Clustering and Association for Active Sonar Tracking and Classification
AREA(S): Battlespace, Electronics, Sensors
PROGRAM: PEO-IWS5, AN/SQQ-89 Program Offices
technology within this topic is restricted under the International Traffic in
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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
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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.
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.
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
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.
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.
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
It is probable that the work under this effort will be classified under Phase
II (see Description section for details).
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.
Gan, Guojun, et al. “Data Clustering: Theory, Algorithms, and Applications.”
ASA-SIAM Series on Statistics and Applied Probability, Philadelphia: SIAM,
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
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/
Sibul, Leon, et al. “Lossless information fusion for active ranging and
detection systems.” IEEE Transactions on Signal Processing 54/10
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/
Anti-submarine Warfare; Submarine Detection; Active Sonar; Data Clustering;
Data Association; Active Sonar Target Tracking
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