In this paper, a track deception scheme by unmanned aerial vehicle (UAV) swarm against radar network with limited communication is proposed. Firstly, a track deception model for single UAV is established, along with a communication framework for information exchange within the swarm. To address packet dropouts, a track deception scheme based on predictive mechanism is introduced, in which cluster head (CH) UAVs estimate the state information of cluster member (CM) UAVs and generate optimal control decisions over multiple time steps. Based on this mechanism, a trajectory planning optimization model is developed with the objective of minimizing the flight velocity of UAVs, subject to kinematic performance constraints and safe distance requirements. This optimization problem is solved by particle swarm optimization (PSO) algorithm. Simulation results demonstrate that the phantom tracks generated with limited communication can effectively pass the homology test from radar network, validating the robustness and feasibility of the proposed scheme.

How to efficiently schedule the transmit beams and power resources in colocated multiple-input-multiple-output (C-MIMO) radar network to achieve better multitarget tracking (MTT) accuracy is a critical research issue. However, in electronic countermeasure environments, excessive resource allocation may lead to the interception of transmit pulses by hostile receivers. As the number of intercepted pulses increases, the transmit signals of C-MIMO radar nodes would be identified and subject to precise suppression jamming, resulting in a sharp degradation in MTT performance. To overcome this shortcoming, we propose a low probability of identification (LPID)-based composed resource allocation (CRA) scheme for MTT in C-MIMO radar network. Closed-form expressions for the predicted conditional Cramer-Rao lower bound (PC-CRLB) and the radar signal identification probability under suppression jamming are derived. Based on the tactical significance map function, we compute the comprehensive threat level for each target in real time. We then employ the sum of products between PC-CRLB values and threat levels, and the worst-case identification probability as the quantitative metrics for MTT performance and LPID performance, respectively. The LPID-CRA is developed to jointly balance these two metrics by dynamically allocating beam-pairing and power resources within each tracking frame. To address the resulting nonconvex nonlinear problem, we perform an equivalent transformation of the optimization model and develop an efficient five-stage solution method that incorporates the Zoutendijk methods of feasible directions and the min-cost flow algorithm. Simulation results demonstrate that the proposed LPID-CRA strategy significantly enhances overall system performance.

To address the survivability challenge of multi-UAVs in hostile environments, a three-dimensional (3D) stealth trajectory planning scheme based on an improved A-star algorithm is proposed for threat avoidance. First, a 3D environment model incorporating radar detection and no-fly zones is established. Subsequently, four evaluation metrics are constructed to assess UAV mission fault tolerance, flight stability, trajectory smoothness, and stealth performance. The weighted sum of these evaluation metrics is defined as the multi-UAV 3D trajectory planning efficiency function. Building upon this framework, an optimization model for multi-UAV stealth trajectory planning in 3D dynamic environments is formulated, with the objective of maximizing the efficiency function while incorporating constraints such as no-fly zones and UAV maneuverability parameters. Finally, a two-step solution method based on the improved 3D A-star algorithm is employed to solve the optimization model. Simulation results demonstrate that, compared to the benchmark, the proposed scheme generates feasible 3D stealth paths for multiple UAVs that satisfy maneuverability constraints and exhibit the low probability of being detected by the radar network.

In this paper, we propose a low probability of identification (LPID)-based composed resource allocation (CRA) scheme for multi-target tracking (MTT) in colocated multiple-input-multiple-output (C-MIMO) radar network under electronic countermeasure environments. Closed-form expressions for the predicted conditional Cramér-Rao lower bound (PC-CRLB) and the radar signal identification probability under suppression jamming are derived. We then employ the sum of PC-CRLB values and the worst-case identification probability as the quantitative metrics for MTT performance and LPID performance, respectively. The LPID-CRA is developed to jointly balance these two metrics by dynamically allocating beam-pairing and power resources within each tracking frame. To address the resulting non-convex non-linear problem, we perform an equivalent transformation of the optimization model and develop an efficient five-stage solution method that incorporates the Zoutendijk methods of feasible directions (ZMFD) and the min-cost flow algorithm. Simulation results demonstrate that the proposed LPID-CRA strategy significantly enhances overall system performance.

In this paper, the joint design of transmit and receive beamformers for transmit subaperturing multiple-input-multiple-output (TS-MIMO) radar is investigated, aiming to enhance its low probability of intercept (LPI) capability. The main objective is to simultaneously minimize the transmission power, suppress the transmit sidelobe levels, and minimize the probability of intercept, thus bolstering the LPI performance of the radar system while maintaining the desired target detection performance. An alternative optimization method is proposed to jointly optimize the transmit and receive beamformers, yielding an unified LPI optimization framework. Particularly, the proposed iterative algorithm based on the Lagrange duality theory for transmit beamforming is more efficient than the conventional convex optimization method. Numerical experiments highlight the effectiveness of the proposed approach in sidelobe suppression and computational efficiency.

