In this paper, we propose a new framework by combining the concepts of maximum distance separable (MDS) code and generalized spatial modulation (GSM) for multiple-input multiple-output (MIMO) transmission, namely MDS-GSM-MIMO. In our design, we exploit the powerful MDS code to increase the minimum Hamming distance (MHD) of the code-words, up to 2, and use the concept of space-domain index modulation to enlarge the minimum Euclidean distance (MED) of the achieved multidimensional GSM constellations. Moreover, we add the maximum minimum distance (MMD) precoder and guaranteed Euclidean distance (GED) precoder to enhance the MED further. Therefore, the MDS-GSM-MIMO we proposed with precoding is capable of optimizing both the overall MHD and MED. Then, we conduct theoretical analyses and comparisons with regard to average bit error rate (ABER) bound, MHD and MED of the conventional GSM-MIMO and the proposed MDS-GSM-MIMO. Simulation results show that the MDS-GSM-MIMO exhibits a BER performance gain of 3 dB compared to conventional GSM-MIMO, while the proposed MDS-GSM-MIMO with precoding exhibits a BER performance gain of up to 4 dB compared to the counterpart without precoding.

Visual tracking is a fundamental task in computer vision with significant practical applications in various domains, including surveillance, security, robotics, and human-computer interaction. However, it may face limitations in visible light data, such as low-light environments, occlusion, and camouflage, which can significantly reduce its accuracy. To cope with these challenges, researchers have explored the potential of combining the visible and infrared modalities to improve tracking performance. By leveraging the complementary strengths of visible and infrared data, RGB-infrared fusion tracking has emerged as a promising approach to address these limitations and improve tracking accuracy in challenging scenarios. In this article, we present a review on RGB-infrared fusion tracking. Specifically, we categorize existing RGBT tracking methods into four categories based on their underlying architectures, feature representations, and fusion strategies, namely feature decoupling based method, feature selecting based method, collaborative graph tracking method, and traditional fusion method. Furthermore, we provide a critical analysis of their strengths, limitations, representative methods, and future research directions. To further demonstrate the advantages and disadvantages of these methods, we present a review of publicly available RGBT tracking datasets and analyze the main results on public datasets. Moreover, we discuss some limitations in RGBT tracking at present and provide some opportunities and future directions for RGBT visual tracking, such as dataset diversity, unsupervised and weakly supervised applications. In conclusion, our survey aims to serve as a useful resource for researchers and practitioners interested in the emerging field of RGBT tracking, and to promote further progress and innovation in this area.

Tracking an Unmanned Aerial Vehicle (UAV) to obtain its locations and trajectory is a crucial task to avoid the unlawful use of UAVs. However, most existing UAV tracking methods fail when facing cluster environments, out-of-view, and occlusions because of their insufficient representation of global context information capacity. To mitigate these issues, we propose a new tracker, namely SiamFusion, to innovate a dual fusion procedure that leverages the advantages in both the feature and decision levels. In particular, we propose a novel feature fusion module named Modality-Fusion to utilize multi-modal information, enhancing the perception of the target. From the decision level, we further develop a local-global converter based on a multi-modal fusion decision-making mechanism to reduce the accumulation during tracking, which significantly increases the robustness of the tracking process. Extensive experiments demonstrate the superiority of the proposed SiamFusion, which achieves the best performance on Anti-UAV in terms of accuracy and speed. In particular, we exceed the state-of-the-art tracking algorithm in the tracking accuracy by 4.2% at a similar frame rate. Our source codes, pre-trained models, and online demos will be released upon acceptance.

In surveillance or search scenarios, exploiting unmanned aerial vehicles (UAVs) as relays to provide wireless data access for task-oriented ground robots (GRs) with remote base station have emerged as a promising application. This paper considers a UAV-enabled wireless network, where communication links could be line-of-sight (LoS) and non-line-of-sight (NLoS) due to obstacles in urban environments. Existing works typically adopted the free-space path loss model or the statistical channel model, which either ignored the impact of obstacles or assumed uniformly distributed obstacles and therefore might fail in practical NLoS scenarios. In this paper, taking the information of randomly distributed obstacles in environments into consideration, we aim to optimize the placement for the UAV-enabled multi-hop network to transfer more data collected by GRs and minimize the time delay in data transmission while satisfying the required communication quality. By reconstructing this complex non-convex optimization problem into two subprob-lems and solving them alternatively, we propose the multi-hop UAVs placement (mUP) method to get the solution, which contains the air-to-ground network formation (ATG-NF) algorithm and the communication quality-aware UAV placement (CQA-UP) algorithm. Simulation results show that in four types of typical urban environments or with different numbers of UAVs, the proposed mUP method achieves substantial performance gains in terms of communication quality and task performance compared to other placement approaches based on statistical channel models. We further discuss the robustness of the mUP method towards terrain measurement error.

