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Target tracking via recursive Bayesian state estimation in cognitive radar networks
- Source :
- Signal Processing. 155:157-169
- Publication Year :
- 2019
- Publisher :
- Elsevier BV, 2019.
-
Abstract
- To cope with complicated environments and stealthier targets, incorporating intelligence and cognition cycles into target tracking is of great importance in modern sensor network management. With remarkable advances in sensor techniques and deployable platforms, a sensing system has freedom to select a subset of available radars, plan their trajectories, and transmit designed waveforms. In this paper, we propose a general framework for single target tracking in cognitive networks of radars, including consideration of waveform design, path planning, and radar selection, which are separately but not jointly taken into account in existing work. The tracking procedure, built on the theories of dynamic graphical models (DGM) and recursive Bayesian state estimation (RBSE), is formulated as two iterative steps: (i) solving a combinatorial optimization problem to select the optimal subset of radars, waveforms, and locations for the next tracking instant, and (ii) acquiring the recursive Bayesian state estimation to accurately track the target. Further, an illustrative example introduces a specific scenario in 2-D space. Simulation results based on the scenario demonstrate that the proposed framework can accurately track the target under the management of the network of radars.
- Subjects :
- Computer science
Bayesian probability
Real-time computing
020206 networking & telecommunications
02 engineering and technology
Tracking (particle physics)
Cognitive network
law.invention
Control and Systems Engineering
law
Signal Processing
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Computer Vision and Pattern Recognition
Motion planning
State (computer science)
Graphical model
Electrical and Electronic Engineering
Radar
Wireless sensor network
Software
Subjects
Details
- ISSN :
- 01651684
- Volume :
- 155
- Database :
- OpenAIRE
- Journal :
- Signal Processing
- Accession number :
- edsair.doi...........0bef4ec2ebd51523c6ed301d74e20d1c