The Navy needs to transmit channel level time-domain data via SATCOM links in order to transmit acoustic array data to onshore signal processing to improve the Detection, Localization, and Classification mission loop. In order for this transmission to be viable, a near-lossless compression codec must be designed to meet at least a 5-to-1 compression ratio in real-time. This 5-to-1 compression ratio will be a starting place for transmitting time-domain data from SURTASS ASW towed arrays for phase I, as the focus of this SBIR is to maximize the compression ratio, while staying within the bounds of processing power and signal integrity. With this SBIR, the ASSETT team will bring extensive knowledge of ambient ocean noise leveraged from previous contract work, as well as the in-depth knowledge of compression theory and SURTASS ASW software from L3 Adaptive Methods. The ASSETT team will develop 4 principle methods that will additively combine to create a codec that meets and exceeds the compression ratio goal while meeting any other requirements on processing power, signal integrity, and latency: Statistical redundancy in the K-Omega domains acoustic zone, Multi-dimensional linear predictive filters, Lossy compression of unimportant data in the K-Omega domains non-acoustic zone, and multi-dimensional entropy encoding.
Benefit: The market for the developments under this effort includes DoD and commercial industry. In the DoD market, the codec could be applied to manned vessels in order to reduce recurring costs of deploying sonar specialists or ACINT specialists to sea; the codec could installed on UUVs to improve the Detection, Localization, and Classification loop; and the codec could theoretically be modified to work with any acoustic data for application to swarm robotics, where communication bandwidth is one of the largest problems it is facing. The commercial market includes UUVs similar to those mentioned above, but to reduce the recurring cost of manned missions in the oil and gas market. The acoustic compression techniques could become more prevalent in the commercial market with machine-learning-based home assistants are utilizing microphone arrays to utilize beamforming technology. Lastly, any developments made in entropy encoding could be applied to any market that uses a form of compression, which includes television broadcast and cellular 5G communication.
Keywords: Entropy Coding, Entropy Coding, beamforming, Multi-dimensional Compression, Acoustic Zone, Linear Predictive Filter, K-Omega, Adaptive Codec, transformation
