High Rez Consulting, Inc. has been awarded the following SBIR/STTR awards:

Automated Acoustic Monitoring & Estimation System (AAME)

Phase I SBIR

Contract #: N00024-15-P-4561 

Award Date: August 2015

Summary:

A robust and effective Automated Acoustic Monitoring System (AAMS) that will alert operators to degraded sensor performance due to platform self-noise and sensor health requires an effective means for gathering, managing, evaluating, associating, thresholding sensor health & performance, and displaying that data in a useful form to the operator for use in determining whether a sensor is too degraded to continue using within the Undersea warfare (USW) Combat system (initiating a Casualty Report – CASREP) or whether to accept the degradation and utilize the associated degraded sensor parameters throughout the USW system in performance detection, classification, and localization processing. In the Phase I research, we were able to define and assess operational concepts and automation algorithms that will accomplish the Phase I Technical Objectives. 

The key to an effective AAMS operational concept is the ability of the aid (system) to automatically ingest, and evaluate sensor health and degradation data to improve an operator’s ability to make informed tactical decisions. The operational concept we have developed will achieve these goals by merging flexible architecture and interfaces, functionality, and intelligent algorithms, such that the system, utilizing available Performance Monitoring & Fault Localization (PMFL) and acoustic data from all available sensors, will provide: 

• Sensor element health and sensor health-dependent beampatterns and sensor parameters as an aid in determining overall system health and performance, 
• Comparison to previous WQM9 reports, and a link to the CASREP generation process, 
• Updated sensor parameters for use in the Sensor Performance Prediction Functional Segment (SPPFS), to estimate performance based on the actual degraded sensor status 

In Phase I, we designed and developed classes of algorithms that support AAMS on multiple sensors in the areas of (a) automated, intelligent control and evaluation of sensor health data based on sensor degradation rules and metrics; (b) sensor degradation metric ranking; (c) in-situ calculation of degraded sensor parameters; and (d) automated, real-time sensor self-noise. 

Results of our analysis are summarized as follows: 

• Sensor element malfunction and sensor deformation affect several terms in the passive and active sonar equations 
• We can quantify these effects to estimate degraded sensor performance 
• Several displays, or modifications to current tactical decision aid displays, can improve operator situational awareness 

Recommended courses of action include re-analysis using actual, rather than canonical, sensor data, refining statistical analysis where behavior of data is non-Gaussian, and modifications to Navy standard performance prediction models to afford efficient computations using degraded sensor information. Future plans include enhancing these intelligent controller algorithms with telemetry wild-pointing & transient detection algorithms and beginning assessment of the integrated classes of algorithms to characterize performance and demonstrate operational benefits. 

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3D Acoustic Model for Geometrically-Constrained Environments

Phase I STTR

Contract #: N00014-16-P-3039

Award Date: July 2016

Summary:

Highly geometrically constrained underwater environments violate the simplifying acoustic modeling assumption that energy can be modeled in a two-dimensional vertical plane. In a complex three-dimensional (3D) environment, current tools to perform navigation and passive and/or active sonar mission planning are inadequate. Furthermore, channel complexity makes it difficult to impossible to establish reliable acoustic communications. The acoustic complexity is due not only to bathymetric variability, which could be graded by the modeling efforts with a metric measuring the significance of out of plane paths, but by the sound speed and bottom environment variations. River out-flow introduces significant amounts of fresh water that can create strong sound speed gradients, and enough silt to affect higher frequency attenuation and scattering. Large scale debris from shipping and industrial use of the coastal regions, such as wrecks could create false alarms. These difficulties may be reduced by simulating the three-dimensional acoustic pressure field, including propagation, scattering, and reverberation in environments with a high 3D-influence metric. Used in conjunction with sonar performance models, one could then address the optimum placement of sensors in these environments. Acoustic communications (ACOMMS) depends on understanding the sound channel between the source and receiver, and successful communications depend on the simplicity of this channel. Ideally, a single acoustic path connects source and receiver. Historically models are used to estimate and optimize ACOMMS that assume no out-of-plane refraction or scattering. In highly constrained environments, this can be a bad assumption, and can lead to the use of ACOMMS in situations where it cannot be successful. 

Threat localization is also an important capability that is hindered by strongly-3D environments. Long-range bearing uncertainties can stem from refraction near fronts and eddies as well as from bathymetric features. Short-range bearing uncertainties in constrained underwater environments can also be large, and problematic. Ideally all models would be Navy standards and be approved for use in Fleet systems. 

Nx2D propagation models are sufficient if the bathymetry and oceanography are relatively benign. Otherwise a 3D model is required. The best known 3D propagation models are based on finite element, finite difference, parabolic equation, and adiabatic normal mode theories. They excel at low-frequency passive applications. Few are efficient for active applications above 1 KHz or readily provide the acoustic travel time and arrival angle parameters.

Our phase I effort will improve acoustic communications, threat detection and localization, and navigation/collision avoidance in complex 3D environments.

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Submarine Sensor Environmental Inference

Phase I SBIR

Contract #: N68335-18-C-0808

Award Date: October 2018

Phase II SBIR

Contract #: N68335-19-C-0674

Award Date: September 2019

Summary:

Within the increasingly contested undersea operational arena, the Navy needs a tactical and competitive advantage in undersea sensing and threat detection through improved situational awareness with respect to environmental parameters, such as sound speed profile (SSP) and bottom properties, that affect overall submarine sonar sensor performance. Current approaches for enhancing environmental situational awareness rely heavily on historical databases and remote numerical ocean models to provide predictions of the acoustic environment and sensor performance within that environment.

To gain a competitive advantage and enhance the tactical decisions and warfighting posture of submarines, the Navy needs to develop advanced environmental inference capabilities to provide in-situ characterizations of the speed and attenuation of sound in the seabed and water column. The U.S. Navy needs a robust, deep-learning multi-sensor environmental inference framework and algorithms that provide enhanced environmental situational awareness and advanced visualization techniques for existing sensor and sonar performance prediction software processes.

The objective of this program is to develop identify, define, and develop a multi-sensor environmental inference concept, architecture, and operational inference algorithms that:

• Provide a framework for exploiting existing in situ data from sonar systems coupled with other measurements and legacy environmental support products to produce a multi-source inference of the submarine’s surroundings.

• Analyze and specify the sonar or other data requirements necessary to develop and support the determination and representation of the multi-source inference and its uncertainty.

The framework must automate ingestion of available in-situ acoustic and non-acoustic sensor data from multiple sources and optimize collaborative machine learning intelligent agents to:

• Exploit existing in-situ data from multiple sensors within current submarine sonar systems

• Utilize supplemental measurements and legacy environmental support products

• Produce a multi-source inference of the submarine’s environmental surroundings

• Refine the methodology using operational, research and development (R&D), academic, or other measurement data

• Assess the impact of the increased skill on the operation of a candidate sonar system