Abstract
We investigate the problem of time-of-arrival (TOA)-based localization under possible non-line-of-sight (NLOS) propagation conditions. To robustify the squared-range-based location estimator, we follow the maximum correntropy criterion, essentially the Welsch M-estimator with a redescending influence function which behaves like ell _0-minimization toward the grossly biased measurements, to derive the formulation. The half-quadratic technique is then applied to settle the resulting optimization problem in an alternating maximization (AM) manner. By construction, the major computational challenge at each AM iteration boils down to handling an easily solvable generalized trust region subproblem. It is worth noting that the implementation of our localization method requires nothing but merely the TOA-based range measurements and sensor positions as prior information. Simulation and experimental results demonstrate the competence of the presented scheme in outperforming several state-of-the-art approaches in terms of positioning accuracy, especially in scenarios, where the percentage of NLOS paths is not large enough.
Highlights
Source localization based on location-bearing information gathered at spatially separated sensors [18] plays a pivotal role in many science and engineering areas such as cellular networks [15], Internet of Things [31], and wireless sensor networks [24]
In addition to SR-maximum correntropy criterion (MCC), state-of-the-art algorithms indicated in Table 1 are included for comparison
The source and sensors are all randomly deployed inside a 20 m × 20 m square region in each Monte Carlo (MC) run
Summary
Source localization based on location-bearing information gathered at spatially separated sensors [18] plays a pivotal role in many science and engineering areas such as cellular networks [15], Internet of Things [31], and wireless sensor networks [24]. The NLOS error in a contaminated TOA appears as a positive bias because of additional propagation delay, indicating that special attention has to be paid to alleviating its adverse impacts on positioning accuracy. The first branch of these methods takes a so-called estimation-based strategy to alleviate the adverse impacts of NLOS conditions on positioning accuracy. As the primary contribution of [23], the authors propose to replace multiple NLOS bias errors by only one (viz., a balancing parameter to be estimated), based on which the effects of NLOS propagation are partially mitigated. The authors discard the constraints between these new variables and NLOS errors and put forward a distinct SDP estimator to eliminate the non-convexity of the established nonlinear LS problem
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