scholarly journals Multi-source localization by using offset residual weight

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Maoshen Jia ◽  
Shang Gao ◽  
Changchun Bao
Keyword(s):  
Source Localization ◽  
Doa Estimation ◽  
Objective Evaluation ◽  
Source Zone ◽  
Multiple Sources ◽  
Time Frequency ◽  
Simulated Environments ◽  
Actual Direction ◽  
The Difference ◽  
Residual Weight

AbstractMultiple sound source localization is a hot issue of concern in recent years. The Single Source Zone (SSZ) based localization methods achieve good performance due to the detection and utilization of the Time-Frequency (T-F) zone where only one source is dominant. However, some T-F points consisting of components from multiple sources are also included in the detected SSZ sometimes. Once a T-F point in SSZ is contributed by multiple components, this point is defined as an outlier. The existence of outliers within the detected SSZ is usually an unavoidable problem for SSZ-based methods. To solve this problem, a multi-source localization by using offset residual weight is proposed in this paper. In this method, an assumption is developed: the direction estimated by all the T-F points within the detected SSZ has a difference along with the actual direction of sources. But this difference is much smaller than the difference between the directions estimated by the outliers along with the actual source localization. After verifying this assumption experimentally, Point Offset Residual Weight (PORW) and Source Offset Residual Weight (SORW) are proposed to reduce the influence of outliers on the localization results. Then, a composite weight is formed by combining PORW and SORW, which can effectively distinguish the outliers and desired points. After that, the outliers are removed by composite weight. Finally, a statistical histogram of DOA estimation with outliers removed is used for multi-source localization. The objective evaluation of the proposed method is conducted in various simulated environments. The results show that the proposed method achieves a better performance compared with the reference methods in sources localization.

scholarly journals The upper bound of multi-source DOA information in sensor array and its application in performance evaluation

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Weilin Tu ◽  
Dazhuan Xu ◽  
Ying Zhou ◽  
Chao Shi
Keyword(s):  
Performance Evaluation ◽  
Upper Bound ◽  
Array Signal Processing ◽  
Additive White Gaussian Noise ◽  
Doa Estimation ◽  
Posteriori Probability ◽  
Theoretical Expression ◽  
Source Information ◽  
Multiple Sources ◽  
Dimensional Parameter

Abstract Direction of arrival (DOA) estimation has been discussed extensively in the array signal processing field. In this paper, the authors focus on the multi-source DOA information which is defined as the mutual information between the DOA and the received signal contaminated by complex additive white Gaussian noise. A theoretical expression of DOA information with multiple sources is derived for the uniform linear array. At high SNRs and under the sparse-source assumption obtained is the upper bound of DOA information contained in K sparse sources which can be regarded as the sum of all single-source information minus the uncertainty of sources’ order logK!. Moreover, because of the uncertainty of multi-sources’ order, the posteriori probability distribution of DOA no longer obeys single peak Gaussian distribution so that the mean square error is unsuitable in evaluating the performance of multi-dimensional parameter estimation. Consequently, entropy error (EE) is used as a new performance evaluation metric, whose relationship with DOA information is given.

scholarly journals Three-dimensional multi-source localization of underwater objects using convolutional neural networks for artificial lateral lines

2020 ◽  
Vol 17 (162) ◽  
pp. 20190616 ◽  
Author(s):  
Ben J. Wolf ◽  
Jos van de Wolfshaar ◽  
Sietse M. van Netten
Keyword(s):  
Neural Network ◽  
Source Localization ◽  
Fluid Velocity ◽  
Three Dimensional ◽  
Sensory System ◽  
Three Dimensions ◽  
Hydrodynamic Theory ◽  
Multiple Sources ◽  
Combined System ◽  
Lateral Lines

This research focuses on the signal processing required for a sensory system that can simultaneously localize multiple moving underwater objects in a three-dimensional (3D) volume by simulating the hydrodynamic flow caused by these objects. We propose a method for localization in a simulated setting based on an established hydrodynamic theory founded in fish lateral line organ research. Fish neurally concatenate the information of multiple sensors to localize sources. Similarly, we use the sampled fluid velocity via two parallel lateral lines to perform source localization in three dimensions in two steps. Using a convolutional neural network, we first estimate a two-dimensional image of the probability of a present source. Then we determine the position of each source, via an automated iterative 3D-aware algorithm. We study various neural network architectural designs and different ways of presenting the input to the neural network; multi-level amplified inputs and merged convolutional streams are shown to improve the imaging performance. Results show that the combined system can exhibit adequate 3D localization of multiple sources.

scholarly journals Asymmetric Temporal Integration of Layer 4 and Layer 2/3 Inputs in Visual Cortex

2011 ◽  
Vol 105 (1) ◽  
pp. 347-355 ◽  
Author(s):  
Giao B. Hang ◽  
Yang Dan
Keyword(s):  
Visual Cortex ◽  
Visual Processing ◽  
Pyramidal Neurons ◽  
Temporal Integration ◽  
Cortical Inhibition ◽  
Multiple Sources ◽  
Layer 2 ◽  
Layer 4 ◽  
The Difference

