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16 pages, 6685 KiB

Open AccessArticle

Signal Processing for Novel Noise Radar Based on de-chirp and Delay Matching

by Xinquan Cao, Shiyuan Zhang, Ke Tan, Jianchao Yang, Xingyu Lu, Zheng Dai and Hong Gu

Sensors 2024, 24(22), 7169; https://doi.org/10.3390/s24227169 - 8 Nov 2024

Viewed by 1062

Modern radar technology requires high-quality signals and detection performance. However, traditional frequency-modulated continuous wave (FMCW) radar often has poor anti-jamming capabilities, and the high sampling rates associated with large time-bandwidth product signals can lead to increased system hardware costs and reduced data processing [...] Read more.

Modern radar technology requires high-quality signals and detection performance. However, traditional frequency-modulated continuous wave (FMCW) radar often has poor anti-jamming capabilities, and the high sampling rates associated with large time-bandwidth product signals can lead to increased system hardware costs and reduced data processing efficiency. This paper constructed a composite radar waveform based on noise frequency modulation (NFM) and linear frequency modulation (LFM) signals, enhancing the signal’s complexity and anti-jamming capability. Furthermore, a method for optimizing the processing of echo signals based on de-chirp and delay matching is proposed. The locally generated LFM signal is used to de-chirp the received echoes, resulting in a narrowband difference frequency noise signal. Subsequently, delay matching is performed in the fast time domain using the locally generated NFM signal according to the number of sampling points in the traversal processing period, allowing for the acquisition of target delay information. While reducing the analog-to-digital (A/D) sampling rate, the detection performance for wideband echo signals under high sampling rates is still maintained, with sidelobe levels and range resolution preserved. Accumulating this information in the slow time domain enables accurate target detection. The effectiveness of the proposed method is validated through simulation experiments. Full article

(This article belongs to the Special Issue Signal Processing in Radar Systems)

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16 pages, 6851 KiB

Open AccessArticle

Range-Dependent Channel Calibration for High-Resolution Wide-Swath Synthetic Aperture Radar Imagery

by Man Zhang, Zhichao Meng, Guanyong Wang and Yonghong Xue

Sensors 2024, 24(11), 3278; https://doi.org/10.3390/s24113278 - 21 May 2024

Viewed by 959

Abstract

High-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) imaging with azimuth multi-channel always suffers from channel phase and amplitude errors. Compared with spatial-invariant error, the range-dependent channel phase error is intractable due to its spatial dependency characteristic. This paper proposes a novel parameterized [...] Read more.

High-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) imaging with azimuth multi-channel always suffers from channel phase and amplitude errors. Compared with spatial-invariant error, the range-dependent channel phase error is intractable due to its spatial dependency characteristic. This paper proposes a novel parameterized channel equalization approach to reconstruct the unambiguous SAR imagery. First, a linear model is established for the range-dependent channel phase error, and the sharpness of the reconstructed Doppler spectrum is used to measure the unambiguity quality. Furthermore, the intrinsic relationship between the channel phase errors and the sharpness is revealed, which allows us to estimate the optimal parameters by maximizing the sharpness of the reconstructed Doppler spectrum. Finally, the results from real-measured data show that the suggested method performs exceptionally for ambiguity suppression in HRWS SAR imaging. Full article

(This article belongs to the Special Issue Signal Processing in Radar Systems)

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13 pages, 2934 KiB

Open AccessArticle

Transient Interference Excision and Spectrum Reconstruction with Partial Samples Using Modified Alternating Direction Method of Multipliers-Net for the Over-the-Horizon Radar

by Zhang Man, Quan Huang and Jia Duan

Sensors 2024, 24(9), 2770; https://doi.org/10.3390/s24092770 - 26 Apr 2024

Viewed by 967

Abstract

Transient interference often submerges the actual targets when employing over-the-horizon radar (OTHR) to detect targets. In addition, modern OTHR needs to carry out multi-target detection from sea to air, resulting in the sparse sampling of echo data. The sparse OTHR signal will raise [...] Read more.

