A wireless network of acoustic multi mission sensors to detect, locate and track simultaneously various helicopters

Abstract

With the number of helicopter flights going up, also the need increases to monitor their trajectories in a 3 D space. For instance, in case of a disaster, where helicopters can bring in first responders, medical aid and food, and evacuate the injured, a rapid deployable air traffic control is a desirable matter. As an alternative to radar, a network of wireless distributed Acoustic Multi Mission Sensors (AMMSs) can be used to detect, locate and track helicopters. Traditionally, arrays of sound pressure transducers have been used to obtain acoustic directional information, estimating the direction of arrival of a sound wave using relative phase differences, which requires spatial coherence. But apart from the fact that such an array obtains a difficult to handle size when trying to cover low frequencies, they need to exchange broad band signals in order to estimate the direction of the sound. It makes unfeasible to use arrays of sound pressure transducers to locate helicopters in long range and low frequency applications, as helicopter localization. An Acoustic Multi Mission Sensor (AMMS) consists of a sensor unit (based upon two orthogonally placed acoustic particle velocity sensors and a collocated sound pressure transducer) that are connected to a Digital Signal Processor (DSP) and covered under a wind and rain resistant open foam wind cap. The 30 cm diameter device weighs around 2 kg and consumes around 2 W electrical power. Since wireless networks cannot handle raw data due bandwidth constrains, some kind of measurement model, source model and/or data compression is needed, i.e. the most important features of the acoustic signature of the detected sources must somehow be sent through the network in order to locate and track multiple sources. Acoustic Multi Mission Sensors can provide a better and simpler measurement or source model than microphone arrays because the AMMS can measure the effective direction of the significant components of the sound at a single point. The distributed (pre)processing of the signals using the on-board DSP has quite some benefits. In this paper a centralized algorithm is presented and tested by using realistic simulations. The signals are generated based on real GPS measurements and real acoustic measurements of two helicopters flying, recorded by a network of AMMSs. The goal of the measurements is to characterize and create a model of the noise of the bearing estimation that can be applied to the simulations. A realistic scenario which assumes that the number of acoustic sources is unknown and time-varying is considered for this research. Each Acoustic Multi Mission Sensor sends the source(s) or measurement model(s) parameters to a central node or main station. The main station or central node runs a centralized algorithm that combines all measurement or source models from all the AMMSs in the network in order to detect, locate and track the acoustic sources in the neighbourhood of the network. Since real sources in motion cannot move randomly with a random speed, some previous knowledge about the motion of the source can be used for tracking. The performance of the proposed system is studied under both single source and multisource scenarios using simulated signals based on real measurements. The results show that the proposed system can locate and track more than one source simultaneously.