Joint target tracking and classification using radar and ESM sensors

Challa, Subhash; Pulford, G.W.

doi:10.1109/7.953266

Cited by 129 publications

(81 citation statements)

References 6 publications

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“…Correctly identifying and evaluateing the target's RCS, more accurate information can be provided for the command and guidance systems to precisely attack the most threatening targets, or provide more help for RCS reduction scientists [1]- [8].…”

Section: Introductionmentioning

confidence: 99%

“…The data is collected through a commercial tracking device, communicating to the computer. In the existing lab, Vision tracking was accomplished by using a joystick to track the target maneuvers as shown in figure (1). To track while measurement process, in order to suppress the unwanted man made error, an online nonlinear controller for nonlinear tracking should be used [9], [10].…”

Section: Introductionmentioning

confidence: 99%

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Modeling and Automating of RCS’s Lab Tracker

Galal¹,

Wang²

2016

IJMO

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Abstract-Radar cross section measurement must be precise and accurate especially for tracking small radar cross section targets (e.g. tracks on Fighters); other targets of interest may be aircraft, tanks, or ships. As a consequence, the need of having more accurate evaluation for the Radar Cross Section (RCS) of the targets is required. This paper describes and demonstrates the modeling/automation of the RCS's antenna holder movements. In order to suppress the unwanted manmade error, fuzzy logic controller was used, to have better target tracking while measurement process. Taking into account the fuzzy nature of human decision making processes and real-time properties, this paper has established different fuzzy controllers then tested and compared with a crisp decision system. The first one has been developed using an expert knowledge, the second one was learned from the recorded videos.Index Terms-Fuzzy logic control, multi-input/ multi output, nonlinear system, RCS lab measurements enhancement, and video guidance.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling and Automating of RCS’s Lab Tracker

Galal¹,

Wang²

2016

IJMO

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“…Classification information can assist in correct data association and false tracks elimination in multiple target tracking systems. The notion of joint tracking and classification (JTC) was introduced by Challa and Pulford in [1].…”

Section: Introductionmentioning

confidence: 99%

“…Grid-based and Monte Carlo methods are representatives of this tendency. Challa and Pulford [1] suggest a grid-based algorithm for JTC using ESM and radar data. However, the computational efficiency of the grid-based algorithms depends on the state vector dimension.…”

Section: Introductionmentioning

confidence: 99%

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Joint target tracking and classification with particle filtering and mixture Kalman filtering using kinematic radar information

Angelova

Mihaylova

2006

Digital Signal Processing

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This paper considers the problem of joint maneuvering target tracking and classification. Based on recently proposed Monte Carlo techniques, a multiple model (MM) particle filter and a mixture Kalman filter (MKF) are designed for two-class identification of air targets: commercial and military aircraft. The classification task is carried out by processing radar measurements only, no class (feature) measurements are used. A speed likelihood function for each class is defined using a prior information about speed constraints. Class-dependent speed likelihoods are calculated through the state estimates of each class-dependent tracker. They are combined with the kinematic measurement likelihoods in order to improve the classification process. The two designed estimators are compared and evaluated over rather complex target scenarios. The results demonstrate the usefulness of the proposed scheme for the incorporation of additional speed information. Both filters illustrate the opportunity of the particle filtering and mixture Kalman filtering to incorporate constraints in a natural way, providing reliable tracking and correct classification. Future observations contain valuable information about the current state of the dynamic systems. In the framework of the MKF, an algorithm for delayed estimation is designed for improving the current modal state estimate. It is used as an additional, more reliable information in resolving complicated classification situations.

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Multitarget Tracking and Classification in Collaborative Sensor Networks via Sequential Monte Carlo Methods

Vercauteren

Wang

2010

Handbook on Array Processing and Sensor Networks

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Joint target tracking and classification using radar and ESM sensors

Cited by 129 publications

References 6 publications

Modeling and Automating of RCS’s Lab Tracker

Modeling and Automating of RCS’s Lab Tracker

Joint target tracking and classification with particle filtering and mixture Kalman filtering using kinematic radar information

Multitarget Tracking and Classification in Collaborative Sensor Networks via Sequential Monte Carlo Methods

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