Past few years have witnessed the widespread adoption of unmanned aerial vehicles (UAVs) in target tracking for regional monitor and strike. Most existing target tracking approaches rely on the target motion frames obtained by the camera equipped, or on ideally assuming a pre-set target trajectory. However, in practice, the real trajectory of the target cannot be perfectly known to the UAVs in advance, and also the target may intelligently adjust its flying strategy according to the environment. Besides, the limited flight performance, as well as information capture and processing capability, of a single UAV can hardly fulfill high tracking success rate requirements. To address aforementioned issues, this paper proposes an end-to-end cooperative multi-agent reinforcement learning (MARL) scheme, where UAVs are enabled to make intelligent flight decisions for cooperative target tracking, on the basis of the past and current states of the target. In order to reduce power consumption and prolong the lifetime of the UAV tracking system, the propulsion power consumption model and energy saving strategy are introduced. Moreover, to further increase the detection coverage, spatial information entropy is introduced in the tracking algorithm. Simulation results show that our proposed algorithm outperfoms the deep reinforcement learning baselines in terms of the mean episode rewards, while also yields high performances with respect to tracking success rates, power saving efficiency and detection coverage.

In this article, we investigate the problem of decentralized federated learning (DFL) in Internet of Things (IoT) systems, where a number of IoT clients train models collectively for a common task without sharing their private training data in the absence of a central server. Most of the existing DFL schemes are composed of two alternating steps, i.e., model updating and model averaging. However, averaging model parameters directly to fuse different models at the local clients suffers from client-drift, especially when the training data are heterogeneous across different clients. This leads to slow convergence and degraded learning performance. As a possible solution, we propose the DFL via a mutual knowledge transfer (Def-KT) algorithm, where local clients fuse models by transferring their learned knowledge to each other. Our experiments on the MNIST, Fashion-MNIST, CIFAR-10, and CIFAR-100 data sets reveal that the proposed Def-KT algorithm significantly outperforms the baseline DFL methods with model averaging, i.e., Combo and FullAvg, especially when the training data are not independent and identically distributed (non-IID) across different clients.

In this letter, we propose two methods for personnel recognition and gait classification using deep convolutional neural networks (DCNNs) based on multistatic radar micro-Doppler signatures. Previous DCNN-based schemes have mainly focused on monostatic scenarios, whereas directional diversity offered by multistatic radar is exploited in this letter to improve classification accuracy. We first propose the voted monostatic DCNN (VMo-DCNN) method, which trains DCNNs on each receiver node separately and fuses the results by binary voting. By merging the fusion step into the network architecture, we further propose the multistatic DCNN (Mul-DCNN) method, which performs slightly better than VMo-DCNN. These methods are validated on real data measured with a 2.4-GHz multistatic radar system. Experimental results show that the Mul-DCNN achieves over 99% accuracy in armed/unarmed gait classification using only 20% training data and similar performance in two-class personnel recognition using 50% training data, which are higher than the accuracy obtained by performing DCNN on a single radar node.

In this article, we consider the problem of federated learning (FL) with training data that are non independent and identically distributed (non-IID) across the clients. To cope with data heterogeneity, an iterative federated clustering algorithm (IFCA) has been proposed. IFCA partitions the clients into a number of clusters and lets the clients in the same cluster optimize a shared model. However, in IFCA, the clusters are nonoverlapping, which leads to an inefficient utilization of the local information since the knowledge of a client is used by only one cluster during each round. To capture the complex nature of real-world data, soft clustering methods with overlapping clusters have been proposed that attain superior performance over the hard ones. Motivated by this, we propose a new algorithm named FL with soft clustering (FLSC) by combining the strengths of soft clustering and IFCA, where the clients are partitioned into overlapping clusters and the information of each participating client is used by multiple clusters simultaneously during each round. The experimental results show that FLSC achieves better learning performance on the classification tasks on the MNIST and Fashion-MNIST data sets, compared with the state-of-the-art baseline methods, i.e., the global model method and IFCA.

In this article, we investigate the problem of federated learning (FL) in a communication-constrained environment of the Internet of Things (IoT), where multiple IoT clients train a global model collectively by communicating model updates with a central server instead of sending raw data sets. To ease the communication burden in IoT systems, several approaches have been proposed for the FL tasks, including sparsification methods and data quantization strategies. To overcome the shortcomings of the existing methods, we propose two new FL algorithms based on compressed sensing (CS) referred to as the CS-FL algorithm and the 1-bit CS-FL algorithm, both of which compress the upstream and downstream data while communicating between the clients and the central server. The proposed algorithms improve upon the existing algorithms by letting the clients send analog and 1-bit data, respectively, to the server after compression with a random measurement matrix. Based on that, in CS-FL and 1-bit CS-FL, the clients update the model locally utilizing the result of sparse reconstruction obtained by iterative hard thresholding (IHT) and binary IHT (BIHT), respectively. Experiments conducted on the MNIST and the Fashion-MNIST data sets reveal the superiority of the proposed algorithm over the baseline algorithms, SignSGD with a majority vote, FL based on sparse ternary compression, and FedAvg.

In this paper, a sparsity-driven method of micro-Doppler analysis is proposed for dynamic hand gesture recognition with radar sensors. Firstly, sparse representations of the echoes reflected from dynamic hand gestures are achieved through the Gaussian-windowed Fourier dictionary. Secondly, the micro-Doppler features of dynamic hand gestures are extracted using the orthogonal matching pursuit (OMP) algorithm. Finally, the nearest neighbor classifier is combined with the modified Hausdorff distance to recognize dynamic hand gestures based on the sparse micro-Doppler features. Experiments with real radar data show that 1) the recognition accuracy produced by the proposed method exceeds 96% under moderate noise, and 2) the proposed method outperforms the approaches based on principal component analysis and deep convolutional neural network with small training dataset.