Clustering and Association for Active Sonar Tracking and Classification
Navy SBIR 2019.1 - Topic N191-016
NAVSEA - Mr. Dean Putnam - dean.r.putnam@navy.mil
Opens: January 8, 2019 - Closes: February 6, 2019 (8:00 PM ET)

N191-016

TITLE: Clustering and Association for Active Sonar Tracking and Classification

 

TECHNOLOGY AREA(S): Battlespace, Electronics, Sensors

ACQUISITION PROGRAM: PEO-IWS5, AN/SQQ-89 Program Offices

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.

OBJECTIVE: Develop novel algorithms using improved energy clustering and association techniques to represent the spatial and Doppler distribution of active sonar returns to improve active sonar tracking and classification performance.

DESCRIPTION: Active sonar performance on Cruisers, Destroyers, Frigates, and Littoral Combat Ships equipped with the Anti-submarine Warfare (ASW) Mission Module currently employ processing algorithms that achieve much less than the theoretically optimal performance. Development of novel algorithms will increase the sonar and combat system automated detection, classification, localization, tracking, and false-alarm capability; and streamline the tasks for reduced operator workload and manning via improved automation.

Active sonar in ASW attempts to differentiate between echoes from submarine targets and the many other echoes from non-submarines, also known as false contacts (clutter). This differentiation is performed by applying a sequence of algorithms to the echoes. These algorithms make up a signal and information processing chain, which is composed of segments associated with detection, localization, tracking and classification. A technology is sought to explore use of spatial and Doppler information to fundamentally improve the detection segment of the active sonar signal and information processing chain.

Detection data, which are indexed by the measurement dimensions of range, bearing, and (for continuous-wave (CW) pulses) Doppler shift, represent a high-energy response relative to the local diffuse background noise and reverberation. The extent of the target and clutter responses in these dimensions can be larger than the system resolution because of their physical size and the spreading induced by acoustic propagation underwater. This results in each contact (target or clutter) being represented by multiple detection points that must be clustered. Current clustering techniques employ an agglomerative hierarchical approach that separates the detection data on a given ping into clusters using a proximity metric. Several approximations to the optimal clustering algorithm are required to enable real-time implementation. Multi-target tracking algorithms then use the cluster data to estimate the position and trajectory of each contact in the scene over multiple pings. Because tracking algorithms generally assume point measurements in range, bearing, and Doppler; a single point (cluster centroid) represents the cluster data.

Limitations of the current approach include reliance on imperfect estimates of the diffuse background (i.e., normalizers); an insensitivity to the anticipated shape of the target and clutter responses (e.g., rings produced by mutual interference, arcs caused by bottom reflections, or separated clusters arising from multipath propagation); an assumption that all clusters within a ping represent independent targets; and a tracking algorithm optimized for point targets. These limitations lead to single contacts being split into multiple clusters and multiple tracks; numerous clutter tracks falsely classified as targets; and true target tracks that are identified late or missed because they are corrupted by clutter clusters.

It is expected that system performance will be substantially refined by new data clustering and data association techniques, expansion of the mathematical representation of the clusters, identification of potentially associated clusters, and use of the new information in target tracking and classification. Potentially applicable emerging science and technology includes alternative cluster representations such as ellipsoids or posterior probability density functions, and (for within-ping cluster association) the use of features that discriminate target clusters from clutter clusters. Research and development is necessary to explore the proper use of these techniques to address the discrimination between categorically different reflectors such that they perform well on data observed in real systems and can be implemented in a real-time system. Target tracking algorithms then need to be developed to exploit the novel cluster representations and cluster-association information. The goal for these improvements to the submarine detection segment of the active sonar signal and information processing chain is to reduce the false track rate by 50% while maintaining probability of true alert and latency, thereby reducing operator workload and staffing requirements.

The intended technology transition will be integration into the PEO-IWS 5 surface ship ASW combat system Advanced Capability Build (ACB) program used to update the AN/SQQ-89 Program of Record.

