Computer Tracking of Moving Point Targets in Space

Mohanty, Nirode

doi:10.1109/tpami.1981.4767153

“…The scan data is converted to binary images by mapping any sensor return with intensity greater than (5) zero to one. This is done to prevent the possibility of losing dim targets.…”

Section: Target Detection and Clutter Rejectionmentioning

confidence: 99%

Neural network directed Bayes decision rule for moving target classification

Yu

¹

,

Azimi-Sadjadi²

2000

IEEE Trans. Aerosp. Electron. Syst.

14

0

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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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“…Having specified the curvature constraint, the acceptable range of the values of (iz,}z) can be determined using (5). Thus, the range of movements from scan t n + , to t n + Z is limited according to the assumed maximum moving curvature [1].…”

Section: Z -mentioning

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%

See 1 more Smart Citation

<title>Neural-network-directed Bayes-decision rule for moving target classification</title>

Azimi-Sadjadi

¹

,

Yu

²

1999

SPIE Proceedings

1

0

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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.

show abstract

“…A number of automatic target detection schemes have been developed over the past years [1][2][3][4][5][6][7]. Among these schemes are spatio-temporal filtering [2,6], maximum likelihood (ML) estimation [3] and recursive Kalman filtering [7].…”

Section: Introductionmentioning

confidence: 99%