Passive Source Localization Using Compressive Sensing

Abstract

This paper presents an underwater passive source localization method by forming an underdetermined linear inversion problem. The signal strength on a specified grid is evaluated using sparse reconstruction algorithms by exploiting the spatial sparsity of the source signals. Our strategy leads to a high ratio of measurements to sparsity (RMS), an increase in the peak sharpness with a low side lobe level, and minimization of the dimensionality of the problem due to the formulation of the system equation of the multiple snapshots based on the data correlation matrix. Furthermore, to reduce the computational burden, pre-locating with Bartlett is presented. Our proposed technique can perform close to Bartlet and white noise gain constraint processes in the single-source scenario, but it can give slightly better results while localizing multiple sources. It exhibits the respective characteristics of traditionally used Bartlett and white noise gain constraint methods, such as robustness to environmental/system mismatch and high resolution. Both the simulated and experimental data are processed to demonstrate the effectiveness of the method for underwater source localization.

Location-Assistive and Real-Time Query IoT-Based Transport System

“…Generally, the GPS traces the longitude and longitude with the locality of the bus system. This information is stored as data on the IoT open platform [4][5][6].…”

Localización de múltiples fuentes basada en información de dirección de llegada mediante el uso de la teoría de sensado comprimido de la posición basado en información de dirección de llegada aplicando sensado comprimido a la matriz de covarianza

“…En la mayoría de casos prácticos de los sistemas de localización, el número de fuentes es mucho menor al de posiciones desde las cuales puede provenir una señal; es decir, al discretizar la región de interés, la cantidad de celdas que efectivamente contienen fuentes transmisoras es mucho menor al tamaño de la grilla como tal, razón por la cual es posible plantear el problema de localización como de reconstrucción dispersa (Zhao, Irshad, Shi y Xu, 2019). Además, basados en la teoría de sensado comprimido desarrollada por (Candès y Romberg, 2006, Candès, Romberg y Tao, 2006a, Candès, Romberg y Tao, 2006b, Donoho, 2006, la señal recibida y r [n] se puede aproximar correctamente como una combinación lineal de algunos pocos elementos de una base de representación conocida o diccionario (Marín-Alfonso, Betancur-Agudelo y Alguello-Fuentes, 2017).…”

Compressive Sensing Approach to Harmonics Detection in the Ship Electrical Network

“…CS is widely used in diverse fields, e.g., biomedical applications, communication systems, and pattern recognition, as well as electrical PQ estimation [15,16]. Various new techniques for identifying and estimating harmonic sources in electricity supply systems have been presented in the literature [17][18][19][20][21][22][23][24][25][26][27][28]. The papers [17][18][19][20][21] concern the distributed monitoring of harmonic and interharmonic pollution in electrical power delivery systems.…”

“…In order to reduce implementation costs, the authors propose a distributed architecture, based on cost-effective nodes that use the CS strategy. Some papers [22][23][24][25][26][27][28] consider the different methods of compressive sampling and innovative CS algorithms for reconstruction of the PQ interference signal.…”