scholarly journals Optimization Algorithm for DOA Estimation of a Shallow Sea Target Based on Active Time Reversal

2018 ◽  
Vol 36 (2) ◽  
pp. 270-275
Author(s):  
Haixia Jing ◽  
Haiyan Wang ◽  
Zhengguo Liu ◽  
Xiaohong Shen ◽  
Zhichen Zhang
Keyword(s):  
Time Reversal ◽  
Array Signal Processing ◽  
Signal To Noise Ratio ◽  
Doa Estimation ◽  
Shallow Sea ◽  
Estimation Model ◽  
Signal To Noise ◽  
Active Time ◽  
Low Snr ◽  
Processing Theory

Time reversal technique is applied to the DOA estimation of a shallow sea target, and a method based on active time reversal (ATR) is proposed to achieve correct estimation under multipath and low signal-to-noise (SNR) conditions. Combining the classical ray theory with array signal processing theory, the conventional multipath DOA estimation model based on uniform line array and the ATR-based DOA estimation model are set up respectively. The Capon algorithm is employed to simulate the models and compare it with conventional one. The simulation results show that the ATR-based estimation model can better estimate the azimuth angle of the target than the conventional counterpart, provide higher resolution and better suppress side lobes with the same signal-to-noise ratio (SNR), especially the low SNR.

A hybrid neural network goal attain optimization for failed sensor(s) radiation pattern in linear array

2019 ◽  
Vol 4 (4) ◽  
pp. 101-109
Author(s):  
Navaamsini Boopalan ◽  
Agileswari K. Ramasamy ◽  
Farrukh Hafiz Nagi
Keyword(s):  
Neural Network ◽  
Radiation Pattern ◽  
Array Signal Processing ◽  
Linear Array ◽  
Signal To Noise Ratio ◽  
Beam Pattern ◽  
Sensor Failure ◽  
Signal To Noise ◽  
Hybrid Neural Network ◽  
Noise Ratio

Array sensors are widely used in various fields such as radar, wireless communications, autonomous vehicle applications, medical imaging, and astronomical observations fault diagnosis. Array signal processing is accomplished with a beam pattern which is produced by the signal's amplitude and phase at each element of array. The beam pattern can get rigorously distorted in case of failure of array element and effect its Signal to Noise Ratio (SNR) badly. This paper proposes on a Hybrid Neural Network layer weight Goal Attain Optimization (HNNGAO) method to generate a recovery beam pattern which closely resembles the original beam pattern with remaining elements in the array. The proposed HNNGAO method is compared with classic synthesize beam pattern goal attain method and failed beam pattern generated in MATLAB environment. The results obtained proves that the proposed HNNGAO method gives better SNR ratio with remaining working element in linear array compared to classic goal attain method alone. Keywords: Backpropagation; Feed-forward neural network; Goal attain; Neural networks; Radiation pattern; Sensor arrays; Sensor failure; Signal-to-Noise Ratio (SNR)

scholarly journals Convolution Network with Custom Loss Function for the Denoising of Low SNR Raman Spectra

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4623
Author(s):  
Sinead Barton ◽  
Salaheddin Alakkari ◽  
Kevin O’Dwyer ◽  
Tomas Ward ◽  
Bryan Hennelly
Keyword(s):  
Raman Spectra ◽  
Loss Function ◽  
Signal To Noise Ratio ◽  
Biomedical Science ◽  
Clinical Tool ◽  
Signal To Noise ◽  
Low Snr ◽  
Low Intensity ◽  
Spectral Peaks ◽  
Noise Ratio

