Target Classification And Tracking Using Acoustic Micro-Doppler Signatures

微多普勒是由目标的组成部分（如车辆上的车轮或人员上摆动的手臂或腿）的运动所引起的对目标回波的扰动。因此，通过分析被无线电频率（如雷达）或声波波长辐射照射的目标的微多普勒回波，有可能获得大量信息。本研究旨在探讨应用于声学微多普勒特征（uDS）数据的处理技术。具体而言，将开发从背景杂波和非目标后向散射信号中提取、分类和跟踪单个目标uDS的方法。近年来，伦敦大学学院在利用雷达数据进行基于uDS的目标识别领域开展了大量工作。这导致了新的算法和技术，可用于识别和分类目标。这项工作特别侧重于根据背景环境的回波确定人员和车辆目标。这项工作是与泰利斯航空航天公司密切合作进行的，并处理了泰利斯和伦敦大学学院使用人员探测雷达获得的现场数据。这项工作的大部分都有可能被映射到声学区域，本提案提出了一项研究，以检查使用雷达数据获得的知识如何在声学系统中存在的非常不同的频率范围和传播条件下使用。声学摄像机将用于记录现场的音频和视频数据。然后将使用理论模型和先前工作中利用雷达数据开发的技术进行信号表征。除了新方法之外，微多普勒分类技术还将适应声学系统，这些方法可能适用于声学频率下可能较长的采集时间。然后将跟踪算法应用于目标退货，并检查自动化整个检测和跟踪过程的方法。这项工作的最终结果应该是一个系统，可以根据目标在一系列不同环境中的声学uDS返回来检测、分类和跟踪一系列目标。

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.