Neocortical neurons in vivo receive concurrent synaptic inputs from multiple sources, including feedforward, horizontal, and feedback pathways. Layer 2/3 of the visual cortex receives feedforward input from layer 4 and horizontal input from layer 2/3. Firing of the pyramidal neurons, which carries the output to higher cortical areas, depends critically on the interaction of these pathways. Here we examined synaptic integration of inputs from layer 4 and layer 2/3 in rat visual cortical slices. We found that the integration is sublinear and temporally asymmetric, with larger responses if layer 2/3 input preceded layer 4 input. The sublinearity depended on inhibition, and the asymmetry was largely attributable to the difference between the two inhibitory inputs. Interestingly, the asymmetric integration was specific to pyramidal neurons, and it strongly affected their spiking output. Thus via cortical inhibition, the temporal order of activation of layer 2/3 and layer 4 pathways can exert powerful control of cortical output during visual processing.

scholarly journals A New Computing Paradigm for Off-Grid Direction of Arrival Estimation Using Compressive Sensing

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Hamid Ali Mirza ◽  
Laeeq Aslam ◽  
Muhammad Asif Zahoor Raja ◽  
Naveed Ishtiaq Chaudhary ◽  
Ijaz Mansoor Qureshi ◽  
...  
Keyword(s):  
Compressive Sensing ◽  
Fitness Function ◽  
Super Resolution ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Estimation Problem ◽  
Direction Of Arrival Estimation ◽  
Grid Refinement ◽  
Computing Paradigm ◽  
The Difference

In this paper, a method for solving grid mismatch or off-grid target is presented for direction of arrival (DOA) estimation problem using compressive sensing (CS) technique. Location of the sources are at few angles as compare to the entire angle domain, i.e., spatially sparse sources, and their location can be estimated using CS methods with ability of achieving super resolution and estimation with a smaller number of samples. Due to grid mismatch in CS techniques, the source energy is distributed among the adjacent grids. Therefore, a fitness function is introduced which is based on the difference of the source energy among the adjacent grids. This function provides the best discretization value for the grid through iterative grid refinement. The effectiveness of the proposed scheme is verified through extensive simulations for different number of sources.

scholarly journals Patch Similarity Modulus and Difference Curvature Based Fourth-Order Partial Differential Equation for Image Denoising

2015 ◽  
Vol 2015 ◽  
pp. 1-12
Author(s):  
Yunjiao Bai ◽  
Quan Zhang ◽  
Hong Shangguan ◽  
Zhiguo Gui ◽  
Yi Liu ◽  
...  
Keyword(s):  
Differential Equation ◽  
Partial Differential Equation ◽  
Image Denoising ◽  
Fourth Order ◽  
Objective Evaluation ◽  
Order Partial Differential Equation ◽  
Partial Differential ◽  
The Difference ◽  
Adaptively Adjusting ◽  
Difference Curvature

The traditional fourth-order nonlinear diffusion denoising model suffers the isolated speckles and the loss of fine details in the processed image. For this reason, a new fourth-order partial differential equation based on the patch similarity modulus and the difference curvature is proposed for image denoising. First, based on the intensity similarity of neighbor pixels, this paper presents a new edge indicator called patch similarity modulus, which is strongly robust to noise. Furthermore, the difference curvature which can effectively distinguish between edges and noise is incorporated into the denoising algorithm to determine the diffusion process by adaptively adjusting the size of the diffusion coefficient. The experimental results show that the proposed algorithm can not only preserve edges and texture details, but also avoid isolated speckles and staircase effect while filtering out noise. And the proposed algorithm has a better performance for the images with abundant details. Additionally, the subjective visual quality and objective evaluation index of the denoised image obtained by the proposed algorithm are higher than the ones from the related methods.

scholarly journals Time-Frequency Intracranial Source Localization of Feedback-Related EEG Activity in Hypothesis Testing

2006 ◽  
Vol 17 (6) ◽  
pp. 1314-1322 ◽  
Author(s):  
D. Papo ◽  
A. Douiri ◽  
F. Bouchet ◽  
J.-C. Bourzeix ◽  
J.-P. Caverni ◽  
...  
Keyword(s):  
Hypothesis Testing ◽  
Source Localization ◽  
Eeg Activity ◽  
Time Frequency

scholarly journals Sparsity-Based DOA Estimation with Gain and Phase Error Calibration of Generalized Nested Array

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Ziang Feng ◽  
Guoping Hu ◽  
Hao Zhou
Keyword(s):  
Degrees Of Freedom ◽  
Phase Error ◽  
Doa Estimation ◽  
Model Errors ◽  
Multiple Sources ◽  
Sparse Arrays ◽  
Estimation Algorithms ◽  
Phase Errors ◽  
Representation Method ◽  
Cramer Rao Bound

Sparse arrays, which can localize multiple sources with less physical sensors, have attracted more attention since they were proposed. However, for optimal performance of sparse arrays, it is usually assumed that the circumstances are ideal. But in practice, the performance of sparse arrays will suffer from the model errors like mutual coupling, gain and phase error, and sensor’s location error, which causes severe performance degradation or even failure of the direction of arrival (DOA) estimation algorithms. In this study, we follow with interest and propose a covariance-based sparse representation method in the presence of gain and phase errors, where a generalized nested array is employed. The proposed strategy not only enhances the degrees of freedom (DOFs) to deal with more sources but also obtains more accurate DOA estimations despite gain and phase errors. The Cramer–Rao bound (CRB) derivation is analyzed to demonstrate the robustness of the method. Finally, numerical examples illustrate the effectiveness of the proposed method from DOA estimation.

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