We study the waveform design problem for a multiple-inputmultiple-output over-the-horizon (MIMO-OTH) radar system faced with a combination of additive Gaussian noise and signal dependent clutter. Considering the oper...
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ISBN:
(纸本)9781479903573
We study the waveform design problem for a multiple-inputmultiple-output over-the-horizon (MIMO-OTH) radar system faced with a combination of additive Gaussian noise and signal dependent clutter. Considering the operational frequency of the MIMO-OTH radar is generally limited to a certain frequency band due to propagation and implementation issues, the waveform transmitted at each antenna is constructed as a weighted sum of discrete prolate spheroidal (DPS) sequences which have good orthogonal and band-limited properties. Optimum waveforms (possibly nonorthogonal) are designed to maximize the target detection performance of the MIMOOTH radar system with the constraint of fixed total transmitted energy. The performance of the proposed waveforms is analyzed.
Among the many parametric problems studied in multiple-inputmultiple-output(MIMO) radar, Direction of Arrival (DOA) estimation is a key issue. With low signal-to-noise ratio (SNR) and low snapshots, the performance o...
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ISBN:
(纸本)9798400707674
Among the many parametric problems studied in multiple-inputmultiple-output(MIMO) radar, Direction of Arrival (DOA) estimation is a key issue. With low signal-to-noise ratio (SNR) and low snapshots, the performance of conventional DOA estimation algorithms may be severely degraded, and even low estimation accuracy may occur. To address this problem, this paper proposes a fast Unitary matrix algorithm for rotational invariant signal parameter estimation. The standard ESPRIT algorithm uses Singular Value Decomposition(SVD) or Eigenvalue Decomposition(EVD), which can certainly obtain accurate DOA estimates, but its operation is huge. In this paper, the space is first transferred from high-dimensional to low-dimensional, and the redundant data in the MIMO radar signal is removed, and then the signal space rational approximation method is used to directly approximate the eigenvalue matrix to obtain the subspace, which is equivalent to forward and backward averaging of the data. The simulation experiments verify the effectiveness of the proposed algorithm.
multiple-inputmultiple-output (MIMO) radar is a new type of radar with excellent performance in target detection and parameter estimations. The MIMO radar equipped with electromagnetic vector sensors (EMVSs) can reco...
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multiple-inputmultiple-output (MIMO) radar is a new type of radar with excellent performance in target detection and parameter estimations. The MIMO radar equipped with electromagnetic vector sensors (EMVSs) can record multi-dimensional information of the electromagnetic (EM) signal. Traditional methods based on long vector model cannot preserve orthogonality between the outputs of each component of the EMVS and have high computational complexity. In this paper, a novel geometric algebra (GA)-based localization method is proposed for direction of arrival (DOA), range, and polarization estimation of near-field (NF) non-circular (NC) sources for a symmetric MIMO array. First, the proposed method converts the output of an EMVS into a multi-vector through a GA matrix transformation. Then, the DOA and range parameters can be estimated by searching the one-dimensional (1-D) spectrum based on the GA-based NC rank reduction (GANC-RARE) algorithm. Finally, the polarization can be estimated by employing estimated DOAs and ranges based on the modified GA-based NC multiple signal classification (MGANC-MUSIC) algorithm. The computational complexity analysis demonstrates that our solution improves memory spaces of the covariance matrix and reduces computation efforts of the eigenvalue decomposition (EVD). The simulation results show the superiority and reliability of our proposed method in model error and coherent noise environment.(c) 2023 Elsevier Inc. All rights reserved.
Unmanned aerial vehicles (UAVs) are increasing in popularity in various sectors, simultaneously rasing the challenge of detecting those with low radar cross sections (RCS). This review paper aims to assess the current...
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Unmanned aerial vehicles (UAVs) are increasing in popularity in various sectors, simultaneously rasing the challenge of detecting those with low radar cross sections (RCS). This review paper aims to assess the current state-of-the-art in radar technology, focusing on multiple-inputmultiple-output (MIMO) and beamforming techniques, to address this growing concern. It explores the challenges associated with detecting UAVs in urban settings and adverse weather conditions, where traditional radar systems often do not succeed. This paper examines the existing literature and technological advancements to understand how these methodologies can significantly boost detection capabilities under the constraints of low RCS. In particular, MIMO technology, renowned for its spatial multiplexing, and beamforming, with its directional signal enhancement, are evaluated for their efficacy in the context of UAV surveillance and defense strategies. Ultimately, a comprehensive comparison is presented, drawing on a variety of studies to illustrate the combined potential of integrating these technologies, providing the way for future developments in radar system design and UAV detection.
77 GHz radar has become a promising approach to enhance automotive safety by quickly detecting and identifying targets around the vehicle, especially in harsh weather conditions. This requires 77 GHz radars to provide...
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77 GHz radar has become a promising approach to enhance automotive safety by quickly detecting and identifying targets around the vehicle, especially in harsh weather conditions. This requires 77 GHz radars to provide environmental imaging with high resolution and reliability. However, radar images are easily blurred by sidelobes and background noises, which makes it difficult to extract real target information. In this paper, a sidelobe suppression algorithm based on the point spread function (PSF) and complex-valued neural network has been proposed to discriminate and suppress unwanted sidelobes while maintaining mainlobes referring to targets. To overcome the scarcity of real-world 77 GHz multiple-inputmultiple-output (MIMO) radar datasets, this paper derives the formula of PSF for 77 GHz MIMO radars in detail and exploits the PSF to generate simulated datasets for training. In addition, to be compatible with the complex-valued radar datasets, a customized neural network model has also been established in this paper. The well-trained neural network is further adopted to suppress sidelobes on real-world radar images. Comprehensive simulations and measurements have proved the superior performance of the proposed method, especially in cases with low signal-to-noise ratio (SNR), large channel mismatches, small target radar Cross-Section (RCS) and large observation angle.
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