TECHNOLOGY AREA(S): Battlespace, Electronics, Sensors

ACQUISITION PROGRAM: PEO-IWS5: Surface ASW Combat System Integration, Surface ASW System Improvement

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 algorithms to use features of acoustic echoes to track and correctly classify multiple targets in noisy, cluttered environments, leveraging extended feature processing and kinematic association to enhance tracking and classification techniques.

DESCRIPTION: Active sonar in anti-submarine warfare (ASW) discriminates between echoes from submarine targets and clutter echoes, which are characterized by signal to noise ratio, echo range, bearing, and Doppler. Traditional tracking methods rely on consistent motion of the high-energy echoes in range, bearing, and Doppler space over multiple active sonar transmissions. While numerous sophisticated algorithms are associated with differentiating submarine targets from surface ships and clutter, these algorithms have traditionally been developed for situations where a single submarine is present.

Future conflicts could involve multi-ship combat operations with a peer competitor that would involve multiple targets in the midst of large amounts of clutter and high ambient noise. In this situation, state-of-the-art trackers, which rely primarily on kinematics, produce large numbers of false tracks, broken tracks, and incorrectly associated tracks. These bad tracks increase false alerts and miss or catastrophically delay true alerts. It is believed that considering features of echoes as well as kinematics can reduce false and broken tracks while retaining or improving correct target identification.

The Navy envisions future multi-ship operations that involve a diversity of sensors on manned and unmanned platforms, increasing the number of potential features that can be considered by a hybrid tracking system.

Tracking algorithms that combine emerging feature-aided detection techniques and kinematic tracking are desired to improve the effectiveness of active sonar against multiple targets in the cluttered, noisy acoustic environment expected during multi-ship ASW conflicts with peer competitors. These improved algorithms will also be crucial to the effectiveness of unmanned platforms utilizing active sonar.

Feature-aided tracking algorithms use measured echo features to inform how the tracker associates� consecutive echoes among the many potential echoes pass the kinematic test. Basic research in this area has been conducted on use of non-kinematic features as it relates to non-acoustic sensors, such as radar [Refs. 1-5]. However, innovation is required to apply emerging feature-aided tracking concepts to active sonar conducting real-time tactical operations in a diverse range of operating environments. The Navy has data sets representing a diverse range of acoustic propagation environments, bathymetric conditions, and operational conditions that can be used to evaluate the benefit of technologies developed under this topic.

For tactical sonar systems such as the AN/SQQ-89 surface ship sonar suite, the performance of the feature-aided tracker is expected to be highly dependent on the transmit waveform, environment, and selection of features to be used. Key tactical propagation environments include direct path, bottom bounce, and convergence zone environments. Anticipated sensor diversity in future multi-ship operations is expected to provide additional target feature data such as measurements of sonar cross-section, physical size, and scattering characteristics. This diversity of expanded feature information can be used with the kinematic information to improve the capabilities of automatic tracking and classification systems, especially in high-clutter, low signal-to-noise ratio and target-dense environments. The solution sought will provide innovative feature-aided tracking algorithms to improve track and classification measures of performance by at least 25% in environments with where acoustic clutter makes target tracking particularly difficult. Key measures of performance include track continuity, false alert reduction, increased true alerts, correct target tracking, and reduced target latency.

By improving these key measures of performance, the technology sought by this SBIR topic will streamline sonar-related tasks to reduce operator workload and enable reduced manning via improved automation. Use of this technology on unmanned platforms is anticipated to improve capability in a manner to enable reduction of acquisition costs.

Work produced in Phase II may 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 a concept for combining new feature-aided tracking tools with kinematic algorithms to track and correctly classify multiple targets in noisy (low signal-to noise ratio) cluttered environments. Demonstrate the concept can feasibly improve tracking performance against multiple targets in noisy, cluttered environments by at least 25% for the active sonar domain. Establish feasibility through analytical modeling and development with simulated or recorded sea data. The Phase I Option, if exercised, will require the initial system specification and capabilities description to build a feature-aided tracking prototype algorithm in Phase II.

PHASE II: Develop and deliver a prototype feature-aided tracking capability and evaluate with tactical active sonar data to show it meets the parameters in the Description. Validate the prototype using diverse data sets to evaluate performance across SQQ-89 transmit waveforms and a representative range of environmental conditions. Develop a Phase III plan.

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 Government in transitioning the technology for Navy use in an operationally relevant environment to allow for further experimentation and refinement. Integrate the software algorithms into an updated PEO-IWS 5 AN/SQQ-89 surface ship ASW combat system program. Validate, test, qualify, and certify the feature-aided tracking algorithms by using the ACB programs current 4-step risk reduction test process for incremental upgrades to the AN/SQQ-89 Program of Record, which will be provided after Phase II.

Commercial applications that currently utilize various forms of active acoustic transmission and reception that could benefit from a feature-aided tracking approach include oil exploration, seismic survey, rescue and salvage, and bathymetric survey.

REFERENCES:

1. Bar-Shalom, Y. and Fortmann, T.E. �Tracking and Data Association.� Academic: San Diego, CA, 1991. https://www.worldcat.org/title/tracking-and-data-association/oclc/634834756

2. Blackman, S.S. �Multiple Target Tracking With Radar Applications.� Artech House: Norwood, MA, 1986. https://www.worldcat.org/title/multiple-target-tracking-with-radar-applications/oclc/506255895

3. Leung, H. and Wu, J. �Bayesian and Dempster-Shafer target identification for radar surveillance.� IEEE Trans. Aerosp. & Electron. Syst., Vol. 36, No., 2 April 2000, pp. 432-447. https://www.worldcat.org/title/bayesian-and-dempster-shafer-target-identification-for-radar-surveillance/oclc/196175614

4. Drummond, O.E. �On Categorical Feature-Aided Target Tracking, Signal and Data Processing of Small Targets.� Proc. SPIE, Vol. 5204, 2003, pp. 544-558. https://www.worldcat.org/title/on-categorical-feature-aided-target-tracking/oclc/5854750699

5. Drummond, O.E. �Feature, Attribute, and Classification Aided Target Tracking, Signal and Data Processing of Small Targets.� Proc. SPIE, Vol. 4473, 2001, pp. 542-558. https://www.worldcat.org/title/feature-attribute-and-classification-aided-target-tracking/oclc/5854947317

KEYWORDS: Active Sonar in Anti-submarine Warfare; Multi-ship Operations that Involve a Diversity of Sensors; Feature-aided Detection Techniques Combined with Kinematic Tracking; AN/SQQ-89 Surface Ship Sonar Suite; Direct Path, Bottom Bounce, and Convergence Zone Environments; Automatic Tracking and Classification Systems