scholarly journals Detection and Direction-of-Arrival Estimation of Weak Spread Spectrum Signals Received with Antenna Array

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2566
Author(s):  
Jarosław Magiera
Keyword(s):  
Antenna Array ◽  
Spread Spectrum ◽  
Additive White Gaussian Noise ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Probability Of Detection ◽  
Joint Detection ◽  
Wide Range ◽  
To Receive ◽  
Spread Spectrum Signals

This paper presents a method for the joint detection and direction of arrival (DOA) estimation of low probability of detection (LPD) signals. The proposed approach is based on using the antenna array to receive spread-spectrum signals hidden below the noise floor. Array processing exploits the spatial correlation between phase-delayed copies of the signal and allows us to evaluate the parameter used to make the decision about the presence of LPD transmission. The DOA estimation is based on the covariance between signals received by sensors for the fixed geometry of the antenna array. Moreover, the paper provides a method for mitigating narrowband interferences prior to signal detection. The presented methods were verified through simulations which proved that the confident detection of a one-second transmission in an additive white Gaussian noise channel is possible even when the noise is 24 dB higher than the power of the received signal. The performance of DOA estimation is analyzed in a wide range of signal-to-noise and interference-to-noise ratios. It is found that the DOA may be estimated with an RMS error not exceeding 10 degrees, even if interference occupies 15% of the analyzed frequency band.

scholarly journals Analysis of Dual-Band Direction of Arrival Estimation in Multipath Scenarios

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1236
Author(s):  
Alessandro Cidronali ◽  
Edoardo Ciervo ◽  
Giovanni Collodi ◽  
Stefano Maddio ◽  
Marco Passafiume ◽  
...  
Keyword(s):  
Multiple Access ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Dual Band ◽  
Second Step ◽  
Frequency Division ◽  
Simulation Environment ◽  
Full Wave ◽  
Wide Range ◽  
Localization Systems

The present paper analyzes the performance of localization systems, based on dual-band Direction of Arrival (DoA) approach, in multi-path affected scenarios. The implemented DoA estimation, which belongs to the so-called Space and Frequency Division Multiple Access (SFDMA) technique, takes advantage of the use of two uncorrelated communication carrier frequencies, as already demonstrated by the authors. Starting from these results, this paper provides, first, the methodology followed to describe the localization system in the proposed simulation environment, and, as a second step, describes how multi-path effects may be taken into account through a set of full-wave simulations. The latter follows an approach based on the two-ray model. The validation of the proposed approach is demonstrated by simulations over a wide range of virtual scenarios. The analysis of the results highlights the ability of the proposed approach to describe multi-path effects and confirms enhancements in DoA estimation as experimentally evaluated by the same authors. To further assess the performance of the aforementioned simulation environment, a comparison between simulated and measured results was carried out, confirming the capability to predict DoA performance.

scholarly journals Spectral Domain Sparse Representation for DOA Estimation of Signals with Large Dynamic Range

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5164
Author(s):  
Jacob Compaleo ◽  
Inder J. Gupta
Keyword(s):  
Sparse Representation ◽  
Antenna Array ◽  
Dynamic Range ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Single Step ◽  
Window Function ◽  
Spectral Domain ◽  
Low Sidelobe ◽  
Weighting Window

Recently, we proposed a Spectral Domain Sparse Representation (SDSR) approach for the direction-of-arrival estimation of signals incident to an antenna array. In the approach, sparse representation is applied to the conventional Bartlett spectra obtained from snapshots of the signals received by the antenna array to increase the direction-of-arrival (DOA) estimation resolution and accuracy. The conventional Bartlett spectra has limited dynamic range, meaning that one may not be able to identify the presence of weak signals in the presence of strong signals. This is because, in the conventional Bartlett spectra, uniform weighting (window) is applied to signals received by various antenna elements. Apodization can be used in the generation of Bartlett spectra to increase the dynamic range of the spectra. In Apodization, more than one window function is used to generate different portions of the spectra. In this paper, we extend the SDSR approach to include Bartlett spectra obtained with Apodization and to evaluate the performance of the extended SDSR approach. We compare its performance with a two-step SDSR approach and with an approach where Bartlett spectra is obtained using a low sidelobe window function. We show that an Apodization Bartlett-based SDSR approach leads to better performance with just single-step processing.

scholarly journals Minimum Array Elements for Resolution of Several Direction of Arrival Estimation Methods in Various Noise-Level Environments

2018 ◽  
Vol 2 ◽  
pp. 87-94
Author(s):  
Ismail El Ouargui ◽  
Said Safi ◽  
Miloud Frikel
Keyword(s):  
Gaussian Noise ◽  
Noise Level ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Direction Of Arrival ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Estimation Methods ◽  
Minimum Number ◽  
Informative Signals

The resolution of a Direction of Arrival (DOA) estimation algorithm is determined based on its capability to resolve two closely spaced signals. In this paper, authors present and discuss the minimum number of array elements needed for the resolution of nearby sources in several DOA estimation methods. In the real world, the informative signals are corrupted by Additive White Gaussian Noise (AWGN). Thus, a higher signal-to-noise ratio (SNR) offers a better resolution. Therefore, we show the performance of each method by applying the algorithms in different noise level environments.

scholarly journals Machine–learning-enabled metasurface for direction of arrival estimation

