Patent ID: 7792598

Claim:
A sparse sampling planner method for providing a plan for long term management of a system of multiple sensors that operates to observe a plurality of tracks/targets wherein each sensor has a number of characteristics including different operational modes and viewing geometries settings, each operational mode incurring cost and providing different information about the tracks, the method comprising the steps of: (a) receiving current track kinematic and classification state inputs representative of a current track picture from a fusion module of the system which receives kinematic and classification measurements of the targets from the multiple sensors during a measurement cycle and performance requirements inputs from a performance data file; (b) beginning with a single root node corresponding to a given state, computing the information need from the inputs of step (a) for generating kinematic and classification information need entropy outputs in terms of a common metric for obtaining an overall value of information need, if the information need of any track is less than or equal to zero, disregard the track for planning purposes; (c) using a generator model, generating sensor action combinations as a permutation of all possible sensor viewing geometries and operational modes; (d) using a sparse sampling planner simulator, generating a predetermined number of samples simulating several measurements of a track for each action resulting in total number of nodes for that sensor defined by the number of sensor modes, the number of viewable geometries and a discount factor value at a current depth corresponding to a layer of a state-action tree structure; (e) reducing the number of sensor action nodes by implementing thresholding for eliminating certain sensor action combinations; (f) for each of the remaining sensor action nodes determined in step (e), computing the value of information gain generated in terms of the common metric of step (b) corresponding to the difference between information gain entropy output of the node from which the node was generated and the information gain entropy output of the remaining node and a reward value corresponding to the rate of information gain; (g) storing a cost value denoting the cost of the sensor action and summing the cost value for each sensor action computed in step (f); (h) repeating the operations of steps (b) and (c) recursively until the cumulative cost value in step (g) reaches a pre-established cost horizon value ending further generation of the action tree structure; (i) determining the value of each chain of actions along the action tree structure by averaging the information gain values of all samples along that chain; and, (j) selecting the chain of actions having the best gain value for carrying out the long term management of the multiple sensors.