Towards Fast and Accurate 3D Direct Emitter Tracking via Laplace Approximation
Ji‐An Luo, Jing He, Dongliang Peng · IEEE Transactions on Vehicular Technology · 2023
In this paper, the location and velocity of an emitter are estimated through spatially distributed receivers based on delay and Doppler-shift induced signal waveforms. In the context of Bayesian filtering, the main challenge in direct emitter tracking is to estimate the high-dimensional parameters based on the non-linear and non-Gaussian marginal posterior distribution. Although Monte Carlo approximations can be set as close to the optimal solution as expected, these methods usually incur a high computational cost and are inefficient in dealing with high-dimensional problems. Therefore, a novel Posterior Laplace Approximated Filter (PLAF) algorithm intended for direct emitter tracking is proposed in this paper to achieve a comparable estimation performance close to the posterior Cramér-Rao lower bound but at a low level of computational complexity. The PLAF method relies on the Laplace approximation technique to approximate the marginal posterior as a Gaussian distribution, the mean and covariance matrix of which are explicitly calculated. Furthermore, an improved approach to unconstrained optimization is developed based on a feature space search technique. The proposed optimization method is capable to jump out of the neighborhood of the local optimum to find a better one. The PLAF algorithm performs well in computationally efficiency because it belongs to a deterministic approximation domain. Besides, it can maintain a superior performance, even in the context of a low signal-to-noise ratio. Simulation results are obtained to demonstrate the performance of the proposed algorithm.