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© 2013, The MITRE Corporation. ALL RIGHTS RESERVED. .

Cognitive Radar*

Dr. John SantaPietro (Presenter)

Dr. Probal Sanyal

Dr. L. D. Tromp

Dr. David Zasada

14 June 2013

Approved for Public Release.

Distribution Unlimited. 13-0123

*Based on the presentation by D. Zasada

at 6th Annual Military Radar Summit, 26 Feb 2013

© 2013, The MITRE Corporation ALL RIGHTS RESERVED

OUTLINE

■ Overview of the Cognitive Radar Concept

■ Program Activities and Research

■ Description of Results

■ Summary & Conclusions

2

OUTLINE





3


Cognitive Radar Background

4

The Cognitive Radar concept & term

was first introduced by Simon Haykin

in 2006 in a Special Issue of IEEE Signal

Processing Magazine on Knowledge-

Based Systems for Adaptive Radar.

Since then many papers on Cognitive

Radar have appeared in the technical

literature.

The textbook “Cognitive Radar: The

Knowledge Aided Fully Adaptive

Approach” by Joseph Guerci appeared

in 2010.


Motivation and Drivers for Cognitive Radar

■ Some challenges facing modern radar

– Increased radar performance is required to provide the ability to see more demanding classes of targets and also operate in a complex environment

■ Advanced Electronic Attack/Protection (EA/EP)

■ Non-intentional interference and spectrum crowding, reallocation & shortage of available spectrum

■ Complex heterogeneous clutter (natural & man-made- urban areas and wind farms )

■ Hypothesis: These challenges can be effectively addressed by Cognitive Radar (CR) i.e. a radar system having the equivalent of Cognitive Actions** (without consciousness) which include perceiving, thinking, reasoning, judging, problem solving, and remembering

5

Cognitive Property* Cognitive Radar Equivalent

Perceiving Sensing/Communicating

Thinking, Reasoning, Judging, Problem

Solving

Expert Systems, Rule-Based Reasoning,

Adaptive Algorithms & Computation

Remembering Memory, Environmental Database

* National Institute for Mental Health

* *J.R. Guerci, “Cognitive Radar: The Knowledge Aided Fully Adaptive Approach, Artech House, Norwood, MA 2010


All 3 are present in the biological example of the echo-location system of the

bat which changes its transmitted sonar wave from “search” to “approach”

to the “terminal phase” in target pursuit

3 Basic Ingredients of Cognitive Radar*

1. Continuous learning about the environment – Builds upon learning through interactions with surrounding

environment & updating the receiver with this information

2. Feedback from receiver to transmitter – The transmitter adjusts its illumination “intelligently” based on 1.

3. The whole system constitutes a dynamic

closed feedback loop in which learning is

preserved

6

*S. Haykin “Cognitive Radar: A way of the future”, IEEE Signal Processing Magazine, 2006


Conventional vs. Cognitive Radar Architecture*

7

Key Cognitive Radar Features

• Fully Adaptive Transmitter (Tx) & Receiver (Rx)

• Rx and Tx have knowledge aided processing and expert reasoning

• Feedback to the Tx

*J.R. Guerci, “Cognitive Radar: The Knowledge Aided Fully Adaptive Approach, Artech House, Norwood, MA 2010


Conventional Radar Only Adapts Based On

Received Pulse Returns “Cognitive Radar – The Knowledge-Aided Fully Adaptive Approach:” by Dr. J.R. Guerci, IEEE International Microwave

Symposium, June 9, 2011 and IDGA Military Radar Summit Feb 7, 2012

■ Vast knowledge sources cannot currently be utilized directly with most conventional radar embedded computing

■ Radar should have access to all kinds of useful information available on the net (weather, terrain, roads, urban features, etc.)

– BUT will need to access & ingest it in “real-time” a challenge that requires “look ahead” scheduling

8


Knowledge Aided Co-Processor

Enables execution of urban propagation models in real time

General Purpose Graphical Processing Units (GPGPU)

based on graphics architecture (NVIDIA)


Summary of Cognitive Radar Features

■ Fully adaptive: Both Rx and Tx – Full spatio-temporal-polarimetric adaptivity on Tx and Rx

■ Rx and Tx use Knowldege-Aided (KA) processing – Use of Environmental dynamic databases

■ Full feedback path to Tx – Essentail component of a “cognitive” system*

10

* S. Haykin “Cognitive Radar: A way of the future”, IEEE Signal Processing Magazine, 2006

© 2013, The MITRE Corporation ALL RIGHTS RESERVED 11

Cognitive Radar Taxonomy

Application: Complex, dynamic, heterogeneous clutter

Crowded spectrum sharing

Electronic protection

Architecture: Knowledge Aided Perceptive Receiver with Adaptive Front End

Modes: Multiple modes and multiple functions

Algorithms

Environment Sensing

Look-ahead Scheduler

Model-based Reasoning

Expert System Inferential Reasoning

Cognitive Processes: Parametric and Adaptive Models

Real-Time Data Bases

Inference Engine

Linear Programming Solver Sp

ectr

um

of

Co

gn

itio

n


Areas where Cognitive Radar can offer a high payoff

■ Cognitive Radar can offer a large payback for the following

– Electronic Warfare (EW) Applications: focus on Electronic Protection (EP) to counter advance threats

– Spectrum Crowding : Counter unintentional interference, minimize interference and co-exist with other users within the operational frequency bands

– Heterogeneous clutter: Mitigate non stationary, heterogeneous clutter (e.g. across land/water boundaries, road networks, etc.)

