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GEOSPATIAL WORLD FORUM

2018

Geo4IR

Emerging Military & Security Solutions

Dr. AMIT MUKHERJEE

(An Academic Perspective)

Geospatial Intelligence with Fuzzy Logic– Reducing the Fog of War


FOG OF WAR

• The fog of war - is the uncertainty in situational awareness experienced by participants in military operations. The

term seeks to capture the uncertainty regarding one's own capability, adversary capability, and

adversary intent during an engagement, operation, or campaign. Military forces try to reduce the fog of war

through military intelligence and friendly force tracking systems. The term is also used to define uncertainty

mechanics in wargames.

• War is the realm of uncertainty; three quarters of the factors on which action in war is based are wrapped in a fog

of greater or lesser uncertainty. A sensitive and discriminating judgment is called for; a skilled intelligence to scent

out the truth.

— Carl von Clausewitz


Geo-Spatial Intelligence

The term "geospatial intelligence" means the exploitation and analysis of imagery and geospatial

information to describe, assess, and visually depict physical features and geographically referenced

activities on the earth. Geospatial intelligence consists of imagery, imagery intelligence, and geospatial

information.

(The NIMA Act of 1996 establishing the National Imagery and Mapping Agency and the subsequent

amended language in the 2003 Defense Authorization Act as codified in the U.S. Code governs the

mission of the National Geospatial-Intelligence Agency (NGA). The de jure definition of Geospatial

Intelligence is found in U.S. Code Title 10, 467)


Geo-Spatial Intelligence

• Geospatial intelligence (GEOINT) is intelligence derived from the exploitation and analysis of imagery and geospatial information about

features and events, with reference to space and time. This definition applies not only to products and services, but also to the process of

conducting analysis. GEOINT is comprised of the following sub-disciplines:

• Imagery Analysis

The process of examining an image collected from satellites or aircraft to identify features, describe activity and interpret what is occurring

at a given place on the Earth’s surface. AGOis responsible for the tasking, collection, processing, dissemination and archiving of imagery

used by the Australian Defence Force and other government agencies.

• Geospatial Analysis

Entails collecting and analyzing information about features on the ground, their relationships to the Earth and to each other. Geographic

features can be hills and valleys, rivers, buildings, streets or even schools. Using geographic information systems (GIS), the data can be

sorted, examined, analyzed and conclusions can be drawn and displayed in a multitude of ways that would not be possible without GIS.

• Geospatial Information and Services

A combination of the precise location information and associated attributes of natural and man-made features. This combination conveys

the 'what' and 'where' of a feature on the Earth's surface and provides the foundation for a wide range of information to

be integrated and displayed.


FUZZY LOGIC - PRINCIPLE

The question as to how GEOINT is different from other geospatial analytic activities is occasionally asked.

Bacastow Postulations:

GEOINT, rooted in the geospatial sciences, geospatial technologies and critical geospatial thinking, seeks knowledge to

achieve a decision advantage. Analysis occurs as a natural human to technical to human sequence of events.

GEOINT reveals how human intent is constrained by the physical landscape and human perceptions of Earth.

GEOINT seeks to anticipate patterns of life through time.

Data and technical systems used by analysts are human creations and reflect human biases.


FUZZY LOGIC - PRINCIPLE

• GIS, at present, has several limitations, which make them inefficient tools for decision-making.

Biggest limitation is that current commercial systems are based on logical foundation. Current GIS

are predominantly based on Boolean logic.

• Fuzzy logic is an alternative logical foundation coming from artificial intelligence (AI) technology

with several useful implications for spatial data handling. Contrary to traditional logic, fuzzy logic

accommodates the imprecision in information, human cognition, perception and thought. This is

more suitable for dealing with real world problems, because most human reasoning is imprecise.

• Major advantage of this fuzzy logic theory is that it allows the natural description, in linguistic

terms, of problems that should be solved rather than in terms of relationships between precise

numerical values. This advantage, dealing with the complex systems in simple way, is the main

reason why fuzzy logic theory is widely applied in technique.

• Fuzzy Logic Theory vs Probability Theory

• Fuzzy Logic – Fuzzification & De-Fuzzification


• Fuzzification: Fuzzification is the first step in the fuzzy inference process.

