David E. Jeffcoat

Simulated annealing for resource-constrained scheduling

Abstract We describe an application of several procedures to a resource-constrained scheduling problem. Basic and augmented neighborhood search procedures are first developed, using a unique way to generate neighborhoods. We then present a simulated annealing procedure which can be viewed as an evolution of the neighborhood search procedures. Computational results indicate that the procedures grow in power as they evolve, with the simulated annealing procedure providing the best results, and further, that it is a viable approach for a very difficult scheduling problem.

Optimal and Feedback Path Planning for Cooperative Attack

This paper considers cooperative path planning for aerial munitions during the attack phase of a mission against ground targets. It is assumed that sensor information from multiple munitions is available to refine an estimate of the target location. Based on models of the munition dynamics and sensor performance, munition trajectories are designed that enhance the ability to cooperatively estimate the target location. The problem is posed as an optimal control problem using a cost function based on the variances in the target-location estimate. These variances are computed by fusing the individual munition measurements in a weighted least-squares estimate. Solutions to the problem are found using a direct-shooting method. These solutions are compared with trajectories developed by an alternative suboptimal feedback-guidance law. This feedback law produces solutions with far less numerical expense and with a performance very close to the best known solutions. The reduction in target-location uncertainty associated with these trajectories could enable the attack of targets with greater precision using smaller, cheaper munitions.

Analysis of dynamic sensor coverage problem using Kalman filters for estimation

Abstract We introduce a theoretical framework for the dynamic sensor coverage problem for the case with multiple discrete time linear stochastic systems placed at spacially separate locations. The objective is to keep an appreciable estimate of the states of the systems at all times by deploying a few limited range mobile sensors. The sensors implement a Kalman filter to estimate the states of all the systems. In this paper we present results for a single sensor executing two different random motion strategies. Under the first strategy the sensor motion is an independent and identically distributed random process and a discrete time discrete state ergodic Markov chain under the second strategy. For both these strategies we give conditions under which a single sensor fails or succeeds to solve the dynamic coverage problem. We also demonstrate that the conditions for the first strategy are a special case of the main result for the second strategy.

On Sensor Coverage by Mobile Sensors

We study the problem of using a small number of mobile sensors to monitor various threats in a geographical area. Using some recent results on stochastic sensor scheduling, we propose a stochastic sensor movement strategy. We present simple conditions under which it is not possible to maintain a bounded estimate error covariance for all the threats. We also study a simple sub-optimal algorithm to generate stochastic trajectories. Simulations are presented to illustrate the results

Formulation and Solution of the Target Visitation Problem

This paper presents the Target Visitation Problem (TVP) for a single unmanned aerial vehicle (UAV). The ability to effectively plan a path for a UAV to visit multiple targets is an increasingly important capability in a variety of applications, including surveillance, attack, assessment, search and rescue, disaster relief, and environmental cleanup. The TVP is related to both the Traveling Salesman Problem and the Linear Ordering Problem, with an objective function that combines elements of both problems. The TVP considers both total travel distance and the order of targets visited. In this paper, we formulate the target visitation problem for a single vehicle, describe a heuristic solution procedure, and report results for problems of various size.

An Analysis and Solution of the Sensor Scheduling Problem

This chapter addresses the scheduling problem of a sensor that constantly collects information from multiple sites. In the existing literature, the problem is solved by probabilistic approaches, potentially generating schedules in which a site is not visited for a long time. To overcome this deficiency, this chapter presents a deterministic approach formulated as an integer linear program. Upon showing that the problem is NP-Hard, the chapter develops valid lower and upper bounds and proposes two constructive heuristic methods. Tested via an extensive computational study, the heuristic methods are proven efficient and effective in solving the problem.

Optimization of Spatiotemporal Clustering for Target Tracking From Multisensor Data

This study focuses on the information extraction from reported sensor data in the communication system of wide-area-search munitions (WASMs). Such sensor data could be erroneous and inconsistent. For example, two WASMs might detect the same target, but associate it with two different targets and tracks. Similarly, two WASMs might detect two distinct targets, but recognize them as the same target. The research challenge is how to fuse both accurate and inaccurate information broadcasted from WASMs, and reconstruct the battle space for accurate target tracking. For each of the detected target points, WASMs provide its location information, detection time, and directional velocity. We, herein, propose a target clustering approach to group target points detected by WASMs and identify the track of individual targets. Our approach differs from traditional clustering techniques as it performs clustering using the time and orientation information, in addition to the distance in the Euclidean space. Our approach employs a network modeling technique to reconstruct all target points and their feasible movement, and a new optimization technique to find the most probable target tracks. Our approach can also determine the optimal number of clusters (targets) automatically from the input data. In this study, distributed interactive simulation, a real-time simulation of a networks information exchange, is used to generate battle space test instances that are used in evaluating the proposed framework. Based on seven realistically simulated instances, the computational results show that our approach provides extremely accurate target-tracking results in a timely fashion. We also compare our results with those obtained using the k-means clustering technique. On average, our approach reconstructs the real target tracks with about 95% accuracy in less than 10 s, while the k-means clustering results yields about 80% accuracy in a similar computational time.

Robust multi-sensor scheduling for multi-site surveillance

This paper presents mathematical programming techniques for solving a class of multi-sensor scheduling problems. Robust optimization problems are formulated for both deterministic and stochastic cases using linear 0–1 programming techniques. Equivalent formulations are developed in terms of cardinality constraints. We conducted numerical case studies and analyzed the performance of optimization solvers on the considered problem instances.

Simultaneous Localization and Planning for Cooperative Air Munitions

This chapter considers the cooperative control of aerial munitions during the attack phase of a mission against ground targets. It is assumed that sensor information from multiple munitions is available to refine an estimate of the target location. Based on models of the munition dynamics and sensor performance, munition trajectories are designed that enhance the ability to cooperatively estimate the target location. The problem is posed as an optimal control problem using a cost function based on the variances in the target-location estimate. These variances are computed by fusing the individual munition measurements in a weighted least squares estimate. Numerical solutions are found for several examples both with and without considering limitations on the munitions’ field of view. These examples show large reductions in target-location uncertainty when these trajectories are used compared to other naively designed trajectories. This reduction in uncertainty could enable the attack of targets with greater precision using smaller, cheaper munitions.

APPLYING SIMULATED ANNEALING TO THE MULTIDIMENSIONAL ASSIGNMENT PROBLEM

A divider plate having a strainer basket depending therefrom is positioned in a skimmer apparatus intermediate the skimming inlet and the lower end. A conduit passes through the basket and communicates with the drain inlet in the bottom of the skimmer which also includes an outlet from which water is drawn by the swimming pool pump. The conduit is aligned with a water passage in the divider plate. A coupling member affixed to the hose associated with vacuum cleaning equipment is passed through the water passage and engaged with the conduit. The coupling blocks water flow from the drain inlet and has an exit therethrough for movement of water from the hose to the outlet of the skimmer. Valve means are provided for selectively closing the water passage and for selectively retarding water flow from the drain inlet.