Improved line‐of‐sight/non‐line‐of‐sight classification methods for pulsed ultrawideband localisation

Abstract

Classification of line-of-sight (LOS) or non-LOS (NLOS) propagation is critical for most pulsed ultrawideband localisation systems. In this letter, first, the authors propose a two-dimensional (2D) LOS/NLOS classification scheme that uses skewness of the channel impulse/pulse response, which has not been used by existing work. The log-likelihood ratio of the 2D LOS/NLOS classification is used to determine the weights for localisation. Then, the authors derive an iterative Gauss-Newton method to solve the location of the target, which provides a higher accuracy than the Gauss-Newton method used in existing work. Finally, they investigate improved localisation by incorporating the statistics of NLOS bias as a result of LOS/NLOS classification into the cost function, and derive the statistics of the corresponding errors of location estimates. The proposed 2D LOS/NLOS classification scheme is compared with an existing 3D scheme to demonstrate its effectiveness.

LOS/NLOS channel identification for improved localization in wireless ultra-wideband networks

“…It is therefore important to identify such conditions and to apply proper NLOS mitigation techniques. As such, there has been strong interest in the literature for addressing this problem [7][8][9].…”

Adaptive robust time-of-arrival source localization algorithm based on variable step size weighted block Newton method

“…The localization performance of traditional approaches, which assume only LOS conditions, is severely degraded under NLOS conditions; thus, mitigation of NLOS errors has become an urgent task and has been extensively investigated in the last decade. In general, research of the LOS/NLOS mixture problem for localization takes one of two approaches: (1) the constrained least squares (LS) method using optimization, such as semidefinite relaxation and second-order cone relaxation [4][5][6][7][8][9] and (2) localization based on the "NLOS identify and discard" [10][11][12][13][14]. Although localization using the optimization method has relatively high accuracy, the computational load is intensive.…”