Raman spectroscopy is a powerful diagnostic tool in biomedical science, whereby different disease groups can be classified based on subtle differences in the cell or tissue spectra. A key component in the classification of Raman spectra is the application of multi-variate statistical models. However, Raman scattering is a weak process, resulting in a trade-off between acquisition times and signal-to-noise ratios, which has limited its more widespread adoption as a clinical tool. Typically denoising is applied to the Raman spectrum from a biological sample to improve the signal-to-noise ratio before application of statistical modeling. A popular method for performing this is Savitsky–Golay filtering. Such an algorithm is difficult to tailor so that it can strike a balance between denoising and excessive smoothing of spectral peaks, the characteristics of which are critically important for classification purposes. In this paper, we demonstrate how Convolutional Neural Networks may be enhanced with a non-standard loss function in order to improve the overall signal-to-noise ratio of spectra while limiting corruption of the spectral peaks. Simulated Raman spectra and experimental data are used to train and evaluate the performance of the algorithm in terms of the signal to noise ratio and peak fidelity. The proposed method is demonstrated to effectively smooth noise while preserving spectral features in low intensity spectra which is advantageous when compared with Savitzky–Golay filtering. For low intensity spectra the proposed algorithm was shown to improve the signal to noise ratios by up to 100% in terms of both local and overall signal to noise ratios, indicating that this method would be most suitable for low light or high throughput applications.

scholarly journals Performance enhancement of MPM using SNR boosting techniques

2016 ◽  
Vol 5 (4) ◽  
pp. 115
Author(s):  
Shimaa Mamdouh ◽  
Amr Hussein ◽  
Hamdy Elmekaty
Keyword(s):  
Signal Processing ◽  
Performance Enhancement ◽  
Mimo Systems ◽  
Signal To Noise Ratio ◽  
Matrix Pencil ◽  
Doa Estimation ◽  
Least Square ◽  
Test Case ◽  
Recursive Least Square ◽  
Low Snr

Signal to noise ratio (SNR) boosting is one of the most important research areas in signal processing. The effectiveness of SNR boosting is not limited to a specific application rather, it is widely used in image processing, signal processing, cognitive radio, MIMO systems, digital beam forming, and direction of arrival (DOA) estimation …etc. In this paper, the recursive least square (RLS) and wavelet based de-noising filters are exploited for SNR boosting in DOA estimation techniques for further performance enhancement. The matrix pencil method (MPM) as an effortlessness and high resolution DOA estimation technique is taken as a test case. That is because it suffers from performance deterioration under low SNR regimes. The simulation results reveal that the MPM based RLS de-noising filter outperforms the MPM based wavelet de-noising filter and the traditional MPM in terms of mean square error (MSE) especially at low SNR regimes.

scholarly journals DOA Estimation for UCAs Based on Virtual Subarray Beamforming Technique

2018 ◽  
Vol 232 ◽  
pp. 01012
Author(s):  
Bo Xu ◽  
Zhigang Huang
Keyword(s):  
Signal To Noise Ratio ◽  
Estimation Method ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Signal To Noise ◽  
Circular Arrays ◽  
Noise Ratio ◽  
Subarray Beamforming ◽  
Beamforming Technique

Direction-of-arrival (DOA) estimation is always a hotspot research in the fields of radar, sonar, communication and so on. And uniform circular arrays (UCAs) are more attractive in the context of DOA estimation since their symmetrical structures have potential to provide two directions coverage. This paper proposed a new DOA estimation method for UCAs via virtual subarray beamforming technique. The method would provide an acceptable DOA estimate even if the number of sources is great than the number of array elements. Also, the performance of the proposed method would hold good when the snapshot length or the signal-to-noise ratio (SNR) is small. Simulations show that the proposed technique offers significantly improved estimation resolution, capacity, and accuracy relative to the existing techniques.

scholarly journals Super resolution DOA estimation based on deep neural network

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Wanli Liu
Keyword(s):  
Neural Network ◽  
Potential Application ◽  
Deep Neural Network ◽  
State Of The Art ◽  
Signal To Noise Ratio ◽  
Super Resolution ◽  
Doa Estimation ◽  
Angular Distance ◽  
Signal To Noise ◽  
Simulation Results