The Phase II effort will likely require secure access, and NAVSEA will process the DD254 to support the contractor for personnel and facility certification for secure access. The Phase I effort will not require access to classified information. If need be, data of the same level of complexity as secured data will be provided to support Phase I work.

Work produced in Phase II will likely become classified. Note: The prospective contractor(s) must be U.S. Owned and Operated with no Foreign Influence as defined by DOD 5220.22-M, National Industrial Security Program Operating Manual, unless acceptable mitigating procedures can and have been be implemented and approved by the Defense Security Service (DSS). The selected contractor and/or subcontractor must be able to acquire and maintain a secret level facility and Personnel Security Clearances, in order to perform on advanced phases of this contract as set forth by DSS 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 IAW DoD 5220.22-M during the advance phases of this contract.

PHASE I: Develop an innovative concept for data clustering and tracking active-sonar detection data with the attributes related in the Description. Establish feasibility through analytical modeling and development with simulated or recorded data that is analogous to Surface Ship sonar data that will be provided by the Navy. Develop a Phase II plan. The Phase I Option, if exercised, will entail development of initial design specification and a capabilities description to build a prototype solution in Phase II.

PHASE II: Design, develop, and deliver a prototype active-sonar data clustering and tracking algorithm. Demonstrate the prototype algorithm’s performance through the required range of parameters given in the Description, including testing with diverse SQQ-89 data sets provided by the Government at a mutually agreed upon Government- or company-provided facility. Prepare a Phase III development plan to transition the technology for Navy production and potential commercial use.

It is probable that the work under this effort will be classified under Phase II (see Description section for details).

PHASE III DUAL USE APPLICATIONS: Assist the Navy in transitioning the technology for Navy use in an operationally relevant environment to allow for further experimentation and refinement. The prototype algorithm will be integrated into the PEO-IWS 5 surface ship ASW combat system Advanced Capability Build (ACB) program used to update the AN/SQQ-89 Program of Record.

Commercial applications that could benefit from the innovative data clustering and data association algorithms include both active and passive remote-sensing systems where the responses of the object of interest or confusable objects are larger than the inherent system resolution in any of the measurement dimensions or where the responses are separated rather than contiguous. Scenarios with moving objects would further benefit from the tracking algorithm developed to exploit the information obtained by the data clustering and data association algorithms. Examples outside of sonar include most applications of radar, lidar, satellite remote sensing, ultrasound, and thermal imaging.

REFERENCES:

1. Gan, Guojun, et al. “Data Clustering: Theory, Algorithms, and Applications.” ASA-SIAM Series on Statistics and Applied Probability, Philadelphia: SIAM, 2007. http://epubs.siam.org/doi/book/10.1137/1.9780898718348

2. Bar-Shalom, Yaakov, et al. “Tracking and Data Fusion.” YBS Publishing, Storrs, CT, 2011. http://www.worldcat.org/title/tracking-and-data-fusion-a-handbook-of-algorithms/oclc/759479036

3. Schupp, Daniel, et al. “Characterization and classification of sonar targets using ellipsoid features.” IEEE Global Conference on Signal and Information Processing (GlobalSIP) 2015:1352-1356. http://ieeexplore.ieee.org/document/7418419/

4. Sibul, Leon, et al. “Lossless information fusion for active ranging and detection systems.” IEEE Transactions on Signal Processing 54/10 2006:3980-3990. http://ieeexplore.ieee.org/document/1703864/

5. Hanusa, Evan, et al. “Contact clustering and classification using likelihood-based similarities.” Proceedings of the Oceans Conference 2012:1-6. http://ieeexplore.ieee.org/document/6404928/

KEYWORDS: Anti-submarine Warfare; Submarine Detection; Active Sonar; Data Clustering; Data Association; Active Sonar Target Tracking

TPOC-1:

Meg Stout

Phone:

202-781-4233

Email:

meg.stout@navy.mil

 

TPOC-2:

Christian Hempel

Phone:

401-832-8648

Email:

christian.hempel@navy.mil

 

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