Active clutter reduction through fusion with passive sonar data.

Abstract

Active and passive sonar systems provide different types and qualities of data. For example, passive sonar typically provides bearing information and classification clues. Active systems can provide the complementary range and Doppler shift information. Similarly, the types of false alarms—passive interferes and active clutter—may be from different objects. The combination of information from both systems should yield a better estimate of the target state, but traditionally, such systems are used separately. A method for fusing active and passive sonar data is presented with example results. The fusion engine is a Bayesian tracker. The key step for fusing active and passive data is the formation of the likelihood function for each type of measurement. This process is described at a high level, but the emphasis on the results of fusing the data. The example problem involves simulated active and passive data, including the presence of passive interferers and active clutter objects. The results show the value of using active systems to refine passive estimates and more importantly suggest the potential power of using passive data to mitigate active clutter. [This work was supported by the Office of Naval Research Contract No. N00014‐06‐G‐0218‐01.]