Evolving Sensor Suites For Enemy Radar Detection

Designing optimal teams of sensors to detect the enemy radars for military operations is a challenging design problem. Many applications require the need to manage sensor resources. There is a tradeoff between the need to decrease the cost and to increase the capabilities of a sensor suite. In this paper, we address this design problem using genetic algorithms. We attempt to evolve the characteristics, size, and arrangement of a team of sensors, focusing on minimizing the size of sensor suite while maximizing its detection capabilities. The genetic algorithm we have developed has produced promising results for different environmental configurations as well as varying sensor resources. © Springer-Verlag Berlin Heidelberg 2003

Designing teams of unattended ground sensors using genetic algorithms

Improvements in sensor capabilities have driven the need for automated sensor allocation and management systems. Such systems provide a penalty-free test environment and valuable input to human operators by offering candidate solutions. These abilities lead, in turn, to savings in manpower and time. Determining an optimal team of cooperating sensors for military operations is a challenging task. There is a tradeoff between the desire to decrease the cost and the need to increase the sensing capabilities of a sensor suite. This work focuses on unattended ground sensor networks consisting of teams of small, inexpensive sensors. Given a possible configuration of enemy radar, our goal is to generate sensor suites that monitor as many enemy radar as possible while minimizing cost. In previous work, we have shown that genetic algorithms (GAs) can be used to evolve successful teams of sensors for this problem. This work extends our previous work in two ways: we use an improved simulator containing a more accurate model of radar and sensor capabilities for out fitness evaluations and we introduce two new genetic operators, insertion and deletion, that are expected to improve the GA\u27s fine tuning abilities. Empirical results show that our GA approach produces near optimal results under a variety of enemy radar configurations using sensors with varying capabilities. Detection percentage remains stable regardless of changes in the enemy radar placements

Designing Teams Of Unattended Ground Sensors Using Genetic Algorithms

Improvements in sensor capabilities have driven the need for automated sensor allocation and management systems. Such systems provide a penalty-free test environment and valuable input to human operators by offering candidate solutions. These abilities lead, in turn, to savings in manpower and time. Determining an optimal team of cooperating sensors for military operations is a challenging task. There is a tradeoff between the desire to decrease the cost and the need to increase the sensing capabilities of a sensor suite. This work focuses on unattended ground sensor networks consisting of teams of small, inexpensive sensors. Given a possible configuration of enemy radar, our goal is to generate sensor suites that monitor as many enemy radar as possible while minimizing cost. In previous work, we have shown that genetic algorithms (GAs) can be used to evolve successful teams of sensors for this problem. This work extends our previous work in two ways: we use an improved simulator containing a more accurate model of radar and sensor capabilities for out fitness evaluations and we introduce two new genetic operators, insertion and deletion, that are expected to improve the GA\u27s fine tuning abilities. Empirical results show that our GA approach produces near optimal results under a variety of enemy radar configurations using sensors with varying capabilities. Detection percentage remains stable regardless of changes in the enemy radar placements

Designing Teams of Unattended Ground Sensors Using Genetic Algorithms

Improvements in sensor capabilities have driven the need for automated sensor allocation and management systems. Such systems provide a penalty-free test environment and valuable input to human operators by offering candidate solutions. These abilities lead, in turn, to savings in manpower and time. Determining an optimal team of cooperating sensors for military operations is a challenging task. There is a tradeoff between the desire to decrease the cost and the need to increase the sensing capabilities of a sensor suite. This work focuses on unattended ground sensor networks consisting of teams of small, inexpensive sensors. Given a possible configuration of enemy radar, our goal is to generate sensor suites that monitor as many enemy radar as possible while minimizing cost. In previous work, we have shown that genetic algorithms (GAs) can be used to evolve successful teams of sensors for this problem. This work extends our previous work in two ways: we use an improved simulator containing a more accurate model of radar and sensor capabilities for out fitness evaluations and we introduce two new genetic operators, insertion and deletion, that are expected to improve the GA’s fine tuning abilities. Empirical results show that our GA approach produces near optimal results under a variety of enemy radar configurations using sensors with varying capabilities. Detection percentage remains stable regardless of changes in the enemy radar placements

Evolving Sensor Suites For Enemy Radar

Designing optimal teams of sensors to detect the enemy radars for military operations is a challenging design problem. Many applications require the need to manage sensor resources. There is a tradeoff between the need to decrease the cost and to increase the capabilities of a sensor suite. In this paper, we address this design problem using genetic algorithms. We attempt to evolve the characteristics, size, and arrangement of a team of sensors, focusing on minimizing the size of sensor suite while maximizing its detection capabilities. The genetic algorithm we have developed has produced promising results for different environmental configurations as well as varying sensor resources