Proc. SPIE. 9096, Open Architecture/Open Business Model Net-Centric Systems and Defense Transformation 2014
KEYWORDS: Defense and security, Data modeling, Sensors, Control systems, Telecommunications, Algorithm development, Data communications, Computer architecture, Systems modeling, Situational awareness sensors
The concept of operations (CONOPS) for unmanned maritime systems (UMS) continues to envision systems that are
multi-mission, re-configurable and capable of acceptable performance over a wide range of environmental and
contextual variability. Key enablers for these concepts of operation are an autonomy module which can execute
different mission directives and a mission payload consisting of re-configurable sensor or effector suites. This level of
modularity in mission payloads enables affordability, flexibility (i.e., more capability with future platforms) and
scalability (i.e., force multiplication). The modularity in autonomy facilitates rapid technology integration, prototyping,
testing and leveraging of state-of-the-art advances in autonomy research. Capability drivers imply a requirement to
maintain an open architecture design for both research and acquisition programs. As the maritime platforms become
more stable in their design (e.g. unmanned surface vehicles, unmanned underwater vehicles) future developments are
able to focus on more capable sensors and more robust autonomy algorithms. To respond to Fleet needs, given an
evolving threat, programs will want to interchange the latest sensor or a new and improved algorithm in a cost effective
and efficient manner. In order to make this possible, the programs need a reference architecture that will define for
technology providers where their piece fits and how to successfully integrate. With these concerns in mind, the US
Navy established the Unmanned Maritime Systems Reference Architecture (UMS-RA) Working Group in August 2011.
This group consists of Department of Defense and industry participants working the problem of defining reference
architecture for autonomous operations of maritime systems. This paper summarizes its efforts to date.
Open architecture (OA) within military systems enables delivery of increased warfighter capabilities in a shorter time at
a reduced cost.i In fact in today's standards-aware environment, solutions are often proposed to the government that
include OA as one of its basics design tenets. Yet the ability to measure and assess OA in an objective manner,
particularly at the subsystem/component level within a system, remains an elusive proposition. Furthermore, it is
increasingly apparent that the establishment of an innovation ecosystem of an open business model that leverages thirdparty
development requires more than just technical modifications that promote openness. This paper proposes a
framework to migrate not only towards technical openness, but also towards enabling and facilitating an open business
model, driven by third party development, for military systems. This framework was developed originally for the U.S. Navy Littoral and Mine Warfare community; however, the principles and approach may be applied elsewhere within the Navy and Department of Defense.
The purpose of this paper is to define the vision and future strategy for advancing the use of automation in underwater
mine recognition. The technical portion of this strategy is founded on the principle of adapting the automation in situ
based on a highly variable environment / context and the occasional availability of the human operator. To frame this
strategy, a survey of past and current algorithm development for underwater mine recognition is presented and includes a
detailed description on adaptive algorithms. This discussion is motivated by illustrating the extreme variability in the
underwater environment and that performance estimation techniques are now emerging that are capable of quantifying
these variations in situ. It is the in situ linkage of performance estimation with adaptive recognition that forms one of the
key technological enablers of this future strategy. The non-technical portion of this strategy is centered on enabling an
effective human-machine team. Enabling this teaming relationship involves both gaining trust and establishing an
overall support system that is amenable to such human-machine interactions. Aspects of trust include both individual
trust and institutional trust, and a path for gaining both is discussed. Overall aspects of the support system are
highlighted and include standards for data and interoperability, network-centric software architectures, and issues in
proliferating knowledge that is learned in situ by multiple distributed algorithms. This paper concludes with an
articulation of several important and timely research questions concerning automation for underwater mine recognition.
The Mine Warfare (MIW) Community of Interest (COI) was established to develop data strategies in support of a future
information-based architecture for naval MIW. As these strategies are developed and deployed, the ability for these datafocused
efforts to enable technology insertion is becoming increasingly evident. This paper explores and provides
concrete examples as to the ways in which these data strategies are supporting the technology insertion process for
software-based systems and ultimately contribute to the establishment of an Open Business Model virtual environment.
