Simulation and Analysis on RTLS Airborne Passive Location Algorithm

In wireless radio frequency injection location, the observation vector and observation matrix are both noisy. The typical least squares (LS) or recursive least squares (RLS) algorithm will lead to the location estimation biased. So total least squares (TLS) algorithm was considered. However, its exact solution is rather complicated and it failure to reach the real-time requirement of wireless RF injection. Therefore, a recursive total least squares (RTLS) algorithm was proposed. Firstly, we built the airborne passive bearing location model. After that recursive form of augmented inverse correlation matrix was obtained according to matrix inversion formula. And then we updated the most right singular vector by using nature of matrix decomposition. Monte Carlo simulations show that the proposed recursive algorithm is more accuracy to large measurement noise than RLS algorithm. The greater noise level, the more improvement to RLS algorithm. It achieves real time estimation of TLS algorithm with lower computational cost, which reaches the requirement of wireless RF injection.