Orthogonal time frequency space (OTFS) is a new multi-carrier modulation technology emerging in recent years. Like orthogonal frequency division multiplexing (OFDM), OTFS also has the problem of high peak to average power ratio (PAPR). Because of the high PAPR, OTFS signals are easy to enter the nonlinear region of the power amplifier (PA), and result in nonlinear distortion. In this paper, we study the PAPR problem for OTFS system and propose an improved algorithm by jointly exploiting the traditional selective mapping (SLM) scheme and the imperialist competition algorithm (ICA), namely ICA-SLM. The simulation results shown that the proposed novel PAPR reduction method is capable of achieving better performance compared to conventional SLM for OTFS systems.

UAV swarms based on cooperative communication networks are widely used in many fields, which have the advantages of high mobility, high flexibility and low cost. However, UAVs face limited spectrum resources in a specific area and may interfere with primary users. Effective communication management between UAVs is a challenging problem. There-fore, this paper proposes a UAV clustering method based on the improved cluster head selection weight, which provides an effective management for the communication between UAVs and improves the efficiency of data collection. The proposed algorithm employs a new cluster head selection strategy based on the searched targets and available channel resources. Moreover, we analyze the weight factors of UAVs in flight and communication energy consumption. Considering the decreasing the member of the UAV clusters, we also design a maintenance strategy to improve the degree of data sharing in the cluster. The experimental results show that, compared with the traditional UAV clustering methods, the proposed method can effectively improve the network management for communication resources, reduce the collision and interference rate with the primary user by 25%, shorten the time required to fully acquire multi-target point data for the first time by 9%, and increase the amount of target point data collected by 26%.

This paper investigates the multi-channel access and rendezvous problem in unmanned aerial vehicle (UAV) relaying system in the absence of pre-allocated control channel. Both the geographical sensing range and the spectrum sensing bandwidth of each UAV are limited due to onboard payload constraints, hence it becomes challenging to design effective and efficient channel rendezvous mechanisms. To address the challenge, this paper first observes and analyzes the effects of UAV relaying network topology and geographical sensing range on the rendezvous, and it is found that a joint exploitation of motion and frequency control is essential to achieve efficient rendezvous. Based on the important insight, this paper formulates the UAV relaying rendezvous problem and proposes a novel joint space-frequency rendezvous (JSFR) method for multi-UAV networks, incorporating with distributed reinforcement learning techniques. The simulation results show that the JSFR method may significantly improve the effectiveness and efficiency of rendezvous in UAV relaying networks, in terms of rendezvous probabilities and convergence rates.

The one-on-one target tracking problem is important in robot vision. Previous studies mainly focused on locating, depth information and control mechanism. In this study, we construct an autonomously visual tracking system called learn-to-track (LtT) by using a novel approach. This system only depends on a monocular camera. The main component is a deep convolutional neural network called the LtT, which trains a supervised image classifier by using images captured by the monocular camera in the follower robot. By operating merely on two adjacent frames, the network can predict the estimated velocity of the target, i.e., the velocity control for the follower. To verify the effectiveness of the LtT system, we construct a large-scale dataset that supports download l in the simulator, in which the LtT network is trained and the LtT system performance is evaluated. Furthermore, a remarkable tracking performance is achieved.

In this paper, we consider a surveillance scenario where a team of sensing robots survey a sensitive area and transmit the monitored data to a remote base station through a mobile relay. In this scenario, it is challenging to autonomously adjust the position of the mobile relay for the sake of minimizing the total communication-motion energy consumption of the system, while maintaining the communication quality of the mobile sensing robots. We first derive the asymptotically optimal transmit powers of the mobile relay and of the sensing robots according to the predefined end-to-end packet error rate (PER) requirement. Then, we propose a joint communication-motion planning (JCMP) method for minimizing the total communication-motion energy consumption in both: single- and multi-sensing-robot scenarios, where the trajectories of the sensing robots are rigorously defined. We further consider the scenario where the sensing robots' trajectories are not fixed but can be optimized in restrained areas. The effectiveness of the proposed JCMP is verified by analysis and numerical results for different system configurations, showing that a substantial energy-efficiency improvement may be achieved in comparison with the benchmark that only optimizes the communication energy consumption.

This paper considers a surveillance scenario where a mobile robot monitors an indoor environment and transmits the monitored data to a base station. Considering the indoor radio environment is complex, we first build the radio environment map (REM) with two different interpolation methods. Then, we combine REM with the structural blueprint of the building to build an integrated map called radio-structural map (RSM). Based on RSM, we propose an optimal surveillance path search (OSPS) method which minimizes the data transmission delay of the patrol robot under a communication reliability constraint. In OSPS, two optimization methods are adopted, which may sharply reduce the computation cost. Besides the numerical simulations, we further discuss the relationship between the communication reliability and data transmission delay. Finally, we test the applicability of OSPS in the stage simulator of ROS.