Tensor-Based Sparsity-Inducing Localization of AAV Swarms-Assisted Mobile Edge Computing Systems

Autonomous aerial vehicle (AAV)-assisted mobile edge computing systems have high mobility and can be deployed in various rugged terrain and emergency scenarios for communication and monitoring. However, the malicious use of AAV swarms poses a potential threat to key areas. Therefore, accurate positioning of AAV swarms is crucial for the security of high-value civilian facilities and equipment. This article investigates angle estimation of coherent signals from AAV swarms in bistatic multiple-input multiple-output radar under nonuniform noise. The nonuniform noise powers are iteratively estimated based on the structural characteristics of the covariance matrix and subsequently removed from the observations. Transmission-reception diversity smoothing is then applied to the signal subspace, obtained through higher order singular value decomposition, to recover the rank deficiency. Furthermore, a block sparse reconstruction method is proposed, utilizing the reweighted smoothed \ell _{0}-norm, to obtain angle estimates. This method automatically pairs the direction-of-arrivals and direction-of-departures of AAVs. Experimental results demonstrate the superiority of our approach over existing solutions.