Renewal models for maneuvering targets

Sworder, David D.; Kent, M.; Vojak, Robert; Hutchins, R.G.

doi:10.1109/7.366300

Cited by 22 publications

(25 citation statements)

References 17 publications

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“…This leads to an (approximate) state-space representation of the renewal model. Simulation results of an application of the renewal model to an agile target that executes constant turns indicates that the performance improvement is not commensurate with the model and algorithm complexity [57], but is significant for an image-enhanced tracking system based on the fusion of a microwave radar and an infrared imaging sensor [59].…”

Section: Semi-markov Jump Process Modelsmentioning

confidence: 98%

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Survey of maneuvering targettracking . part I: dynamic models

Jilkov

2003

IEEE Trans. Aerosp. Electron. Syst.

1,800

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This is the first part of a comprehensive and up-to-date survey of the techniques for tracking maneuvering targets without addressing the so-called measurement-origin uncertainty. It surveys various mathematical models of target motion/dynamics proposed for maneuvering target tracking, including 2D and 3D maneuver models as well as coordinate-uncoupled generic models for target motion. This survey emphasizes the underlying ideas and assumptions of the models. Interrelationships among models and insight to the pros and cons of models are provided. Some material presented here has not appeared elsewhere. CONTENTS

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Section: Semi-markov Jump Process Modelsmentioning

confidence: 98%

“…This is not consistent with the distribution of the durations of practical target motions. To correct this deficiency, it was proposed in [57] that the sojourn time be assumed an independent process having a gamma distribution (see Fig. 5), which includes exponential as a special case with ® = 1.…”

Section: Semi-markov Jump Process Modelsmentioning

confidence: 99%

Survey of maneuvering targettracking . part I: dynamic models

Jilkov

2003

IEEE Trans. Aerosp. Electron. Syst.

1,800

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“…Some studies [13], [14] have used this technique to include heading information in the dynamic equations. Here, the continuous correction of target velocity with segment orientation allows both adaptation to turns and a high degree of smoothing.…”

Section: Curvilinear Modelmentioning

confidence: 99%

“…The best state sequence can be retrieved by keeping track of the argument that maximizes (14) for each k and j. The complete procedure can be described as follows: 1) Initialization:…”

Section: Hmm-based Tracking Techniquesmentioning

confidence: 99%

Comparison of Techniques for Ground Target Tracking

Ke¹,

Herrero²,

Llinas³

2000

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There have been a lot of studies addressing target-tracking problems, in which targets like aircraft and missiles can move freely in the air without hard spatial constraints. Tracking ground targets is a completely different case. Variable terrain structures not only limit the target's moving capability, but also degrade the quality of measurement data. This paper describes an exploratory research project which studied the tracking of a single ground target via traditional and atypical approaches. Traditional Kalman techniques taking into account the additional information provided by the ground restrictions in the tracking process, a road network in our study, were implemented. Additionally, another tracker using the Hidden Markov Model (HMM) with transition array was also developed under the same scenario. The results showed that Kalman techniques with available road information significantly outperform the conventional Kalman approaches in terms of longitudinal and transversal errors at the time when the target maneuvers. The proposed adaptive HMM tracker, composed of some regional HMM trackers, is not sensitive to transversal maneuvers, but may yield large longitudinal errors at the time when the target approaches the boundary of each subscenario.

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“…In [16], for example, modal transitions are modeled with a renewal process. The resulting "memory" this refinement gives the filter can lead to improved performance in both filtering and predictions.…”

Section: Prediction Performancementioning

confidence: 99%

Predicting a hybrid system

Boyd

Sworder

Hutchins

2003

Communications in Information and Systems

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Abstract. Predicting the future state of a hybrid system is made exceptionally difficult by uncertainty in the current operating mode of the plant and the possibility of a plant modal transition during the interval over which a prediction is to be made. Unjustified approximations and errors in the system model typically create large errors in the prediction and, for some applications even worse, yield an unrealistic estimate of prediction error covariance. The effect can be an unreasonably optimistic appraisal of the accuracy of a prediction. In this paper, we document the progress to date of an investigation into hybrid prediction using the recently developed Gaussian Wavelet Estimator. We show that the path-length 2 version of this sophisticated estimator can provide consistent predictions and error covariance estimates when applied to an example ship defense problem.1. Introduction. Hybrid systems have a characteristic state space decomposition connecting a finite dimensional Euclidean space (representing, for example, position and velocity), and a set with S elements (representing the operating regimes). A time-continuous process {x t } in the former, called the base state, is modulated by a modal-state process, which can be specified by an indicator process {φ t } from the discrete set 1 . Hybrid models are useful for representing multimode systems for pur-

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Renewal models for maneuvering targets

Cited by 22 publications

References 17 publications

Survey of maneuvering targettracking . part I: dynamic models

Survey of maneuvering targettracking . part I: dynamic models

Comparison of Techniques for Ground Target Tracking

Predicting a hybrid system

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