cervus defence and security ltd - introduction to · 2018. 8. 13. · cervus • we were formed in...
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Introduction to Cervus
• We were formed in 2013
• We come from Force Development and Collective Training
Backgrounds
• We exploit military and security training data to provide our
customers with a comprehensive understanding of their
operational performance.
• Using pioneering data capture systems, industry standard
analytics and secure data storage solutions, we provide
services to exploit your training data.
The Col lect ive Training System
• Environment System – what is
required to create the appropriate
Environment
• Scenario System – what is
required to wrap around the
environment with a logical and
consistent scenario
• Capture System – what is
required to capture appropriate
contextual data and performance
data on the training subjects
• Analysis System – that which is
required to collate, analyse and
manage data
• Exploitation System – what is
required to exploit the data for
AAR and as long term actionable
insights
MANAGEMENT RULES
SCENARIO
ENVIRONMENT
CAPTURE SYSTEM
ANALYSIS SYSTEM
EXPLOITATION SYSTEM
Commander Force Development and Training (Comd FDT) July 2011
The central idea is unlocking
power through applying a concept
of exploitation [of] what we can
learn from current operations,
from our training and from
experimentation within training.
Balancing Act
Training Experimentation
Focussed on the
achievement of
Collective Training
Objectives (CTOs)
Data capture
typically to
support an after action review
Known costs and budgeted
Limited opportunity for
repeatability
Focussed on the
experimentation
objectives and evidence
Data capture to
inform measures of
performance / effectiveness
Requiring
repeatabilityUnknown costs and
limited budget
Our Vis ion for Tra in ing Data Explo i ta t ion
Harvest All Data
LVC and C2
Objective Data
Intelligent Analysis
Analytics Broad Utility
F r o m s e l e c t i v e d a t a g a t h e r i n g t o h a r v e s t i n g a l l d a t a .
• The reduced cost, size and power consumption of
modern sensors, power storage technologies,
communications systems and data storage
devices means there is no longer a need to limit
data gathering to small data sets.
• It is now technically possible to harvest and record
virtually all data, linked to all participants
(BLUEFOR, OPFOR, wider COEFOR and
observers themselves) associated with every
training event: allowing for an exponentially richer
training data set to mine and exploit – enabled by
a “capture everything now, use anything later”
approach.
F r o m u s e o f L i v e t r a i n i n g d a t a t o t h e u s e o f L i v e , V i r t u a l , C o n s t r u c t i v e & C o m m a n d d a t a .
• Traditionally, the main focus for training data gathering has
been within the Live training environment, and then mostly
positional and firing data. However, there is a plethora of
wider useful training data that can be harvested: human
biometrics; vehicle usage, performance and health; C4I
(voice, data and meta-data); ISR; topographical and
meteorological data to name but a few.
• Also as the Live, Virtual and Constructive training
environments continue to blend ever more seamlessly into
a single synthetic training environment, there is an
opportunity to draw (and merge) training data from each of
these synthetic domains to provide a far more holistic
training data set to mine and exploit.
• Comms data and HUMS are both undervalued and
underused training data sources.
F r o m p r i m a r i l y g a t h e r i n g s u b j e c t i v e d a t a t o t h e h a r v e s t i n g o f o b j e c t i v e d a t a .
• Whilst some limited objective data is currently gathered,
new and emerging technologies allow for the routine and
automatic harvesting and storage of far more, and far
wider, objective data sets.
• This will free the training observer to concentrate more
on expertise-based subjective observation but, as
importantly, will provide a wealth of context within which
to ultimately frame far more meaningful and useful
insights from training.
F r o m m a n u a l a n a l y s i s t o a u t o m a t e d , i n t e l l i g e n t ( A I ) a n a l ys i s .
• The relentless development of ever-more ‘intelligent’
machines provides an exceptional opportunity to move
rapidly away from the resource-heavy, time-consuming
activity of manual data analysis to an automated – even
intelligent – analytical approach.
• Self-adapting algorithms, pattern recognition
technologies and machine learning approaches now
mean that the drawing of meaningful insights from
masses of data is simple, time-efficient, ever-improving,
self-teaching and increasingly affordable. The obstacles
that the manual processing and analysis of large
amounts of training data once presented are now easily
surmountable.
F r o m d e s c r i p t i v e a n a l y s i s t o p r e d i c t i v e & p r e s c r i p t i v e a n a l y s i s .
• This machine learning capability now allows for a
genuine step-change from purely Descriptive analytics
(What has just happened? What is happening now?) to
Predictive analytics (What is likely to happen next,
based upon experience?) to Prescriptive analytics
(What could/should be done about what is likely to
happen next, so as to achieve a positive outcome?).
• From retrospective after-action consideration of training
data to real-time interpretation and understanding.
