Effective management of sensor collaboration is crucial to the success of any distributed unattended ground sensor
(UGS) system. A successful management scheme must allow nodes to share enough information to form and maintain
tracks while minimizing unnecessary or excessive collaboration. Systems developed with the traditional unidirectional
or request/response models are typically susceptible to excessive collaboration in the presence of a persistent loud sound
source. The work presented in this paper addresses the challenge of suppressing excessive sensor collaboration in the
presence of loud targets. The Loud Target Suppression (LTS) algorithm utilizes Voronoi tessellation as a means to
allow sensor nodes to autonomously determine alert regions that support track formation with neighboring nodes. By
replying only with sensor measurements that fall within the alert regions, the LTS algorithm is able to significantly
reduce message quantities without impacting track accuracy. This paper will demonstrate that an alert-based sensor
collaboration scheme, employed by Distributed Cluster Management (DCM), greatly reduces sensor collaboration in the
presence of loud targets which results in a more scalable system.
Interest in the distribution of processing in unattended ground sensing (UGS) networks has resulted in new technologies and system designs targeted at reduction of communication bandwidth and resource consumption through managed sensor interactions. A successful management algorithm should not only address the conservation of resources, but also attempt to optimize the information gained through each sensor interaction so as to not significantly deteriorate target tracking performance. This paper investigates the effects of Distributed Cluster Management (DCM) on tracking performance when operating in a deployed UGS cluster. Originally designed to reduce communications bandwidth and allow for sensor field scalability, the DCM has also been shown to simplify the target tracking problem through reduction of redundant information. It is this redundant information that in some circumstances results in secondary false tracks due to multiple intersections and increased uncertainty during track initiation periods. A combination of field test data playback and Monte Carlo simulations are used to analyze and compare the performance of a distributed UGS cluster to that of an unmanaged centralized cluster.
Smart Sensor Networks are becoming important target detection and tracking tools. The challenging problems in such networks include the sensor fusion, data management and communication schemes. This work discusses techniques used to distribute sensor management and multi-target tracking responsibilities across an ad hoc, self-healing cluster of sensor nodes. Although miniaturized computing resources possess the ability to host complex tracking and data fusion algorithms, there still exist inherent bandwidth constraints on the RF channel. Therefore, special attention is placed on the reduction of node-to-node communications within the cluster by minimizing unsolicited messaging, and distributing the sensor fusion and tracking tasks onto local portions of the network. Several challenging problems are addressed in this work including track initialization and conflict resolution, track ownership handling, and communication control optimization. Emphasis is also placed on increasing the overall robustness of the sensor cluster through independent decision capabilities on all sensor nodes. Track initiation is performed using collaborative sensing within a neighborhood of sensor nodes, allowing each node to independently determine if initial track ownership should be assumed. This autonomous track initiation prevents the formation of duplicate tracks while eliminating the need for a central “management” node to assign tracking responsibilities. Track update is performed as an ownership node requests sensor reports from neighboring nodes based on track error covariance and the neighboring nodes geo-positional location. Track ownership is periodically recomputed using propagated track states to determine which sensing node provides the desired coverage characteristics. High fidelity multi-target simulation results are presented, indicating the distribution of sensor management and tracking capabilities to not only reduce communication bandwidth consumption, but to also simplify multi-target tracking within the cluster.
Multisensor-multitarget sensor management is viewed as a problem in nonlinear control theory. This paper applies newly developed theories for sensor management based on a Bayesian control-theoretic foundation. Finite-Set-Statistics (FISST) and the Bayes recursive filter for the entire multisensor-multitarget system are used
with information-theoretic objective functions in the development of the sensor management algorithms. The theoretical analysis indicate that some of these objective functions lead to potentially tractable sensor management algorithms when used in conjunction with
MHC (multi-hypothesis correlator)-like algorithms. We show examples of such algorithms, and present an evaluation of their performance against multisensor-multitarget scenarios. This sensor management formulation also allows for the incorporation of target preference, and experiments demonstrating the performance of sensor management with target preference will be presented.
In multi-hypothesis target tracking, given the time-predicted tracks, we consider the sensor management problem of directing the sensors' Field of View (FOV) in such a way that the targets detection rate is improved. Defining a (squared) distance between a sensor and a track as the (squared) Euclidean distance between the centers of their respective Gaussian distributions, weighted by the sum of the covariance matrices, the problem is formulated as the minimization of the Hausdorff distance from the set of tracks to the set of sensors. An analytical solution for the single sensor case is obtained, and is extended to the multiple sensors case. This extension is achieved by performing the following: (1) It is first proved that for an optimal solution, there exists a partition of the set of tracks into subsets, and an association of each subset with a sensor, such that each subset-sensor pair is optimal in the Hausdorff distance sense; (2) a brute force search is then conducted to check all possible subset-partitions of the tracks as well as the permutations of sensors; (3) for each subset-sensor pair, the optimal solution is obtained analytically; and (4) the configuration with the smallest Hausdorff distance is declared as the optimal solution for the given multi-target multi-sensor problem. Some well established loopless algorithms for generating set partitions and permutations are implemented to reduce the computational complexity. A simulation result demonstrating the proposed sensor management algorithm is also presented.
For the last three years at this conference we have been describing the implementation of a unified, scientific approach to performance estimation for various aspects of data fusion: multitarget detection, tracking, and identification algorithms; sensor management algorithms; and adaptive data fusion algorithms. The proposed approach is based on finite-set statistics (FISST), a generalization of conventional statistics to multisource, multitarget problems. Finite-set statistics makes it possible to directly extend Shannon-type information metrics to multisource, multitarget problems in such a way that information can be defined and measured even though any given end-user may have conflicting or even subjective definitions of what informative means. In this presentation, we will show how to extend our previous results to two new problems. First, that of evaluating the robustness of multisensor, multitarget algorithms. Second, that of evaluating the performance of multisource-multitarget threat assessment algorithms.
KEYWORDS: Sensors, Molybdenum, Data fusion, Detection and tracking algorithms, Silicon, Monte Carlo methods, Solids, Metrology, Switches, Analytical research
For the last two years at this conference, we have described the implementation of a unified, scientific approach to performance measurement for data fusion algorithms based on FINITE-SET STATISTICS (FISST). FISST makes it possible to directly extend Shannon-type information metrics to multisource, multitarget problems. In previous papers we described application of information Measures of Effectiveness (MoEs) to multisource-multitarget data fusion and to non-distributed sensor management. In this follow-on paper we show how to generalize this work to DISTRIBUTED sensor management and ADAPTIVE DATA FUSION.
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