AbstractRecently, deep neural network (DNN) studies on direction-of-arrival (DOA) estimations have attracted more and more attention. This new method gives an alternative way to deal with DOA problem and has successfully shown its potential application. However, these works are often restricted to previously known signal number, same signal-to-noise ratio (SNR) or large intersignal angular distance, which will hinder their generalization in real application. In this paper, we present a novel DNN framework that realizes higher resolution and better generalization to random signal number and SNR. Simulation results outperform that of previous works and reach the state of the art.

scholarly journals Data-mining Based Expert Platform for the Spectral Inspection

2014 ◽  
Vol 10 (S306) ◽  
pp. 292-294
Author(s):  
Haijun Tian ◽  
Yang Xu ◽  
Yang Tu ◽  
Yanxia Zhang ◽  
Yongheng Zhao ◽  
...  
Keyword(s):  
Data Mining ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Sky Surveys ◽  
Low Snr ◽  
Effective Role ◽  
Noise Ratio

AbstractWe propose and preliminarily implement a data-mining based platform to assist experts to inspect the increasing amount of spectra with low signal to noise ratio (SNR) generated by large sky surveys. The platform includes three layers: data-mining layer, data-node layer and expert layer. It is similar to the GalaxyZoo project and it is VO-compatible. The preliminary experiment suggests that this platform can play an effective role in managing the spectra and assisting the experts to inspect a large number of spectra with low SNR.

DOA Estimation Algorithm for Non-Uniform Linear Array with Parallel Factor and Power Loading

2014 ◽  
Vol 556-562 ◽  
pp. 3361-3364
Author(s):  
Chi Jiang ◽  
Xiao Fei Zhang ◽  
Li Cen Zhang
Keyword(s):  
Linear Array ◽  
Simulation Analysis ◽  
Signal To Noise Ratio ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Signal Source ◽  
Signal To Noise ◽  
Uniform Linear Array ◽  
Power Loading ◽  
Parallel Factor

The algorithm of DOA estimation for non-uniform linear array with Parallel Factor (PARAFAC) and power loading is carried out detailed studies and simulation in this paper, and we use Trilinear Alternating Least Squares (TALS) estimation algorithm to estimate the DOA of signal source. In addition, we make a simulation analysis and comparison of different parameters (Signal-to-noise ratio, the number of snapshots, the number of antenna elements, the number of targets, the similar angles) and different algorithm. Finally the thesis summarizes the work.

scholarly journals Improvement of Noise Uncertainty and Signal-To-Noise Ratio Wall in Spectrum Sensing Based on Optimal Stochastic Resonance

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 841 ◽  
Author(s):  
Di He ◽  
Xin Chen ◽  
Ling Pei ◽  
Lingge Jiang ◽  
Wenxian Yu
Keyword(s):  
Stochastic Resonance ◽  
Spectrum Sensing ◽  
Signal To Noise Ratio ◽  
Multipath Fading ◽  
Signal To Noise ◽  
Noise Uncertainty ◽  
Low Snr ◽  
Dynamic Stochastic ◽  
Snr Wall ◽  
Noise Ratio

Noise uncertainty and signal-to-noise ratio (SNR) wall are two very serious problems in spectrum sensing of cognitive radio (CR) networks, which restrict the applications of some conventional spectrum sensing methods especially under low SNR circumstances. In this study, an optimal dynamic stochastic resonance (SR) processing method is introduced to improve the SNR of the receiving signal under certain conditions. By using the proposed method, the SNR wall can be enhanced and the sampling complexity can be reduced, accordingly the noise uncertainty of the received signal can also be decreased. Based on the well-studied overdamped bistable SR system, the theoretical analyses and the computer simulations verify the effectiveness of the proposed approach. It can extend the application scenes of the conventional energy detection especially under some serious wireless conditions especially low SNR circumstances such as deep wireless signal fading, signal shadowing and multipath fading.

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