PROJECTED CMMC LEVEL REQUIREMENT
Level 2 (Self)
TECHNOLOGY AREAS
None
MODERNIZATION PRIORITIES
Advanced Computing and Software
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Human-Machine Interfaces
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Integrated Sensing and Cyber
KEYWORDS
SONAR; Array Signal Processing; Surveillance Automation; Acoustic Detection; Acoustic Target Classification; Sonar Operator Workload
OBJECTIVE
Develop an auto-focus signal processing capability to optimize detection of quiet contacts by arrays of hydrophones.
ITAR
The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with section 3.5 of the Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws.
DESCRIPTION
Arrays of hydrophones are used to detect, classify, and localize contacts in the ocean environment. Finding a contact, especially a quiet contact, is extremely challenging due to the large volume of data that needs to be searched as well as the large number of other noise sources (e.g., shipping, fishing, whales, etc.) that generate clutter on the displays.
Array signal processing, also known as beamforming, steers many beams to spatially filter the noise environment and generate a 3-D data volume that is a function of time, frequency, and bearing (i.e., steered beam) that are processed to generate several detection surfaces.
Several parameters can be adjusted to optimize the detection of a signal on an array. One of these parameters is focus range. (Other parameters are more sensitive and will be provided to Phase II awardees). However, only a limited number of display surfaces are typically generated due to processing constraints, and this may not provide the best opportunity to detect all signals. Furthermore, the operators typically have a large workload and are only able to search for a limited number of the available display surfaces.
Automation approaches have been developed for decades to help reduce operator workload. However, a well-trained operator can still detect lower Signal to Noise Ratio (SNR) signals than the state-of-the-art automation. The main reason for this is if the automation detection threshold is adjusted to detect lower SNR signals, it will cause an increase in the number of false alerts that detracts from the search process.
Another approach that is used to reduce the operator workload is ORing, which combines multiple Passive Narrow Band (PNB) displays by taking the maximum value at each time/frequency bin and then combines all contacts found on any of the displays onto a single display; however, it also takes the maximum of the noise bins. This results in ORing loss by increasing the noise floor and reducing the overall SNR.
As a result, automation has not yet solved the operator workload problem and operators are still required to conduct manual search on a limited number of detection surfaces. This leads to system losses that can at times be significant and offers an opportunity to mitigate those losses with a new processing paradigm.
The objective of this SBIR topic is to develop a signal processing approach that will auto-focus on the signal processing (much like a digital camera does) with respect to parameters such as focus range. There is currently nothing available commercially.
The easiest example to understand is range focusing. Let's assume we are trying to track whales and there are several of them at different ranges. If we process a single far field (i.e., distant) focus range, then the close-range whales may barely be detected. Instead, if we process several focus ranges, let's say 10, from close to far, there will be one focus range where each of the whales displays the clearest signal with the highest SNR. Over time, the whales will swim closer and farther, and the best detection range will change. The problem is that the operator doesn't have time to look at the detection surfaces for all 10 focus ranges so instead we need to combine them into a single display that contains the higher SNR instance of each whale regardless of the range where they are.
Different whales will also have different broadband signatures and would be more detectable when averaging over different frequency bands. The optimal frequency band may also vary as the ambient noise environment (such as nearby shipping and weather conditions) changes. If the processing generates a large number of detections in multiple frequency bands, then a user will be able to find the most detectable instance of each whale over time.
Processing multiple focus ranges is relatively straightforward and is largely just brute force processing. The innovative part of this SBIR topic is the use of this larger data volume to build a combined display that contains the best representation of every available signal. This combined display would be the primary search space for the operators and would also be provided with other automation algorithms.
One of the keys to success will be developing an alternative to standard ORing that takes the maximum value at each pixel across the beams being ORed. It is speculated that improvements are possible since the SNR of the signals will be well behaved across the ORing dimension. For example, if multiple focus ranges are combined, there will be one focus range where the signal is strongest, but the signal will gradually degrade as the difference between the focus range and the actual range increases. For pixels that contain noise instead of signal, it is expected that the levels will be more random and that this could be exploited to enhance the signal without increasing the background noise.
