An accurate AOA localization method based on unreliable sensor detection

2022

Entropy

Self Cite

This paper addresses the problem of robust angle of arrival (AOA) target localization in the presence of uniformly distributed noise which is modeled as the mixture of Laplacian distribution and uniform distribution. Motivated by the distribution of noise, we develop a localization model by using the ℓp-norm with 0≤p<2 as the measurement error and the ℓ1-norm as the regularization term. Then, an estimator for introducing the proximal operator into the framework of the alternating direction method of multipliers (POADMM) is derived to solve the convex optimization problem when 1≤p<2. However, when 0≤p<1, the corresponding optimization problem is nonconvex and nonsmoothed. To derive a convergent method for this nonconvex and nonsmooth target localization problem, we propose a smoothed POADMM estimator (SPOADMM) by introducing the smoothing strategy into the optimization model. Eventually, the proposed algorithms are compared with some state-of-the-art robust algorithms via numerical simulations, and their effectiveness in uniformly distributed noise is discussed from the perspective of root-mean-squared error (RMSE). The experimental results verify that the proposed method has more robustness against outliers and is less sensitive to the selected parameters, especially the variance of the measurement noise.

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust AOA-Based Target Localization for Uniformly Distributed Noise via ℓp-ℓ1 Optimization

Chen

Wang

2022

Entropy

Self Cite

“…As a result, the estimated AOAs at each node will deviate significantly from the true values. Such outliers have been found to be detrimental to the PLE [ 7 , 8 ]. Thus, it is important to identify these erroneous data to improve the localization performance or perform a repair of the data.…”

Section: Introductionmentioning

confidence: 99%

“…The intersection points (IPs)-based method [ 11 ] calculates the source position by taking the centroid of the set of intersections obtained by pairs of bearing lines; however, this method cannot significantly improve the localization performance, even eliminating the IPs obtained by two bearing lines close to parallel. The proposed unreliable AOA detection method in [ 7 ] can improve the localization accuracy; however, many threshold parameters need to be set. The steered-response power phase transform (SRP-PHAT) [ 12 ] source localization approaches have demonstrated robustness when operating in reverberant and noisy environments.…”

Section: Introductionmentioning

confidence: 99%

An Outlier Detection Method Based on Mahalanobis Distance for Source Localization

Chen

Strycker

2018

Sensors

Self Cite

This paper addresses the problem of localization accuracy degradation caused by outliers of the angle of arrival (AOA). The problem of outlier detection of the AOA is converted into the detection of the estimated source position sets, which are obtained by the proposed division and greedy replacement method. The Mahalanobis distance based on robust mean and covariance matrix estimation method is then introduced to identify the outliers from the position sets. Finally, the weighted least squares method based on the reliable probabilities and distances is proposed for source localization. The simulation and experimental results show that the proposed method outperforms representative methods when unreliable AOAs are present.

“…Different measurement models are defined to localize the source, such as time of arrival (TOA) [ 10 , 11 , 12 , 13 ], time difference of arrival (TDOA) [ 14 , 15 , 16 , 17 ], angle of arrival (AOA) [ 18 ], received signal strength (RSS) [ 19 , 20 , 21 ], received signal energy [ 5 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ], distance measurement (DM) [ 31 ], and a combination of part of them [ 32 , 33 ]. The range information between sensor nodes and the source is reflected in TOA, TDOA, and RSS, while the angular information of the emitting signal relative to self-nodes is reflected in AOA.…”

Section: Introductionmentioning

confidence: 99%

Efficient Convex Optimization for Energy-Based Acoustic Sensor Self-Localization and Source Localization in Sensor Networks

Wang

Shen

et al. 2018

Sensors

The energy reading has been an efficient and attractive measure for collaborative acoustic source localization in practical application due to its cost saving in both energy and computation capability. The maximum likelihood problems by fusing received acoustic energy readings transmitted from local sensors are derived. Aiming to efficiently solve the nonconvex objective of the optimization problem, we present an approximate estimator of the original problem. Then, a direct norm relaxation and semidefinite relaxation, respectively, are utilized to derive the second-order cone programming, semidefinite programming or mixture of them for both cases of sensor self-location and source localization. Furthermore, by taking the colored energy reading noise into account, several minimax optimization problems are formulated, which are also relaxed via the direct norm relaxation and semidefinite relaxation respectively into convex optimization problems. Performance comparison with the existing acoustic energy-based source localization methods is given, where the results show the validity of our proposed methods.