SPRT-Based track confirmation and rejection

Li, X.R.; Li, Ning; Jilkov, Vesselin P.

doi:10.1109/icif.2002.1020914

Cited by 12 publications

(5 citation statements)

References 7 publications

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“…In terms of tracking, the SPRT calculates the joint probability density of the measurements associated to a track for the hypothesis that all the measurements are target originated and for the hypothesis that all the measurements are false. It then takes the ratio of the two probability densities and either rejects the track if the ratio is below a threshold, confirms the track if the ratio is above a threshold or continues to gather more information as new measurements are received until the track can be confirmed or rejected [6]. The SPRT is used in a variety of tracking algorithms including the Multiple Hypothesis Tracker (MHT) [7].…”

Section: Introductionmentioning

confidence: 99%

The Integrated Probabilistic Data Association Filter Adapted to Lie Groups

Petersen

Beard

2023

IEEE Trans. Aerosp. Electron. Syst.

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The Integrated Probabilistic Data Association Filter (IPDAF) is a target tracking algorithm based on the Probabilistic Data Association Filter that calculates a statistical measure that indicates if an estimated representation of the target properly represents the target or is generated from non-target-originated measurements. The main contribution of this paper is to adapt the IPDAF to constant velocity target models that evolve on connected, unimodular Lie groups, and where the measurements are also defined on a Lie group. We present an example where the methods developed in the paper are applied to the problem of tracking a ground vehicle on the special Euclidean group SE(2).

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Section: Introductionmentioning

confidence: 99%

The Integrated Probabilistic Data Association Filter Adapted to Lie Groups

Petersen

Beard

2023

IEEE Trans. Aerosp. Electron. Syst.

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“…If it is above a threshold, it is confirmed. If it is between the two thresholds, no decision is made and new measurements are continually added until a decision can be made [6]. The SPRT based track confirmation and rejection method is used in a variety of tracking algorithms including the Multiple Hypothesis Tracker (MHT) [7].…”

Section: Introductionmentioning

confidence: 99%

The Integrated Probabilistic Data Association Filter Adapted to Lie Groups

Petersen¹,

Beard²

2021

Preprint

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The Integrated Probabilistic Data Association Filter is a target tracking algorithm based on the Probabilistic Data Association Filter (PDAF) that calculates a statistical measure that indicates if a track should be rejected or confirmed to represent a target. The main contribution of this paper is to adapt the IPDA filter to target models that evolve on connected unimodular Lie groups, and where the measurements models also involve a Lie group. The paper contains a high level introduction to Lie groups, and then shows applications of the theory to tracking a car from an overhead UAV using camera information.

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“…For a binary hypothesis testing problem with independent and identically distributed observations, the SPRT optimally minimizes the average number of observations that are required to reach a decision [21]. The SPRT has been successfully used in many different applications including target detection and tracking [22][23][24][25][26]. In the sensor manager considered here, the SPRT is used within each discrete grid cell location to make a decision about whether a target is or is not present in the cell based on the data that has been observed.…”

Section: Introductionmentioning

confidence: 99%

Managing landmine detection sensors: results from application to AMDS data

Kolba

Collins

2007

SPIE Proceedings

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Previous work by the authors using information-based sensor management for static target detection has utilized a probability of error performance metric that assumes knowledge of the number of targets present in a grid of cells. Using this probability of error performance metric, target locations are estimated as the N cells with the largest posterior state probabilities of containing a target. In a realistic application, however, the number of targets is not known a priori. The sequential probability ratio test (SPRT) developed by Wald is therefore implemented within the previously developed sensor management framework to allow cell-level decisions of "target" or "no target" to be made based on the observed sensor data. Using these cell-level decisions, more traditional performance metrics such as probability of detection and probability of false alarm may then be calculated for the entire region of interest.The resulting sensor management framework is implemented on a large set of data from the U.S. Army's autonomous mine detection sensors (AMDS) program that has been collected using both ground penetrating radar (GPR) and electromagnetic induction (EMI) sensors. The performance of the sensor manager is compared to two different direct search techniques, and the sensor manager is found to achieve the same P d performance at a lower cost than either of the direct search techniques. Furthermore, uncertainty in the sensor performance characteristics is also modeled, and the use of uncertainty modeling allows a higher P d to be obtained than is possible when uncertainty is not modeled within the sensor management framework.

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SPRT-Based track confirmation and rejection

Cited by 12 publications

References 7 publications

The Integrated Probabilistic Data Association Filter Adapted to Lie Groups

The Integrated Probabilistic Data Association Filter Adapted to Lie Groups

The Integrated Probabilistic Data Association Filter Adapted to Lie Groups

Managing landmine detection sensors: results from application to AMDS data

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