Overall, it is expected that this auto focus approach will allow system gains that are currently not being realized with the current signal processing and automation approach. This would significantly improve system performance by providing earlier detections and longer holding times of contact without increasing the operator workload or requiring a complete overhaul of the signal processing and automation framework. And although this does come at an increased computational cost, it would allow us to squeeze every dB out of the signal processing.
Work produced in Phase II may become classified. The prospective contractor(s) must be U.S. owned and operated with no foreign influence as defined by 32 U.S.C. 2004.20 et seq., National Industrial Security Program Executive Agent and Operating Manual, unless acceptable mitigating procedures can and have been implemented and approved by the Defense Counterintelligence and Security Agency (DCSA) formerly Defense Security Service (DSS). The selected contractor must be able to acquire and maintain a secret level facility and Personnel Security Clearances. This will allow contractor personnel to perform on advanced phases of this project as set forth by DCSA and NAVSEA in order to gain access to classified information pertaining to the national defense of the United States and its allies; this will be an inherent requirement. The selected company will be required to safeguard classified material during the advanced phases of this contract IAW the National Industrial Security Program Operating Manual (NISPOM), which can be found at Title 32, Part 2004.20 of the Code of Federal Regulations.
PHASE I
Develop a concept for a technical approach for implementing an auto -focus signal processing and automation capability that accomplishes the intent identified in the Description. Demonstrate this approach by using the focus range parameter as an example using an unclassified simulated dataset. Establish feasibility through analysis and modelling.
During the Phase I period of performance, the government team will provide the simulated dataset that was developed to stress test the various conditions. If the performer does not have experience with array signal processing, this dataset would be preprocessed display surfaces for different focus ranges. If the performer does have experience with array signal processing, then this dataset could be either preprocessed display surfaces or element level timeseries array data. The Phase I Option, if exercised, will include the initial design specifications and capabilities description to build a prototype solution in a Phase II plan.
PHASE II
Develop and deliver a prototype auto-focus signal processing and automation capability using additional optimization parameters identified by the Government team. This will be implemented as a research code and tested against classified datasets provided by the Government to the Phase II awardee(s). Based on the results of the testing, this approach will be refined to improve the system's performance.
It is important to note that this is not intended to be a tracker development effort unless the awardee's solution requires it. If needed, the government will process the test data using an existing tracker solution and provide the track data to the awardee.
Performance and technical requirements to be evaluated include earlier detections and increased time holding of quiet contacts, improvements in the operator's ability to search the data space, and/ or improvements in operator workflow.
Deliver a prototype, software description document, a working copy of the development code, and test results from processing two or more classified datasets.
At the end of Phase II, the development code may be used by the government team for an independent evaluation using additional datasets not provided to the awardee to determine whether follow-on Phase III efforts are justified.
It is probable that the work under this effort will be classified under Phase II (see the Description section for details).
PHASE III DUAL USE APPLICATIONS
Support the Navy in transitioning the technology to Navy use to allow for further experimentation and refinement. Develop production level code that is containerized and assist with integration efforts to incorporate the SBIR-developed code into the Government's Processing Testbed (PTB) which allows development software to be tested on real-time or playback data. Successful integration and testing in PTB would lead to further integration into production software.
This technology does have significant dual-use applicability. The underlying concept is built upon array processing fundamentals that are applicable to SONAR, RADAR, communications, geophysical exploration, astrophysics, and biomedical imaging.
REFERENCES
Benesty, Jacob. "Fundamentals of Signal Enhancement and Array Signal Processing." Hoboken, NJ: John Wiley and Sons, 2017. https://onlinelibrary.wiley.com/doi/epdf/10.1002/9781119293132
Zhu, Jiahua (Editor). "Advanced Array Signal Processing for Target Imaging and Detection." Basel,Switzerland: MDPI, 2024. https://www.mdpi.com/books/reprint/9396-advanced-array-signal-processing-for-target-imaging-and-detection
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