A coarse to accurate noise-tolerant positioning evaluation for mobile target based on modified genetic algorithm

Abstract

The location information of mobile target can be mapped between physical space and signal space, which is one of the key technologies for real-time tracking and multi-machine cooperation. Benefiting from the distributed perception and ubiquitous communication capabilities, wireless sensor networks (WSNs) can be used for target real-time positioning indoor and outdoor. However, various sensor noises and environmental interferences bring uncertainty to target positioning, resulting in inconsistent positioning performance. Hence, this paper proposes a coarse to accurate noise-tolerant positioning evaluation for mobile target based on modified genetic algorithm. Considering the uncertainty of various measurements in WSNs, the preliminary positioning results are estimated by total least squares. Taking the preliminary results as the initial search values, the modified genetic algorithm is used to refine the target positioning accuracy with use of adaptively adjusted crossover probability and improved mutation operation. In addition, the theoretical accuracy model is derived by the Cramer–Rao Lower Bound (CRLB) as a benchmark. Comprehensive experiments are conducted through the simulation and testing platform. The experimental results of the proposed algorithm have a 0.25 m average error and 0.152 error variance. The result trends of simulation are consistent with the result of experimental platform, which can validate the superior accuracy of the proposed algorithm compared with relevant positioning algorithms.