A Fast Estimation Method for Direction of Arrival Using Tripole Vector Antenna

Zhang, Bodong; Zou, Xuan; Zhang, Tingyi; Tang, Yunong; Zeng, Hao

doi:10.3390/s20175008

Cited by 4 publications

(2 citation statements)

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“…In order to extract c k , we follow Ref. [19] to estimate the first L DoAs from D k (T 1 ) to D k (T L ) as shown in (4). We average these L DoAs and let the averaged DoA Dk to be invariant through the training and the predicting stages.…”

Section: Proposed Prediction Schemementioning

confidence: 99%

Polarization-Aware Prediction of Mobile Radio Wave Propagation Based on Complex-Valued and Quaternion Neural Networks

2022

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It is important to predict the characteristics of radio waves propagating in wireless environment for compensating information loss caused by fading effects. Though fading is often analyzed as the changes of intrinsic electric-field phasor describing a communication channel, it is also attributed greatly to its polarization state (PS). In this paper, we propose PS and phasor prediction methods based on quaternion neural networks (QNNs) and complex-valued neural networks (CVNNs). Quaternion is a hypercomplex number system for representing three dimensional (3-D) spatial rotation and orientation. It realizes adaptive prediction more stably and effectively than a real-valued rotation matrix. The time trajectories of PS are expressed as 3-D rotational motions on the Poincare sphere. We show that QNNs learn the spatiotemporal movements of the PS with high generalization ability and thus provide a superior prediction performance. In addition, CVNNs are efficient predictors for processing complex-valued signals such as the phasor of an incident wave. We evaluate the precision of the proposed QNN-CVNN prediction scheme in channel equalization in a time-division-duplex (TDD) orthogonal-frequency-division-multiplexing (OFDM) communication system. In physical wireless-environment experiments, we find that the proposed scheme provides 3.0 dB and 6.0 dB improvements of bit error rate (BER) at 10 −4 , showing almost the same BER performance as a system perfectly knowing the actual radio-wave characteristics in future TDD frames. Accordingly, this work also reveals that PS needs to be considered as one of the significant characteristics in various radio-wave systems in the near-future technology.

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Section: Proposed Prediction Schemementioning

confidence: 99%

Polarization-Aware Prediction of Mobile Radio Wave Propagation Based on Complex-Valued and Quaternion Neural Networks

2022

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“…The direction of arrival (DOA) [1][2][3] estimation problem is a fundamental problem in the application of wireless communications and radar systems [1,[3][4][5], and an array is adopted to estimate the DOA from the different phases of the received signals. Usually, discrete Fourier transform (DFT)-based methods [6][7][8] can be used to estimate the DOA, where the phase difference of the received signals between different antennas can be described as spatial sampling of the signal, and the DOA estimation problem is similar to the frequency estimation problem in the time-frequency domain [9,10].…”

Section: Introductionmentioning

confidence: 99%

Atomic Network-Based DOA Estimation Using Low-Bit ADC

Sheng

Chen

Yao³

et al. 2021

Electronics

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In the direction of arrival (DOA) estimation problem, when a low-bit analog to digital converter (ADC) is used, the estimation performance severely deteriorates. In this paper, the DOA estimation problem is considered in a low-cost direction finding system with low-bit ADC. To eliminate quantization noise, we propose a novel network ADCnet, which is a composition of fully connected layers and exponential linear unit (ELU) layers, and the input signals are the received signals using low-bit ADC. After the ADCnet, an AtomicNet is also proposed to estimate the DOA from the denoised signals, where atomic vectors are corresponding to the steer vectors. A loss function considering both the reconstruction performance and the sparsity is proposed in the AtomicNet. Different from the exiting atomic norm-based methods, the proposed method can avoid an optimization problem and estimate the DOA with lower computational complexity. Simulation results show that the proposed method outperforms the existing methods in the DOA estimation performance using low-bit ADC.

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