Deep learning can exploit high dimensional data in tracking systems to improve snippet screening and reduce latency of contact follower creation and classification. A robust machine learning capability will improve the performance of the active off board sensors deployed from on the P-8A aircraft; increasing the speed of its search mission. Signal Systems Corporation (SSC) is a small business specializing in signal processing for distributed acoustic sensors able to provide machine learning capabilities to platforms that are informed by deep domain knowledge of underwater acoustics, multistatic sonar, and tracking. The risk of performance degradation in operation is being mitigated by segregating representative flights from the normal process of training and evaluation. SSC seeks to integrate this technology into existing field level detection and tracking systems as either a primary or a sub-contractor.
Anti-submarine warfare (ASW) is a vital, challenging, naval fleet task currently executed by various platforms and sensors. Carrier ASW mission operations employ the MH-60 Romeo(R) AN/AQS-22 Airborne Low Frequency Sonar (ALFS) dipping sonar. Signal Systems’ approach includes sensor data from Directional Frequency Analysis and Recording (DIFAR) sonobuoys, using the captured direct pulse to process pings and unknown waveforms from sources. The DIFAR sonobuoys are employed as multi-static receivers to enhance acoustic detection opportunities. Multi-statics and automation techniques create an improved, robust ASW mission package for the MH-60R and AN/SQQ-34 Aircraft Carrier Tactical Support System (CV-TSC). Signal Systems, a high-tech business with expertise in acoustic signal processing, develops cutting edge technology for multi-static sonar processing, suitable for integration with US Navy Anti-Submarine Warfare (ASW) combat systems.