Efficient 2D Sensor Location Estimation using Targets of Opportunity

Due to their low cost and ease of deployment, the use of passive acoustic sensors for target tracking has seen increasing popularity. Such systems might consist of individual microphones or hydrophones that selfassemble into arrays [24], or, perhaps, sensors consisting of clusters of microphones or hydrophones, each producing measurements consisting of arrival angles and features/attributes for use in data association [19].1 The clusters of microphones or hydrophones form individual sensors, which can also be referred to as “nodes” in the system. This paper focusses on the latter scenario, localizing sensors with measurements taken with respect to a common, unknown coordinate axis. Determining which detection on one sensor corresponds to the same target on another sensor (measurement association) might be accomplished, for example, by utilizing acoustic patterns for classification, as has previously been done to aid acoustic tracking [19]. Target tracking is not considered here. The scenarios considered focus on angular noise levels up to 2± (root-mean squared error), which is the accuracy of the sensors in [19], though acoustic sensors can often have significantly worse angular accuracies. When considering the construction of land-based sensor networks, it cannot be assumed that satellitebased localization systems, such as GPS (USA) or GLONASS (Russia), will be available, and such signals cannot penetrate far underwater. However, many nonsatellite-based location estimation algorithms, which have been primarily designed for use in underwater and wireless networks may be used. A number of methods applied to sonar channels are described in [4]. These approaches typically utilize the communication characteristics between sensors and are divided into two categories: range-based and range-free. Range-based methods utilize range (distance) measurements. Range-free schemes do not utilize range information. Both techniques might take advantage of moving anchor nodes that broadcast their position [6, 9, 13, 22]. Our focus is on algorithms for node localization based on the angle-only observations of the nodes, though we do consider the case where range measurements are also available. Estimates based on angle-only measurements are particularly useful when the sensors have a limited broadcast range. Underwater, this might be the case when the sensor network is built using data MULEs (Mobile Ubiquitous LAN2 Extensions) [21]. A data MULE is a mobile device that approaches the sensors to collect data. In such a network, traditional methods of sensor localization, which rely on communication channels between sensors, are not applicable.