Maneuvering target tracking using jump processes

Lim, Sang Seok; Farooq, M.

doi:10.1109/cdc.1991.261779

Cited by 11 publications

(11 citation statements)

References 19 publications

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“…Xk+1 FCVXk + GWk (17) The above models (1 1) and (1 7) are known as the continuous-and discrete-time constant-velocity (CV) models, or more precisely "nearly-constant-velocity models," respectively. Note that the control input u = 0 in the nonmaneuvering models, although the actual thrust of the target has to be present to maintain the motion.…”

Section: Nonmaneuver Target Dynamic Modelsmentioning

confidence: 99%

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<title>Survey of maneuvering target tracking: dynamic models</title>

2000

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This is the first part of a series of papers that provide a comprehensive and up-to-date survey of the problems and techniques of tracking maneuvering targets in the absence of the so-called measurement-origin uncertainty. It surveys the various mathematical models of target dynamics proposed for maneuvering target tracking, including 2D and 3D maneuver models as well as coordinate-uncoupled generic models for target dynamics. This survey emphasizes the underlying ideas and assumptions of the models. Interrelationships among the models surveyed and insight to the pros and cons of the models are provided. Some material presented here has not appeared elsewhere.

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Section: Nonmaneuver Target Dynamic Modelsmentioning

confidence: 99%

“…for the unknown input u is a discrete-time, finite-mode (semi-)Markov process. A continuous-time counterpart was proposed in [17]. A sojourn-time dependent Markov chain model for target motion is proposed in [18] in the context of the multiple-model approach.…”

Section: Semi-markov Jump Process Modelsmentioning

confidence: 99%

<title>Survey of maneuvering target tracking: dynamic models</title>

2000

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“…The Gaussian premise stipulates that such drastic changes are very unlikely, producing large estimation errors when they occur. More sophisticated models, which include a jump process in addition to the original Gaussian process such as the jump diffusion models or Markov/semi-Markov jump models, show improved inference results for manoeuvring targets [32,14]. In contrast, we utilise in this paper an α-stable Lévy process as the driving noise, which consists of fewer parameters and leads to competitive inference performance.…”

Section: B Related Workmentioning

confidence: 99%

Lévy State-Space Models for Tracking and Intent Prediction of Highly Maneuverable Objects

Gan

Ahmad

Godsill

2021

IEEE Trans. Aerosp. Electron. Syst.

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In this paper, we present a Bayesian framework for manoeuvring object tracking and intent prediction using novel α-stable Lévy state-space models, expressed in continuous time as Lévy processes. In contrast to conventional (fully) Gaussian formulations, the proposed models are driven by heavy-tailed αstable noise and are thus much more able to capture extreme values/behaviours. This can better characterise sharp changes in the state, which may be induced by sudden and frequent manoeuvres such as swift turns or abrupt accelerations. In particular, they are represented in a conditionally Gaussian series form which ensures the tractability of the applied inference algorithms. A corresponding estimation strategy with the Rao-Blackwellised particle filter is then proposed and an efficient intent inference procedure is introduced. Here, the underlying intent, driving the target's long-term behaviour (e.g. reaching its final destination), is modelled as a latent variable. Real vessel data from maritime surveillance and human computer interactions (e.g. cursor data from motor-impaired interface users) are utilised to demonstrate the effectiveness of the proposed approach. It is shown to deliver noticeable improvements in the tracking and intent prediction performance (whenever relevant) compared with a more conventional Gaussian dynamic model.

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“…The approximation (25) preserves the autocorrelation function of the random process z whenever a single evasive maneuver is expected 25 and tracks a piecewise-constant input provided that the value of Q a is chosen to be sufficiently large. 26 However, it is known that the introduction of the jerk process in the estimation process degrades filtering of the Gaussian noise in the original system. The matrices of the linear system employed by the Kalman filter augmented with a Wiener process acceleration model (F,G 1 ,G 2 ,H , andQ w ) are provided in Appendix A.…”

Section: B the Bank Of State Estimatorsmentioning

confidence: 99%

Decision-Directed Adaptive Estimation and Guidance for an Interception Endgame

Dionne¹,

Michalska²,

Shinar³

et al. 2006

Journal of Guidance, Control, and Dynamics

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A new integrated estimation and guidance design approach is presented as a computationally effective procedure for interception of maneuvering targets. This is an adaptive approach that uses the following elements: banks of state estimators, and guidance laws, a maneuver detector for the onset of the target's maneuver, and a hierarchical decision law for online selection of the estimator/guidance law pair to be employed. Simulation results confirm that the adaptive approach leads to a reduction in the miss distance as compared with cases in which only a single estimator/guidance law combination is available.

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Maneuvering target tracking using jump processes

Cited by 11 publications

References 19 publications

<title>Survey of maneuvering target tracking: dynamic models</title>

<title>Survey of maneuvering target tracking: dynamic models</title>

Lévy State-Space Models for Tracking and Intent Prediction of Highly Maneuverable Objects

Decision-Directed Adaptive Estimation and Guidance for an Interception Endgame

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