This paper describes an experimental demonstration of a distributed, decentralized, low communication sensor
management algorithm. We first review the mathematics surrounding the method, which includes a novel combination
of particle filtering for predictive density estimation and information theory for maximizing information
flow. Earlier work has shown the utility via Monte Carlo simulations. Here we present a laboratory demonstration
to illustrate the utility and to provide a stepping stone toward full-up implementation. To that end, we
describe an inverted Unmanned Aerial Vehicle (UAV) test-bed developed by The General Dynamics Advanced
Information Systems (GDAIS) Michigan Research and Development Center (MRDC) to facilitate and promote
the maturation of the research algorithm into an operational, field-able system. Using a modular design with
wheeled robots as surrogates to UAVs, we illustrate how the method is able to detect and track moving targets
over a large surveillance region by tasking a collection of limited field of view sensors.
This paper describes a decentralized low communication approach to multi-platform sensor management. The
method is based on a physicomimetic relaxation to a joint information theoretic optimization, which inherits the
benefits of information theoretic scheduling while maintaining tractability. The method uses only limited message
passing, only neighboring nodes communicate, and each node makes its own sensor management decisions.
We show by simulation that the method allows a network of sensor nodes to automatically self organize
and perform a global task. In the model problem, a group of unmanned aerial vehicles (UAVs) hover above a
ground surveillance region. An initially unknown number of moving ground targets inhabit the region. Each
UAV is capable of making noisy measurements of the patch of ground directly below, which provide evidence as
to the presence or absence of targets in that sub-region. The goal of the network is to determine the number of
targets and their individual states (positions and velocities) in the entire surveillance region through repeated
interrogation by the individual nodes. As the individual nodes can only see a small portion of the ground, they
must move in a manner that is both responsive to measurements and coordinated with other nodes.
KEYWORDS: Sensors, Signal to noise ratio, Target detection, Intelligent sensors, Error analysis, Monte Carlo methods, Sensor performance, Yield improvement, Remote sensing, Computing systems
This paper analyzes the impact on target detection of several alternative sensor management schemes. Past work in this area has shown that myopic discrimination optimization can be a useful heuristic. In this paper we compare the performance obtained using discrimination with direct optimization of the detection error rate using both myopic and non-myopic optimization techniques. Our model consists of a gridded region containing a set of targets with known priors. Each grid location contains at most one target. At each time step, the sensor can sample a grid location, returning sample values that may or may not be thresholded. The sensor output distribution conditioned on the content of the location is known. Bayesian methods are used to recursively update the posterior probability that each location contains a target. These probabilities can then in turn be used to classify each location as either containing a target or not. At each time step, sensor management is used to determine which location to test next. For non-myopic optimization, graph search techniques are used. When the sensor output is thresholded, the performance obtained using myopic optimization of the expected error rate is worse then that obtained using our other three approaches. Interestingly, we find that for non-thresholded measurements on symmetric distributions, the performance is the same for the four cases tested (myopic/non-myopic discrimination gain/expected error rate). This supports that discrimination is a useful heuristic that provides near-optimal performance under the given assumptions.
KEYWORDS: Sensors, General packet radio service, Land mines, Magnetometers, Electromagnetic coupling, Binary data, Iron, Metals, Magnetic sensors, Target detection
In this presentation, we compare the gain in performance offered by combing the result of a ground-penetrating radar, an electromagnetic induction metal detector, and a magnetometer (MAG) against the performance offered by any one of these sensors alone on the problem of buried mine and unexploded ordnance detection. Using the community-wide DARPA background clutter data set, we characterize the single-channel performance of each of these detectors, describing the preprocessing and detection processing used for each. We then combine the sensor results, using a variety of binary decision-level Boolean methods. A performance gain was observed as a two-to-threefold reduction in the false alarm rate, operating at an 80 percent probability of detection, for 'majority voting', which was the best of the combining methods.
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