Nanophotonics ◽  
2022 ◽  
Vol 0 (0) ◽  
Author(s):  
Min Huang ◽  
Bin Zheng ◽  
Tong Cai ◽  
Xiaofeng Li ◽  
Jian Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Incident Angle ◽  
Wide Band ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Signal Acquisition ◽  
Reconstruction Algorithms ◽  
Time Saving ◽  
Wide Range

Abstract Metasurfaces, interacted with artificial intelligence, have now been motivating many contemporary research studies to revisit established fields, e.g., direction of arrival (DOA) estimation. Conventional DOA estimation techniques typically necessitate bulky-sized beam-scanning equipment for signal acquisition or complicated reconstruction algorithms for data postprocessing, making them ineffective for in-situ detection. In this article, we propose a machine-learning-enabled metasurface for DOA estimation. For certain incident signals, a tunable metasurface is controlled in sequence, generating a series of field intensities at the single receiving probe. The perceived data are subsequently processed by a pretrained random forest model to access the incident angle. As an illustrative example, we experimentally demonstrate a high-accuracy intelligent DOA estimation approach for a wide range of incident angles and achieve more than 95% accuracy with an error of less than 0.5 ° $0.5{\degree}$ . The reported strategy opens a feasible route for intelligent DOA detection in full space and wide band. Moreover, it will provide breakthrough inspiration for traditional applications incorporating time-saving and equipment-simplified majorization.

scholarly journals Reduced-Complexity Direction of Arrival Estimation Using Real-Valued Computation with Arbitrary Array Configurations

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Feng-Gang Yan ◽  
Jun Wang ◽  
Shuai Liu ◽  
Yi Shen ◽  
Ming Jin
Keyword(s):  
State Of The Art ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Low Complexity ◽  
Eigenvalue Decomposition ◽  
Music Algorithm ◽  
New Approach ◽  
Multiple Signal Classification ◽  
Wide Range ◽  
Complexity Algorithm

A low-complexity algorithm is presented to dramatically reduce the complexity of the multiple signal classification (MUSIC) algorithm for direction of arrival (DOA) estimation, in which both tasks of eigenvalue decomposition (EVD) and spectral search are implemented with efficient real-valued computations, leading to about 75% complexity reduction as compared to the standard MUSIC. Furthermore, the proposed technique has no dependence on array configurations and is hence suitable for arbitrary array geometries, which shows a significant implementation advantage over most state-of-the-art unitary estimators including unitary MUSIC (U-MUSIC). Numerical simulations over a wide range of scenarios are conducted to show the performance of the new technique, which demonstrates that with a significantly reduced computational complexity, the new approach is able to provide a close accuracy to the standard MUSIC.

Adaptive Antenna Array Receivers for Spread-Spectrum Signals in Non-Gaussian Noise

2001 ◽  
Vol 11 (2) ◽  
pp. 144-158 ◽  
Author(s):  
Dimitris A. Pados ◽  
Michael J. Medley ◽  
Stella N. Batalama
Keyword(s):  
Antenna Array ◽  
Gaussian Noise ◽  
Spread Spectrum ◽  
Adaptive Antenna ◽  
Adaptive Antenna Array ◽  
Non Gaussian ◽  
Spread Spectrum Signals

scholarly journals Direction-of-Arrival Estimation over Sea Surface from Radar Scattering Based on Convolutional Neural Network

2021 ◽  
Vol 13 (14) ◽  
pp. 2681
Author(s):  
Xiuyi Zhao ◽  
Ying Yang ◽  
Kun-Shan Chen
Keyword(s):  
Neural Network ◽  
Point Source ◽  
Convolutional Neural Network ◽  
Array Signal Processing ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Sea Surface ◽  
Estimation Methods ◽  
Wind Speeds ◽  
Wide Range

Conventional direction-of-arrival (DOA) estimation methods are primarily used in point source scenarios and based on array signal processing. However, due to the local scattering caused by sea surface, signals observed from radar antenna cannot be regarded as a point source but rather as a spatially dispersed source. Besides, with the advantages of flexibility and comparably low cost, synthetic aperture radar (SAR) is the present and future trend of space-based systems. This paper proposes a novel DOA estimation approach for SAR systems using the simulated radar measurement of the sea surface at different operating frequencies and wind speeds. This article’s forward model is an advanced integral equation model (AIEM) to calculate the electromagnetic scattered from the sea surface. To solve the DOA estimation problem, we introduce a convolutional neural network (CNN) framework to estimate the transmitter’s incident angle and incident azimuth angle. Results demonstrate that the CNN can achieve a good performance in DOA estimation at a wide range of frequencies and sea wind speeds.

scholarly journals Investigations on antenna array calibration algorithms for direction-of-arrival estimation

2016 ◽  
Vol 14 ◽  
pp. 181-190 ◽  
Author(s):  
Michael Eberhardt ◽  
Philipp Eschlwech ◽  
Erwin Biebl
Keyword(s):  
Antenna Array ◽  
Array Signal Processing ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Calibration Procedure ◽  
Estimation Algorithms ◽  
Modeling Errors ◽  
Array Calibration ◽  
History Of ◽  
The Common

Abstract. Direction-of-arrival (DOA) estimation algorithms deliver very precise results based on good and extensive antenna array calibration. The better the array manifold including all disturbances is known, the better the DOA estimation result. A simplification or ideally an omission of the calibration procedure has been a long pursued goal in the history of array signal processing. This paper investigates the practicability of some well known calibration algorithms and gives a deeper insight into existing obstacles. Further analysis on the validity of the common used data model is presented. A new effect in modeling errors is revealed and simulation results substantiate this theory.

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