It is through the creation of such a collaborative research platform that a truly transformation MIW capability can be realized.
The purpose of this paper is to outline the requisite technologies and enabling capabilities for network-centric sensor data
analysis within the mine warfare community. The focus includes both automated processing and the traditional humancentric
post-mission analysis (PMA) of tactical and environmental sensor data. This is motivated by first examining the
high-level network-centric guidance and noting the breakdown in the process of distilling actionable requirements from
this guidance. Examples are provided that illustrate the intuitive and substantial capability improvement resulting from
processing sensor data jointly in a network-centric fashion. Several candidate technologies are introduced including the
ability to fully process multi-sensor data given only partial overlap in sensor coverage and the ability to incorporate
target identification information in stride. Finally the critical enabling capabilities are outlined including open
architecture, open business, and a concept of operations. This ability to process multi-sensor data in a network-centric
fashion is a core enabler of the Navy's vision and will become a necessity with the increasing number of manned and
unmanned sensor systems and the requirement for their simultaneous use.
This paper describes our attempts to model sea bottom textures in high-frequency synthetic aperture sonar imagery using
a Gaussian Markov random field. A least-squares estimation technique is first used to estimate the model parameters of
the down-sampled grey-scale sonar images. To qualitatively measure estimation results, a fast sampling algorithm is then
used to synthesize the sea bottom textures of a fourth-order Gaussian Markov random field which is then compared with
the original sonar image. A total of four types of sea floor texture are used in the case study. Results show that the 4th
order GMRF model mimics patchy sandy textures and sand ripple, but does not reproduce more complex textures
exhibited by coral and rock formations.
The purpose of this research is to jointly learn multiple classification tasks by appropriately sharing information between
similar tasks. In this setting, examples of different tasks include the discrimination of targets from non-targets by
different sonars or by the same sonar operating in sufficiently different environments. This is known as multi-task
learning (MTL) and is accomplished via a Bayesian approach whereby the learned parameters for classifiers of similar
tasks are drawn from a common prior. To learn which tasks are similar and the appropriate priors a Dirichlet process is
employed and solved using mean field variational Bayesian inference. The result is that for many real-world instances
where training data is limited MTL exhibits a significant improvement over both learning individual classifiers for each
task as well as pooling all data and training one overall classifier. The performance of this method is demonstrated on
simulated data and experimental data from multiple imaging sonars operating over multiple environments.
Many small Unmanned Underwater Vehicles (UUVs) currently utilize inexpensive, low resolution sonars that are either
mechanically or electronically steered as their main sensors. These sonars do not provide high quality images and are
quite dissimilar from the broad area search sonars that will most likely be the source of the localization data given to the
UUV in a reacquisition scenario. Therefore, the acoustic data returned by the UUV in its attempt to reacquire the target
will look quite different from the original wide area image. The problem then becomes how to determine that the UUV is
looking at the same object. Our approach is to exploit the maneuverability of the UUV and currently unused information
in the echoes returned from these Commercial-Off-The-Shelf (COTS) sonars in order to classify a presumptive target as
an object of interest. The approach hinges on the ability of the UUV to maneuver around the target in order to insonify
the target at different frequencies of insonification, ranges, and aspects. We show how this approach would allow the
UUV to extract a feature set derived from the inversion of simple physics-based models. These models predict echo
time-of-arrival and inversion of these models using the echo data allows effective classification based on estimated
surface and bulk material properties. We have simulated UUV maneuvers by positioning targets at different ranges and
aspects to the sonar and have then interrogated the target at different frequencies. The properties that have been extracted
include longitudinal, and shear speeds of the bulk, as well as longitudinal speed, Rayleigh speed, and density of the
surface. The material properties we have extracted using this approach match the tabulated material values within 8%.
We also show that only a few material properties are required to effectively segregate many classes of materials.