Target localization based on structured total least squares with hybrid TDOA-AOA measurements

In this paper, we focus on the target localization problem which finds broad applications in radar, sonar and wireless sensor networks. A pseudolinear overdetermined system of equations is constructed from the nonlinear hybrid TDOA-AOA measurements about target location. Considering the matrix and vector in the constructed pseudolinear system are both contaminated by the measurement noise, a new weight least squares (WLS) method which is based on the first order Taylor expansions of the noise terms is developed in this paper and it can reduce the estimation bias that arise from the least squares (LS) method. In particular we focus on constructing a localization algorithm to reduce the bias that easily arise from the traditional methods. Thus in addition, a novel structured total least squares (STLS) method is also developed in this paper to further reduce the estimation bias specially when the target is outside the convex hull formed by sensors. Numerical examples show the superiority of the proposed STLS method in estimation accuracy compared with the LS method, total least squares (TLS) method and the proposed WLS method.