Motion Parameter Estimation of Near-Field Drone Based on DNSP Under Sensor Position Error

Utilizing acoustic signals from the rotor or power engines is an important approach of identifying and localizing the drone. When localize the near-field drone, the uncertainties in the environment may introduce errors both in the signals propagation speed and the sensors positions. To address the issue, this letter proposes a double null space projection (DNSP) closed-form algorithm based on time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements to achieve high precision motion parameter estimation of the drone. Firstly, the null space projection method is introduced to the constructed localization equation to obtain the initial solution of drone motion parameter. However, due to the relationship between the actual unknowns and nuisance parameters in the projection process is neglected, this solution is suboptimal. Secondly, DNSP ultimately refines the localization performance by error compensation. Simulation results and performance analysis show that DNSP has Cramér–Rao lower bound (CRLB) localization accuracy under small noise.