Optimization of LPI-FDA-MIMO Radar and MIMO Communication for Spectrum Coexistence

Gong, Pengcheng; Xu, Kaiyan; Wu, Yuntao; Zhang, Jing; So, Hing Cheung

doi:10.1109/lwc.2023.3261419

Cited by 11 publications

(5 citation statements)

References 10 publications

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“…This problem has the same form as problem (28), for which the similarity constraint solution is shown in Equation (35). Therefore, we can follow the same steps as before to derive the spectral-MPI algorithm, the main steps of which are listed in Algorithm 2.…”

Section: Constrained By Spectral Constraint Under Similarity Constraintmentioning

confidence: 99%

“…The advent of new regime radars then amplified the signal's bandwidth, resulting in a wider signal bandwidth and a larger frequency range for energy distribution. Furthermore, MIMO radar waveform design minimizes the peak power captured by the interception system through the transmission beamforming technique [35,36]. The emergence of advanced frequency diversity array (FDA) radars [37], which utilize electronically scanned methods, further broadened the technical scope of LPI radar waveform design research.…”

Section: Introductionmentioning

confidence: 99%

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LPI Sequences Optimization Method against Summation Detector Based on FFT Filter Bank

Liu,

Guo,

Xiong

et al. 2024

Remote Sensing

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Waveform design is a crucial factor in electronic surveillance (ES) systems. In this paper, we introduce an algorithm that designs a low probability of intercept (LPI) radar waveform. Our approach directly minimizes the detection probability of summation detectors based on FFT filter banks. The algorithm is derived from the general quadratic optimization framework, which inherits the monotonic properties of such methods. To expedite overall convergence, we have integrated acceleration schemes based on the squared iterative method (SQUAREM). Additionally, the proposed algorithm can be executed through fast Fourier transform (FFT) operations, enhancing computational efficiency. With some modifications, the algorithm can be adjusted to incorporate spectral constraints, increasing its flexibility. Numerical experiments indicate that our proposed algorithm outperforms existing ones in terms of both intercept properties and computational complexity.

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Section: Constrained By Spectral Constraint Under Similarity Constraintmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

LPI Sequences Optimization Method against Summation Detector Based on FFT Filter Bank

Liu,

Guo,

Xiong

et al. 2024

Remote Sensing

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show abstract

“…A total of 100, 000 samples are generated in this process. 5 2) OUTPUT DATA GENERATION Fusing decisions from all radars can result in excessive data traffic over the data links connected to the fusion center, which can increase the global false alarm probability as more radars are fused. To account for this, we limit the network complexity by assuming that at most l radars can be combined to detect each target.…”

Section: A Dataset Information 1) Input Data Generationmentioning

confidence: 99%

“…M ULTI-RADAR to multi-target assignment (MRMTA) is crucial for achieving low probability of intercept (LPI) support and better information retrieval in distributed radar networks [1], [2], and it has recently attracted considerable attention from radar engineers [3], [4], [5], [6], [7], [8]. LPI radars are designed to search or track targets while remaining hidden from the enemy's equipment, and this property can be adapted to distributed radar networks that are netted together [9], [10], [11], [12], [13], [14].…”

Section: Introductionmentioning

confidence: 99%

Efficient Radar-Target Assignment in Low Probability of Intercept Radar Networks: A Machine-Learning Approach

Amiriara,

Andargoli,

Meghdadi

2023

IEEE Open J. Commun. Soc.

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To achieve low probability of intercept (LPI) in radar networks for multiple target detection, it is necessary to find the optimal assignment of distributed radars to targets. The multi-radar to multi-target assignment (MRMTA) problem aims to find the best radar combination, but its brute-force (BF)-based approach over all possible sensor combinations has exponential complexity, making it challenging to implement in networks with a large number of radars or targets. This limits the implementation of the BF approach in networks that prioritize low latency and complexity. To address this challenge, we propose a supervised machine-learning (ML)-based solution for the MRMTA problem. Our proposed implementation scheme performs the training procedure offline, leading to a significant reduction in assignment complexity and processing latency. We conducted extensive numerical simulations to design an ML structure with high accuracy, convergence speed, and scalability. Simulation results demonstrate the efficiency and effectiveness of our proposed ML-based MRMTA solution, which achieves near-optimal LPI performance with considerably lower computation time than benchmark schemes. Our proposed solution has the potential to optimize the assignment of distributed radars to targets in LPI radar networks and improve the performance of complex networks with low latency and complexity requirements.INDEX TERMS Multi-radar to multi-target assignment (MRMTA), supervised machine-learning (ML), low probability intercept radars, feed-forward neural network (FNN), radar network, information fusion.

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“…In 2006, Antonik et al proposed the FDA radar [5], which has a small frequency increment between each transmitting array element and produces a directional graph with range-angle-time coupling characteristics [6][7][8]. The FDA radar's performance is enhanced by unique features, such as beamforming [9], target parameter estimation [10,11], detection [12], tracking [13], imaging [14], clutter suppression [15], and low intercept performance [16][17][18][19]. In addition, the FDA radar can effectively suppress forwarded spoofing jamming located in the main lobe.…”

Section: Introductionmentioning

confidence: 99%

Frequency Diversity Array Radar and Jammer Intelligent Frequency Domain Power Countermeasures Based on Multi-Agent Reinforcement Learning

Zhou,

Wang,

Bao

et al. 2024

Remote Sensing

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With the development of electronic warfare technology, the intelligent jammer dramatically reduces the performance of traditional radar anti-jamming methods. A key issue is how to actively adapt radar to complex electromagnetic environments and design anti-jamming strategies to deal with intelligent jammers. The space of the electromagnetic environment is dynamically changing, and the transmitting power of the jammer and frequency diversity array (FDA) radar in each frequency band is continuously adjustable. Both can learn the optimal strategy by interacting with the electromagnetic environment. Considering that the competition between the FDA radar and the jammer is a confrontation process of two agents, we find the optimal power allocation strategy for both sides by using the multi-agent deep deterministic policy gradient (MADDPG) algorithm based on multi-agent reinforcement learning (MARL). Finally, the simulation results show that the power allocation strategy of the FDA radar and the jammer can converge and effectively improve the performance of the FDA radar and the jammer in the intelligent countermeasure environment.

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Optimization of LPI-FDA-MIMO Radar and MIMO Communication for Spectrum Coexistence

Cited by 11 publications

References 10 publications

LPI Sequences Optimization Method against Summation Detector Based on FFT Filter Bank

LPI Sequences Optimization Method against Summation Detector Based on FFT Filter Bank

Efficient Radar-Target Assignment in Low Probability of Intercept Radar Networks: A Machine-Learning Approach

Frequency Diversity Array Radar and Jammer Intelligent Frequency Domain Power Countermeasures Based on Multi-Agent Reinforcement Learning

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