A Novel Subspace Approach for Bearing-Only Target Localization

Luo, Ji-An; Shao, Xuehui; Peng, Dongliang; Zhang, Xiaoping

doi:10.1109/jsen.2019.2919899

Cited by 31 publications

(10 citation statements)

References 28 publications

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“…The tr(CRLB) is minimum when cos θ a b = cos(θ a c ) = cos θ b c = 0. Note that when tr(CRLB) becomes minimum, the FIM becomes diagonal, so the optimal sensor placement is obtained by diagonalizing the FIM [33].…”

Section: Problem Formulationmentioning

confidence: 99%

“…We used the parameters of Case B except P 0 = using(20) and(21), i.e., α = 7.10 • , β = 358.88 • , γ = 22.26, and P 0 can be rewritten as P r 0 = rest of simulation parameters can be obtained from(22), and the tr(CRLB) was computed using(33). The sensor trajectory is shown in Figure5c, and the final angles were θ = −0.03 • and φ = −0.01 • , which matches Configuration 3 (c > b > a) in Table1.…”

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confidence: 99%

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Optimal 3D Angle of Arrival Sensor Placement with Gaussian Priors

Zhou

Chen

Tan

et al. 2021

Entropy

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Sensor placement is an important factor that may significantly affect the localization performance of a sensor network. This paper investigates the sensor placement optimization problem in three-dimensional (3D) space for angle of arrival (AOA) target localization with Gaussian priors. We first show that under the A-optimality criterion, the optimization problem can be transferred to be a diagonalizing process on the AOA-based Fisher information matrix (FIM). Secondly, we prove that the FIM follows the invariance property of the 3D rotation, and the Gaussian covariance matrix of the FIM can be diagonalized via 3D rotation. Based on this finding, an optimal sensor placement method using 3D rotation was created for when prior information exists as to the target location. Finally, several simulations were carried out to demonstrate the effectiveness of the proposed method. Compared with the existing methods, the mean squared error (MSE) of the maximum a posteriori (MAP) estimation using the proposed method is lower by at least 25% when the number of sensors is between 3 and 6, while the estimation bias remains very close to zero (smaller than 0.15 m).

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Section: Problem Formulationmentioning

confidence: 99%

mentioning

confidence: 99%

Optimal 3D Angle of Arrival Sensor Placement with Gaussian Priors

Zhou

Chen

Tan

et al. 2021

Entropy

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“…The parameters to be estimated for the two modes of the TWLAS, as defined by (5) in Section III-A, are different. We denote the FIMs of the two modes by F Mode 1 for Mode 1 and F Mode 2 for Mode 2, respectively.…”

Section: B Crlb Analysismentioning

confidence: 99%

Optimal Two-way TOA Localization and Synchronization for Moving User Devices with Clock Drift

Zhao,

Zhang,

Cui

et al. 2020

Preprint

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In two-way time-of-arrival (TOA) systems, a user device (UD) obtains its position and timing information by roundtrip communications to a number of anchor nodes (ANs) at known locations. Compared with the one-way TOA technique, the two-way TOA scheme is easy to implement and has better localization and synchronization accuracy. Existing two-way TOA localization and synchronization methods assume a stationary UD. This will cause uncompensated position and timing errors. In this article, we propose an optimal maximum likelihood (ML) based two-way TOA localization and synchronization method, namely TWLAS, which takes the UD motion into account to compensate the error caused by the UD movement. We analyze its estimation error and derive the Cramér-Rao lower bound (CRLB). We show that the conventional two-way TOA method is a special case of the TWLAS when the UD is stationary, and the TWLAS has better estimation accuracy than the conventional one-way TOA method. We also derive the estimation error in the case of deviated UD velocity information. Numerical result demonstrates that the estimation accuracy of the new TWLAS for a moving UD reaches CRLB, better than that of the conventional one-way TOA method, and the estimation error caused by the deviated UD velocity information is consistent with the theoretical analysis.

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“…The problem of BOSL is to estimate the source location from a set of noise-corrupted bearing measurements where its main challenge originates in the highly nonlinear nature of the angle observations with the true source position. Under the assumption of Gaussian measurement noise, many estimation approaches have been proposed to handle the nonlinearity, including the grid search method, the pseudolinear estimators [ 6 , 7 ], the iterative maximum likelihood methods [ 8 ] and the subspace approaches [ 9 ]. However, in the complex field environment, the sensor is vulnerable to external interference, enemy attack or node failure.…”

Section: Introductionmentioning

confidence: 99%

A Total Lp-Norm Optimization for Bearing-Only Source Localization in Impulsive Noise with SαS Distribution

Luo

Xue

Rong

et al. 2021

Sensors

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This paper considers the problem of robust bearing-only source localization in impulsive noise with symmetric α-stable distribution based on the Lp-norm minimization criterion. The existing Iteratively Reweighted Pseudolinear Least-Squares (IRPLS) method can be used to solve the least LP-norm optimization problem. However, the IRPLS algorithm cannot reduce the bias attributed to the correlation between system matrices and noise vectors. To reduce this kind of bias, a Total Lp-norm Optimization (TLPO) method is proposed by minimizing the errors in all elements of system matrix and data vector based on the minimum dispersion criterion. Subsequently, an equivalent form of TLPO is obtained, and two algorithms are developed to solve the TLPO problem by using Iterative Generalized Eigenvalue Decomposition (IGED) and Generalized Lagrange Multiplier (GLM), respectively. Numerical examples demonstrate the performance advantage of the IGED and GLM algorithms over the IRPLS algorithm.

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A Novel Subspace Approach for Bearing-Only Target Localization

Cited by 31 publications

References 28 publications

Optimal 3D Angle of Arrival Sensor Placement with Gaussian Priors

Optimal 3D Angle of Arrival Sensor Placement with Gaussian Priors

Optimal Two-way TOA Localization and Synchronization for Moving User Devices with Clock Drift

A Total Lp-Norm Optimization for Bearing-Only Source Localization in Impulsive Noise with SαS Distribution

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