A controllable sensor management algorithm capable of learning

Abstract

Sensor management technology progress is challenged by the geographic space it spans, the heterogeneity of the sensors, and the real-time timeframes within which plans controlling the assets are executed. This paper presents a new sensor management paradigm and demonstrates its application in a sensor management algorithm designed for a biometric access control system. This approach consists of an artificial intelligence (AI) algorithm focused on uncertainty measures, which makes the high level decisions to reduce uncertainties and interfaces with the user, integrated cohesively with a bottom up evolutionary algorithm, which optimizes the sensor network's operation as determined by the AI algorithm. The sensor management algorithm presented is composed of a Bayesian network, the AI algorithm component, and a swarm optimization algorithm, the evolutionary algorithm. Thus, the algorithm can change its own performance goals in real-time and will modify its own decisions based on observed measures within the sensor network. The definition of the measures as well as the Bayesian network determine the robustness of the algorithm and its utility in reacting dynamically to changes in the global system.

Research on Multi-sensor Cooperative Tracking Mission Planning of Aerospace Hypersonic Vehicles

“…Therefore, it is the key problem in the integration framework how to dynamically distribute the resources of sensors [12][13] . Many-to-many task allocation mechanism is used in air-space high-speed flight vehicle synergetic tracking.…”

Using Swarm Intelligence and Bayesian Inference for Aircraft Interrogation

“…The algorithm inherently handles sensor degradation by ignoring sensors that have poor status or are off-line. The algorithm scales to larger problems easily and, even more importantly, has the flexibility to redefine the mission based on its performance requirement needs based on geographical location [ 4 ]. Thus, a very flexible and scalable algorithm, consisting of both the Bayesian network for defining the requirements and swarm intelligence for optimizing management decisions, can be designed for all types of sensor networks as pointed out in [ 1 ].…”