Active Signal Processing Enhancements for Classification of Low Signal-to-Noise Ratio (SNR) Sonar Signals in Doppler Clutter
Navy SBIR 2015.1 - Topic N151-035
NAVSEA - Mr. Dean Putnam - email@example.com
Opens: January 15, 2015 - Closes: February 25, 2015 6:00am ET
N151-035 TITLE: Organic Submarine Multi-Sensor Fusion
TECHNOLOGY AREAS: Sensors, Electronics, Battlespace
ACQUISITION PROGRAM: PEO IWS 5, Undersea Warfare Systems
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 5.4.c.(8) of the solicitation. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws.
OBJECTIVE: Develop an automated organic submarine multi-sensor data fusion capability for submarine sensor systems that meets submarine tactical group requirements.
DESCRIPTION: Submarine combat systems require manual processes and procedures to assimilate information gathered by physical sensors into a tactical picture. The tactical picture is used by a submarine’s command team and crew to understand and respond to the operating environment. To generate the tactical picture, the submarine crew evaluates contact or track information across sensor classes for a number of factors related to similarities in kinematic and spectral properties. If properties across tracks are sufficiently correlated, contacts are "fused". This process provides more information for tactical level tracking to improve the track and reduce the number of contacts improving situational clarity and enabling the Submarine’s command team and crew to understand and respond to the operating environment more effectively. Fully automated systems exist in a variety of DoD, DHS, and commercial systems. Radar systems are such an example. However, providing a unified tactical picture through sensors with weak range resolution, in contact rich environments while searching for weak and elusive targets, remains a difficult problem. Submarine sonar systems are an example of this. Providing a solution to this remains a difficult problem.
This topic pursues a software subsystem that would interact with the tactical system of a submarine to fully automate data fusion techniques to produce a tactical picture through association of contact information across multiple sensors, both acoustic and non-acoustic [ref 1]. Although automated association of all contacts all of the time is extremely difficult, if not impossible, there are many cases where sufficient information is available to produce high confidence associations and to improve the command teams understanding of a contact’s position and velocity. For example, existence of strong narrowband content across spectrally overlapped acoustic sensors is useful to initiate and maintain association of two independent sensor level tracks for the purposes of generating a single composite fused contact.
The small business will need to develop a collection of physics-based techniques operating within a probabilistic framework designed to exploit contact features across both similar and dissimilar sensing systems [ref 2] and innovations on what data can be fused reliably. For example, it should be clear that acoustic data from an array of towed hydrophones is unlikely to share any spectral information with optical data from the periscope; however, environmental effects may encode closing geometry characteristics in both the acoustic and optical data. Also, for example, raw spectral information from a towed sensor may not permit a direct signature level correlation with a hull-mounted sensor due to separation in the operating spectra; however, known engine characteristics may allow the determination that these different separate spectral bands hold narrow band components of a common mechanical origin. The approach to interface with the hosting Tactical Control System should be best suited to the proposed data fusing concept(s) and should provide salient metrics to measure and monitor in-situ.
Like few other platforms, the submarine is vitally dependent on its sensors during periods of total submersion. Collecting, associating, and assimilating acoustic data to generate the tactical and operational picture is the highest priority. Means to use non-acoustic sensor data to compare and fuse acoustic evidence is desired for periods when the submarine is at periscope depth; however, this is a secondary consideration.
Of great importance to any concept transitioning to operational use will be a means to provide confidence to the command team and crew that the automated systems are working correctly and accurately. A critical factor for success is then a demonstrable means for the concept to provide transparency to the operator on all facets of the data collection, association, and assimilation. In addition to this transparency, a means to "self-regulate" is of equal importance. We define self-regulation as the property of the system to assess inputs and accurately characterize its fused contact output in terms of uncertainty or confidence. Empirical and analytic techniques for this self-assessment are well known [refs 3, 4]. A successful concept must then self-regulate to report when operational thresholds for confidence are not satisfactory to remain under automated contact fusion. Effective approaches will provide a means for rapid and effective operator interaction with the system to act when manual attention is required.
PHASE I: The company will develop a concept for an organic submarine multi-sensor data fusion capability that meets the stated requirements in the description section. The company will demonstrate the feasibility of the concept in meeting Navy needs and will establish that the concept can be feasibly developed into a useful product for the Navy. Simulated testing and analytical modeling will establish feasibility.
PHASE II: Based on the results of Phase I and the Phase II contract statement of work, the company will develop a prototype for evaluation. The prototype will be evaluated to determine its capability in meeting Navy requirements for the organic submarine multi-sensor data fusion capability. System performance will be demonstrated through prototype evaluation and modeling or analytical methods over the required range of parameters including but not limited to its ability to fuse data from multiple acoustic and non-acoustic sensors into correlated contacts, operate autonomously to provide a tactical picture of fused data contacts, interface with existing submarine Tactical Control Systems, provide salient metrics to measure and monitor contacts in-situ, and provide a means to determine/report confidence level of the automated data fusing contact reporting. Evaluation results will be used to refine the prototype into a design that will meet Navy requirements. The company will prepare a Phase III development plan to transition the technology to Navy use. Phase II has the potential to be classified.
PHASE III: The company will be expected to support the Navy in transitioning the technology for Navy use. The company will develop an organic submarine multi-sensor data fusion capability according to the Phase III development plan for evaluation to determine its effectiveness in an operationally relevant environment. The company will support the Navy for test and validation to certify and qualify the system for Navy use.
PRIVATE SECTOR COMMERCIAL POTENTIAL/DUAL-USE APPLICATIONS: Technology developed under this effort could be of benefit to areas such as persistent surveillance and homeland defense, where significant diversity of data types and minimal geometric perspective sensors makes finding and exploiting spectral and kinematic feature information difficult.
2. Stone, Lawrence; et al., Bayesian Multiple Target Tracking Second Edition, Artech House, 2014.
3. Ristic, Branko; et al., Beyond the Kalman Filter: Particle Filters for Tracking Applications. Artech House, 2004.
4. Edited by, Van Trees, Harry and Bell, Kristine, Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking, Wiley Interscience, 2007.
KEYWORDS: Submarine combat systems; automated data fusion; assimilating acoustic data; automated association of contacts; acoustic sensors; non-acoustic sensors
Offical DoD SBIR FY-2015.1 Solicitation Site: