Feature Aided Switching Model Set Approach for Maneuvering Target Tracking

Fan, Jianpeng; Zhu, Yanbo; Fan, Sisi; Fan, Hongqi; Fu, Qiang

doi:10.2528/pierb12082803

Cited by 4 publications

(4 citation statements)

References 19 publications

(25 reference statements)

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“…The above expression of (almost) axisymmetric beam wave has been inspired by several works in theoretical physics on internal gravity wave beams, in particular the group of Akylas, see for instance [33,21]. As explained in [34], laboratory experiments on internal wave beams are realized by means of a beam wave generator, that is for instance a cylinder oscillating at a fixed frequency in a uniform stratification (see Figure 2).…”

Section: Figure 1 Coordinate Systemsmentioning

confidence: 99%

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Reflection of internal gravity waves in the form of quasi-axisymmetric beams

Bianchini¹,

Paul²

2022

Preprint

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Preservation of the angle of reflection when an internal gravity wave hits a sloping boundary generates a focusing mechanism if the angle between the direction of propagation of the incident wave and the horizontal is close to the slope inclination (near-critical reflection). We establish a rigorous analysis of this phenomenon in two space dimensions, by providing an explicit description of the form of the leading approximation of the unique Leray solution to the near-critical reflection of internal waves from a slope within a certain (nonlinear) time-scale. More precisely, we construct a consistent and Lyapunov stable approximate solution, L 2 -close to the Leray solution, in the form of a beam wave.Besides, beams being physically more meaningful than plane waves, their spatial localization plays a key role in improving the previous mathematical results from a twofold viewpoint: 1) our beam wave approximate solution is the sum of a finite number of terms, each of them is a consistent solution to the system and there is no artificial/non-physical corrector; 2) thanks to 1) and the special structure of the nonlinear term, we can improve the expansion of our solution up to two next orders, so providing a more accurate approximation leading to a longer consistency time-scale.Finally, our results provide a set of initial conditions localized on rays, for which the Leray solution maintains approximately in L 2 the same localization. Contents 1 3 , ω ∼ ± sin γ. 3.2. Proof of Proposition 3.1 3.3. Linear analysis in different regimes 4. The weakly nonlinear system 4.1. Interactions of type (a) 4.2. Interactions of type (b) 4.3. Proof of Proposition 4.1 5. Higher-order Approximation 5.1. Proof of Theorem 2.10 Appendix A. Proof of Lemma 2.4 Appendix B. A useful lemma

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Section: Figure 1 Coordinate Systemsmentioning

confidence: 99%

“…To conclude, the beam wave approach is crucial in this work and it marks a strong difference with respect to [1]: besides the physical relevance of beam waves ( [33,21]), it allows to characterize the Leray solution in the physical space, to obtain consistency and to improve the accuracy of the approximate solution.…”

Section: Figure 1 Coordinate Systemsmentioning

confidence: 99%

Reflection of internal gravity waves in the form of quasi-axisymmetric beams

Bianchini¹,

Paul²

2022

Preprint

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“…As an approximate implementation of PHD filter, the particle PHD filter is a promising candidate for MTT problems [10,21]. The diagram of operations in the implementation of particle PHD filter is depicted in Figure 1, where, {x…”

Section: Analysis Of Time Delay In Hardware Implementation Of Particlmentioning

confidence: 99%

Threshold-Based Resampling for High-Speed Particle PHD Filter

Shi¹,

Zheng²,

Bian³

et al. 2013

PIER

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Abstract-In recent years, particle probability hypothesis density (PHD) filtering has become an active research topic for multiple targets tracking in dense clutter scenarios. However, it is highly required to improve the real-time performance of particle PHD filtering because it is a kind of Monte Carlo approach and the computational complexity is very high. One of major difficulties to improve the realtime performance of particle PHD filtering lies in that, resampling, which is usually a sequential process, is crucial to the fully-parallel implementation of particle PHD filter. To overcome this difficulty, this paper presents a novel threshold-based resampling scheme for the particle PHD filter, in which the particle weights are all set below a proper threshold. This specific threshold is determined using a distinguishing feature of the particle PHD filters: The weight sum of all particles in weight update is equal to the total target number in the current iteration. This proposed resampling scheme allows the use of fully-pipelined architecture in the hardware design of particle PHD filter. Theoretical analysis indicates that the particle PHD filter employing the proposed resampling technique can reduce the time complexity by 33% around in a typical multi-target tracking (MTT) scenario compared with that employing the traditional systematic resampling technique, while simulation results show that it can maintain the almost same performance of estimation accuracy.

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“…The main problem of MTT is how to deal with the unknown fast change in the maneuvering acceleration. Many techniques and methods [2][3][4][5] have been suggested to solve the problem during last years. Acceleration modeling techniques [6], input estimation (IE) techniques [7] and multiple-model (MM) methods [8,9] are three main approaches.…”

Section: Introductionmentioning

confidence: 99%

Absolute Adaptive Cs Model and Modified Strong Tracking Unscented Filter for High Maneuvering Target Tracking

Zhou¹,

Jin-mang²,

Liu³

2013

PIER C

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Abstract-Absolute adaptive current statistical (AACS) model and modified strong tracking unscented filter (MSTUF) are proposed for maneuvering target tracking (MTT) under nonlinear measurement in this paper. The key point of the AACS model is to associate the instantaneous acceleration variance with some elements of state covariance matrix by constructing acceleration increment models of the acceleration limit and acceleration mean in the CS model, while the maneuvering frequency can adjust itself according to the change of the measurement residual. MSTUF is proposed for high maneuver tracking under nonlinear measurement by incorporating the modified strong tracking filter (STF) into the unscented filter (UF). Since the state covariance, process noise covariance and maneuvering frequency can adjust themselves jointly according to the residual, the proposed algorithm, called the AACS-MSTUF, has a good performance on both maneuver and non-maneuver. Simulation results indicate that the overall performance of the proposed algorithm is better than the interacting multiple-model unscented filter (IMM-UF), UF and original strong tracking unscented filter (STUF) based on the CS model (CS-STUF) when considering tracking accuracy, stability, convergence and computational complexity.

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Feature Aided Switching Model Set Approach for Maneuvering Target Tracking

Cited by 4 publications

References 19 publications

Reflection of internal gravity waves in the form of quasi-axisymmetric beams

Reflection of internal gravity waves in the form of quasi-axisymmetric beams

Threshold-Based Resampling for High-Speed Particle PHD Filter

Absolute Adaptive Cs Model and Modified Strong Tracking Unscented Filter for High Maneuvering Target Tracking

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