Flocking is a fundamental self-organizing behavior observed in networked agent systems (NASs), wherein agents achieve coordinated group dynamics through mutual interactions. While in dynamic environmental contexts, the demand for flocking behavior to demonstrate rapid responsiveness and robust stability becomes more critical. With this in mind, this paper addresses the adaptive bipartite flocking control problem for NASs, particularly in the presence of compound uncertainties and convergence time constraints. A robust adaptive neural network-based prescribed-time bipartite flocking controller is developed to ensure that, despite uncertainties, all agents achieve flocking behavior within a predefined time. Notably, the settling time can be predefined and remains independent of system parameters such as controller gain , initial agent states, and the communication topology among agents. Additionally, by analyzing the stability conditions of the closed-loop error system, an adaptive weight update law for the neural network estimator is formulated. This updated law allows for effective uncertainty estimation through backpropagation of the flocking control error. Finally, the effectiveness and superiority of the proposed prescribed-time bipartite flocking control scheme are validated through numerical simulations.

In nature, populations of organisms (e.g., wolves) exhibit a remarkable ability to coordinate their group actions, such as hunting prey or evading predators, despite the coexistence of cooperative and competitive interactions among individuals. Motivated by this intriguing phenomenon, this article investigates the cooperative consensus tracking control problem of multiagent systems (MASs) over cooperation-competition networks with asynchronous communications. That is, all followers can simultaneously achieve trajectory tracking of the leader agent, even if there exist competitive interactions between the followers and the leader. To portray the cooperation and competition level among agents, a new distance-based weight function is designed, which is more flexible than the fixed weight values in existing research works. Theoretically, the sufficient conditions for achieving consensus tracking control are obtained based on the convergence analysis method of infinite products of super-stochastic matrices. Finally, some numerical simulations are given to verify the effectiveness of the proposed consensus tracking control scheme.

Due to multiplicative speckle noise in Synthetic Aperture Radar(SAR) image and significant intensity difference between different data, it is difficult to match SAR and optical image accurately. In this paper, we propose a novel template matching method for SAR and optical image named multi-Dimensional Matching Histogram of Oriented Gradient (mDM-HOG). Firstly, in order to reduce the negative effect of speckle noise on gradient calculation, the ratio of exponentially weighted averages(ROEWA) operator is introduced to calculate the gradient magnitude and orientation in SAR image. Then, using the obtained gradient information, we extract the 3-D pixelwise HOG feature for both images. Finally, we separate the 3-D feature map to nine sub-maps and measure the similarity of the sub-maps to obtain the template matching result. The experimental result shows that in comparison with the existing methods, the proposed template matching method has higher accuracy when locating the position of SAR image in optical image.

Recently, MIMO (multiple-input-multiple-output) physical layer secure transmission has attracted great attentions. However, current schemes cannot defend against the passive eavesdroppers with arbitrary number of antennas. To address this problem, in this work, we propose a practical physical layer MIMO secure communication scheme (PLSC) to defend against such an eavesdropper with arbitrary number of antennas. In the proposed scheme, the transmitter first independently generates a random binary sequence as the “key bits (KB)” to “encrypt” (XOR) the confidential information. After that, the transmitter sends the “encrypted information” over the wireless channel, along with mapping key bits to the legitimate receiver simultaneously. The key principle lies in that the KB information is coded in the indexes of the activated/non-activated antennas combination of the legitimate user. Then, the legitimate receiver first observes his/her activated antenna indexes to obtain the corresponding key bits. After that, he/she demodulates the “encrypted information” at the activated antennas, and finally “decrypts” (XOR) the confidential information by using the observed key bits. However, due to the uniqueness and independence of MIMO wireless channel, for any other eavesdroppers who suffer an independent channel from legitimate users, we prove that it cannot observe any information about KB from the received signals, regardless of how many antennas it has used. Consequently, without knowledge of KB, it cannot decrypt any information about the confidential information, too. The reliability and security of PLSC are theoretically demonstrated. The simulation and numerical results fully verified the validity and effectiveness of the proposed scheme.

Physical-layer security (PLS) in Internet of Things (IoT) has attracted great attentions recently. Although mobility is an intrinsic property of IoT networks, most of the existing works only investigate the secure transmission design for static users. To fill this gap, this article specifically investigates the secrecy throughput maximization problems for the mobile IoT user under two typical mobility models: 1) random waypoint model (RWP) and 2) random direction model (RD). The insights about how the mobility patterns, and security–reliability requirements affect the mobile user’s secrecy throughput are revealed. First, in order to establish the relationship between security and reliability of the mobile user, a general analytic framework is provided to derive the closed-form expressions of transmit secrecy outage probability (TSOP) for the mobile user. Second, two transmission schemes are proposed to maximize the secrecy throughput of the mobile user by ensuring a certain level of transmit probability (TP) and TSOP requirements. The numerical and simulation results verify the validity and effectiveness of the proposed schemes, and indicate that by adopting appropriate mobility pattern, the user’s secrecy throughput can be improved, and the constraint on its moving region can be largely reduced. Those properties are lightweight and feasible to enhance security for many mobile IoT scenarios.

In this paper, we propose a novel greedy iteration algorithm, called block matching pursuit (BMP), for arbitrary block sparse signal recovery. BMP can recover the target signal without prior information of block structure or block length and can estimate the true sparsity level. In each iteration, BMP processes a correlation test to estimate nonzero entries of signal with a fixed step size, and then gathers all possible nonzero entries to form a candidate list, finally checks the list to choose correct entries according to current estimated sparsity level. The simulation results show that BMP can recover the signal of interest in noiseless or noisy case and achieves an outstanding recovery performance.

A new modulated hopping Discrete Fourier Transform (mHDFT) algorithm which is characterized by its merits of high accuracy and constant stability is presented. The proposed algorithm, which is based on the circular frequency shift property of DFT, directly moves the k-th DFT bin to the position of k = 0, and computes the DFT by incorporating the successive DFT outputs with arbitrary time hop L. Compared to previous works, since the pole of mHDFT precisely settles on the unit circle in the Z-plane, the accumulated errors and potential instabilities, which are caused by the quantization of the twiddle factor, are always eliminated without increasing much computational effort. The numerical simulation results verify the effectiveness and superiority of the proposed algorithm.