Transient interference often submerges the actual targets when employing over-the-horizon radar (OTHR) to detect targets. In addition, modern OTHR needs to carry out multi-target detection from sea to air, resulting in the sparse sampling of echo data. The sparse OTHR signal will raise serious grating lobes using conventional methods and thus degrade target detection performance. This article proposes a modified Alternating Direction Method of Multipliers (ADMM)-Net to reconstruct the target and clutter spectrum of sparse OTHR signals so that target detection can be performed normally. Firstly, transient interferences are identified based on the sparse basis representation and then excised. Therefore, the processed signal can be seen as a sparse OTHR signal. By solving the Doppler sparsity-constrained optimization with the trained network, the complete Doppler spectrum is reconstructed effectively for target detection. Compared with traditional sparse solution methods, the presented approach can balance the efficiency and accuracy of OTHR signal spectrum reconstruction. Both simulation and real-measured OTHR data proved the proposed approach’s performance. Full article

(This article belongs to the Special Issue Signal Processing in Radar Systems)

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16 pages, 7278 KiB

Open AccessArticle

Migration through Resolution Cell Correction and Sparse Aperture ISAR Imaging for Maneuvering Target Based on Whale Optimization Algorithm—Fast Iterative Shrinkage Thresholding Algorithm

by Xinrong Guo, Fengkai Liu and Darong Huang

Sensors 2024, 24(7), 2148; https://doi.org/10.3390/s24072148 - 27 Mar 2024

Cited by 1 | Viewed by 1107

Abstract

Targets faced by inverse synthetic aperture radar (ISAR) are often non-cooperative, with target maneuvering being the main manifestation of this non-cooperation. Maneuvers cause ISAR imaging results to be severely defocused, which can create huge difficulties in target identification. In addition, as the ISAR [...] Read more.

Targets faced by inverse synthetic aperture radar (ISAR) are often non-cooperative, with target maneuvering being the main manifestation of this non-cooperation. Maneuvers cause ISAR imaging results to be severely defocused, which can create huge difficulties in target identification. In addition, as the ISAR bandwidth continues to increase, the impact of migration through resolution cells (MTRC) on imaging results becomes more significant. Target non-cooperation may also result in sparse aperture, leading to the failure of traditional ISAR imaging algorithms. Therefore, this paper proposes an algorithm to realize MTRC correction and sparse aperture ISAR imaging for maneuvering targets simultaneously named whale optimization algorithm–fast iterative shrinkage thresholding algorithm (WOA-FISTA). In this algorithm, FISTA is used to perform MTRC correction and sparse aperture ISAR imaging efficiently and WOA is adopted to estimate the rotational parameter to eliminate the effects of maneuvering on imaging results. Experimental results based on simulation and measured datasets prove that the proposed algorithm implements sparse aperture ISAR imaging and MTRC correction for maneuvering targets simultaneously. The proposed algorithm achieves better results than traditional algorithms under different signal-to-noise ratio conditions. Full article

(This article belongs to the Special Issue Signal Processing in Radar Systems)

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17 pages, 3718 KiB

Open AccessArticle

Enhancing Radar Echo Extrapolation by ConvLSTM2D for Precipitation Nowcasting

by Farah Naz, Lei She, Muhammad Sinan and Jie Shao

Sensors 2024, 24(2), 459; https://doi.org/10.3390/s24020459 - 11 Jan 2024

Cited by 5 | Viewed by 2181

Abstract

Precipitation nowcasting in real-time is a challenging task that demands accurate and current data from multiple sources. Despite various approaches proposed by researchers to address this challenge, models such as the interaction-based dual attention LSTM (IDA-LSTM) face limitations, particularly in radar echo extrapolation. [...] Read more.

