Single base station positioning based on multipath parameter clustering in NLOS environment

Abstract

This paper proposes a scattering area model for processing multipath parameters achieve single base station positioning. First of all, we construct a scattering area model based on the spatial layout of obstacles near the base station and then collect the multipath signals needed for positioning and extract parameters. Second, we use the joint clustering algorithm improved by k-means clustering and mean shift clustering algorithm to process the parameters and extract useful information. Third, the processed information is combined with the spatial layout information of the scattering area model to construct equations, and then the solving problem of equations is converted into a least-squares optimization problem. Finally, the Levenberg-Marquardt (LM) algorithm is used to solve the optimal solution and estimate the mobile target position. The simulation results show that the positioning algorithm in this paper can be used by a single base station to locate the target in an outdoor non-line-of-sight (NLOS) environment, and the accuracy is improved compared with the traditional positioning algorithm.

Cellular Positioning in an NLOS Environment Applying the COPSO-TVAC Algorithm

“…The LM algorithm [7] and the TRR algorithm [8] give very similar results and interpolate between the Gauss-Newton algorithm (GN) and the method of gradient descent. The LM does not require constraints, while the TRR allows only bounds or linear equality constraints, but not both.…”

“…The proposed location methods are suited for cases where the objective function is highly nonlinear. Based on scattering propagation information collected using one base station, nonlinear least-squares equations are formed in [7] and solved by the Levenberg-Marquardt (LM) algorithm. Similarly, the ML objective function obtained in the mmWave location estimation process performed in [8] is optimized using the Trust Region Reflective algorithm.…”

“…To verify the localization performance of the proposed metaheuristic algorithms, we compared them to the classical positioning methods such as gradient-based algorithms (the Levenberg-Marquardt (LM) and the Trust Region Reflective (TRR) algorithm) [7,8], and the Taylor Series-based Least Squares algorithm (TSLS) [13]. The objective function ( 13) is an excellent benchmark function for comparing the proposed metaheuristics with gradient methods.…”

“…The accuracy of these methods depends on the accuracy of the presumed NLOS error distribution model. The ML estimators that implemented the gradient-based optimization algorithms such as the Gauss-Newton (GN), the Steepest Descent (SD), the Levenberg-Marquardt (LM), and the Trust Region Reflective (TRR) are suggested in papers [5][6][7][8]. The proposed location methods are suited for cases where the objective function is highly nonlinear.…”

“…The simulation results show the effectiveness of the COPSO-TVAC algorithm for different numbers of NLOS BSs and the NLOS error levels in the suburban and the urban environment, compared to the standard PSO and PSO-TVAC metaheuristic algorithms [22,24,36], as well as compared to the conventional algorithms such as the TSLS [13] and gradient-based algorithms [7,8].…”