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Cognitive sensor/processor system framework for target tracking

Cognitive sensor/processor system framework for target tracking

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In this chapter we examine the emerging topic of artificial cognition and its application to target tracking. Most sensor/processor systems employ a feed-forward processing chain in which they first perform some low-level processing of received sensor data (such as obtaining target detections) and then pass the processed data on to some higher-level processor (such as a tracker or classifier), which extracts information (such as target kinematic parameters and target type) to achieve a system objective. Tracking and classification can be improved using adaptation between the information extracted from the sensor/processor and the design and transmission of subsequent illuminating waveforms. As such the application of artificial cognition to sensor/processor systems offers much promise for improved sensing and processing [1-7].

Chapter Contents:

  • 8.1 Introduction
  • 8.2 Framework
  • 8.2.1 Cognitive sensor/processor system framework
  • 8.2.2 Cognitive single target tracking
  • 8.2.3 Cognitive MAP-PF single target tracking
  • 8.2.4 Summary
  • 8.3 Distributed sensor example
  • 8.3.1 Model
  • 8.3.2 Implementation
  • 8.3.3 Simulation results
  • 8.4 Software defined radar example
  • 8.4.1 Model
  • 8.4.2 Implementation
  • 8.4.3 Collected data results
  • 8.5 Conclusions
  • 8.6 Appendix A: Bayesian Cramér-Rao lower Bound
  • References

Inspec keywords: sensors; cognition; object detection; target tracking

Other keywords: low-level processing; target type; feedforward processing chain; target tracking; target detections; cognitive sensor; processor system framework; target kinematic parameters; artificial cognition

Subjects: Sensing devices and transducers; Signal processing theory; Signal processing and detection

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