Simultaneous Sensor and Sound Source Localization in Urban Environments

The localization of sound sources in urban environments is a challenging task and the self-position and self-orientation of the individual sensor nodes can usually not be assumed to be precisely known. Therefore in this paper, a method for simultaneous sensor and source localization under line-of-sight conditions in urban environment is proposed. The method is based on acoustic bearing and relative time difference of arrival measurements as well as self-position and self-orientation measurements of an asynchronous acoustic sensor network. Here, both direct wave and first-order reflections are taken into account. We derive the Cramér-Rao bound for the corresponding localization problem and compare it with results from a Monte Carlo simulation. We consider cases where the sensors provide all measurements as well as cases where parts of the measurements fail, resulting in sub-measurement data sets. Here, a focus is on scenarios with fully unknown sensor states. Depending on the measurement data set under consideration, the initial self-position measurement is improved by taking into account the acoustic measurements, which in turn has a positive impact on the source localization accuracy in many considered cases.