Dual-function radar-communications (DFRC) system has been recognized as a promising solution to alleviate the radio frequency spectrum congestion and the shortage of spectrum resources. In this article, we address the problem of designing the joint subcarrier selection and power allocation scheme to minimize the power consumption of a DFRC system, under a general scenario in which the DFRC system is capable of performing a primary radar purpose and a secondary communications purpose in the meantime. In particular, the key mechanism is to minimize the total radiated power of the multicarrier DFRC system by jointly selecting the best possible subcarriers for radar and communications purposes in sequence and allocating the optimal power resource on the corresponding subcarriers, under the constraints of a predefined mutual information for target characterization and a desired communications data rate for information transmission. The resulting problem is formulated as a two-variable nonconvex optimization problem, one for subcarrier selection and the other for power allocation. Then, after convex relaxation reformulation and problem partition, an efficient three-step solution technique is developed for the joint optimization scheme, which combines the cyclic minimization algorithm and Karush–Kuhn–Tuckers optimality conditions. Finally, numerical results are provided to validate the theoretical findings and to verify the effectiveness of the proposed joint optimization scheme.

This paper investigates the problem of low probability of intercept-based optimal power allocation (LPI-OPA) for an integrated multistatic radar and communication system, which consists of multiple transmitters operating at different frequencies, a radar receiver, and a communication receiver (CR). The integrated multistatic radar and communication system is capable of fulfilling the requirements of both radar and communication subsystems. The key tenet of the integrated system is to minimize the total power consumption by optimizing the transmit power allocation at each transmitter for radar waveforms and information signals, which is constrained by a predetermined target detection performance for the RR and a desired information rate for the CR. Since the analytical closed-form expression of the probability of detection is not tractable, its upper bound is derived. We analytically show that the resulting optimization problem can be reformulated as two subproblems, which can be solved by an efficient solution procedure based on the approach of linear programming and the Karush–Kuhn–Tuckers optimality conditions. Simulation results are provided to show that the LPI performance of the integrated multistatic radar and communication system can significantly be enhanced by employing our proposed LPI-OPA scheme.

In this article, we investigate the problem of joint route optimization and multidimensional resource management (JRO-MDRM) for an airborne radar network in target tracking application. The mechanism of the proposed JRO-MDRM scheme is to adopt the optimization technique to collaboratively design the flight route, transmit power, dwell time, waveform bandwidth, and pulselength of each airborne radar node subject to the system kinematic limitations and several resource budgets, with the aim of simultaneously enhancing the target tracking accuracy and low probability of intercept (LPI) performance of the overall system. The predicted Bayesian Cramér–Rao lower bound and the probability of intercept are calculated and employed as the metrics to gauge the target tracking performance and LPI performance, respectively. It is shown that the resulting optimization problem is nonlinear and nonconvex, and the corresponding working parameters are coupled in both objective functions, which is generally intractable. By incorporating the particle swarm optimization and cyclic minimization approaches, an efficient four-step solution algorithm is proposed to deal with the above problem. Extensive numerical results are provided to demonstrate the correctness and advantages of our developed scheme compared with other existing benchmarks.

Distributed radar network systems have been shown to have many unique features. Due to their advantage of signal and spatial diversities, radar networks are attractive for target detection. In practice, the netted radars in radar networks are supposed to maximize their transmit power to achieve better detection performance, which may be in contradiction with low probability of intercept (LPI). Therefore, this paper investigates the problem of adaptive power allocation for radar networks in a cooperative game‐theoretic framework such that the LPI performance can be improved. Taking into consideration both the transmit power constraints and the minimum signal to interference plus noise ratio (SINR) requirement of each radar, a cooperative Nash bargaining power allocation game based on LPI is formulated, whose objective is to minimize the total transmit power by optimizing the power allocation in radar networks. First, a novel SINR‐based network utility function is defined and utilized as a metric to evaluate power allocation. Then, with the well‐designed network utility function, the existence and uniqueness of the Nash bargaining solution are proved analytically. Finally, an iterative Nash bargaining algorithm is developed that converges quickly to a Pareto optimal equilibrium for the cooperative game. Numerical simulations and theoretic analysis are provided to evaluate the effectiveness of the proposed algorithm.

Due to the high demand for new wireless services and shortage of available radio frequency (RF) spectrum, joint radar and communication system is considered as a coexistence solution to the RF spectrum congestion problem. Therefore, joint radar and communication system has become an attractive platform for target parameter estimation. In this study, the problem of low probability of intercept (LPI) performance based orthogonal frequency division multiplexing (OFDM) radar jamming power allocation is addressed for a joint radar and communication system. Given the knowledge of the radar transmitted signal, the communication signal, the channel impulse responses, and the propagation losses of the corresponding channels provided by the jammer, three different LPI‐based criteria for radar noise jamming power allocation are proposed, whose purposes are to minimise the total noise jamming power by optimising the multicarrier jamming power allocation while the achieved mutual information between the received echoes and the target impulse response is enforced to be less than a predefined threshold. The presented optimisation problems are solved analytically and their solutions represent the optimum power allocation for each subcarrier in the OFDM jamming waveform. Numerical simulations show that the LPI performance of the jammer is considerably improved by the proposed strategies.

In this paper, the problem of joint beam forming and power allocation in a multiple-inputmultiple-output(MIMO) radar network is investigated in the presence of signal-dependent interference and multiple jammers. Specifically, the main objective of the radar system is to improve the performance in terms of total power consumption and beampattern peak sidelobe level (PSL) under the signal-to-interference-plus-noise ratio (SINR) constraints at the radars, whereas the jammers aim to maximize their side effects on the radars. The interactions between the radar system and the jammers are formulated as a noncooperativegame.Anefficientiterativealgorithmisproposedtominimize the total weighted transmit power of the radar system based on the Nash equilibrium of the game. Additionally, in order to suppress the sidelobe level of both the transmit and receive beampatterns for the radars, this paper further studies the problem of minimizing the sidelobe level by introducing a pricing mechanism that takes into account the accumulated sidelobe level of the radar system. The sidelobe control problems are effectively solved using the Lagrange duality and the Lagrange multiplier method. Finally, simulation results are provided to confirm the convergence of the proposed algorithms and assess the performance in terms of total power consumption and PSL.