Abstract
In this work, the terrain data is exploited for passive source localization with Doppler frequency shift (DFS) measurements. The localization problem is modeled as a maximum likelihood estimation (MLE) problem. Firstly, in order to avoid carrying out optimization step on a highly nonlinear objective function, the likelihood function is reformulated as a constrained weighted least squares (CWLS) problem. Then it is further relaxed into the semidefinite programming (SDP) problem by a semidefinite relaxation (SDR) method, which can be solved by modern convex optimization methods. By incorporating the terrain data, the localization accuracy is promoted. The performance of the proposed algorithm is examined via simulations in a typical scenario.
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
