Target Tracking Using Energy Based Detections Xuezhi Wang Melbourne System Laboratory University of Melbourne Victoria, Australia Email: xu.wang@ee.unimelb.edu.au Darko Mus ˇ icki Melbourne System Laboratory University of Melbourne Victoria, Australia Email: d.musicki@ee.unimelb.edu.au Abstract— Energy based detection measures sensor received signal strength (RSS) transmitted from a target. In this paper, we propose a new approach for estimating a moving target trajectory over a sensor field via energy based detections as an alternative to trilateration positioning or nonlinear estimation. In 2D case, possible target locations described by a RSS ratio from two sensors are approximated using a set of Gaussian random variables which are refereed to as location measurements. At each data collection time, several sets of such measurements can be found from RSS ratios which are due to multiple sensor detections. A track splitting filter is used to perform either measurement fusion and target state estimation using these measurements. The RSS ratio data mapping via Gaussian density approxima- tion plays a key role in the proposed target tracking method and is robust in the sense that it can tolerate larger RSS noise and using additional sensor detections to improve tracking perfor- mance over trilateration based techniques. The effectiveness of the propose method is demonstrated via an example of tracking a moving target over a sensor network of small acoustic sensors. Keywords: RSS, Data Mapping, Target Tracking, Gaussian Density Approximation, Nonlinear filtering. I. I NTRODUCTION Integrated sensing processing using multiple sensor detec- tions is one of the most popular target detection strategies for sensor networks of small sensors, which are usually constrained by cost, size and information processing ability, etc [1], [2]. Among signal detection techniques, the energy based signal detection, which is referring the RSS (or RSS indicator) from a target to a signal propagation model to find parameters of the target, can easily be made by low cost sensors such as acoustic sensors. As observed in [3], it has a relaxed requirement for synchronization of sensors as opposite to those which measure time difference of arrival (TDOA) or time of arrival (TOA). In the literature, application examples may be found in localizing mobile users in wireless communications systems [4], [5], sensor network localizations [6], [7] and target tracking in distributed sensor networks [3], [8], [9], etc. In principle, finding target location from the RSS collected from multiple sensors is a common idea of these applications. In this paper, a moving target tracking over a network of small sensors in a 2D Cartesian coordinate system is consid- 0 This work was supported in part by the Defense Advanced Research Projects Agency of the US Department of Defense and was monitored by the Office of Naval Research under Contract No. N00014-04-C-0437. ered. A set of motes are distributed with known locations to cover the whole surveillance area and they can transmit the detected RSS to a fusion center via wireless communication links. A target detection will be declared by a sensor if the RSS exceeds a threshold. In this case, we say that the sensor is activated. At each data collection time, a set of RSSs from activated sensors are expected to be received by fusion center. Our problem is to estimate the trajectory of the underlying target based on RSSs received by the fusion center. With negligible RSS noise, approaches based on trilateration can provide exact solution of closed form to the problem [3], [5], [8], where at each time, the location “measurement” of the target is computed from the RSSs of multiple sensors and the target trajectory can be estimated based on the target location measurement sequence. The methodology of these approaches are quite similar to those with TOA and TDOA signals as seen in the literature [10]–[12]. However, trilateration based methods usually treat the un- derlying location state as unknown constant in the localization process and are therefore sensitive to data noise. In fact, with RSS of low SNR, these methods can either generate significant error or fail to have a real solution as pointed in [13]. In addition, deterministic localization scheme does not offer filtering any unbiased noise out and improving localization accuracy by using more data. In the presence of RSS noise, optimization procedures as in [3], [5]–[7], which minimize the distance error based on the least square criterion, can be an option to improve localization accuracy in the trilateration framework given that there will be enough data. The underlying target location may be estimated under the maximum likelihood estimation criterion. However, as strong nonlinearity is present between the RSS detection and the target location, nonlinear filters such as particle filters have to be considered. In this paper, we propose a new method to deal with the underlying target tracking problem in the presence of noisy energy based detections of a sensor network. A target detec- tion, which is characterized by a RSS ratio transmitted from two sensors, defines a nonlinear and non-Gaussian density of target location. We approximate the possible target locations by a set of Gaussian densities with assumption that one of them represents the true target location density. Therefore, at each data collection time (scan), a set of RSS ratios which arise due to the detection of target by multiple sensors are mapped