In this paper, an adaptive resource scheduling (ARS) strategy tailored for multiple maneuvering targets tracking (MMTT) task with the consideration of anti-jamming is put forward for the radar-enabled unmanned aerial vehicles (UAVs). More specifically, the developed ARS strategy for radar-enabled UAVs (RUAVs) is framed as a non-convex mathematical optimization problem, where the node-target assignment, route planning and transmit beampattern synthesis are jointly considered for the MMTT task. Hereafter, in order to tackle the established non-convex optimization problem effectively, an efficient partition based three-stage solution technique is developed. Finally, numerical simulation results are provided to demonstrate the effectiveness of the proposed ARS strategy.

The low probability of intercept (LPI) performance is pivotal for the self-safeguarding of dual-function radar-communication (DFRC) systems. This paper proposes an LPI-based resource optimization algorithm for the multi-carrier DFRC system. Different subcarriers are allocated reasonably to realize radar detection or communication transmission functions, which establishes the DFRC system model. The radar conditional mutual information and data transmission rate are derived to quantify the performance of radar and communication systems, respectively. These indicators are imposed on the constraints, aiming to ensure the performance of multi-target detection and multi-user communication. The optimization objective is to minimize the total transmit power to improve the LPI performance. To address the non-convexity of the optimization problem, a convex relaxation-based alternating algorithm is proposed. The variables are placed within an alternating framework, and the nonlinear inequality constraints are relaxed to apply Karush-Kuhn-Tucker (KKT) conditions for each iteration. Simulations validate that the proposed algorithm can provide lower transmit power for multi-target detection and multi-user communication, thereby facilitating the LPI performance of the DFRC system.

Low probability of interception (LPI) technology is critical for the self-protection of dual-function radar-communication (DFRC) systems in hostile environments. However, the range-dimensional uncontrollability of the beampattern imposes a bottleneck limitation on LPI development. This article investigates a transceive beamforming for frequency diverse array (FDA)-multiple-input-multiple-output (MIMO)-based DFRC systems. Initially, we innovatively construct the signal model for the FDA-MIMO DFRC system, which surmounts the inseparability between frequency offsets and transmitting antennas. Subsequently, the equivalent transmitted beampattern and communication channel are derived into 2-D range-angle expressions, aiming to implement joint range-angle beamforming. The communication symbols are strategically placed in predetermined range-angle positions of each user through the beampattern modulation technique. The LPI optimization problem is formulated as the minimization of transmitting beamforming power, subject to the constraints of radar signal-to-interference-plus-noise ratio (SINR) and communication sum rate. To tackle the nonconvex problem with high-dimensional nonlinear constraints, a distributed alternating algorithm is developed for fast solving, involving sequence convex approximation, weighted mean-square error minimization, and alternating direction method of multipliers. Simulation results demonstrate that our method achieves punctate multitarget detection and multiuser communication in 2-D range-angle planes, enormously facilitating the LPI capability.

This paper deals with the dual function waveform design problem for a knowledge-aided integrated radar and jamming (IRAJ) system. Supposing the IRAJ system has access to an information database obtained from a reconnaissance system for the threatening radar’s transmit waveform knowledge, the signal-to-jamming and noise ratio (SJNR) at the output of the threatening radar filter is considered as a figure of merit to reduce its detection probability, which represents the performance of blanket jamming. Besides, along with energy and peak-to-average ratio (PAR) constraints to comply with the hardware realization, let us minimize the weighted peak sidelobe level (WPSL) or maximize the signal-to-interference and noise ratio (SINR) of the IRAJ system for the detection performance according to the delay structure of reflected echoes. To tackle the resulting nonconvex multi-objective optimization problems, iterative fractional programming algorithms (IFPA) leveraging cyclic algorithmnew (CAN) and alternating direction method of multipliers (ADMM) are proposed, respectively. Finally, simulation results are provided to demonstrate the competition between radar and jamming functions within the proposed Pareto optimization framework and validate the effectiveness of the conceived algorithms.

The integration of multiple-input multiple-output (MIMO) technology with the dual-functional radar and communication (DFRC) system is pivotal for enhancing the capabilities of the Internet of Vehicles (IoV). However, conventional MIMO-DFRC designs lack support for simultaneous multi-user communication in the same direction, limiting their utility in IoV applications. To overcome this limitation, we propose a novel robust transmit beamforming approach for the MIMO-DFRC system, facilitating multi-user communication through beampattern modulation. Initially, the orthogonal Oppermann sequences are allocated to each user. Subsequently, these sequences are embedded into sub-beampatterns, which encode communication symbols for multi-user information transmission. Furthermore, the encoded information is strategically placed within the sidelobes of the transmit beampattern, aiming to minimize the integrated sidelobe levels. This goal serves as the objective function while maintaining mainlobe detection performance, which imposes a constraint, forming an optimization model for transmit beamforming. Due to the non-convex nature of the optimization model, it is decomposed using a coordinate rotation strategy and addressed iteratively via a convex optimization algorithm. Simulations validate that our method facilitates reliable unidirectional communication among multiple users and preserves the integrity of radar detection performance, contributing to the IoV application of the DFRC technology.

