A De-ghosting Technique for Passive Coherent Location With Transmitter Origin Uncertainty

Abstract

Passive Coherent Location (PCL) has attracted attention of many researchers for its advantages such as low cost, covert operation, and low vulnerability to electronic countermeasures in air defense system. However, in cases such as transmitters of a single frequency network(SFN) are used, it is not a trivial problem to resolve signals of multiple transmitters reflected from multiple targets, which adds another dimension of uncertainty into data association. To address such transmitter-object-measurement 3D data association problem, many effective assignment-based algorithms are proposed. Nevertheless, research shows that in multidimensional assignment based algorithms, candidate assignment tree building consumes about 95% of the time. Consequently, it is necessary to develop proper de-ghosting techniques with less time consumption. The major contribution of this paper is that we propose a distribution geometry of stations based de-ghosting strategy, by taking such information into consideration, we can easily decrease the scale of the candidate assignment tree with very little time consumption, which will surely lighten the burden of data association. Both theoretical analysis and simulation are provided to demonstrate the feasibility and effectiveness of our algorithm.