scholarly journals A Compressive Sensing-Based Dynamic Estimation Algorithm in Unified Laser TTC System

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 29963-29972
Author(s):  
Jianping An ◽  
Lichen Zhu ◽  
Xiangyuan Bu ◽  
Kai Yang
Keyword(s):  
Compressive Sensing ◽  
Estimation Algorithm ◽  
Dynamic Estimation

scholarly journals Exploiting Fine-Grained Subcarrier Information for Device-Free Localization in Wireless Sensor Networks

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3110 ◽  
Author(s):  
Yan Guo ◽  
Dongping Yu ◽  
Ning Li
Keyword(s):  
Compressive Sensing ◽  
Estimation Algorithm ◽  
Surface Model ◽  
Frequency Diversity ◽  
Fine Grained ◽  
Area Of Interest ◽  
Radio Signals ◽  
Device Free ◽  
Target Locations ◽  
Spatial Sparsity

Device-free localization (DFL) that aims to localize targets without carrying any electronic devices is addressed as an emerging and promising research topic. DFL techniques estimate the locations of transceiver-free targets by analyzing their shadowing effects on the radio signals that travel through the area of interest. Recently, compressive sensing (CS) theory has been applied in DFL to reduce the number of measurements by exploiting the inherent spatial sparsity of target locations. In this paper, we propose a novel CS-based multi-target DFL method to leverage the frequency diversity of fine-grained subcarrier information. Specifically, we build the dictionaries of multiple channels based on the saddle surface model and formulate the multi-target DFL as a joint sparse recovery problem. To estimate the location vector, an iterative location vector estimation algorithm is developed under the multitask Bayesian compressive sensing (MBCS) framework. Compared with the state-of-the-art CS-based multi-target DFL approaches, simulation results validate the superiority of the proposed algorithm.

scholarly journals Two-Dimensional Direction of Arrival (DOA) Estimation for Rectangular Array via Compressive Sensing Trilinear Model

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Huaxin Yu ◽  
Xiaofeng Qiu ◽  
Xiaofei Zhang ◽  
Chenghua Wang ◽  
Gang Yang
Keyword(s):  
Compressive Sensing ◽  
Direction Of Arrival ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Two Dimensional ◽  
Rectangular Array ◽  
Angle Estimation ◽  
Estimation Performance ◽  
Trilinear Model ◽  
2D Doa Estimation

We investigate the topic of two-dimensional direction of arrival (2D-DOA) estimation for rectangular array. This paper links angle estimation problem to compressive sensing trilinear model and derives a compressive sensing trilinear model-based angle estimation algorithm which can obtain the paired 2D-DOA estimation. The proposed algorithm not only requires no spectral peak searching but also has better angle estimation performance than estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm. Furthermore, the proposed algorithm has close angle estimation performance to trilinear decomposition. The proposed algorithm can be regarded as a combination of trilinear model and compressive sensing theory, and it brings much lower computational complexity and much smaller demand for storage capacity. Numerical simulations present the effectiveness of our approach.

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