Weiwei Zhang

To enhance the energy-utilization performance of distributed radar networks, this paper presents an adaptive beamforming and power-control strategy for unmanned aerial vehicles (UAVs) detection. The array-gain formulation and monostatic detection probabilities are first derived and subsequently fused to obtain the detection probabilities of the distributed radar networks using the rank-fusion rule. The resulting optimization problem—aimed at minimizing the total transmit power under resource and detection-probability constraints—is characterized as a high-dimensional, non-convex, and discontinuous mixed-Boolean optimization model. To address this NP-hard problem, the particle swarm optimization (PSO) algorithm is employed. Based on the optimized array gain, linearly constrained minimum variance (LCMV) transmit multi-beamforming is further utilized to obtain the final beam patterns and corresponding power allocation. Simulation results verify that the proposed strategy yields a substantial improvement in energy-utilization efficiency compared with existing approaches.

In order to enhance the energy utilization efficiency of the colocated multiple-input multiple-output (MIMO) radar system, the joint aperture and transmit resource allocation strategy is proposed for the application of multi-target detection. The objective of the proposed strategy is to minimize the total transmit power of the colocated MIMO radar system by optimally allocating the aperture and transmitting power to illuminate each target under the constraints of the detection performance, the limitation of the radar system infrastructure as well as the corresponding natural attributes. To achieve this, the proposed strategy is firstly formulated as an NP-hard optimization model with the adaptable vectors of the aperture and transmitting power. Given the unique characteristics of this optimization problem, the standard particle swarm optimization (SPSO) and analytic method that involves relaxation and Lagrangian multiplier method are subsequently exploited to address the problem. Finally, extensive numerical simulation confirms the superiority of the proposed analytical method to the SPSO algorithm, while validating the higher energy utilization efficiency of the proposed resource strategy over existing resource allocation strategies.

In this article, the game theoretic power control for a distributed radar network in the presence of a smart jammer is investigated. The main objective of the radar network is to minimize its total transmit power while maintaining desirable estimation rates, taking into account the available power of each radar. The jammer, on the other hand, aims at maximizing the damaging effect on the radar system. By formulating the adversarial interaction between the radar system and the jammer as a Nash game and a Stackelberg game, respectively, we compute the optimal power allocation strategies for the radar system incorporating the effect of signal-dependent interference. We begin by studying a radar network where each radar can benefit from the other radars’ transmission. The conditions for the existence of the Nash equilibrium and the Stackelberg equilibrium are examined, and algorithms to obtain the optimal transmission strategies for the radar system are developed. Next, we extend the equilibrium analyses to a more general distributed radar network with inter-radar interference. Finally, numerical results are presented to confirm the theoretical analysis.

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.

In practical application, the resources related to infrastructure and electromagnetic radiation are often limited for phased array radar network (PARN). To more effectively exploit these limitation resources, a joint aperture and transmit resource allocation (JATRA) strategy is built for the application of multi-target localization. The principle of the JATRA strategy is to minimize the total transmit power of the PARN by optimally allocating the aperture, power and effective bandwidth under the constraints of the system resources, while achieving the predefined localization accuracy for each target. To begin with, the expressions of Cramér–Rao lower bound (CRLB) of target localization are derived for time of arrival (TOA), direction of arrival (DOA) as well as joint TOA and DOA, and adopted as localization metric. Subsequently, the JATRA strategy is constructed as a mixed-integer, nonlinear and nonconvex optimization problem involving three adaptable vectors. In view of the characteristics presented in the construction model, a fast convergent two-step iterative optimization algorithm is designed to distribute three kinds of resources, together with its convergence and complexity well analyzed. Finally, the convergence of the two-step iterative algorithm is verified by simulation results, providing insights into underlying superiority of the JATRA strategy in comparison with other existing resource allocation schemes.

To more effectively exploit the omnipresent public non-cooperative illuminators of opportunity (IOs) to improve the low probability of intercept (LPI) performance, a power allocation strategy is developed for target localization in distributed hybrid non-coherent active-passive radar networks. The core of the strategy is to optimize each active radar power allocation to minimize the total intercepted power for the hostile intercept receiver in view of power constraints, while achieving the predetermined collaborative target localization accuracy with the assistance of public non-cooperative IOs. Considering the timing uncertainty of public non-cooperative IOs, the localization performance is determined by joint Cráme-Rao lower bound (CRLB) of the target position and the time-of-arrival (TOA) of the direct-path in a distributed passive radar network. Then, the Bayesian CRLB (BCRLB) is derived to evaluate collaborative target localization accuracy, adopted as the optimization criterion for the power allocation strategy. Furthermore, the optimization problem of the power allocation strategy is strictly proved to be a convex optimization, which implies that the global optimal lower bound can be easily obtained. By introducing a semi-definite matrix, the optimization problem is subsequently converted to semi-definite programming (SDP), resolved by the exiting classical algorithm directly. Finally, the simulation results verify the theoretical analysis and the LPI performance of the proposed strategy which outperforms existing strategies.

In this article, an optimization scheme integrating power allocation, bandwidth assignment and radar node selection (PABARNS) is developed to improve power utilization efficiency (PUE) of noncoherent distributed multiple-input–multiple-output (MIMO) radar system for target localization. The basis of the proposed scheme is to minimize the total transmit power subject to a predetermined target localization accuracy requirement, where the target localization accuracy is evaluated with the Crámer–Rao lower bound. Subsequently, the PABARNS scheme results in a mixed nonlinear Boolean-nonconvex optimization problem. In view of the unique feature of the optimization model, a two-step technique based on the heuristic algorithm and alternating direction method of multipliers method is presented to solve the underlying optimization problem, with merit of that it is more workable than other existing approaches when the power and bandwidth lie in any bound set. Finally, the simulation reveals the higher accuracy and lower complexity of the developed algorithm compared with the benchmarks, as well as the superiority of the PABARNS scheme compared with over other existing schemes in the view of the achievable PUE.