Localization and Classification using an Acoustic Sensor Network - experimental data processing for urban acoustic surveillance

The Acoustic Sensor Network (ASN) has emerged as an important research area, because acoustic sensors can significantly increase situational awareness in many situations. Although little is currently known about acoustic surveillance, Thales Nederland is interested in the potential offered by an ASN in urban environments and specifically for classification. This is because the operational problem is not merely to detect targets, but also to localize and classify them in a robust way. Current radar implementations do not provide enough performance to classify targets in complex urban environments while now acoustic sensors are seen as an extra source of information. Therefore, a challenging project was initiated to investigate the potential and feasibility of a passive ASN to localize and classify targets. Three different kind of targets were investigated for acoustic surveillance: guns (muzzle blast), vehicles (running piston engine) and humans (walking pedestrian). Can a passive ASN be deployed in urban environments to localize and classify them only by their emitted sound? It is a great challenge to cope with the received signals using a passive ASN in urban environment, because signals can - even within the same classes - differ significantly. This leads to a technical challenge when it comes to achieving robust localization and correct classification. Two project objectives were defined. Firstly, extract target information and use propagation models to localize the targets. Secondly, extract features which allow a classification method to discriminate between the different target classes. Experimental data processing had to be designed, implemented and evaluated with measured data for a performance indication. To find target features and to investigate localization possibilities, extensive acoustic analysis is done on the three targets. The emitted energy of the gun was the dominant feature of the muzzle blast. The dominant features of the running piston engine were the harmonics. The walking pedestrian had characteristic time interval features between the footsteps. An experimental framework was designed with a signal processor, localizer and classifier. The signal processor has to process the recorded signal in such a way that the localizer and classifier can use the result. The localizer is designed which can localize targets time-based and power-based. The feature extraction of the classifier provided discriminative features which allowed a classification method to discriminate between the classes. The designed components are combined and experimentally implemented (proof of concept) with four microphones and tested for a system performance indication. The time-based localization performed well, but the power-based localization requires extensive calibration to perform proper. The dominant target features were extracted and allowed an experimental classification tree to discriminate between the classes. Passive acoustic surveillance is possible, but the system performance depends very much on the operational situation (e.g. background noise). The performance mainly depends on the signal to noise ratio (SNR) and the SNR depends on the target class. The potential for localizing and classifying walking pedestrians is very low. Vehicle power-based localization and detection has potential, but good microphone hardware is required. Gun localization and classification has the highest potential and feasibility. Although there are some difficulties, throughout this project it became clear that acoustic sensors are able to provide extra information and features. This can be used to further increase the robustness and integrity of urban surveillance systems.MTS-RadarElectrical Engineering, Mathematics and Computer Scienc