Precipitation nowcasting in real-time is a challenging task that demands accurate and current data from multiple sources. Despite various approaches proposed by researchers to address this challenge, models such as the interaction-based dual attention LSTM (IDA-LSTM) face limitations, particularly in radar echo extrapolation. These limitations include higher computational costs and resource requirements. Moreover, the fixed kernel size across layers in these models restricts their ability to extract global features, focusing more on local representations. To address these issues, this study introduces an enhanced convolutional long short-term 2D (ConvLSTM2D) based architecture for precipitation nowcasting. The proposed approach includes time-distributed layers that enable parallel Conv2D operations on each image input, enabling effective analysis of spatial patterns. Following this, ConvLSTM2D is applied to capture spatiotemporal features, which improves the model’s forecasting skills and computational efficacy. The performance evaluation employs a real-world weather dataset benchmarked against established techniques, with metrics including the Heidke skill score (HSS), critical success index (CSI), mean absolute error (MAE), and structural similarity index (SSIM). ConvLSTM2D demonstrates superior performance, achieving an HSS of 0.5493, a CSI of 0.5035, and an SSIM of 0.3847. Notably, a lower MAE of 11.16 further indicates the model’s precision in predicting precipitation. Full article

(This article belongs to the Special Issue Signal Processing in Radar Systems)

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17 pages, 21450 KiB

Open AccessArticle

Agricultural Application Prospect of Fully Polarimetric and Quantification S-Band SAR Subsystem in Chinese High-Resolution Aerial Remote Sensing System

by Yabo Liu, Luhao Wang, Shuang Zhu, Xiaojie Zhou, Jia Liu and Binghong Xie

Sensors 2024, 24(1), 236; https://doi.org/10.3390/s24010236 - 31 Dec 2023

Cited by 2 | Viewed by 1487

Abstract

The synthetic aperture radar (SAR) is a type of active radar that can obtain polarization scattering information of ground objects, which is an important supplement to optical remote sensing. This paper designs a high-precision quantitative SAR system that combines radiation and polarization calibration [...] Read more.

The synthetic aperture radar (SAR) is a type of active radar that can obtain polarization scattering information of ground objects, which is an important supplement to optical remote sensing. This paper designs a high-precision quantitative SAR system that combines radiation and polarization calibration processing to achieve a subtle perception of the changes in soil moisture and straw coverage. In Yushu, Jilin, we conducted the first S-band agricultural remote sensing application experiment. The backscattering coefficient was measured under different water content and straw coverage conditions, and the results showed that the backscattering coefficient increased by about 2 dB and 6 dB, respectively. We estimated that the soil water content increased by about 0.01 cm³/cm³, which was consistent with the theoretical analysis. The polarization scattering characteristics also showed significant differences under different straw coverage. The results indicated that S-band quantitative SAR had an excellent response ability to water content and straw coverage, which provided a technical basis for further radar agricultural applications in the future. Full article

(This article belongs to the Special Issue Signal Processing in Radar Systems)

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16 pages, 3502 KiB

Open AccessArticle

Resolution Enhancement for Millimeter-Wave Radar ROI Image with Bayesian Compressive Sensing

by Pengfei Xie, Jianxin Wu, Lei Zhang, Guanyong Wang and Xue Jin

Sensors 2022, 22(15), 5757; https://doi.org/10.3390/s22155757 - 2 Aug 2022

Cited by 2 | Viewed by 2525

Abstract

For millimeter-wave (MMW) imaging security systems, the image resolution promisingly determines the performance of suspicious target detection and recognition. Conventional synthetic aperture radar (SAR) imaging algorithms only provide limited resolution in active MMW imaging, which is limited by the system. In terms of [...] Read more.

For millimeter-wave (MMW) imaging security systems, the image resolution promisingly determines the performance of suspicious target detection and recognition. Conventional synthetic aperture radar (SAR) imaging algorithms only provide limited resolution in active MMW imaging, which is limited by the system. In terms of enhancing the resolution of a region of interest (ROI) image containing suspicious targets, super-resolution (SR) imaging is adopted via Bayesian compressive sensing (BCS) implemented by fast Fourier transform (FFT). The spatial sparsity of MMW ROI images is well exploited with BCS to achieve resolution enhancement without computational cost. Both simulated and measured experiments confirm that the proposed scheme effectively improves the resolution of ROI images. Full article

(This article belongs to the Special Issue Signal Processing in Radar Systems)

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19 pages, 9136 KiB

Open AccessArticle

Costas DFC-Based Random Stepped Wideband Waveform for Interference Countermeasure in SAR Imagery

by GanE Dai, Lei Zhang and Sha Huan

Sensors 2022, 22(9), 3197; https://doi.org/10.3390/s22093197 - 21 Apr 2022

Cited by 1 | Viewed by 2069

Abstract

Interference in SAR imagery will induce false targets or form a mask in specific areas to prevent accurate scene assessment. Traditional anti-jamming methods based on waveform agility require a trade-off between anti-jamming performance and imaging quality in waveform design. In this paper, we [...] Read more.

