Abstract
We propose a line-of-sight (LOS)/non-line-of-sight (NLOS) mixture source localization algorithm that utilizes the weighted least squares (WLS) method in LOS/NLOS mixture environments, where the weight matrix is determined in the algebraic form. Unless the contamination ratio exceeds 50 %, the asymptotic variance of the sample median can be approximately related to that of the sample mean. Based on this observation, we use the error covariance matrix for the sample mean and median to minimize the weighted squared error (WSE) loss function. The WSE loss function based on the sample median is utilized when statistical testing supports the LOS/NLOS state, while the WSE function using the sample mean is employed when statistical testing indicates that the sensor is in the LOS state. To testify the superiority of the proposed methods, the mean square error (MSE) performances are compared via simulation.
Highlights
The aim of the source localization system is to find a geometrical point of intersection using the measurements from each receiver, such as the time difference of arrival (TDOA), time of arrival (TOA), or received signal strength (RSS)
The LOS/NLOS mixture localization method for multiple sample case has the advantage that all sensors can be utilized compared to the single sample-based LOS/NLOS mixture localization method if the contamination ratio for the samples in each sensor is lower than the breakdown point
Is smaller than σi2,LOS, the following weighted squared error (WSE) loss function based on the sample mean is used since the judgment that the ith sensor is in the LOS condition is supported
Summary
The aim of the source localization system is to find a geometrical point of intersection using the measurements from each receiver, such as the time difference of arrival (TDOA), time of arrival (TOA), or received signal strength (RSS). The performance of the proposed closed-form LOS/NLOS mixture localization method using the two-step WLS method is similar to that of the Taylor series-based iteration method, with the advantages of low computational complexity and avoidance of the divergence problem of the solution. When solution diverges, it can reach a solution, which is far from the true solution, or sometimes it fails to produce a solution when the initial value is not appropriately chosen. The two methods are discussed briefly in terms of the formulation of the LOS/NLOS mixture source localization
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