The existing linear-frequency-modulated (or step frequency) and random noise synthetic aperture radar (SAR) systems may correspond to the frequency-hopping and direct-sequence spread spectrum systems in the past second- and third-generation wireless communications. Similar to the current and future wireless communications generations, in this paper, we propose the orthogonal frequency-division multiplexing (OFDM) SAR imaging, where a sufficient cyclic prefix (CP) is added to each OFDM pulse. The sufficient CP insertion converts an intersymbol interference (ISI) channel from multipaths into multiple ISI-free subchannels as the key in a wireless communications system, and analogously, it provides an inter-range-cell interference (IRCI)-free (high range resolution) SAR image in a SAR system. The sufficient CP insertion along with our newly proposed SAR imaging algorithm, particularly for the OFDM signals, also differentiates this paper from all the existing studies in the literature on OFDM radar signal processing. Simulation results are presented to illustrate the high-range-resolution performance of our proposed CP-based OFDM SAR imaging algorithm.

Long time coherent integration (LTCI) technique enhances radar detection of maneuvering weak targets. Unfortunately, range migration (RM) and Doppler frequency migration (DFM) would occur during the integration period. Existing LTCI methods are all based on the assumption that the target is of single motion model (i.e., the target's motion model is fixed) during the long integration time. However, with increasing target's maneuverability and long observation time (needed to detect weak targets), the target's motion model is often changing (i.e., target is of multiple motion models) during the integration time, which would reduce the performance of existing LTCI methods significantly. In this paper, we address the LTCI problem for detecting a maneuvering weak target with multiple motion models, where the target signal model is first established. Thereafter, the short-time generalized Radon Fourier transform (STGRFT) based LTCI algorithm is proposed. The STGRFT method could not only eliminate RM and DFM, but also estimate the model-changing point (i.e., the time when target's motion model changes) and accumulate the target energy distributed in different motion stages (corresponding to different motion models). Finally, numerical and real-data experiments are carried out to demonstrate its effectiveness. The results confirm that in terms of integration gain, input-output signal-to-noise ratio (SNR) performance and detection ability, STGRFT-based method is superior to the existing popular LTCI algorithms.

For a Multiple-Input Multiple-Output (MIMO)-Orthogonal Frequency-Division Multiplexing (OFDM) based dual-function radar and communication (DFRC), the problem of transmit/receive beamforming with the constraints of Quality of Server (QoS) and transmit power is studied in this paper. The degrees of freedom for system design are the transmit/receive beampatterns at the radar array, and the receive beampattern at the communication array. Firstly, we adopt Kullback-Leibler divergence (KLD) as design metrics and formulate the design problem as a KLD maximization problem with QoS and transmit power constraints. Secondly, we solve the original non-convex problem through the proposed alternating direction sequential relaxation programming (ADSRP) algorithm, which iteratively optimizes three subproblems: 1) dual-function transmit beamforming with the constraints of QoS and transmit power; 2) radar receive beamforming; and 3) communication receive beamforming. We analytical provide the monotonic improvement and bound of KLD, which guarantees the convergence of the ADSRP algorithm. Finally, simulation results are provided to evaluate the effects of different QoS requirements and peak-to-average-power ratio (PAPR) constraints in terms of power allocation scheme and radar detection performance, compared to the traditional radar system (TRS).

Our previously proposed OFDM with sufficient cyclic prefix (CP) synthetic aperture radar (SAR) imaging algorithm is inter-range-cell interference (IRCI) free and achieves ideally zero range sidelobes for range reconstruction. In this OFDM SAR imaging algorithm, the minimum required CP length is almost equal to the number of range cells in a swath, while the number of subcarriers of an OFDM signal needs to be more than the CP length. This makes the length of a transmitted OFDM sequence at least almost twice of the number of range cells in a swath and for a wide swath imaging, the transmitted OFDM pulse length becomes long, which may cause problems in some radar applications. In this paper, we propose a CP-based OFDM SAR imaging with arbitrary pulse length, which has IRCI free range reconstruction and its pulse length is independent of a swath width. We then present a novel design method for our proposed arbitrary length OFDM pulses. Simulation results are presented to illustrate the performances of the OFDM pulse design and the arbitrary pulse length CP-based OFDM SAR imaging.

Thanks to the low detectability and low cost of unmanned aerial vehicles (UAVs), UAV-enabled front swarm jamming has been considered a promising method to counter netted radar systems. Nevertheless, the task assignment problem of the UAV-enabled front jammer swarm has rarely been studied. Due to the low load capacity of the UAV and dynamic task environment, the task assignment of the UAV-enabled front jammer swarm faces the following two main challenges: 1) The hardware performance of the jammer nodes is limited; 2) The optimal task assignment scheme is dynamic. To address the above issues, a distributed dynamic task assignment approach is studied for the UAV-enabled front jammer swarm in this paper. Firstly, we design the utility function of UAV-enabled front jammer swarm based on the quality of service (QoS) framework, and formulate the task assignment of the UAV-enabled front jammer swarm as a multi-agent coordination problem. Then, the coalition formation game (CFG) of jammers for the task assignment is formulated to reveal the coordination process of the jammers. Finally, a distributed coalition formation based UAV-enabled front jammer swarm dynamic task assignment algorithm is proposed, which can achieve similar performance to the centralized optimization via a distributed approach and is suitable for dynamic environment. Experimental results demonstrate that the proposed approach is highly competitive to deal with the task assignment problem of the UAV-enabled front jammer swarm.

Task assignment is crucial for multitarget tracking of the radar network and is mainly solved by centralized optimization methods, which results in the issues of robustness deficiency, high computational cost, and inflexible adaptability in complex environments. To overcome these issues, task assignment of the radar network for multitarget tracking is formulated as a multiagent decision-making and learning problem, where each radar node in the network acts as an intelligent agent that can make the tracking decision according to its task preference and interact with other agents for the sake of the best network utility. To describe the decision references of radar nodes for various tasks, the criterion for the design of the utility function is presented, and a utility function under this criterion is devised based on the quality of service framework. Then, the coalition game with transferrable utility is developed for task assignment, where the utility of the coalition is completely transferred to all coalition members. The existence of the stable coalition partition of the developed game is analyzed theoretically, and the model-based multiagent random Fourier features reinforcement learning (RFFRL) algorithm is proposed to solve the optimal solution to the game in the high dimensional state space, which is proven to be converged at a Nash-stable coalition partition. Some numerical simulation results are provided to illustrate the effectiveness of the proposed algorithm in terms of tracking performance and resource conservation.