This paper considers the problem of power minimization based robust orthogonal frequency division multiplexing (OFDM) radar waveform design, in which the radar coexists with a communication system in the same frequency band. Recognizing that the precise characteristics of target spectra are impossible to capture in practice, it is assumed that the target spectra lie in uncertainty sets bounded by known upper and lower bounds. Based on this uncertainty model, three different power minimization based robust radar waveform design criteria are proposed to minimize the worst-case radar transmitted power by optimizing the OFDM radar waveform, which are constrained by a specified mutual information (MI) requirement for target characterization and a minimum capacity threshold for communication system. These criteria differ in the way the communication signals scattered off the target are considered: (i) as useful energy, (ii) as interference or (iii) ignored altogether at the radar receiver. Numerical simulations demonstrate that the radar transmitted power can be efficiently reduced by exploiting the communication signals scattered off the target at the radar receiver. It is also shown that the robust waveforms bound the worst-case power-saving performance of radar system for any target spectra in the uncertainty sets.

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 power minimization-based joint subcarrier assignment and power allocation (PM-JSAPA) for an integrated radar and communications system (IRCS). In the considered system, the integrated transmitter is responsible to fulfill both radar and communications purposes simultaneously. The proposed PM-JSAPA strategy aims to minimize the total radiated power of the IRCS by jointly optimizing the available subcarriers and power resource allocation in order to achieve improved power-saving performance, subject to a predetermined mutual information (MI) for target parameter estimation and a specified data information rate (DIR) for wireless communications. The formulated problem falls into a mixed integer nonlinear programming (MINLP) problem, which is generally NP-hard. Then, an efficient three-step resource allocation framework is designed to solve the resulting optimization problem. Specifically, in Step 1, the subcarrier assignment for both radar and communications purposes is determined. In Step 2 and Step 3, we perform power allocation for different applications with the obtained subcarrier assignment results in Step 1, respectively. Finally, numerical simulation results are presented to demonstrate the superior power-saving performance of the proposed PM-JSAPA strategy and the effectiveness of the proposed solutions.

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 order to simultaneously enhance the low probability of intercept (LPI) performance and resource utilization of distributed radar network system, an LPI-based collaborative power and bandwidth allocation (CPBA) strategy is developed for multi-target tracking in the current work. The primary aim of the proposed LPI-CPBA strategy is to exploit the optimization technique to minimize the total power consumption of the distributed radar network by collaboratively optimizing the radar node selection, transmit power and signal bandwidth allocation, while satisfying the predetermined tracking accuracies of multiple targets and several resource constraints. The closedform expression for the predicted Bayesian Cramér-Rao lower bound (BCRLB) incorporating the above controllable parameters is analytically computed and employed to gauge the multi-target tracking performance. Subsequently, an efficient two-stage solution methodology is proposed to solve the resulting mixed-Boolean, non-linear and non-convex optimization problem, which collaboratively determines the radar node selection as well as the transmit power and bandwidth allocation between the designated radar nodes. Extensive numerical simulations are conducted to verify the advantage of the presented LPI-CPBA strategy in terms of the achievable LPI performance of distributed radar network system.