Multi-Channel Back-Projection Algorithm for mmWave Automotive MIMO SAR Imaging With Doppler-Division Multiplexing

Zhang, Bangjie; Xu, Gang; Zhou, Rui; Zhang, Hui; Wang, Hong

doi:10.1109/jstsp.2022.3207902

Cited by 45 publications

(10 citation statements)

References 38 publications

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“…In an experiment to evaluate the ground-penetrating performance of the TDBP algorithm for a designed UWB radar system with multiple transmissions, the system uses a Vivaldi antenna and a vector network analyzer (P2023B) with an iron skin made of conductive material as the target [11] . The target is placed in a 100 cm x 60 cm x 80 cm (L*W*H) sandbox and a simple method is used to estimate the dielectric constant of the sand.…”

Section: Ground-penetrating Experiments Resultsmentioning

confidence: 99%

An ultra-wideband MIMO ground-penetrating imaging system

Li,

Fan,

Wang

et al. 2023

International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023)

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Ultra-Wideband (UWB) Ground Penetrating Radar (GPR) is a short-range target detection based on UWB technology for the detection and identification of buried objects, e.g. for mine detection, earthquake rescue, etc. In this paper, an ultrawideband MIMO radar system is investigated for the detection of underground targets. Different measurements of several stationary targets were carried out using the radar system and the experimental results agreed well with the simulation results. The reconstructed images show that the designed MIMO radar system is able to successfully detect and image subsurface targets.

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Section: Ground-penetrating Experiments Resultsmentioning

confidence: 99%

An ultra-wideband MIMO ground-penetrating imaging system

Li,

Fan,

Wang

et al. 2023

International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023)

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show abstract

“…All the received signals are used to reconstruct SAR images based on different kinds of algorithms. The back-projection algorithm is the most common algorithm [ 5 , 27 , 28 ]. To construct an antenna array with a low side lobe level, the distance between the array elements should be less than half the wavelength, which is practically impossible in most cases.…”

Section: Mimo Concept Of Radarmentioning

confidence: 99%

An Improved Performance Radar Sensor for K-Band Automotive Radars

El-Hameed

Ouf

Elboushi

et al. 2023

Sensors

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This paper presents a new radar sensor configuration of a planar grid antenna array (PGAA) for automotive ultra-wideband (UWB) radar applications. For system realisation, the MIMO concept is adopted. The proposed antenna is designed to operate over the 24 GHz frequency band. It is based on split-ring resonator (SRR) elements to enhance the operating bandwidth and increase the antenna gain, leading to a better-performing radar system. The PGAA consists of thirty-one radiating elements, in which each element excitation is obtained using a common transmission line centre fed by a 50 Ω coaxial probe. By introducing a superstrate dielectric layer at a distance of λ/2 from the top of the antenna array, the PGAA gain and impedance bandwidth are further improved. The gain is improved by 2.7 dB to reach 16.5 dBi at 24 GHz, and the impedance bandwidth is enhanced to 9.3 GHz (37.7%). The measured impedance bandwidth of the proposed antenna array ranges from 20 GHz to 29.3 GHz for a reflection coefficient (S11) of less than −10 dB. The proposed antenna is validated for automotive applications.

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“…The offset of target echo in the Doppler frequency domain can also play an important role in multiple input multiple output (MIMO) radar SAR imaging. In [ 1 ], high-resolution imaging is achieved by adopting the Doppler-division multiplexing (DDM) technique for MIMO channel separation and combining it with a single-channel BP algorithm and multi-channel synthesis.…”

Section: Introductionmentioning

confidence: 99%

Radar Human Activity Recognition with an Attention-Based Deep Learning Network

Huan

Zhang

et al. 2023

Sensors

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Radar-based human activity recognition (HAR) provides a non-contact method for many scenarios, such as human–computer interaction, smart security, and advanced surveillance with privacy protection. Feeding radar-preprocessed micro-Doppler signals into a deep learning (DL) network is a promising approach for HAR. Conventional DL algorithms can achieve high performance in terms of accuracy, but the complex network structure causes difficulty for their real-time embedded application. In this study, an efficient network with an attention mechanism is proposed. This network decouples the Doppler and temporal features of radar preprocessed signals according to the feature representation of human activity in the time–frequency domain. The Doppler feature representation is obtained in sequence using the one-dimensional convolutional neural network (1D CNN) following the sliding window. Then, HAR is realized by inputting the Doppler features into the attention-mechanism-based long short-term memory (LSTM) as a time sequence. Moreover, the activity features are effectively enhanced using the averaged cancellation method, which improves the clutter suppression effect under the micro-motion conditions. Compared with the traditional moving target indicator (MTI), the recognition accuracy is improved by about 3.7%. Experiments based on two human activity datasets confirm the superiority of our method compared to traditional methods in terms of expressiveness and computational efficiency. Specifically, our method achieves an accuracy close to 96.9% on both datasets and has a more lightweight network structure compared to algorithms with similar recognition accuracy. The method proposed in this article has great potential for real-time embedded applications of HAR.

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Multi-Channel Back-Projection Algorithm for mmWave Automotive MIMO SAR Imaging With Doppler-Division Multiplexing

Cited by 45 publications

References 38 publications

An ultra-wideband MIMO ground-penetrating imaging system

An ultra-wideband MIMO ground-penetrating imaging system

An Improved Performance Radar Sensor for K-Band Automotive Radars

Radar Human Activity Recognition with an Attention-Based Deep Learning Network

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