• From observed facts (pure data) to informed insights
(via analysed data in context).
• From simple observation of training, mostly after the
event, to helpful, proactive interventions during (and
even before) training.
F r o m l i m i t e d t o b r o a d u t i l i t y .
• By making use of open architectures and common
standards and modern cloud-based storage and
processing technologies, the training data gathered, and
the analysis drawn from it, will be of use not just to the
immediate training community but also to individuals
and the wider field army, force development,
research, experimentation & acquisition communities
– all of whom will be able to access the data they need
whenever, and from wherever, necessary.
HIVE- a solut ion
• HIVE will be unparalleled in its adaptive ability to
harvest, categorise, store, and analyse data
from collective training environments.
• More than that, HIVE can deliver genuinely
comprehensive and exploitable insights via its
innovative machine learning engine and unique
visual reporting systems: thus, allowing
commanders to quickly spot and interpret trends
from training, gain context-derived insights from
them, and to rapidly and clearly identify
opportunities for enhancements to warfighting.
• HIVE can truly build ‘winning foundations’.
Matthew Syed
You need judgement and creat iv i ty to
determine how to f ind solut ions to what
the data is te l l ing you, but those
judgements, in turn, are tested as par t of
the next opt imisat ion loop. Creat iv i ty not
guided by a feedback mechanism is l i t t le
more than whi te noise. Success is a
complex interp lay between creat iv i ty and
measurement , the two operat ing together ,
the two sides of the opt imisat ion loop. ”
HIVE Demonst ra t ionCollective Training Event
Assessment ProgrammeId Name Start Date End Date
1
Initial
Assessment 07/05/2018 07:35 07/05/2018 12:13
2 Mission 1 07/05/2018 13:35 07/05/2018 16:13
3 Mission 2 08/05/2018 07:35 08/05/2018 10:11
4 Mission 3 08/05/2018 12:35 07/05/2018 14:10
5 Mission 4 09/05/2018 07:35 09/05/2018 12:26
6
Initial
Assessment 08/05/2018 06:55 08/05/2018 12:13
7 Mission 1 08/05/2018 12:35 07/05/2018 14:10
8 Mission 2 09/05/2018 07:35 09/05/2018 12:26
9 Mission 3 09/05/2018 14:13 09/05/2018 16:16
10Mission 4 10/05/2018 07:05 10/05/2018 14:15
11
Initial
Assessment 09/05/2018 07:05 09/05/2018 12:11
12Mission 1 09/05/2018 14:40 09/05/2018 16:43
13Mission 2 10/05/2018 07:05 10/05/2018 12:11
14Mission 3 10/05/2018 14:10 10/05/2018 16:32
15Mission 4 11/05/2018 07:09 11/05/2018 12:23
Id Msn 4 Events
1 Battle Preparation
2 Vehicle Move to LoD
3 Pre designated Fires on Enemy Target
4 Adjustment of Fires
5 Fire Support from MIV
6 Cross LoD
7 Assault Building 1 Room 1
8 Assault Building 1 Room 2
9 Assault Building 1 Room 3
10 Enemy Fires onto BLUEFOR
11
Enemy Counter Attack from Building 2 onto
Building 1
12
Exploit and Assault from Building 1 to
Building 2
13 Assault Building 2 Room 1
14 Reorganisation
15 Casualty Extraction
16 POW Extraction
17 Hot Washup
18 Training System Reset
Scenario and Environment
Fires Bde Fires
Fires BG Fires HICON Coy HQ
Dismount Sect Comd
Dismount Sharpshooter
Dismount Grenadier
Dismount Grenadier
Dismount NLAW
Dismount ASM
Dismount Sect 2IC
Dismount Rifleman
MIV AFV
Crew Driver
Crew Gunner
Crew Commander
Building Enemy Position
Building Enemy Position
Observer Mentor Primary OM
Observer Mentor Secondary OM
Observer Mentor Primary OM
Observer Mentor Secondary OM
Enemy Dismount Commander
Enemy Dismount Sharpshooter
Enemy Dismount Grenadier
Enemy Dismount Anti Tank
Enemy Fires Combat Team Fires
Simulation:
• Noise
• Smell
• Blast /Vibration
Capture Systems
• I-DES
• DFWES
• AWES
• HR Monitor
• HUMS
• Joint Fires Virtual
• I-DES
• I-DES
• Joint Fires Virtual
• AWES
• Warrior Metric
• Training
Management and
Capture Tablets
• DFWES
• AWES
• I-DES
• DFWES
• AWES
• Joint Fires Virtual
• I-DES
• Joint Fires Virtual
• AWES
Current Systems
Analysis and Exploi tat ion
Harvest All Data
LVC and C2
Objective Data
Intelligent Analysis
Analytics
Broad Utility