Waveform Adaptation for Target Classification Using HRRP in a Cognitive Framework

Warnke, Marcel; Bruggenwirth, Stefan

doi:10.1109/taes.2022.3230659

Cited by 3 publications

(2 citation statements)

References 37 publications

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“…For instance, the possible optimization criteria for cognitive waveform design in the detection task are either the signal to interference-plus-noise power ratio (SINR) [5] or detection probability [6], while the waveform can be optimized based on the estimation error [7] in the tracking scenario. For classification purposes, the separability of different targets could be a criterion to guide the waveform design [8,9]. On the other hand, the information-driven paradigm adopts an information-theoretic perspective to enhance the information acquisition capability by improving the system capacity for targetof-interest information.…”

Section: Motivationsmentioning

confidence: 99%

Waveform Design for Target Information Maximization over a Complex Circle Manifold

Yu,

Fu,

Yang

et al. 2024

Remote Sensing

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The cognitive radar framework presents a closed-loop adaptive processing paradigm that ensures the efficient acquisition of target information while exploring the environment and enhancing overall sensing performance. In this study, instead of mutual information, we employed the squared Pearson correlation coefficient (SPCC) to measure the target information in observations specifically considering only linear dependency. A waveform design method is proposed that simultaneously maximizes target information and minimizes the integrated sidelobe level (ISL) under the constant modulus constraint (CMC). To enhance computational efficiency, we reformulated the constrained problem as an unconstrained optimization problem by leveraging the inherent geometric property of CMC. Additionally, we present two conditional equivalences associated with waveform design in relation to target information. The simulation results validate the feasibility and effectiveness of the proposed method.

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Section: Motivationsmentioning

confidence: 99%

Waveform Design for Target Information Maximization over a Complex Circle Manifold

Yu,

Fu,

Yang

et al. 2024

Remote Sensing

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“…HRRP is an essential structural feature of the target and, compared to Synthetic Aperture Radar (SAR) and Inverse Synthetic Aperture Radar (ISAR) images, it is easier to process and acquire 2 . As a result, HRRP target recognition has garnered significant attention within the Radar Automatic Target Recognition (RATR) community [3][4][5][6] .…”

Section: Introductionmentioning

confidence: 99%

Ship HRRP target recognition method based on transformer and convolutional attention network

ZIJIE,

GUANGFEN,

et al. 2024

Fifteenth International Conference on Signal Processing Systems (ICSPS 2023)

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The radar's high-resolution range profile (HRRP) data structure is complex, and extracting stable and reliable features from it is crucial for HRRP target recognition. In this paper, we propose to use the convolution module to extract local spatial features of HRRP and use the positional encoding to embed the position information to generate new temporal features, and then capture the long-term dependency within the distance unit of HRRP through the multi-head new selfattention mechanism of the Transformer encoder, to construct a reliable feature extraction method for the HRRP target. Finally, a new deep learning model CNN-TEAN (CNN TransEncoder-Attention Network), based on one-dimensional residual convolution, Transformer encoder, and attention mechanism, is formed using the attention mechanism, fully connected layer, and softmax for classification. Using six simulated ship target data types for experimental validation, the CNN-TEAN model proposed in this paper can achieve a higher recognition rate than RNN, LSTM, and SVM models.

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Reinforcement Learning Approach for a Cognitive Framework for Classification

Barth

Bruggenwirth

2023

2023 IEEE Radar Conference (RadarConf23)

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Waveform Adaptation for Target Classification Using HRRP in a Cognitive Framework

Cited by 3 publications

References 37 publications

Waveform Design for Target Information Maximization over a Complex Circle Manifold

Waveform Design for Target Information Maximization over a Complex Circle Manifold

Ship HRRP target recognition method based on transformer and convolutional attention network

Reinforcement Learning Approach for a Cognitive Framework for Classification

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