This involves a domain transformation where crisp inputs are transformed

into fuzzy inputs. Crisp inputs are exact inputs measured by sensors and

passed into the control system for processing, such as temperature,

pressure, rpm's, etc..

• Defuzzification: Defuzzification is the process of producing a quantifiable

result in Crisp logic, given fuzzy sets and corresponding membership

degrees. It is typically needed in fuzzy control systems.


Weighted Overlay


Reliability Score Distance Terrain Conditions

Rules Based Evaluation Output


Fuzzy Logic Operator Overlay


Fuzzy Logic Operator


Artillery Accuracy with Uni-variate Membership Model : Range


Artillery Accuracy with bi-variate Membership Model : Range & Barrel Condition.


Naval System Impact Analysis on Operational Effectiveness –

Multivariate Model.

Propulsion

Navigation

Weapons Specs.

Command & Control

Sea Condition

Distance


Naval System Impact Analysis on Operational Effectiveness –

Multivariate Model.


FUZZY LOGIC IN COUNTER-INSURGENCY

OPERATIONS

• - with Fuzzy Logic

• Mobility Maps and Training

• Cross Country Route

• Along track route

• Mine Location - > Energy Focusing Ground Penetrating Radar + Automatic Target Recognition

• Ambush points

• Obstacles

• Attack –Counter attack


Is line-of-sight enough to analyze a vehicles vulnerability to an attack?

• Speed?

• Continuous line-of-sight?

• Kill Chain Sequence Time?

• Goal: Improve current methodology to incorporate vehicle speed, target

acquisition time, and continuous line-of-sight to better analyze a moving target.


Current Model: Overhead Angle of Attack Model

Original Problem: Locate and characterize overhead firing opportunities

Programming Language: Python using ArcGIS

Current Model

LIDAR

Route Points

Compute viewshed and point

summaries at each individual point

Excel Summary Table

Viewshed for each point


Identifies every point the vehicle can see and be seen by a 2m tall fire-position above the vehicle (>1° angles).

Viewshed Analysis

0:7,000


Angle and distance are calculated for every visible point (>1° angles).

Higher angles represent a threat from a higher vantage point.

Green = 1-7°

Yellow = 7-14°

Red = 14-80°

Angle and Distance Calculation

0:7,000


• Threat occurrences over total firing opportunities

• Averaged over all route points.

• Table for distribution.

Statistical Aggregation


Infantry - Platoon Level Attack Simulation

DESIGN OBJECTIVES STEP 1. Receive the mission.

STEP 2. Issue a warning order.

STEP 3. Make a tentative plan.

STEP 4. Start necessary movement.

STEP 5. Reconnoiter.

STEP 6. Complete the plan.

STEP 7. Issue the complete order.

STEP 8. Supervise.



• Movement – Guidelines

Make enemy contact with the smallest element possible.

Prevent detection of elements not in contact until they are in the assault.

Maintain 360-degree security at all times.

Report all information quickly and accurately.

Maintain contact once it is gained.

Generate combat power rapidly upon contact.

Fight through at the lowest level possible.



• Infiltration - Guidelines To gather information.

To attack enemy positions from the rear.

To conduct raids or ambushes in enemy rear areas.

To capture prisoners.

To seize key terrain in support of other operations.

To aid a main attack.


Attack Simulation

Receive new Mission Parameters

Tactical deployment Final

Goal / Mission Accomplished

Enemy Analysis Weapons Analysis Terrain Analysis

Issue Warning

Issue Order

Reconnoiter

Tentative Plan

Complete Action plan

Fight / Attack

Fuzzy Supervisor / ANN


PL1-SEC 1 PL1-SEC 2

PL1-SEC 3

PL2-SEC 2

PL2-SEC 3

PL3-SEC 2 PL3-SEC 3 PL3-SEC 1

PL3-SEC 3 PL3-SEC 3

Forest Area – Tree Cover

Hillock 1 Hillock 2

Stream

Bridge 2

Bridge 1

Mound

Cover

Enemy Section 1 Enemy Section 2

Enemy Section 2


THANKS

JAI HIND

reducing the fog of war dr. amit mukherjee (an … · add your company logo here on master slide...

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