Optimal 3D Angle of Arrival Sensor Placement with Gaussian Priors

Zhou, Rongyan; Chen, Jianfeng; Tan, Weijie; Yan, Qingli; Cai, Chang

doi:10.3390/e23111379

Cited by 6 publications

(6 citation statements)

References 33 publications

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“…A few papers analyze the problem of optimizing sensor placement for multilateration problems. This is done in 2 dimensions [62], [131], [132] and in 3 dimensions [98]. One [132] determines that the optimal positions for three sensors is in an equilateral triangle.…”

Section: ) Optimized Sensor Placementmentioning

confidence: 99%

A Review of Linear Multilateration Techniques and Applications

Widdison,

Long

2024

IEEE Access

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In multilateration, distances between an unknown location and several known points are used to determine the location of the unknown point. Papers on algorithms for multilateration, such as time of arrival (TOA), time difference of arrival (TDOA), and angle of arrival (AOA), have proliferated in the literature. These use the same equations, but are often written in a way that hides the underlying geometry and make it difficult to see the similarity in approaches between different papers. This paper provides a review of TOA and AOA-based localization approaches, with a focus on linear positioning algorithms. This paper presents simple and consistent versions of TOA, TDOA, and AOA position equations with intuitive explanations for how those equations relate to the geometry of the localization problem. This perspective can make it easier to implement these algorithms in a way that produces stable results and provides insight into the underlying geometric problem. We also review the literature relating to TDOA and AOA localization algorithms and multistatic radar.

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Section: ) Optimized Sensor Placementmentioning

confidence: 99%

A Review of Linear Multilateration Techniques and Applications

Widdison,

Long

2024

IEEE Access

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“…In three-dimensional space, the vectors of the bearing-only sensor node pair and the target are

and

. By utilizing the AOA measurement method [ 26 ], it is possible to derive the spatial coordinates of the target.

when

and

, the target line of sight of the sensor node pair coincides, the system of equations has no solution.…”

Section: System Modelmentioning

confidence: 99%

Distributed Cubature Information Filtering Method for State Estimation in Bearing-Only Sensor Network

Chen,

Fu,

Zhang

et al. 2024

Entropy

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The problem of state estimation based on bearing-only sensors is increasingly important while existing research on distributed filtering solutions is rather limited. Therefore, this paper proposed the novel distributed cubature information filtering (DCIF) method for addressing the state estimation challenge in bearing-only sensor networks. Firstly, the system model of the bearing-only sensor network was constructed, and the observability of the system was analyzed. The sensor nodes are paired to measure relative angle information. Subsequently, the coordinated consistency theory is employed to achieve a unified state estimation of the maneuvering target. The DCIF method enhances the observability of the system, addressing the issues of large accuracy errors and divergence in traditional nonlinear filtering algorithms. Building upon the theoretical proof of consistency convergence in DCIF, four simulation experiments were conducted for comparison. These experiments validate the effectiveness and superiority of the DCIF method in bearing-only sensor networks.

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“…Using s 0 = (x 0 , y 0 , z 0 ) T as a reference, and {θ k , φ k } denotes the bearing measurement with azimuth and elevation angle in spherical coordinates [28]. The azimuth angle measurement of the kth sensor, takes the form…”

Section: Problem Formulationmentioning

confidence: 99%

Sensor Selection for Optimal Target localization with 3-D Angle of Arrival Estimation in Underwater Wireless Sensor Networks

Zhou

Chen

Tan

et al. 2022

JMSE

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This paper investigates a sensor selection scheme for optimal target localization with three-dimensional (3-D) Angle of Arrival (AOA) estimation in Underwater Wireless Sensor Networks (UWSN). Specifically, we present a new 3-D AOA-based localization measurement model, which considers correlated noises and Gaussian priors. The trace of Cramer–Rao lower bound (CRLB) for the 3-D AOA measurement model is derived by introducing a special vector to denote the selected sensors with the azimuth and elevation angle measurements. Based on the presenting expressions of the CRLB, we formulate the sensor selection problem as an optimization problem, which has been transformed into the semidefinite problem program by convex relaxation, and a randomization method is adopted to improve the quality of the SDP solution. Simulation results illustrate that the proposed method receives better estimation performance over the reference methods and approaches the exhaustive search method.

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Optimal 3D Angle of Arrival Sensor Placement with Gaussian Priors

Cited by 6 publications

References 33 publications

A Review of Linear Multilateration Techniques and Applications

A Review of Linear Multilateration Techniques and Applications

Distributed Cubature Information Filtering Method for State Estimation in Bearing-Only Sensor Network

Sensor Selection for Optimal Target localization with 3-D Angle of Arrival Estimation in Underwater Wireless Sensor Networks

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