A Correlation Algorithm for Geo-Location Position Measurements
Geo-location of ground targets is often performed by surveillance aircraft using measurements of target position. These aircraft typically conduct missions where multiple target signal sources exist within the area of operation. As a result, each new position measurement must be correlated with a specific target emitter before that target's position estimate can be updated by the geo-location algorithm. Geo-location performance is typically degraded in a dense emitter environment due to the difficulty of correlating each position measurement with the correct target.
Many correlation algorithms form measurement clusters by evaluating all possible combinations of measurements using some performance metric. The method described here forms clusters based on a Mahalanobis distance association criterion and does not require that the number of clusters be specified in advance, as with methods such as k-means clustering. This approach accounts for measurement error statistics and avoids the computational complexity of an exhaustive combinatorial search. Also, many correlation algorithms will simply compute a target position estimate using the cluster having the most measurements. This method computes target position using the cluster that maximizes the associated target position likelihood function, which is justified by statistical estimation theory.
Once the optimal cluster of measurements has been determined and an associated target position estimate has been computed, the cluster is removed from the set of all measurements and the process is repeated. This continues until no additional clusters can be formed.CONTACT: