Fuzzy Logic Multisensor Association Algorithm: Multiple Emitters, Computational Complexity, and Noisy Data

摘要

A recursive multisensor association algorithm based on fuzzy logic has been developed. It simultaneously determines fuzzy grades of membership and fuzzy cluster centers. It is capable of associating data from various sensor types. In its simplest form, it makes no assumption about noise statistics as many association algorithms do. The algorithm is capable of performing without operator intervention. It associates data from the same target for multiple sensor types. The algorithm also provides an estimate of the number of targets present, reduced noise estimates of the quantities being measured, and a measure of confidence to assign to the data association. A comparison of the algorithm to a more conventional Bayesian association algorithm is provided. Data from both the electronic support measures (ESM) and radar systems are noisy, and ESM data are also intermittent. The data has probability of detection less than unity. The effect of a large number of targets being present in the data on parameter estimation, determination of the number of targets, and multisensor data association is examined. Finally, issues related to computational complexity are discussed.