Trajectory Optimization Using Cramér-Rao Lower Bound for Bearings-Only Target Tracking

Roh, Hyunchul; Cho, Min-Hyun; Tahk, Min-Jea

doi:10.2514/6.2018-1591

Cited by 8 publications

(5 citation statements)

References 23 publications

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“…The A-optimality criterion maximizes the decrease in the error covariance matrix trace rather than the inverse of the determinant of the covariance matrix because the latter is computationally expensive [ 17 ]. The A-optimality criterion has been used to determine optimal flight paths for multiple UAVs using AOA measurements to localize targets in 2D [ 31 , 38 , 39 , 43 , 50 ] and 3D [ 51 , 52 ].…”

Section: Related Workmentioning

confidence: 99%

Optimal Maneuvering for Autonomous Vehicle Self-Localization

McGuire

Law

Doğançay

et al. 2022

Entropy

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We consider the problem of optimal maneuvering, where an autonomous vehicle, an unmanned aerial vehicle (UAV) for example, must maneuver to maximize or minimize an objective function. We consider a vehicle navigating in a Global Navigation Satellite System (GNSS)-denied environment that self-localizes in two dimensions using angle-of-arrival (AOA) measurements from stationary beacons at known locations. The objective of the vehicle is to travel along the path that minimizes its position and heading estimation error. This article presents an informative path planning (IPP) algorithm that (i) uses the determinant of the self-localization estimation error covariance matrix of an unscented Kalman filter as the objective function; (ii) applies an l-step look-ahead (LSLA) algorithm to determine the optimal heading for a constant-speed vehicle. The novel algorithm takes into account the kinematic constraints of the vehicle and the AOA means of measurement. We evaluate the performance of the algorithm in five scenarios involving stationary and mobile beacons and we find the estimation error approaches the lower bound for the estimator. The simulations show the vehicle maneuvers to locations that allow for minimum estimation uncertainty, even when beacon placement is not conducive to accurate estimation.

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Section: Related Workmentioning

confidence: 99%

Optimal Maneuvering for Autonomous Vehicle Self-Localization

McGuire

Law

Doğançay

et al. 2022

Entropy

View full text Add to dashboard Cite

show abstract

“…The A-optimality criterion minimizes the trace of the inverse FIM, which minimizes the mean squared error (MSE) of the estimates. The Aoptimality criterion has been used to determine optimal flight paths for multiple UAVs using AoA measurements to localize a stationary target [20,21] or a moving target [22][23][24], and for 3D AoA target localization [25,26]. The diversity of the eigenvalues of the FIM has been used as an alternative criterion for the optimal placement of sensors for target position estimation [27].…”

Section: Related Workmentioning

confidence: 99%

Optimal Beacon Placement for Self-Localization Using Three Beacon Bearings

et al. 2020

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Autonomous vehicles need to localize themselves within the environment in order to effectively perform most tasks. In situations where a Global Navigation Satellite System such as the Global Positioning System cannot be used for localization, other methods are required. One self-localization method is to use signals transmitted by beacons at known locations to determine the relative distance and bearing of the vehicle from the beacons. Estimation performance is influenced by the beacon–vehicle geometry and the investigation into the optimal placement of beacons is of interest to maximize the estimation performance. In this article, a new solution to the optimal beacon placement problem for self-localization of a vehicle on a two-dimensional plane using angle-of-arrival measurements is proposed. The inclusion of heading angle in the estimation problem differentiates this work from angle-of-arrival target localization, making the optimization problem more difficult to solve. First, an expression of the determinant of the Fisher information matrix for an arbitrary number of beacons is provided. Then, a procedure for analytically determining the optimal angular separations for the case of three beacons is presented. The use of three beacons is motivated by practical considerations. Numerical simulations are used to demonstrate the optimality of the proposed method.

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“…The aim of the trajectory optimisation is to determine optimal manoeuvres that minimise target localisation error and also respect the physical constraints given in (2). To accomplish the aim, this paper formulates a discrete-time trajectory optimisation problem which is denoted as CTO 1 .…”

Section: B Problem Formulationmentioning

confidence: 99%

Trajectory Optimization for Target Localization With Bearing-Only Measurement

Shin

Tsourdos

2019

IEEE Trans. Robot.

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This paper considers the problem of twodimensional (2D) constrained trajectory optimisation of a pointmass aerial robot for constant-manoeuvring target localisation using bearing-only measurement. A performance metric that can be utilised in trajectory optimisation to maximise target observability is proposed first based on geometric conditions. One-step optimal manoeuvre that maximises the observability criterion is then derived analytically for moving targets. The heading angle constraint is also incorporated in the proposed optimal manoeuvre derivation to support practical application. Numerical simulations with some comparisons are presented to validate the analytical findings.

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Trajectory Optimization Using Cramér-Rao Lower Bound for Bearings-Only Target Tracking

Cited by 8 publications

References 23 publications

Optimal Maneuvering for Autonomous Vehicle Self-Localization

Optimal Maneuvering for Autonomous Vehicle Self-Localization

Optimal Beacon Placement for Self-Localization Using Three Beacon Bearings

Trajectory Optimization for Target Localization With Bearing-Only Measurement

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