Target tracking for a correlated multi-sensor system with constant time delay

Wang, Qilong; Li, Jianyong

doi:10.1177/0959651817721619

Cited by 3 publications

(6 citation statements)

References 30 publications

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“…There are target tracking motivated techniques that are applicable to other problems, such as tracking missiles using a radar, surveillance of sensitive buildings and systems, navigation, robots and unmanned vehicle guidance, machine vision, and collision avoidance and obstacles. 3,4 In recent years, the research of maneuvering target tracking has been a hot issue in the radar field. The problem of moving target tracking using noisy sensor measurement has been addressed by several researchers over the past decades.…”

Section: Introductionmentioning

confidence: 99%

“…These target tracking filters must be robust against modeling noise and uncertainties. 3 As an example, weapons guidance represents an example of an autonomous tracking filter application based on the interaction with sensors, and control process is a complex problem. It is generally a subset based on recursive estimation.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the estimation of the parameters depends mainly on the state of the target, its environment, and the noise measurement of each sensor. 3 The concept of data fusion in sensor networks is to take advantage of the maximum amount of information gathered about the data, to reduce the weaknesses of some with the help of others. Indeed, it is interesting to be able to jointly use several redundant or complementary sources of information.…”

Section: Introductionmentioning

confidence: 99%

“…When aircrafts are tracked using radar stations, the distance between the sensor and the target is essentially only one sensor resolution cell. 3 As a result, each target generates at most a measure of range, bearing, and velocity. Target tracking establishes the state of moving objects in a given time interval from observations of the objects with some uncertainty.…”

Section: Introductionmentioning

confidence: 99%

“…Target tracking establishes the state of moving objects in a given time interval from observations of the objects with some uncertainty. 3,4 A state can be one or more random processes that completely describes the behavior of the moving objects at any moment of the considered time interval. The motion of objects is described mathematically by one or more dynamic models that characterize the motion of these objects through the observations of the objects that are made with sensors.…”

Section: Introductionmentioning

confidence: 99%

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A new filtering strategy for target tracking application using the second form of Smooth Variable Structure Filter

Demim

Rouigueb

Nemra

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part I:

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Target tracking process consists of tracking and measuring a quantity over time, which is a difficult estimation problem. The latter gives erroneous measurements that are affected by the unknown number of targets to be tracked, as well as, measurements that are corrupted by noise. Thus, overcoming the target tracking problem includes basically solving the filtering problem which leads to a data association problem. Tracking problems that force lightly nonlinear models are solved appropriately with the Extended Kalman Filter. Although, problems that use nonlinear models, applying analytical expressions of the Jacobian of dynamic systems, or measurement vector functions are potentially solved by the Unscented Kalman Filter. The first technique is well built, but not precise, while the second technique yields very accurate results, but in terms of complexity, it is not suitable for real-time applications. Another filter called Particle Filter used a Monte Carlo method based on probability distribution over the state is proposed. Subsequently, we used a Gaussian Mixture Model which is based on the Expectation Maximization algorithm for fitting mixture-of-Gaussian models is considered to guarantee the estimation of optimal parameters set for each target. In order to achieve better trade between optimality and robustness of the tracking effect, the Smooth Variable Structure Filter is proposed as an alternative. This latter filter does not make any assumptions about noise characteristics and provides accurate estimation results. Furthermore, in this article, we propose a new target tracking algorithm based on the second form of Smooth Variable Structure Filter, which is implemented in a simulation using different scenarios. Experimental scenarios, under realistic conditions, are presented and the obtained results confirm the effectiveness of the Smooth Variable Structure Filter approach compared to other filters.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A new filtering strategy for target tracking application using the second form of Smooth Variable Structure Filter

Demim

Rouigueb

Nemra

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part I:

View full text Add to dashboard Cite

show abstract

Time Delay Error Online Correction of LiDAR-IMU System Through MSCKF Integrated DLRNN Method

Liu,

Li,

et al. 2024

IEEE/ASME Trans. Mechatron.

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Design and Research of Computer Network Education and Teaching System Based on Multisensor Information Fusion

2022

Mobile Information Systems

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With the rapid development of information technology, information network has become the fundamental guarantee of social progress. The network is the basic material foundation of the information society. It is ubiquitous, and human activities are highly dependent on it. Education is also inseparable from the Internet. Online learning is emerging as a new form of basic education. At the same time, it needs to be considered that the information received by the sensor is always incomplete, reflecting the information measured in a particular part of the object but not understanding the total content. Therefore, it is necessary to use multiple sensors to identify an object from multiple angles, obtain additional dimensional information about the object, and combine multidimensional information. This paper introduces the design and implementation of a teaching network system for computer network courses, starting from the overall concept of the system, the selection of development methods and the specific implementation plan of the system, and then introduces the specific content of each module in the teaching system in detail. The recognition success rates of five groups of experiments based on multisensor information fusion are 96.9%, 95.7%, 98.4%, 96.3%, and 98.7%, respectively.

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Target tracking for a correlated multi-sensor system with constant time delay

Cited by 3 publications

References 30 publications

A new filtering strategy for target tracking application using the second form of Smooth Variable Structure Filter

A new filtering strategy for target tracking application using the second form of Smooth Variable Structure Filter

Time Delay Error Online Correction of LiDAR-IMU System Through MSCKF Integrated DLRNN Method

Design and Research of Computer Network Education and Teaching System Based on Multisensor Information Fusion

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