Target Classification And Tracking Using Acoustic Micro-Doppler Signatures

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Micro-Doppler is a perturbation on an echo returned from a target which results from the movement of its component parts such as wheels on vehicles or swinging arms or legs on personnel. A great deal of information can potentially therefore be gained from analysing Micro-Doppler returns from a target which has been illuminated by radio frequency (eg radar) or acoustic wavelength radiation.This study aims to investigate the processing techniques which may be applied to acoustic micro-Doppler signature (uDS) data. Specifically, methods to extract, classify and track the uDS of individual targets from the background clutter and non-target backscatter signals will be developed.UCL has carried out extensive work in the area of uDS based target recognition using radar data in recent years. This has resulted in new algorithms and techniques which can be used in identifying and classifying targets. This work has particularly concentrated on identifying personnel and vehicle targets against the returns from the background environment. The work has been carried out in close collaboration with Thales Aerospace and has dealt with field data obtained by both Thales and UCL using personnel detecting radar. Much of this work could potentially be mapped on to the acoustic region and this proposal presents a study to examine how the knowledge gained using radar data can be used in the very different frequency ranges and propagation conditions that exist in the acoustic regime.An acoustic camera will be used to record audio and video data from a scene. Signal characterisation will then be performed using theoretical models and techniques developed using radar data in the previous work. Micro-Doppler classification techniques will be adapted to the acoustic regime, in addition to new methods, which may be suitable for the potentially longer acquisition times at acoustic frequencies. Tracking algorithms will then be applied to the target returns and methods to automate the entire detection and tracking process will be examined.The end result of the work should be a system that can detect, classify and track a range of targets based on their acoustic uDS returns in a range of different environments.