Fast Target Localization Method for FMCW MIMO Radar via VDSR Neural Network

Cong, Jingyu; Wang, Xianpeng; Lan, Xiang; Huang, Mengxing; Wan, Liangtian

doi:10.3390/rs13101956

Cited by 24 publications

(13 citation statements)

References 25 publications

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“…It achieved accuracy of 88.4%. Many other applications have been tackled in the literature [ 9 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ]. Most of the systems presented in the aforementioned works suffer from the shadow effect.…”

Section: State Of the Artmentioning

confidence: 99%

A Convolutional Neural Network-Based Method for Discriminating Shadowed Targets in Frequency-Modulated Continuous-Wave Radar Systems

Mohanna

Gianoglio

Rizik

et al. 2022

Sensors

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The radar shadow effect prevents reliable target discrimination when a target lies in the shadow region of another target. In this paper, we address this issue in the case of Frequency-Modulated Continuous-Wave (FMCW) radars, which are low-cost and small-sized devices with an increasing number of applications. We propose a novel method based on Convolutional Neural Networks that take as input the spectrograms obtained after a Short-Time Fourier Transform (STFT) analysis of the radar-received signal. The method discerns whether a target is or is not in the shadow region of another target. The proposed method achieves test accuracy of 92% with a standard deviation of 2.86%.

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Section: State Of the Artmentioning

confidence: 99%

A Convolutional Neural Network-Based Method for Discriminating Shadowed Targets in Frequency-Modulated Continuous-Wave Radar Systems

Mohanna

Gianoglio

Rizik

et al. 2022

Sensors

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“…By definition, the third-order signal X ∈ C N×M×L is decomposed into three parts in three directions. In order to simplify the expression, the mode-3 matrix unfolding of the third-order signal X ∈ C N×M×L is represented by X = [X ] (3) , where [X ] (3) can be expressed as [35] X…”

Section: Tensor-based Data Modelmentioning

confidence: 99%

“…In 2004, Fishler and others proposed the concept of Multiple input Multiple output (MIMO) radar based on the idea of spatial diversity [1][2][3][4]. Owing to the use of waveform diversity technology, MIMO radar has lots of advantages including flexible operation mode, high angle measurement accuracy, low probability of interception and strong multitarget tracking ability.…”

Section: Introductionmentioning

confidence: 99%

Tensor-Based Target Parameter Estimation Algorithm for FDA-MIMO Radar with Array Gain-Phase Error

Guo

Wang

Shi³

et al. 2022

Remote Sensing

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As a new radar system, FDA-MIMO radar has recently developed rapidly, as it has broad prospects in angle-range estimation. Unfortunately, the performance of existing algorithms for FDA-MIMO radar is greatly degrading or even failing under the condition of array gain-phase error. This paper proposes an innovative solution to the joint angle and range estimation of FDA-MIMO radar under the condition of array gain-phase error and an estimation algorithm is developed. Moreover, the corresponding Cramér-Rao bound (CRB) is derived to evaluate the algorithm. The parallel factor (PARAFAC) decomposition technique can be utilized to calculate transmitter and receiver direction matrices. Taking advantage of receiver direction matrix, the angle estimation can be obtained. The range estimation can be estimated by transmitter direction matrix and angle estimation. To eliminate the error accumulation effect of array gain-phase error, the gain error and phase error are obtained separately. In this algorithm, the impact of gain-phase error on parameter estimation is removed and so is the error accumulation effect. Therefore, the proposed algorithm can provide excellent performance of angle-range and gain-phase error estimation. Numerical experiments prove the validity and advantages of the proposed method.

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“…Moreover, frequency-modulated continuous wave (FMCW) radar has good range measurement capability as well as low power consumption [8]. Likewise, FMCW MIMO radar plays an important role in in-vehicle sensing systems [13], allowing for efficient estimation of target's DOA, velocity, and range.…”

Section: Introductionmentioning

confidence: 99%

“…Although they possess excellent nonlinear mapping ability and better robustness and have achieved tremendous results in single-parameter estimation, such methods are limited by the data dimensionality, and it is difficult to achieve good results with them in multiparameter estimation. In particular, for FMCW MIMO radars, it is challenging to build end-to-end parameter estimation neural networks, due to a large data dimensionality, and hence, it is more suitable to use traditional methods with lower complexity to obtain the 2D spatial spectrum, translate the problem to a computer vision problem, and then implement parameter estimation using very mature networks [13], [17]. However, such estimation techniques are limited by the accuracy and computational complexity of conventional imaging algorithms, and cannot fully exploit the advantages of high-performance FMCW MIMO radars.…”

Section: Introductionmentioning

confidence: 99%

Fast Joint DOA, Range, and Velocity Estimation Method for FMCW MIMO Radar via PARAFAC

Cong

Sun

Wang

et al. 2023

Preprint

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Considering the ever-increasing data size, the traditional subspace-based frequency-modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) radar parameter estimation method is no longer suitable, due to its high computation complexity in eigenvalue decomposition and multidimensional spectrum peak search, which makes it difficult to process the data in real time. In addition, with this method, the accuracy of parameter estimation is also limited by the subspace orthogonality. In order to solve such problems, this paper takes Texas instruments (TI) cascade FMCW MIMO radar system with a large data size as the research object, derives and establishes a tensor domain data model, and designs a fast joint estimation method for direction-of-arrival (DOA), velocity, and range, using the compressed parallel factorization (PARAFAC) technique. Firstly, a data model conforming to the TI radar hardware is established, and its tensor domain model is obtained. Then, the Tucker3 decomposition is used to substantially compress the dimension of original tensor, to obtain a compressed tensor model. After that, the trilinear decomposition problem is solved by trilinear alternating least square (TALS), following by its decompression to original tensor dimension. Finally, the parameter estimation is achieved by phase extraction. Furthermore, in order to minimize the effect of ground clutter and interference on the received signal, a data domain beamforming is employed. Simulations and experiment were carried out to validate the computational efficiency and effectiveness of the proposed method.

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Fast Target Localization Method for FMCW MIMO Radar via VDSR Neural Network

Cited by 24 publications

References 25 publications

A Convolutional Neural Network-Based Method for Discriminating Shadowed Targets in Frequency-Modulated Continuous-Wave Radar Systems

A Convolutional Neural Network-Based Method for Discriminating Shadowed Targets in Frequency-Modulated Continuous-Wave Radar Systems

Tensor-Based Target Parameter Estimation Algorithm for FDA-MIMO Radar with Array Gain-Phase Error

Fast Joint DOA, Range, and Velocity Estimation Method for FMCW MIMO Radar via PARAFAC

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