Adaptive Strong Tracking Square-Root Cubature Kalman Filter for Maneuvering Aircraft Tracking

Zhang, Haowei; Xie, Junwei; Ge, Jiaang; Lu, Wenlong; Zong, Binfeng

doi:10.1109/access.2018.2808170

Cited by 53 publications

(36 citation statements)

References 26 publications

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“…The smoothing performance index is derived using (35). We assume that x o,k contains noise with variance B x and the target moves with a constant jerk because the α-β-γ-δ filter uses the constant-jerk motion model, as indicated in (4). Thus, the true motion assumed in (35) is…”

Section: Derivation Of Smoothing Performance Indexmentioning

confidence: 99%

“…Here, to calculate the steady-state bias error (mean error), we assume that the mean error in x o,k becomes zero because of the white Gaussian noise. Thus, X o (z) = Z[s c (kT) 4 /24] is satisfied. By substituting (A13) and (A11) into (A14), we obtain…”

Section: Conflicts Of Interestmentioning

confidence: 99%

“…In monitoring systems for aircraft, robots, and intelligent cars using remote sensors such as radar and cameras, the accurate tracking of moving objects is essential. For such applications, Kalman filter- [1][2][3][4] and Bayesian filter-based trackers [5] are commonly used to track the position, velocity, and acceleration of the targets. However, in recent years, the importance of jerk, which is the derivative of acceleration with respect to time, has been realized to address tracking problems [4,6,7].…”

Section: Introductionmentioning

confidence: 99%

“…For such applications, Kalman filter- [1][2][3][4] and Bayesian filter-based trackers [5] are commonly used to track the position, velocity, and acceleration of the targets. However, in recent years, the importance of jerk, which is the derivative of acceleration with respect to time, has been realized to address tracking problems [4,6,7]. In [4,6], it was proved that the use of a high-dimensional parameter such as jerk can improve the performance of the tracking filters for highly maneuvering targets.…”

Section: Introductionmentioning

confidence: 99%

“…However, in recent years, the importance of jerk, which is the derivative of acceleration with respect to time, has been realized to address tracking problems [4,6,7]. In [4,6], it was proved that the use of a high-dimensional parameter such as jerk can improve the performance of the tracking filters for highly maneuvering targets. Thus, the consideration of jerk in tracking filters is promising.…”

Section: Introductionmentioning

confidence: 99%

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Correct Stability Condition and Fundamental Performance Analysis of the α-β-γ-δ Filter

Shibata

Saho

2018

Applied Sciences

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This paper theoretically analyzes the fundamental performance of a fourth-order steady-state moving object tracking filter, called an α - β - γ - δ filter. The α - β - γ - δ filter considers estimations of jerk (time-derivative of acceleration) in the motion of targets. First, regarding the stability conditions of the α - β - γ - δ filter, we prove that there are unstable cases even when the conventionally derived stability conditions are satisfied, and then we derive the correct stability conditions. Next, we analytically derive performance indices that indicate the steady-state errors for targets with typical motions. Based on the derived indices, the optimal performance of the α - β - γ - δ filter is theoretically analyzed and compared with that of the traditional second- and third-order steady-state tracking filters, i.e., α - β and α - β - γ filters. Numerical analyses and simulations are used to verify the advantages and disadvantages of the α - β - γ - δ filter over the above-mentioned filters. The practicality of the use of jerk for the tracking filtering problem is revealed in this paper.

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Section: Derivation Of Smoothing Performance Indexmentioning

confidence: 99%

Section: Conflicts Of Interestmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Correct Stability Condition and Fundamental Performance Analysis of the α-β-γ-δ Filter

Shibata

Saho

2018

Applied Sciences

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Robust adaptive Kalman filter for structural performance assessment

Yi,

Su,

Tang

2024

Intl J Robust & Nonlinear

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SummaryStructural performance assessment is a critical stage in the health monitoring for the maintenance and risk management of engineering structures, in which the interstory drift is a key indicator. In this paper, a robust adaptive Kalman filter is proposed for an interstory drift estimation problem to show the health condition of steel structures in the case that the statistics or internal dynamics describing the signals and measurements are not known precisely. More precisely, we build an adaptive current Jerk model (ACJM) where the model parameters are updated in each time step to presuppose the statistics characterization of the steel dynamic, while the unknown measurement noise covariance is adapted based on a fixed‐lag innovation with respect to measurements. Moreover, a robust adaptive Kalman filter is designed for the modelling uncertainties in each time increment by solving a minimax game: one player tries to select an “actual” model far from the proposed ACJM with an exponential decay tolerance, while its “hostile” player, namely, the optimum filter, is designed by minimizing the estimation error according to the selected “actual” model. Finally, some simulation and experimental results show the effectiveness of the proposed algorithm.

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Trajectory Tracking of Maneuvering Reentry Vehicle Using Nonlinear Estimate Model

Cheng

Jia

et al. 2023

Lecture Notes in Electrical Engineering

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Adaptive Strong Tracking Square-Root Cubature Kalman Filter for Maneuvering Aircraft Tracking

Cited by 53 publications

References 26 publications

Correct Stability Condition and Fundamental Performance Analysis of the α-β-γ-δ Filter

Correct Stability Condition and Fundamental Performance Analysis of the α-β-γ-δ Filter

Robust adaptive Kalman filter for structural performance assessment

Trajectory Tracking of Maneuvering Reentry Vehicle Using Nonlinear Estimate Model

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