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We develop an optimization approach for the planning problem of configuring the positions of sensors within a sensor network for minimization of the travel length required to service the sensor locations. This is in contrast to existing approaches whereby the coverage of the sensor network is an objective of the optimization; in this new optimization approach the level of coverage is treated as a constraint. By beginning with an over-populated sensor network and then alternating between sensor repositioning and sensor removal, we create an optimization procedure that solves this difficult practical planning problem. A formal rule for switching between the repositioning and removal components of the optimization strategy is developed. Numerical example problems are presented to illustrate the method and show its performance against a heuristic optimization approach.

This paper develops and applies a numerical optimization procedure to compute broadband noise-adaptive weights for delay and sum beamforming that are conditioned to maximize the deflection coefficient at the output of a square law detector for a given set of underwater pressure measurements. The resulting optimal weights mitigate the effects of noise and interferers and maximize signal detection. Comparison of the optimal weights with minimum variance distortionless response weights show that the presented algorithm provides higher attenuation of interferers. We also use the noise-adaptive algorithm to find the optimal sparse array geometry for a given number of sensors and aperture. Comparison of the resulting optimal array with coprime, nested, and semi-coprime arrays shows that the proposed sparse array suppresses interferers more than the other sparse arrays.

A new method is derived for finding the best positions in which to locate the sensors in a distributed sensor network in order to achieve a desired variation, or pattern, in spatial coverage over a specified domain. Such patterning is important in situations when there are not enough sensors to completely cover a region adequately. By providing coverage based on a desired pattern, this approach allows a user/designer to specify which sub-regions of the domain are more important to cover, and to what level that is desired. The method that is developed is novel in that it is an analytic approach, as opposed to existing numerical optimization approaches, and thus provides solutions rapidly and can also be applied to provide online repositioning for existing sensor networks to respond to changes in the environment. The method is based on deriving an expression for the probabilistic density of sensor locations that best matches the desired coverage under given spatially varying environmental conditions; and then samples from that sensor density to determine specific sensor locations. The performance of the method is demonstrated on numerical examples in both one-dimensional and two-dimensional settings. Comparisons are made between solutions found from this approach and solutions obtained by a numerical optimization technique.

The use of a spatially distributed set of sensors has become a cost-effective approach to achieve surveillance coverage against moving targets. As more sensors are utilized in a collaborative manner, the optimal placement of sensors becomes critical to achieve the most efficient coverage. In this paper, we develop a numerical optimization approach to place distributed sets of sensors to perform surveillance against moving targets over extended areas. In particular, we develop a genetic algorithm solution to find spatial sensor density functions that maximize effectiveness against moving targets, where the surveillance performance of individual sensors is dependent on their absolute position in the region as well as their relative position to both the expected target(s) and any asset that is being protected. The density function representation of optimal sensor locations is shown to provide a computationally efficient method for determining sensor asset location planning. We illustrate the effective performance of this method on numerical examples based on problems of general area surveillance and risk-based surveillance in protection of an asset.

We consider a problem of finding the effectiveness of applying varying amounts of motion in fields of autonomous unmanned vehicles performing group search operations. These fields are typically deployed to provide extended coverage over that provided by a single sensor in spatially extended search regions. The autonomous nature of these distributed sensing assets is attributable to constraints on communications bandwidth and manpower. Due to the potential large cost discrepancy between fielding moving searchers and fixed searchers, the potential benefit of searcher motion relative to that achievable by deployment of larger numbers of fixed nodes is a critical operational consideration. We build a parametric analytical model of searcher performance for fields of moving searchers, and through a perturbation analysis examine the effectiveness of searcher motion vice nonmotion in terms of numbers of searchers required to achieve similar goals. Numerical simulations support the conclusions that are obtained.

The localization accuracy of a track-before-detect search for a target moving across a distributed sensor field is examined in this paper. The localization accuracy of the search is defined in terms of the area of intersection of the spatial-temporal sensor coverage regions, as seen from the perspective of the target. The expected value and variance of this area are derived for sensors distributed randomly according to an arbitrary distribution function. These expressions provide an important design objective for use in the planning of distributed sensor fields. Several examples are provided that experimentally validate the analytical results.