Direction Finding for Bistatic MIMO Radar with Unknown Spatially Colored Noise

Wen, Fangqing; Shi, Junpeng; Zhang, Zijing

doi:10.1007/s00034-019-01260-5

Cited by 35 publications

(26 citation statements)

References 28 publications

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“…Another fully referenced quality evaluation index of image is the peak signal to noise ratio (PSNR), which is the most common and widely used objective evaluation index of images, directional of arrive estimation [19]- [23] and wireless communications [6], [24]- [29]. It is based on the error between corresponding pixel points, that is, the quality evaluation of image based on error sensitivity.…”

Section: A Attention Gan Evaluationmentioning

confidence: 99%

Attention GAN-Based Method for Designing Intelligent Making System

et al. 2019

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Design of the intelligent making system (iMAS) poses a big technical challenge to recognize both printed and handwriting characters from the scanned images. In this paper, we propose an iMAS method based on the attention generative adversarial network (GAN), which innovatively applies the idea of removing raindrops through attention GAN to remove the printed text first, and then generates the image of handwriting text. The proposed method introduces a classical attention model in the visual field and generates the attention maps which focus only on the important areas through recurrent neural network (RNN), and adopts you only look once at object detection (YOLOv3) method to recognize the characters. Experimental results show that the structural similarity (SSIM) of the image generated by attention GAN is 0.89 and the accuracy of recognition is 91.34%. INDEX TERMS Attention model, generative adversarial network (GAN), recurrent neural network (RNN), object detection.

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Section: A Attention Gan Evaluationmentioning

confidence: 99%

Attention GAN-Based Method for Designing Intelligent Making System

et al. 2019

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“…The other is the collocated MIMO radar, where both transmitting and receiving antennas are arranged close to each other [7]. According to whether the receiver and transmitter are located in the same place, the collocated MIMO radar is further categorized into the monostatic MIMO radar [8,9] and bistatic MIMO radar [10,11]. A monostatic MIMO radar is superior in its excellent maneuverability and synchronization between the transmitter and receiver.…”

Section: Introductionmentioning

confidence: 99%

A Novel Unitary ESPRIT Algorithm for Monostatic FDA-MIMO Radar

Liu

Wang

Huang

et al. 2020

Sensors

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A novel unitary estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm, for the joint direction of arrival (DOA) and range estimation in a monostatic multiple-input multiple-output (MIMO) radar with a frequency diverse array (FDA), is proposed. Firstly, by utilizing the property of Centro-Hermitian of the received data, the extended real-valued data is constructed to improve estimation accuracy and reduce computational complexity via unitary transformation. Then, to avoid the coupling between the angle and range in the transmitting array steering vector, the DOA is estimated by using the rotation invariance of the receiving subarrays. Thereafter, an automatic pairing method is applied to estimate the range of the target. Since phase ambiguity is caused by the phase periodicity of the transmitting array steering vector, a removal method of phase ambiguity is proposed. Finally, the expression of Cramér–Rao Bound (CRB) is derived and the computational complexity of the proposed algorithm is compared with the ESPRIT algorithm. The effectiveness of the proposed algorithm is verified by simulation results.

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“…For instance, the combination of the OFDM technique and the SAR playing a critical role in remote sensing [18], marine, and terrestrial surveillance, together with the reconnaissance of enemy situation [19] has been investigated [20]. The multiple-input and multiple-output (MIMO) technique radar can be broadly applied to the direction-of-arrival (DOA) estimation [21][22][23][24] and wide-swath imaging [25] in a big way. Further, a newer system, so-called coprime array, becomes a key technology enabling a huge number of applications, including the DOA [26,27], and adaptive beamforming [28], and the orthogonality between the subcarriers of the OFDM signal can provide frequency diversity for the waveform of these array structures.…”

Section: Introductionmentioning

confidence: 99%

ECCM Schemes against Deception Jamming Using OFDM Radar with Low Global PAPR

Wang

Zhang

Wang

et al. 2020

Sensors

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In this paper, a type of effective electronic counter-countermeasures (ECCM) technique for suppressing the high-power deception jamming using an orthogonal frequency division multiplexing (OFDM) radar is proposed. Concerning the velocity deception jamming, the initial phases of the pulses transmitted in a coherent processing interval (CPI) are designed to minimize the jamming power within a specific range, forming a notch around the jamming in the Doppler spectrum. For the purpose of suppressing the range deception jamming and the joint range-velocity deception jamming, the phase codes of the subcarriers belonging to the OFDM pulses are optimized to minimize the jamming power, distributing some specific bands in the range and the range-velocity domain, respectively. According to Parseval's theorem, the phase encoding, acting as the coding manner of the OFDM subcarriers can ensure that the energy of each OFDM symbol stays the same. It is worth noticing that the phase codes of the OFDM subcarriers can influence the peak-to-average power ratio (PAPR). Thus, an optimization problem is formulated to optimize the phase codes of the subcarriers under the constraint of global PAPR, which can regulate the PAPRs of multiple OFDM symbols at the same time. The proposed problem is non-convex; therefore, it is a huge challenge to tackle. Then we present a method named by the phase-only alternating direction method multipliers (POADMM) to solve the aforementioned optimization problem. Some necessary simulation results are provided to demonstrate the effectiveness of the proposed radar signaling strategy 4 of 26 norm. In addition, arg{x} denotes the phase vector of x.|·|, {·} and {·} are the magnitude, real part, and image part of a complex-valued input. The operators ⊕ and ⊗ are the Hadamard operator and Kronecker product operator, respectively. Let I, 1, and 0 denote the identity matrix, matrix of 1 and zero matrix (their size is determined from the context). The operators, Diag(·) and diag(·), are, respectively, the diagonal matrices formed by the entries of the vector argument and the column vector containing the principal diagonal of the matrix argument. denotes the gradient operator and vec(·) represents the vectorization of an input matrix.

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Direction Finding for Bistatic MIMO Radar with Unknown Spatially Colored Noise

Cited by 35 publications

References 28 publications

Attention GAN-Based Method for Designing Intelligent Making System

Attention GAN-Based Method for Designing Intelligent Making System

A Novel Unitary ESPRIT Algorithm for Monostatic FDA-MIMO Radar

ECCM Schemes against Deception Jamming Using OFDM Radar with Low Global PAPR

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