Use of measurements from an imaging sensor for precision target tracking

Bar‐Shalom, Yaakov; Shertukde, H.M.; Pattipati, Krishna R.

doi:10.1109/7.40726

Cited by 38 publications

(17 citation statements)

References 9 publications

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“…Nevertheless, when the number of targets is more than two, one can classify each pair of targets first, and then use the likelihood ratio test to make the final decision for all the targets. Using this algorithm, if for targets j and k, (9) …”

Section: Let Us Definementioning

confidence: 99%

“…The operations are done in the frequency domain by passing the image through a bank of directional filters each tuned to extract line features of the tracks with certain orientations. Bar-Shalom, et al [9,10] used two different centroid-based measurements to track targets in forward looking IR images. In a more recent paper [11], Shertukde and Bar-Shalom extended this approach by using the joint probabilistic data association (JPDA) in conjunction with a Kalman state estimator.…”

Section: Introductionmentioning

confidence: 99%

“…These include spatio-temporal filtering [3,4], maximum likelihood (ML) estimation [5,12], recursive Kalman filtering [6][7][8][9][10][11] and neural network-based methods [13]. In [4], a 3-D spatio-temporal filtering scheme is developed.…”

Section: Introductionmentioning

confidence: 99%

“…Several different schemes [3][4][5][6][7][8][9][10][11][12][13] for moving target detection have been developed and applied to IR, radar and sonar imagery data. These include spatio-temporal filtering [3,4], maximum likelihood (ML) estimation [5,12], recursive Kalman filtering [6][7][8][9][10][11] and neural network-based methods [13].…”

Section: Introductionmentioning

confidence: 99%

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Neural network directed Bayes decision rule for moving target classification

Azimi-Sadjadi²

2000

IEEE Trans. Aerosp. Electron. Syst.

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In this paper, a new neural network directed Bayes decision rule is developed for target classification exploiting the dynamic behavior of the target. The system consists of a feature extractor, a neural network directed conditional probability generator and a novel sequential Bayes classifier. The velocity and curvature sequences extracted from each track are used as the primary features. Similar to hidden Markov model (HMM) scheme, several hidden states are used to train the neural network, the output of which is the conditional probability of occurring the hidden states given the observations. These conditional probabilities are then used as the inputs to the sequential Bayes classifier to make the classification. The classification results are updated recursively whenever a new scan of data is received. Simulation results on multiscan images containing heavy clutter are presented to demonstrate the effectiveness of the proposed methods.

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Section: Let Us Definementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Neural network directed Bayes decision rule for moving target classification

Azimi-Sadjadi²

2000

IEEE Trans. Aerosp. Electron. Syst.

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“…The remainder of the experimental setup is identical to the previous two scenarios. In the tracking literature, it is common to model a target manoeuvre as a Gaussian noise process [14,18,24,25,26], and use it as an input into the constant course and speed signal model. This assumption was made for the Kalman filter equations in Chapter 2.…”

Section: Ex~erirnent 3 -Fusine Smooth Track and Measurement Datamentioning

confidence: 99%

Bearings-only tracking using data fusion and instrumental variables

Chan

Rea²

2000

Proceedings of the Third International Conference on Information Fusion

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See and Avoid Using Onboard Computer Vision

Lai¹,

Ford²,

Alvarez³

et al. 2012

Sense and Avoid in UAS

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An unmanned aerial vehicle (UAV), also known as a drone, refers to a pilotless aircraft, a flying machine without an onboard human pilot or passengers. As such, 'unmanned' implies the total absence of a human who directs and actively pilots the aircraft. Control functions for unmanned aircraft may be either onboard or off-board (remote control). That is why the terms remotely operated aircraft (ROA) and remotely piloted vehicle (RPV) are in common use as well [1]. The term UAV has been used for several years to describe unmanned aerial systems. Various definitions have been proposed for this term, like [2]:A reusable 1 aircraft designed to operate without an onboard pilot. It does not carry passengers and can be either remotely piloted or pre-programmed to fly autonomously.Recently, the most reputable international organizations -like the International Civil Aviation Organization (ICAO), EUROCONTROL, the European Aviation Safety Agency (EASA), the Federal Aviation Administration (FAA) -as well as the US Department of Defense (DoD), adopted unmanned aircraft system (UAS) as the correct official term. The changes in acronym are caused by the following aspects:1 The characterization reusable is used to differentiate unmanned aircraft from guided weapons and other munitions delivery systems.

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Use of measurements from an imaging sensor for precision target tracking

Cited by 38 publications

References 9 publications

Neural network directed Bayes decision rule for moving target classification

Neural network directed Bayes decision rule for moving target classification

Bearings-only tracking using data fusion and instrumental variables

See and Avoid Using Onboard Computer Vision

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