## 3 Detection, Localization, and Tracking in Multi-Agent Systems  
The third and last group of papers specifically addresses location-awareness and tracking, which is essential for the implementation of multi-agent applications, by exploiting multi-channel techniques. The review paper, \"Distributed Localization with Complemented RSS and AOA Measurements: Theory and Methods\", by [PERSON], [PERSON], [PERSON], and [PERSON] [11], presents a survey (from a signal processing perspective) of existing distributed solutions for multi-agent localization (IoT devices such as sensors, machines, and vehicles) in 5G systems, which may not rely on GNSS due to energy and/or cost limitations or because of lack of coverage. The paper focuses in particular on (distributed) hybrid solutions based on received signal strength (RSS) and angle of arrival (AOA), which offer very promising performances in the considered scenarios. When the localization of a mobile agent is concerned, suitable tracking techniques are also needed. This problem becomes more complex in multi-agent systems, for instance in terms of formation tracking. In the paper, \"A Dropout Compensation ILC Method for Formation Tracking of Heterogeneous Multi-Agent Systems with Loss of Multiple Communication Packets\", by [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [12], the formation tracking problem for heterogeneous multi-agent systems with loss of multiple communication packets is addressed. A dropout compensation iterative learning control method is presented to construct effective distributed iterative learning protocols, and convergence conditions are theoretically established. Another tracking-related problem concerns the correct association of detections and measurements in multi-agent/multi-target scenarios. In the paper, \"Labeled Multi-Bernoulli Filter Joint Detection and Tracking of Radar Targets\", by [PERSON], [PERSON], and [PERSON] [13], a labeled multi-Bernoulli (LMB) filter is presented to jointly detect and track radar targets. In fact, new or close tracks often violate the conventional assumption that measurements of different targets do not overlap, leading to a bias in the cardinality estimate. To address this problem, a one-to-one association method between measurements and tracks is proposed, and an approximate method for calculating the point spread function of radar is developed to improve the computational efficiency. Results demonstrate the effectiveness of the proposed algorithm in low SNR scenarios with closely spaced targets. The last two papers of the Special Issue consider more specific aspects and applications of multi-agent radar systems. In \"Radar Application: Stacking Multiple Classifiers for Human Walking Detection Using Micro-Doppler Signals\", by [PERSON] and [PERSON] [14], radar sensors are considered for the problem of human walking recognition based on micro-Doppler signals. Several types of background noise typically found in operational contexts (line of sight, fan, snow, and rain) are considered, and a deep neural network approach for signal classification is devised, based on experimental data. Then, stacking methods are proposed to combine base classifiers of different structures in a single (ensemble) classifier with improved performance. Test results report an accuracy of more than 95%. Finally, in \"Reinforcement Learning-Based Anti-Jamming in Networked UAV Radar Systems\", by [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [15], the problem of quantifying and optimizing the anti-jamming performance of networked UAV radar systems is addressed. The latter may exploit inter-UAV cooperation for enhancing information acquisition capabilities, meanwhile its inter-UAV communications may be interfered with by external jammers. A modified Q-Learning method is proposed based on double greedy algorithm to optimize the anti-jamming performance, through joint programming in the frequency-motion-antenna domain. This research received no external funding.