In this chapter we aim to exploit experience of the natural world in which echolocating mammals are able to detect and classify objects with apparent ease. These observations suggest that waveform diversity and orientation strategies play an important role. It is this hypothesis that we test and show to be valid, as confirmed through real-world radar experiments. Specifically, there is additional information contained in different perspectives of a target that can help classify it, thus boosting performance. There is also a law of diminishing returns, in that the opportunity to extract additional new information reduces as the number of perspectives increases.

Chapter Contents:

• 7.1 Introduction
• 7.2 Biologically inspired NCTR
• 7.2.1 Waveform design
• 7.2.2 Nectar-feeding bats and bat-pollinated plants
• 7.2.3 Classification of flowers
• 7.2.3.1 Data collection
• 7.2.3.2 Data pre-processing and results
• 7.2.4 Classification of insects
• 7.3 Acoustic micro-Doppler
• 7.3.1 Description of the acoustic radar
• 7.3.2 Experimentation
• 7.3.3 Classification performance results
• 7.4 Multi-aspect NCTR
• 7.4.1 Data preparation
• 7.4.2 Feature extraction
• 7.4.3 Multi-perspective classifiers
• 7.4.4 Multi-perspective classification performance
• 7.5 Summary
• References

Inspec keywords: image classification; object detection; radar detection; radar cross-sections; waveform analysis; echo; radar target recognition

Other keywords: biologically inspired target recognition; echolocating mammals; waveform diversity; orientation strategies; multiperspective target recognition; object detection; real-world radar experiment; object classification; natural world

Subjects: Mathematical analysis; Image recognition; Radar equipment, systems and applications