and be the unknown position of the m -th sensor (target) for . We assume that all anchor nodes have array antenna or directional antenna to measure AOA (azimuth and elevation angle) [ 37 ] via the line-of-sight (LoS) path from the target [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ]. In the presence of multiple targets, we assume that each anchor node receives signals from the targets, and the RSS, azimuth angle, and elevation angle are measured from these signals.…”

“…When every RSS/AOA measurement set is identifiable by the originated target , the multi-target localization problem can be solved based on M single-target localization problems. Several intensive studies regarding linear single-target localization [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 22 , 23 ] exist, and the most well-known approach is to consider the following linear relationship between the target position and RSS/AOA measurements: where and denotes parameter errors.…”

“…Most existing studies regarding multi-target detection in WSNs assume that the targets are sparsely located over networks and, therefore, each anchor node contains a single set of RSS/AOA measurements from a single target. Various multi-target localization methods that exploit the network topology [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ] or apply compressive sensing [ 25 , 26 , 27 ] have been proposed. Some existing studies assume that each anchor node can identify the origin of the RSS/AOA measurements using, for example, code division multiplexing access codes [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ], therefore solving multiple single-target localization problems.…”

A Dynamic Self-Tuning Maximum Correntropy Kalman Filter for Wireless Sensors Networks Positioning Systems

“…However, the range-based positioning algorithm is seriously affected by non-Gaussian noise (NGN) [9,10], which results in its inaccuracy in practical application. Huang et al [11], Fang et al [12] and Navon et al [13] all pointed out that WSNs positioning systems are affected by various NGNs, which seriously reduce the positioning accuracy.…”

Coal mine personnel positioning algorithm based on improved adaptive unscented Kalman filter with wireless channel fading and unknown noise statistics

“…With the development of wireless communication technology, the potential applications of wireless sensor networks (WSNs) in the personnel positioning have attracted the significant attention (Chen et al, 2013; Vaseghi et al, 2018; Xie et al, 2019). In general, WSNs are comprised of a large number of sensor nodes deployed in the monitoring area, which are highly compatible and easy to deploy (Kang et al, 2020; Zhang and Xing Zheng, 2018). In a specific monitoring area, sensor nodes can share the local information with each other through wireless information transmission, which help the system to execute complex tasks cooperatively (Ge et al, 2017).…”