Interference in SAR imagery will induce false targets or form a mask in specific areas to prevent accurate scene assessment. Traditional anti-jamming methods based on waveform agility require a trade-off between anti-jamming performance and imaging quality in waveform design. In this paper, we proposed a SAR ECCM scheme including a Costas DFC-based random stepped wideband waveform and corresponding imaging processing method. The waveform exhibits high flexibility against forwarding interference due to the decomposition of a wideband signal into multiple pulses with different Costas discrete frequency encoding, carrier frequency and phase modulation. Furthermore, the combination of FCDC and the imaging processing successfully overcomes the Doppler sensitivity of the proposed waveform. Extensive simulations confirmed the superiority of this waveform and processing method under different interference strategies. Full article

(This article belongs to the Special Issue Signal Processing in Radar Systems)

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15 pages, 3722 KiB

Open AccessArticle

Channel Phase Calibration for High-Resolution and Wide-Swath SAR Imaging with Doppler Spectrum Sharpness Optimization

by Man Zhang, Sha Huan, Zeya Zhao and Zhibin Wang

Sensors 2022, 22(5), 1781; https://doi.org/10.3390/s22051781 - 24 Feb 2022

Cited by 1 | Viewed by 1878

Abstract

Channel phase calibration is a crucial issue in high resolution and wide swath (HRWS) imagery with azimuth multi-channel synthetic aperture radar (SAR) systems. Precise phase calibration is definitely required in reconstructing the full Doppler spectrum for precise HRWS imagery without high-level ambiguities. In [...] Read more.

Channel phase calibration is a crucial issue in high resolution and wide swath (HRWS) imagery with azimuth multi-channel synthetic aperture radar (SAR) systems. Precise phase calibration is definitely required in reconstructing the full Doppler spectrum for precise HRWS imagery without high-level ambiguities. In this paper, we propose a novel calibration for HRWS SAR imagery by optimizing the reconstructed unambiguous Doppler spectrum. The sharpness of the reconstructed Doppler spectrum is applied as the metric to measure the unambiguity quality, which is maximized to retrieve the element phase error caused by channel imbalance. Real data experiments demonstrate the performance of the proposed calibration for ambiguity suppression in HRWS SAR imagery. Full article

(This article belongs to the Special Issue Signal Processing in Radar Systems)

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17 pages, 792 KiB

Open AccessArticle

Hybrid Interacting Multiple Model Filtering for Improving the Reliability of Radar-Based Forward Collision Warning Systems

by Jung Min Pak

Sensors 2022, 22(3), 875; https://doi.org/10.3390/s22030875 - 24 Jan 2022

Cited by 7 | Viewed by 2910

Abstract

Automotive forward collision warning (FCW) systems based on radar sensors attracted widespread attention in recent years. To achieve a reliable FCW, it is essential to accurately estimate the position and velocity of a preceding vehicle. To this end, this study proposed a novel [...] Read more.

Automotive forward collision warning (FCW) systems based on radar sensors attracted widespread attention in recent years. To achieve a reliable FCW, it is essential to accurately estimate the position and velocity of a preceding vehicle. To this end, this study proposed a novel estimation algorithm, which is a hybrid of interacting multiple model probabilistic data association (IMM-PDA) and finite impulse response (FIR) filters. Although the IMM-PDA filter is one of the most successful algorithm for tracking a maneuvering target in clutters, it sometimes exhibits divergence owing to modeling errors. In this study, the divergent IMM-PDA filter in the novel algorithm was reset and recovered using an assisting FIR filter. Consequently, this enabled reliable estimation for FCW. The improved reliability of the proposed algorithm was demonstrated through the simulation of preceding vehicle tracking using automotive radars. Full article

(This article belongs to the Special Issue Signal Processing in Radar Systems)

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