Locating multiple Radio Frequency (RF) sources by Unmanned Aerial Vehicles (UAVs) using Received Signal Strength (RSS) measurements attracts extensive attention for its intrinsic simplicity in hardware and low cost. However, the signals from multiple RF sources are aggregated at the airborne sensors, making it hard to achieve multi-emitter localization with a limited number of UAVs. In this paper, the Bayesian compressive sensing (BCS) approach is applied to estimating ground targets’ locations exploiting the RSS data collected by the onboard sensors. Meanwhile, to support the BCS approach, which requires sufficient measurements to achieve multi-emitter localization, a sensing architecture comprising a limited number of UAVs is presented. Besides, to optimize the localization result, we put forward a trajectory planning-based Bayesian compressive sensing (TPBCS) algorithm. This algorithm dynamically plans UAVs’ trajectories based on the adaptive simulated annealing (ASA) algorithm, which can bring down the computational cost in UAV path planning compared to the traditional simulated annealing (SA) algorithm. The effectiveness and robustness of the approach are validated by simulations in the end.

Device-free localization (DFL) plays an increasingly important role in many security and military applications. It can realize localization without the requirement of equipping targets with any devices for signal transmitting or receiving. To reduce the number of measurements in DFL, compressive sensing (CS) theory has been applied. By exploiting the sparse nature of location finding problem, the target location vector can be estimated from a few measurements. However, in changing environments, measurements may diverge from those in a fixed dictionary (sensing matrix), and the mismatches between the dictionary and runtime measurements can significantly deteriorate the localization performance of CS-based DFL methods. To address this, we propose a novel dictionary refinement-based DFL method. It adopts the saddle surface model to characterize the shadowing effects caused by targets and parameterizes the dictionary with the shadowing rate of each link as the underlying parameters. Then, the variational expectation-maximization algorithm is adopted to realize joint localization and dictionary refinement. Simulation results show that the proposed approach achieves higher accuracy and robustness compared with the state-of-the-art fixed dictionary DFL methods.

As an emerging technique, device-free localization (DFL) is promising to localize the target without attaching any transceivers. Recently, the benefits of channel state information (CSI) on DFL have been revealed in literature. Motivated by this, in this paper, we propose to exploit the channel diversity of CSI measurements for multi-target DFL under the compressive sensing (CS) framework. The CSIbased multi-target DFL problem is formulated as a joint sparse recovery problem which reconstructs the unknown sparse vectors of multiple channels. Moreover, in practice, some faulty prior information (e.g., coarse positions) is usually available. To take advantage of these information for joint sparse recovery, a novel support knowledge-aided multiple sparse Bayesian learning (SA-M-SBL) algorithm is introduced, which incorporates the prior information into a three-layer hierarchical prior model. With this model, the joint sparsity of the sparse vectors can be induced, and their values can be estimated via the variational Bayesian inference (VBI). Numerical simulation results demonstrate the outstanding performance of the proposed method compared with the state-of-the-art CS-based multi-target DFL methods.

Device-free localization (DFL) that aims to localize targets without carrying any electronic devices is addressed as an emerging and promising research topic. DFL techniques estimate the locations of transceiver-free targets by analyzing their shadowing effects on the radio signals that travel through the area of interest. Recently, compressive sensing (CS) theory has been applied in DFL to reduce the number of measurements by exploiting the inherent spatial sparsity of target locations. In this paper, we propose a novel CS-based multi-target DFL method to leverage the frequency diversity of fine-grained subcarrier information. Specifically, we build the dictionaries of multiple channels based on the saddle surface model and formulate the multi-target DFL as a joint sparse recovery problem. To estimate the location vector, an iterative location vector estimation algorithm is developed under the multitask Bayesian compressive sensing (MBCS) framework. Compared with the state-of-the-art CS-based multi-target DFL approaches, simulation results validate the superiority of the proposed algorithm.

As an emerging and promising technique, device-free localization (DFL) estimates target positions by analyzing their shadowing effects. Most existing compressive sensing (CS)-based DFL methods use the changes of received signal strength (RSS) to approximate the shadowing effects. However, in changing environments, RSS readings are vulnerable to environmental dynamics. The deviation between runtime RSS variations and the data in a fixed dictionary can significantly deteriorate the performance of DFL. In this paper, we introduce ComDec, a novel CS-based DFL method using channel state information (CSI) to enhance localization accuracy and robustness. To exploit the channel diversity of CSI measurements, the DFL problem is formulated as a joint sparse recovery problem that recovers multiple sparse vectors with common support. To solve this problem, we develop a joint sparse recovery algorithm under the variational Bayesian inference framework. In this algorithm, dictionaries are parameterized based on the saddle surface model. To adapt to the environmental changes and different channel characteristics, dictionary parameters are modelled as tunable parameters. Simulation results verified the superior performance of ComDec as compared with other state-of-the-art CS-based DFL methods.

The security of Unmanned Aerial Vehicle (UAV) swarms is threatened by the deployment of anti-UAV systems under complicated environments such as battlefield. Specifically, the faults caused by anti-UAV systems exhibit sparse and compressible characteristics. In this paper, in order to improve the survivability of UAV swarms under complicated environments, we propose a novel multi-abnormality self-detecting and faults location method, which is based on compressed sensing (CS) and takes account of the communication characteristics of UAV swarms. The method can locate the faults when UAV swarms are suffering physical damages or signal attacks. Simulations confirm that the proposed method performs well in terms of abnormalities detecting and faults location when the faults quantity is less than 17% of the quantity of UAVs.