Biased Estimation Properties of the Pseudolinear Tracking Filter

Aidala, Vincent J.; Nardone, S.

doi:10.1109/taes.1982.309250

Cited by 163 publications

(65 citation statements)

References 5 publications

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“…In practice, a system designer may be constrained in their choice of parameter estimator. Likely, the estimation technique used in practice will be biased [21], [22]. The Cramer-Rao bound for unbiased estimators is still an interesting benchmark with which intuitively pleasing results and performance measures can be derived.…”

Section: B Discussion On the Cramer-rao Boundmentioning

confidence: 99%

Doppler Shift Target Localization

Shames

Bishop

Smith³

et al. 2013

IEEE Trans. Aerosp. Electron. Syst.

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This is an author produced version of a paper published in IEEE Transactions on Aerospace and Electronic Systems.This paper has been peer-reviewed but does not include the final publisher proofcorrections or proceedings pagination.© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Abstract-This paper outlines the problem of doppler-based target position and velocity estimation using a sensor network. The minimum number of doppler shift measurements at distinct generic sensor positions to have a finite number of solutions, and later, a unique solution for the unknown target position and velocity is stated analytically. Furthermore, we study the same problem where not only doppler shift measurements are collected, but also other types of measurements are available, e.g. bearing or distance to the target from each of the sensors. Later, we study the Cramer-Rao inequality associated with the doppler-shift measurements to a target in a sensor network, and use the Cramer-Rao bound to illustrate some results on optimal placements of the sensors when the goal is to estimate the velocity of the target. Some simulation results are presented in the end.

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Section: B Discussion On the Cramer-rao Boundmentioning

confidence: 99%

Doppler Shift Target Localization

Shames

Bishop

Smith³

et al. 2013

IEEE Trans. Aerosp. Electron. Syst.

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“…2 Pseudolinear filter formulations and modified polar coordinate filters are proposed by Aidala et al, 1,3 attempting to improve filter convergence and remove biases, and comparisons of performance for different filtering algorithms are explored by Nardone et al…”

Section: Ic Literature Reviewmentioning

confidence: 99%

Trajectory Optimization for Target Localization Using Small Unmanned Aerial Vehicles

Ponda

Kolacinski

Frazzoli

2009

AIAA Guidance, Navigation, and Control Conference

121

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Small unmanned aerial vehicles (UAVs) equipped with navigation and video capabilities can be used to perform target localization. Combining UAV state estimates with image data leads to bearing measurements of the target that can be processed to determine its position. This 3-D bearings-only estimation problem is nonlinear and traditional filtering methods are prone to biases, noisy estimates, and filter instabilities. The performance of the target localization is highly dependent on the vehicle trajectory, motivating the development of optimal UAV trajectories. This work presents methods for designing trajectories that increase the amount of information provided by the measurements and shows that these trajectories lead to enhanced estimation performance.The Fisher Information Matrix (FIM) is used to quantify the information provided by the measurements. Several objective functions based on the FIM are considered and the A-optimality criterion is shown to be the best suited for trajectory optimization in the 3-D bearings-only target localization problem. The resulting trajectories produce spirals, which increase the angular separation between measurements and reduce the range to the target, supporting geometric intuition. The problem of simultaneous target estimation and vehicle trajectory optimization is explored and the resulting algorithms produce vehicle trajectories that increase the information provided by the measurements, enhancing the target estimation performance by increasing accuracy, reducing uncertainty and improving filter convergence.

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“…Toyon Research Corporation,75 Aero Camino Suite A,Goleta,CA,93117 8. PERFORMING ORGANIZATION REPORT NUMBER…”

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confidence: 99%

“…Another algorithm, dubbed the pseudo-linear least-squares (PLLS), is obtained through some manipulation of the measurement equation and has very good convergence properties for stationary targets (Aidala, Nardone) [7]. However, the PLLS does lead to a biased estimator [8]. In this algorithm the set of pseudo-linear measurements are linear in the Cartesian state.…”

Section: Supplementary Notesmentioning

confidence: 99%

See 1 more Smart Citation

Tracking and interception of ground-based RF sources using autonomous guided munitions with passive bearings-only sensors and tracking algorithms

Ezal

Agate

2004

Acquisition, Tracking, and Pointing XVIII

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This paper considers the problem of tracking and intercepting a potentially moving ground-based RF source with an autonomous guided munition that has a passive bearings-only sensor located on its nose. It is assumed that the munition has lost GPS signal lock and that it relies only on its noisy inertial measurement unit (IMU) for guidance and navigation. Bearings-only target motion analysis (TMA) algorithms are used to obtain a position and velocity estimate for the RF source using the position, velocity and attitude estimates of the munition as well as the azimuth and elevation measurements obtained from the bearings-only RF sensor. Six degree-of-freedom (6-DOF) and three degree-of-freedom (3-DOF) munition models are used to evaluate the tracking and intercept/seeker performance of a hybrid coordinate (HC) extended Kalman filter (EKF), a particle filter (PF), a multiple hypothesis (MH) HC-EKF, and a pseudo-linear least squares (PLLS) filter.

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Biased Estimation Properties of the Pseudolinear Tracking Filter

Cited by 163 publications

References 5 publications

Doppler Shift Target Localization

Doppler Shift Target Localization

Trajectory Optimization for Target Localization Using Small Unmanned Aerial Vehicles

Tracking and interception of ground-based RF sources using autonomous guided munitions with passive bearings-only sensors and tracking algorithms

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