Adaptive Modified Unbiased Minimum-Variance Estimation for Highly Maneuvering Target Tracking With Model Mismatch

He, Shuping; Wu, Panlong; Li, Xingxiu; Bo, Yuming; Peng, Yun

doi:10.1109/tim.2023.3239639

Cited by 10 publications

(9 citation statements)

References 41 publications

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“…The first two moments of the conditional probability density function p x k jk; Z k � � can be readily approximated by Equations ( 22), ( 23) and (25). As a result, the filtered estimate and the corresponding estimation error covariance become…”

Section: System Propagationmentioning

confidence: 99%

“…Several filtering schemes have been developed to cope with the lateral manoeuvres of MaRVs [18][19][20][21][22][23][24][25]. However, they have mainly focused on the accuracy of target tracking rather than target identification.…”

Section: Introductionmentioning

confidence: 99%

“…As a remedy for the shortcomings of the MaRV filter, the non-linear Markov process model was suggested [20,25]. To approximate the dynamics of manoeuvre parameters as a Markov process, the aerodynamic coefficients and their derivatives with respect to altitude and velocity were used as prior knowledge about reentry targets.…”

Section: Introductionmentioning

confidence: 99%

“…where z k η k À � is the predicted measurement and P νν,k (η k ) is the residual covariance, which are updated along with the hypothesis sequence η k . Refer to Equation (25) for the definitions of z k andP νν,k . Assuming the Markovian transition, the hypothesis transition probability of Equation ( 30) can be simplified.…”

mentioning

confidence: 99%

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Knowledge‐based multiple hypothesis tracking and identification of manoeuvring reentry targets

Lee

Whang

2023

IET Radar Sonar & Navi

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This paper addresses the integrated tracking and identification problem of a manoeuvring reentry target that performs intentional lateral manoeuvres to disrupt ground radars. Unlike previous approaches, prior knowledge of the lift‐induced drag is incorporated into a new manoeuvring model to describe the reentry target dynamics more explicitly. This model can account for the constraint between lift and drag, which is beneficial in ensuring the reliability of target state estimation. Noticing that the lift‐induced drag is an inherent characteristics of a reentry target that distinguishes the target's identity from others belonging to the same class, the integrated target tracking and identification problem is formulated within the framework of the multiple hypothesis testing about a set of manoeuvring models constructed by different prior knowledge. The proposed approach enables the authors to derive the optimal solution to the given problem in a mathematically rigorous manner. To cope with the real‐time implementation issue, a hypothesis merging strategy is also devised in view of maintaining the target identification performance. Simulation results demonstrate that the proposed scheme provides superior performance and reliability both in target tracking and identification compared to the existing method, despite imperfectness of prior knowledge.

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Section: System Propagationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Knowledge‐based multiple hypothesis tracking and identification of manoeuvring reentry targets

Lee

Whang

2023

IET Radar Sonar & Navi

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“…Kalman filter (KF) has been widely used in engineering applications related to measurement and tracking, such as navigation guidance, signal processing, etc [1,2]. However, KF can only obtain optimal state estimation when both the process and measurement noises obey the Gaussian distribution [3]. In actual industrial processes, due to various factors such * Author to whom any correspondence should be addressed.…”

Section: Introductionmentioning

confidence: 99%

A Gaussian–Pearson type VII adaptive mixture distribution-based outlier-robust Kalman filter

Wang,

Wu,

et al. 2023

Meas. Sci. Technol.

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Considering that existing robust filtering algorithms rely on the selection of initial values of degree of freedom (DOF) parameters in outlier interference environments and cannot effectively cope with unknown non-stationary heavy-tailed measurement noises (HMN), aGaussian–Pearson type VII (PTV) adaptive mixture distribution-based outlier-robust Kalman filter (GPTVMAKF) is proposed. In order to determine whether the current measurement is a normal value or an outlier, a judgment factor subject to the Beta-Bernoulli distribution is introduced. PTV distribution is used to model HMN caused by outliers, and two Gamma distributions are used to model the two different DOF parameters, which can make the PTV distribution have the adaptive adjustment ability. By introducing the inverse Wishart distribution as the prior distribution of the measurement noise covariance, which is adaptively estimated to cope with the unknown time-varying measurement noises. The state and parameters are jointly estimated by variational Bayesian. Finally, the simulation experiments verify that the proposed GPTVMAKF can obtain more accurate state estimation than existing filters in the environments with varying degrees of HMN and unknown non-stationary HMN.

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A novel constrained skew-Gaussian filter and its application to maneuverable reentry vehicle tracking

Wang,

Wang

2024

Aerospace Science and Technology

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Adaptive Modified Unbiased Minimum-Variance Estimation for Highly Maneuvering Target Tracking With Model Mismatch

Cited by 10 publications

References 41 publications

Knowledge‐based multiple hypothesis tracking and identification of manoeuvring reentry targets

Knowledge‐based multiple hypothesis tracking and identification of manoeuvring reentry targets

A Gaussian–Pearson type VII adaptive mixture distribution-based outlier-robust Kalman filter

A novel constrained skew-Gaussian filter and its application to maneuverable reentry vehicle tracking

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