Modeling of target motion constrained on straight line

Duan, Zhansheng; Li, X. Rong

doi:10.1109/taes.2015.140970

Cited by 18 publications

(7 citation statements)

References 19 publications

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“…This has formed the majority of extensions of the model-driven framework, e.g., a huge number of works for noise covariance estimation [17], but also formed new challenges to the real time implementation due to escalated computational requirement. More importantly, it is unclear how to optimally use some important but fuzzy information such as a linguistic context that the target moves close to a straight line, which might not be easily defined as constraints [14], [15], [35]- [37] and [2,Chapter 6]. This class of information is very common and useful to targets like aircraft, satellites, large cruise ships, and trains, which are supposed to move on pre-defined runways.…”

Section: A Challenges To Hmmmentioning

confidence: 99%

Joint Smoothing and Tracking Based on Continuous-Time Target Trajectory Function Fitting

Chen

Sun

et al. 2019

IEEE Trans. Automat. Sci. Eng.

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We present a continuous time state estimation framework that unifies traditionally individual tasks of smoothing, tracking, and forecasting (STF), for a class of targets subject to smooth motion processes, e.g., the target moves with nearly constant acceleration or affected by insignificant noises. Fundamentally different from the conventional Markov transition formulation, the state process is modeled by a continuous trajectory function of time (FoT) and the STF problem is formulated as an online data fitting problem with the goal of finding the trajectory FoT that best fits the observations in a sliding timewindow. Then, the state of the target, whether the past (namely, smoothing), the current (filtering) or the near-future (forecasting), can be inferred from the FoT. Our framework releases stringent statistical modeling of the target motion in real time, and is applicable to a broad range of real world targets of significance such as passenger aircraft and ships which move on scheduled, (segmented) smooth paths but little statistical knowledge is given about their real time movement and even about the sensors. In addition, the proposed STF framework inherits the advantages of data fitting for accommodating arbitrary sensor revisit time, target maneuvering and missed detection. The proposed method is compared with state of the art estimators in scenarios of either maneuvering or non-maneuvering target.

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Section: A Challenges To Hmmmentioning

confidence: 99%

Joint Smoothing and Tracking Based on Continuous-Time Target Trajectory Function Fitting

Chen

Sun

et al. 2019

IEEE Trans. Automat. Sci. Eng.

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“…In many practical scenarios, the state estimation is usually expected to be localized in some particular subspace [23,24,25]. For example, in a blind multiuser detection problem, it is necessary to ensure that the target value associated with the desired user is located in the specific searching space [23].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Set Membership Filter with State Estimation Constraints via Consensus-ADMM

Li¹,

Zhang²,

Wang³

et al. 2022

Preprint

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This paper considers the state estimation problem for nonlinear dynamic systems with unknown but bounded noises. Set membership filter (SMF) is a popular algorithm to solve this problem. In the set membership setting, we investigate the filter problem where the state estimation requires to be constrained by a linear or nonlinear equality. We propose a consensus alternating direction method of multipliers (ADMM) based SMF algorithm for nonlinear dynamic systems. To deal with the difficulty of nonlinearity, instead of linearizing the nonlinear system, a semi-infinite programming (SIP) approach is used to transform the nonlinear system into a linear one, which allows us to obtain a more accurate estimation ellipsoid. For the solution of the SIP, an ADMM algorithm is proposed to handle the state estimation constraints, and each iteration of the algorithm can be solved efficiently. Finally, the proposed filter is applied to typical numerical examples to demonstrate its effectiveness.

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“…Generally, trajectory tracking and estimation are fulfilled through a filter, which combines measured data received from the radar and that of the reference model [2]. Accordingly, the reference model has a significant impact on the estimation accuracy of the trajectory [3], [4]. This is especially more pronounced for state variables, which cannot be measured directly.…”

Section: Introductionmentioning

confidence: 99%

Aerodynamic Statistics-Based Trajectory Estimation of Hypersonic Maneuvering Target

et al. 2020

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Modeling of target motion constrained on straight line

Cited by 18 publications

References 19 publications

Joint Smoothing and Tracking Based on Continuous-Time Target Trajectory Function Fitting

Joint Smoothing and Tracking Based on Continuous-Time Target Trajectory Function Fitting

Nonlinear Set Membership Filter with State Estimation Constraints via Consensus-ADMM

Aerodynamic Statistics-Based Trajectory Estimation of Hypersonic Maneuvering Target

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