Semantic Learning for Analysis of Overlapping LPI Radar Signals

Chen, Kuiyu; Wang, Lipo; Zhang, Jingyi; Chen, Si; Zhang, Shuning

doi:10.1109/tim.2023.3242013

Cited by 7 publications

(5 citation statements)

References 54 publications

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“…In this paper, we stop the training procedure when the validation loss does not decrease for a certain number of epochs. 6 6. It's worth mentioning that even though retraining is unnecessary for changes in the target's position, updating the trained ML model is necessary when the number of radars and/or targets is changed.…”

Section: ML Structure and Training Proceduresmentioning

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%

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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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Section: ML Structure and Training Proceduresmentioning

confidence: 99%

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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show abstract

“…However, these techniques also inadvertently increase the processing complexity of the reconnaissance system. Furthermore, the issue of designing a low probability of identification (LPID) waveforms is being examined, with a specific focus on the aspect of time complexity [16,17].…”

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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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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“…Therefore, this paper utilizes the bispectrum to extract signal characteristics and simultaneously suppress noise, thereby enabling the recognition of UAV radar signal modulation types through the exploitation of geometric features inherent in bispectral slices. The studies referenced in [23][24][25][26][27] utilize deep neural networks for modulation signal recognition. However, deep neural networks require a large amount of training data and have high hardware requirements.…”

Section: Introductionmentioning

confidence: 99%

Modulation Recognition of Low-SNR UAV Radar Signals Based on Bispectral Slices and GA-BP Neural Network

Liu

Song

Chen

et al. 2023

Drones

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In this paper, we address the challenge of low recognition rates in existing methods for radar signals from unmanned aerial vehicles (UAV) with low signal-to-noise ratios (SNRs). To overcome this challenge, we propose the utilization of the bispectral slice approach for accurate recognition of complex UAV radar signals. Our approach involves extracting the bispectral diagonal slice and the maximum bispectral amplitude horizontal slice from the bispectrum amplitude spectrum of the received UAV radar signal. These slices serve as the basis for subsequent identification by calculating characteristic parameters such as convexity, box dimension, and sparseness. To accomplish the recognition task, we employ a GA-BP neural network. The significant variations observed in the bispectral slices of different signals, along with their robustness against Gaussian noise, contribute to the high separability and stability of the extracted bispectral convexity, bispectral box dimension, and bispectral sparseness. Through simulations involving five radar signals, our proposed method demonstrates superior performance. Remarkably, even under challenging conditions with an SNR as low as −3 dB, the recognition accuracy for the five different radar signals exceeds 90%. Our research aims to enhance the understanding and application of modulation recognition techniques for UAV radar signals, particularly in scenarios with low SNRs.

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Semantic Learning for Analysis of Overlapping LPI Radar Signals

Cited by 7 publications

References 54 publications

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

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

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

Modulation Recognition of Low-SNR UAV Radar Signals Based on Bispectral Slices and GA-BP Neural Network

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