A multi-sensor approach to airport surface traffic tracking

Stauffer, Donald R.; French, H. B.; Lenz, James; Rouse, Gordon F.

doi:10.1109/dasc.1993.283512

Cited by 4 publications

(4 citation statements)

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“…Where, T is the interval of sampling. Motion state vector of the aircraft is , and observation matrix is In the period when aircraft is not perceptible, it maintains CV for about 30 seconds at the velocity obtained from step (3) and then operates to the next detection node.…”

Section: Preparation For Simulationmentioning

confidence: 99%

“…The study of surface moving surveillance system based on event-driven non-cooperative can fundamentally solve the above-mentioned defects. Honeywell developed a dual infrared/magnetic sensor, and thousands of such sensors are equipped at airports for detection of the aircraft [3] . Chartier et al proposed that the position of the aircraft could be determined though the information of coil sensor installed on the boundary of the airfield pavement segmentation [4] .…”

Section: Introductionmentioning

confidence: 99%

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A Method of Target Tracking and Prediction Based on Geomagnetic Sensor Technology

Tang¹,

Shangfeng²,

Han³

et al. 2015

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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In view of the inherent defects in current airport surface surveillance system, this paper proposes an asynchronous target-perceiving-event driven surface target surveillance scheme based on the geomagnetic sensor technology. Furthermore, a surface target tracking and prediction algorithm based on I-IMM is given, which is improved on the basis of IMM algorithm in the following aspects: Weighted sum is performed on the mean of residual errors and model probabilistic likelihood function is reconstructed, thus increasing the identification of a true motion model; Fixed model transition probability is updated with model posterior information, thus accelerating model switching as well as increasing the identification of a model. In the period when a target is non-perceptible, prediction of target trajectories can be implemented through the target motion model identified with I-IMM algorithm. Simulation results indicate that I-IMM algorithm is more effective and advantageous in comparison with the standard IMM algorithm.

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Section: Preparation For Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Method of Target Tracking and Prediction Based on Geomagnetic Sensor Technology

Tang¹,

Shangfeng²,

Han³

et al. 2015

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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show abstract

“…Honeywell has developed a dual infrared/ magnetic sensor and arranged a set of these sensors to detect aircraft. 3 Chartier and Hashemi 4 proposed that the position of an aircraft could be determined through information from loop sensors located at segment boundaries. Gao et al 5 proposed to detect a magnetic target using a magnetic sensor network and to transfer the resulting data to A-SMGCS.…”

Section: Introductionmentioning

confidence: 99%

Improved interacting multiple model algorithm airport surface target tracking based on geomagnetic sensors

Tang

Zhao

Shangfeng

2020

International Journal of Distributed Sensor Networks

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To avoid the inherent defects of current airport surface surveillance systems, a distributed non-cooperative surface surveillance scheme based on geomagnetic sensor technology is proposed in this article. Furthermore, a surface target tracking algorithm based on improved interacting multiple model (WIMM) is presented for use when the target is perceptible. In this algorithm, the weighted sum of the mean values of the residual errors, which is used to reconstruct the model probabilistic likelihood function, and the Markov model transition probability are updated using posterior information. When a target is imperceptible, its trajectory can be predicted by the target identified motion model and the adaptive model transition probability. Simulation results show that the WIMM algorithm can be used efficiently together with an observed small sample of velocity information for target tracking and trajectory prediction. Compared with the interacting multiple model and residual-mean interacting multiple model algorithms, the frequency of model switching and the rate of model identification were increased during the imperceptible period, and target prediction error was greatly reduced.

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“…Distributed non-cooperative sensors only cover a local area, so they usually provide more accurate information at a high-update rate. Stauffer [3] proposed a measurement programme for collecting data from the magnetic and infrared signatures of aircraft and other vehicles on airport surfaces. These data suggested that short range sensors could facilitate the detection and tracking of vehicles.…”

Section: Introductionmentioning

confidence: 99%

Runway incursion prevention method based on a discrete object sensing event‐driven model

Tang

Xing

Han

2014

IET Intelligent Transport Systems

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A multi-sensor approach to airport surface traffic tracking

Cited by 4 publications

References 0 publications

A Method of Target Tracking and Prediction Based on Geomagnetic Sensor Technology

A Method of Target Tracking and Prediction Based on Geomagnetic Sensor Technology

Improved interacting multiple model algorithm airport surface target tracking based on geomagnetic sensors

Runway incursion prevention method based on a discrete object sensing event‐driven model

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