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Towards small target recognition with photonics-based high resolution radar range profiles

Jinhu Li, Fangzheng Zhang, Yu Xiang, and Shilong Pan

Open Access Open Access

Abstract

Photonics-based radar expands the bandwidth of traditional radars and enhances the radar range resolution. This makes it possible to recognize small-size targets using the high resolution range profiles (HRRPs) acquired by a photonics-based broadband radar. In this paper, we investigate the performance of small target recognition using HRRPs of a photonics-based radar with a bandwidth of 8 GHz (28-36 GHz), which is built based on photonic frequency multiplication and frequency mixing. A convolutional neural network (CNN) is used to extract features of the HRRPs and classify the targets. In the experiment, recognition of four types of small-size targets is demonstrated with an accuracy of 97.16%, which is higher than target recognition using a 77-GHz electronic radar by 31.57% (2-GHz bandwidth) and 8.37% (4 GHz-bandwidth), respectively. Besides the accuracy, target recognition with photonics-based radar HRRPs is proved to have good generalization capability and stable performance. Therefore, photonics-based radar provides an efficient solution to small target recognition with one-dimension HRRPs, which is expected to find import applications in air defense, security check, and intelligent transportation.

© 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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Data availability

Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

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Figures (7)
Fig. 1.
Fig. 1. Range profiles of a pistol acquired by a low-resolution radar and a high-resolution radar.

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Fig. 2.
Fig. 2. (a) Picture of photonics-based radar prototype, and (b) schematic diagram of the photonics-based radar.

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Fig. 3.
Fig. 3. Structure of the 1D convolutional neural network used for feature extraction and classification.

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Fig. 4.
Fig. 4. (a), (c), (e), (g) Pictures of target A, target B, target C, and target D. (b), (d), (f), (h) HRRPs of the four targets.

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Fig. 5.
Fig. 5. (a) The training and testing accuracy curves considering all the three detection conditions, and (b) the relationship between the bandwidth and the final recognition accuracy.

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Fig. 6.
Fig. 6. t−SNE feature visualizations for (a) TI Radar (2 GHz), (b) TI Radar (4 GHz), and (c) Photonics-based Radar.

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Fig. 7.
Fig. 7. Confusion matrix of (a) TI Radar (2 GHz), (b) TI Radar (4 GHz), (c) Photonics-based Radar (8 GHz).

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Tables (1)

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Table 1. Parameters of the optimized 1D CNN

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