MLP-CFAR for improving coherent radar detectors robustness in variable scenarios

Mata-Moya, David; del-Rey-Maestre, Nerea; Peláez-Sánchez, V. M.; Jarabo-Amores, María-Pilar; Martín-de-Nicolás, Jaime

doi:10.1016/j.eswa.2014.12.055

Cited by 21 publications

(3 citation statements)

References 34 publications

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“…Both [33] and [34] perform detection using a deep ANN that is trained to replicate the behavior of a cell-averaging CFAR (CA-CFAR) detector on range processed data with Gaussian interference, and [35] uses an ANN trained to perform peak detection in the frequency domain. Mata-Moya et al [36] use a deep neural network (DNN)-based approach for adaptive thresholding in range-Doppler processed data. Both random decision forests and recurrent neural networks (RNNs) for detection based on I/Q data are explored in [37].…”

Section: Introductionmentioning

confidence: 99%

A Metacognitive Approach to Adaptive Radar Detection

Stringer,

Dolinger,

Hogue

et al. 2024

IEEE Trans. Aerosp. Electron. Syst.

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Detecting objects of interest is one of the core functions of radar systems and doing so in the presence of interference is an ongoing challenge in the domain. Clutter is an especially problematic form of interference that can result in a large number of false alarms. In general, the goal of radar detection systems is to maximize the likelihood of detecting targets while maintaining a constant false alarm rate (CFAR). Adaptive detectors like the Generalized Likelihood Ratio Test (GLRT) have been developed to achieve this. However, they are derived assuming that the clutter can be modeled according to a consistent probability distribution. This assumption typically does not hold true in many real-world applications, particularly on airborne or naval systems, which degrades detection performance and eliminates the desired CFAR behavior. In this work, a metacognitive approach to adaptive detection is proposed to achieve CFAR-like behavior over a range of clutter distributions. It is demonstrated that this meta-cognitive detector maintains CFAR -like behavior when presented with data randomly selected from a range of clutter distribution models (Gaussian, K, and Pareto) and that it matches the performance of the traditional GLRT in Gaussian interference.

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Section: Introductionmentioning

confidence: 99%

A Metacognitive Approach to Adaptive Radar Detection

Stringer,

Dolinger,

Hogue

et al. 2024

IEEE Trans. Aerosp. Electron. Syst.

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“…As deep learning models have achieved good performance in ATR, it is feasible and reasonable to explore deep learning-based models in RTD. As one of the most reliable classifiers, ANN has been utilized to improve radar detection performance [46,47]. Many recent literatures utilize DNN to tackle RTD and also present performance improvements.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks and Deep Learning Techniques Applied to Radar Target Detection: A Review

et al. 2022

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Radar target detection (RTD) is a fundamental but important process of the radar system, which is designed to differentiate and measure targets from a complex background. Deep learning methods have gained great attention currently and have turned out to be feasible solutions in radar signal processing. Compared with the conventional RTD methods, deep learning-based methods can extract features automatically and yield more accurate results. Applying deep learning to RTD is considered as a novel concept. In this paper, we review the applications of deep learning in the field of RTD and summarize the possible limitations. This work is timely due to the increasing number of research works published in recent years. We hope that this survey will provide guidelines for future studies and applications of deep learning in RTD and related areas of radar signal processing.

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“…With respect to the above problems, this paper presents a novel algorithm for life signal extraction and reconstruction based on the MTI-Autocorrelation-EEMD (MAE), where the MTI algorithm is utilized to eliminate the interference from fixed object clutters. Compared with PCA method in [15], MTI directly performs cancel operation to remove the fixed object clutter without eigenvalue decomposition [20]. Thus, the anti-noise performance is improved greatly.…”

Section: Introductionmentioning

confidence: 99%

Non-Contact Life Signal Extraction and Reconstruction Technique Based on MAE

2019

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Non-contact life signal extraction could be used in natural disaster rescue, health care and other fields. As the energy of respiration and heartbeat signals is extremely weak in reality, they are usually submerged in noise and clutter. As a result, the traditional life signal extraction algorithms always fail at low signal-to-noise ratio (SNR) conditions. This paper proposes a novel life signal extraction and reconstruction algorithm based on MTI-Autocorrelation-EEMD (MAE) so as to enhance the accuracy and stability of life signal detection at low SNR condition. Taking advantage of its strong robustness with regard to noise, this technique utilizes a moving target indicator (MTI) algorithm to eliminate the interference of fixed object clutter on the echo signal after pulse compression. Combined with the autocorrelation algorithm with stable detection performance, the precise location of the human body micro-motion signal can be determined. Through the integration of an ensemble empirical mode decomposition (EEMD) algorithm, the phase of human body micro-motion signal is adaptively decomposed, overcoming the problem of mode mixing so that the signals of respiration and heartbeat can be reconstructed in the time domain according to the mode judgement criteria. Extensive real data experiments illustrate the effectiveness and robustness of the proposed algorithm under low SNR.INDEX TERMS Non-contact life signal extraction and reconstruction, ensemble empirical mode decomposition (EEMD), moving target indicator (MTI), autocorrelation detection, respiration and heartbeat frequencies estimation.

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MLP-CFAR for improving coherent radar detectors robustness in variable scenarios

Cited by 21 publications

References 34 publications

A Metacognitive Approach to Adaptive Radar Detection

A Metacognitive Approach to Adaptive Radar Detection

Artificial Neural Networks and Deep Learning Techniques Applied to Radar Target Detection: A Review

Non-Contact Life Signal Extraction and Reconstruction Technique Based on MAE

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