&lt;title&gt;A survey of maneuvering target tracking: approximation techniques for nonlinear filtering&lt;/title&gt;

Li, Xiaorong; Jilkov, Vesselin P.

doi:10.1117/12.553357

Cited by 96 publications

(43 citation statements)

References 118 publications

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“…In most systems, tracking is achieved through sequential localization [3][16] [2] or moving velocity measurement [5] [7], cooperating with target movement modelling, estimation and filtering [1] [6][11] (e.g., Kalman filter [10], Particle filters [9], Bayesian networks [33]). However, model based methods not only bring about a complex system design, but also require some maneuver-related assumptions about the mobile target.…”

Section: Introductionmentioning

confidence: 99%

Tracking with Unreliable Node Sequences

et al. 2009

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Abstract-Tracking mobile targets using sensor networks is a challenging task because of the impacts of in-the-filed factors such as environment noise, sensing irregularity and etc. This paper proposes a robust tracking framework using node sequences, an ordered list extracted from unreliable sensor readings. Instead of estimating each position point separately in a movement trace, we convert the original tracking problem to the problem of finding the shortest path in a graph, which is equivalent to optimal matching of a series of node sequences. In addition to the basic design, multidimensional smoothing is developed to enhance tracking accuracy. Practical system deployment related issues are discussed in the paper, and the design is evaluated with both simulation and a system implementation using Pioneer III Robot and MICAz sensor nodes. In fact, tracking with node sequences provides a useful layer of abstraction, making the design framework generic and compatible with different physical sensing modalities.

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Section: Introductionmentioning

confidence: 99%

Tracking with Unreliable Node Sequences

et al. 2009

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“…where T is the time interval and Φ k|k−1 is the state transition matrix; W k is the process noises at time k. From Equation (8), the discrete state transition matrix can be written as:…”

Section: α-Jerk Modelmentioning

confidence: 99%

Maximum Correntropy Criterion Kalman Filter for α-Jerk Tracking Model with Non-Gaussian Noise

Hou

Zhou

et al. 2017

Entropy

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Abstract:As one of the most critical issues for target track, α-jerk model is an effective maneuver target track model. Non-Gaussian noises always exist in the track process, which usually lead to inconsistency and divergence of the track filter. A novel Kalman filter is derived and applied on α-jerk tracking model to handle non-Gaussian noise. The weighted least square solution is presented and the standard Kalman filter is deduced firstly. A novel Kalman filter with the weighted least square based on the maximum correntropy criterion is deduced. The robustness of the maximum correntropy criterion is also analyzed with the influence function and compared with the Huber-based filter, and, moreover, the kernel size of Gaussian kernel plays an important role in the filter algorithm. A new adaptive kernel method is proposed in this paper to adjust the parameter in real time. Finally, simulation results indicate the validity and the efficiency of the proposed filter. The comparison study shows that the proposed filter can significantly reduce the noise influence for α-jerk model.

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“…To estimate the position, the velocity, and the acceleration of a target, the Kalman filter has been widely used as a tracking filter, but in the presence of a maneuver, its performance may be seriously degraded. In order to have higher tracking accuracy, researchers have developed techniques for solving maneuvering target tracking problems by various approaches [3,4]. Recently, the H ∞ filter has been introduced [5], but few applications in maneuvering target tracking.…”

Section: Introductionmentioning

confidence: 99%

Genetic interacting multiple model algorithm based on H ∞ filter for maneuvering target tracking

Ruan

Pan

2011

Int. J. Control Autom. Syst.

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In order to have a good performance for maneuvering target tracking, a genetic interacting multiple model (GIMM) algorithm based on the H ∞ filter is proposed in this paper. It introduces the H ∞ filter as model-conditional filter, which keeps its robustness by constantly adjusting parameters, to improve the performance and the precision. Meanwhile, it optimizes model probabilities using the genetic algorithm (GA), chooses sub-models which are close to true models from a set of models, adjusts the number of models and parameters in real-time, reduces excessive competition, and improves the performance of the algorithm. The simulation results indicate that, the algorithm has higher tracking accuracy and stronger robustness than the standard IMM algorithm.

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<title>A survey of maneuvering target tracking: approximation techniques for nonlinear filtering</title>

Cited by 96 publications

References 118 publications

Tracking with Unreliable Node Sequences

Tracking with Unreliable Node Sequences

Maximum Correntropy Criterion Kalman Filter for α-Jerk Tracking Model with Non-Gaussian Noise

Genetic interacting multiple model algorithm based on H ∞ filter for maneuvering target tracking

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