Sensor Network-Based Rigid Body Localization via Semi-Definite Relaxation Using Arrival Time and Doppler Measurements

This paper addresses the rigid body localization problem using a convex optimization approach. We propose a semi-definite relaxation (SDR) method for locating a stationary rigid body using arrival time measurements, and extend it for moving rigid body using both arrival time and Doppler measurements. Localization of a stationary (moving) rigid body involves not only the position (and velocity) but also the rotation angles (and angular velocity), making it a challenging optimization problem with nonlinear constraints. We approximate the maximum likelihood problem with a constrained weighted least-squares (CWLS) minimization and apply SDR to obtain a coarse estimate. The orthogonalization and refinement procedures are followed next to recover the performance loss caused by relaxation and approximation. It is shown analytically that the CWLS solution can achieve the Cramér–Rao lower bound accuracy for Gaussian noise when the noise level is not significant. The simulations show that the proposed method achieves better accuracy than the previously developed methods.