Unsupervised Tracking of Maneuvering Vehicles

Hampton, Robert L.; Cooke, James R. H.

doi:10.1109/taes.1973.309767

Cited by 24 publications

(8 citation statements)

References 9 publications

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“…However, neglect of any attempt to model acceleration will imply a preference for constant velocity trajectories . For the formulation in which position and velocity are estimated from range measurements only [19], the dynamics (one dimension only) are given by…”

Section: Singer Model For Target Acceleration Probability Densitymentioning

confidence: 99%

Estimation of aircraft target motion using pattern recognition orientation measurements

Kendrick¹,

Maybeck

Reid

1978

1978 IEEE Conference on Decision and Control Including the 17th Symposium on Adaptive Processes

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Section: Singer Model For Target Acceleration Probability Densitymentioning

confidence: 99%

Estimation of aircraft target motion using pattern recognition orientation measurements

Kendrick¹,

Maybeck

Reid

1978

1978 IEEE Conference on Decision and Control Including the 17th Symposium on Adaptive Processes

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“…Methods directly estimating the optimal gain of the KF instead of the noise CMs were proposed as well . The methods often follow the concept of the Bayesian or the correlation methods.…”

Section: Discussion and Comparison Of Noise CM Estimation Methodsmentioning

confidence: 99%

Noise covariance matrices in state‐space models: A survey and comparison of estimation methods—Part I

Duník

Straka

Kost

et al. 2017

Adaptive Control & Signal

118

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This paper deals with the estimation of the noise covariance matrices of systems described by state-space models. Stress is laid on the systematic survey and classification of both the recursive and batch processing methods proposed in the literature with a special focus on the correlation methods. Besides the correlation methods, representatives of other groups are introduced also with respect to their basic idea, estimate properties, assumptions and possible extensions, and user-defined parameters. Common and dual properties of the methods are highlighted, and a simulation comparison using exemplary MATLAB implementations of the methods is provided. KEYWORDSadaptive systems, noise covariance matrix, state estimation, state-space model, system identification INTRODUCTIONKnowledge of a system model is a key prerequisite for many state estimation, signal processing, fault detection, and optimal control problems. The model is often designed to be consistent with random behaviour of the system quantities and properties of the measurements. While the deterministic part of the model often arises from mathematical modelling on the basis of physical, chemical, or biological laws governing the behaviour of the system, the statistics of the stochastic part are often difficult to find by the modelling and have to be identified using the measured data. Incorrect description of the noise statistics may result in significant worsening of estimation, signal processing, detection, or control quality or even in a failure of the underlying algorithms.In the last 5 decades, therefore, a significant research interest has been focused on a design of the methods for the estimation of the properties of the stochastic part of the model. The attention has been devoted to both the input-output models 1-5 and the state-space (SS) models 6-14 and both recursive and batch processing methods. This paper focuses on the methods estimating the properties of the stochastic part of the system described by an SS model discrete in time. In particular, the methods estimating the covariance matrices (CMs) † of noises in the state and measurement equation from a sequence of measured data are of interest. The methods are further denoted as the noise CM estimation methods.

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“…If this ratio exceeds the threshold, then HI is accepted and the null hypothesis Ho: AV(k) < 0 (5.11) is rejected because it has "low level of significance." Hypothesis H1 is accepted usually only if the significance level of H. is less than 5 A new tracking scheme that incorporates a simple Kalman filter and a detector was presented. The detector requires a minimum amount of computation and memory.…”

Section: State Estimationmentioning

confidence: 99%

“…Spingarn and Weidemann [15] presented a linear regression filter for tracking maneuvering targets. An adaptive filtering algorithm to track maneuvering vehicles was developed by Hampton and Cooke [5].The acceleration during maneuver was considered to be correlated in time as in Singer [14]. The filter adjusts its gain to account for a maneuver using a stochastic approximation approach.…”

mentioning

confidence: 99%

Variable Dimension Filter for Maneuvering Target Tracking

Bar‐Shalom

Birmiwal

1982

IEEE Trans. Aerosp. Electron. Syst.

286

106

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A novel approach to tracking a maneuvering target is developed. This approach does not rely on a statistical description of the maneuver as a random process. Instead, the state model for the target is changed by introducing extra state components when a maneuver is detected. The maneuver, modeled as an acceleration, is estimated recursively. The performance of this estimator is shown to be superior to a recent algorithm presented by Chan et al. that handles the maneuver by estimating it as an unknown input. A significant departure from the current practice of comparison of algorithms is made: a recently introduced rigorous statistical methodology is used in the comparison of these estimators.

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Unsupervised Tracking of Maneuvering Vehicles

Cited by 24 publications

References 9 publications

Estimation of aircraft target motion using pattern recognition orientation measurements

Estimation of aircraft target motion using pattern recognition orientation measurements

Noise covariance matrices in state‐space models: A survey and comparison of estimation methods—Part I

Variable Dimension Filter for Maneuvering Target Tracking

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