Radar moving target detection is one of the important applications in modern military. However, existing moving target detection algorithms have shortcomings in processing complex targets and computational timeliness. To solve this problem, this paper proposes a two-level data collaborative association(TDCA) track fusion detection algorithm based on Krank(KR) method. This method adds spatiotemporal registration and track correlation technology on the basis of the KR method, solving the breakpoint problem in track detection using the KR method, and improves the timeliness of track fusion while ensuring the accuracy of track fusion. In the simulation process, this method is compared with the KR method, asynchronous track (AT) fusion algorithm, covariance intersection (CI) fusion algorithm and explicit recursive (ER) fusion algorithm. The results show that the TDCA fusion algorithm performs well in both timeliness and accuracy.

In this study, a joint detection threshold optimization and multidimensional resource allocation (JDTO-MRA) scheme based on low probability of intercept is put forward for multitarget tracking in phased-array radar networks. The foundation of the proposed JDTO-MRA scheme is to adopt the optimization methodology to adaptively coordinate the detection threshold, radar node selection, transmit power, and signal bandwidth of each radar node to minimize the total power consumption of the underlying system, subject to given target detection probability and tracking accuracy constraints and several system resource budgets. The analytical expressions of the average target detection probability and Bayesian Cram & eacute;r-Rao lower bound are derived and utilized as the metrics to depict the detection and tracking performance of multiple targets. By incorporating the simultaneous detection and tracking concept, resource-aware design, and improved probabilistic data association algorithm into a coherent framework, the JDTO-MRA model is established in phased-array radar networks. Due to the optimization parameters are all coupled in both the constraints and criterion function, the resulting JDTO-MRA model demonstrates a nonlinear and nonconvex problem. Combined with the semidefinite programming method and the sequential quadratic programming method, an appropriate five-step solution technique is proposed to solve the original problem. Several numerical results are developed to verify the superiority and effectiveness of the JDTO-MRA scheme.

This article investigates the problem of joint transmit and receive beamforming for a colocated multiple-input multiple-output radar network with multiple targets and signal-dependent interferences. At first, a distributed beamforming design scheme is developed using noncooperative game theory. The basis of this scheme is to minimize the maximum transmit power of antennas for each radar while adhering to the constraint of signal-to-interference-plus-noise ratio for the targets. By transforming the formulated optimization problem into a convex problem, the Lagrangian duality theory is utilized to decompose the original problem into two sub-problems. Then, a subgradient projection method and an equivalent receive beamforming optimization method are developed to tackle the two sub-problems, yielding the optimal transmit beamformers for the radars. After obtaining the transmit and receive beamformers iteratively, the game relationship among radars is then formulated as a power control game, and the paper proves the existence and uniqueness of the Nash equilibrium. Furthermore, a centralized beamforming design problem is explored for comparative analysis, where the beamformers of all radars are jointly optimized to suppress power fluctuations among transmit antennas. Simulation results are provided to validate the effectiveness of the proposed schemes.

This paper proposes a joint optimization algorithm of stealthy trajectory planning and task allocation in unmanned aerial vehicle (UAV) cluster under multi-threat environment. Firstly, a model for the multi-threat environment is established. Then, a comprehensive cost function is designed for UAV cluster stealthy trajectory planning and task allocation. Combining the scattering characteristics of the UAV itself, taking the comprehensive cost function as the optimization objective, and the platform security, the UAV's own dynamic limitations and the task allocation scheme as the constraints, to construct a joint optimization model of stealthy trajectory planning and task allocation in UAV cluster under multi- threat environment. Based on this, an improved algorithm combining A* algorithm and genetic algorithm is employed to solve the optimization model. The simulation results validate the effectiveness and superiority of the proposed algorithm.

This paper investigates the joint design of low probability of intercept (LPI) waveform and receive filter for MIMO radar under constant modulus and similarity constraints. First, the Signal to Interference Plus Noise Ratio (SINR) is adopted as the detection performance metric of MIMO radar. Then, under the constraints of meeting the predetermined SINR requirement, constant modulus, and waveform similarity, a joint design model of LPI waveform and receive filter for MIMO radar is constructed, with the optimization objective of minimizing transmit energy. On this basis, a cyclic optimization method is proposed to solve the above non-convex optimization model. Simulation results show that the proposed algorithm can not only effectively improve the LPI performance of MIMO radar, but also exhibits good detection performance.

The waveform design method for Multiple-input-multiple-output (MIMO) radar based on phase encoding is studied in this paper. By designing the covariance matrix of the transmitted waveform, the desired beampattern of the transmitted waveform is generated with nulls in predetermined directions, thereby effectively suppressing clutter interference. Then a method based on coordinate descent (CD) optimization is proposed to generate the waveform that matches the previously designed covariance matrix, achieving better null depth. To meet the detection requirement meanwhile, an enhanced genetic algorithm (GA) is proposed, concurrently addresses two fundamental design objectives, minimizing spatial synthetic signal autocorrelation sidelobes and maintaining exact beampattern matching. Simulation results confirm that the proposed method achieves expected suppression of spatial synthetic signal autocorrelation sidelobes while generating well-defined nulls in specified directions when implemented with quadriphase discrete phase-coded waveforms.