12

EP

ES

EAEW

MITRE worked

this application

OUTLINE





13


Background and Program Details ■ FY 12 MITRE funded Research “Seedling” : Feb – Aug 2012

■ OASD(R&E)/RD/Space & Sensors Systems Directorate- led

Radar S&T IPT surveyed projects at service labs

– Among other findings, the IPT identified Cognitive Radar as among

“game changing” technology candidates suitable for additional

research

– Identified potential for significantly improved radar performance,

reduced costs and/or enhance spectrum interoperability

■ OASD(R&E)/RD/Space & Sensors Systems Directorate selected

MITRE to investigate the viability of the cognitive radar paradigm

using Electronic Protection (EP) as example application: July-Dec

2012

– Paper for Tri Service Radar Proceedings 2013 in preparation.

14


FY12 MITRE Seedling Outcomes

■ Survey the technical literature to determine the current state of the art in Cognitive Radar

■ Enumerate example problems that current radars have difficulty handling and where cognitive radar offers a payback

■ Sketch a comparison of CR and conventional STAP solutions and quantify potential performance gain

■ Generate Seedling Products – Report documenting state of the art of Cognitive Radar

and seedling findings and recommendations – Phase II proposal write up and briefing

15


MITRE Seedling Literature Survey ■“Top Ten” reviewed articles

– adaptive waveform design (Goodman et al)

– joint transmit/receive design for clutter estimation (Stoica et al -foreign)

– knowledge based approach to CFAR with results based on measured data (Farina

et al -foreign)

– explicit designs of feedback to the transmitter to optimize tracking error (Haykin

et al -foreign)

■Several major clusters of current activity

– DARPA/AFRL/GTRI (Guerci, Wicks, Melvin) – primarily an outgrowth of KASSPER*

– Comms Research Lab, McMaster Univ, Ontario (Haykin & collaborators)

– ASU/Univ of OK/Naval Post Grad Sch (Goodman/Romero et al)

■J. Guerci consulted on the MITRE Seedling – Meeting at MITRE Mclean 28 March

– Classified meeting to discuss EP application –week of 7 May

16

*KASSPER = Knowledge Aided Sensor Signal Processing & Expert Reasoning


17

OUTLINE





17


Activity for OASD(R&E)/RD/Space & Sensors

■ Electronic Warfare (EW) Applications: focus on Electronic Protection (EP) to counter advance threats

■ investigate the viability of the cognitive radar paradigm using Electronic Protection (EP) as example application

18

EP

ES

EAEW


(1) Perceptive Receiver

(2) Expert System

Learning Tree

(3)

Solver

(4)

Look-Ahead

Scheduler

(5) Adaptive Front End

19

Mitigate

Detect

Assess

How Advanced Radar Hardware And Computational Capabilities Might Be Utilized To Create A Cognitive Cycle


Initial Implementation

■ Simulated cognitive radar Front End with an Agile Active Electronically Scanned Antenna (AESA) executing GMTI surveillance

– Based on previously collected data

■ Built cognitive extensions to baseline waveforms

– Adaptive controls for carrier frequency, pulse repetition frequency, duty cycle, bandwidth, waveform, coherent processing interval, dwell times, beam steering

■ Simulated interference environment

– Linear combination of simulated interference and baseline interference-free recorded data

■ Built expert system learning tree

– Generated objective function incorporating multiple mitigation techniques

■ Used COTS solver to optimize mitigation solutions

– Solved beam by beam and mode by mode

■ Invoked Look-Ahead Scheduler to optimize satisfaction of multiple radar service requests given priorities, costs, and constraints

■ Iterates over multiple radar cognitive cycles and frame times

20


Notional Boundaries of Constraint Space

■ Maximum Frame Times

– Wide Area Search

– Focused Area Search

– Surface Moving Target Track Revisit

■ Volumetric Search Limits

– Maximum Azimuth Search

– Minimum Azimuth Search

– Maximum Range

■ Density of Threshold Crossings

■ False Track Generation Rate

■ Probability of Detection

■ Track Location Error

■ Beam splitting

■ Waveform autocorrelation properties:

– Integrated sidelobe level

– Peak sidelobe level

21

Inputs To

Constrained

Optimization


Results: A Cognitive Cycle Generating Constrained Optimizations of Transmit Schedules

22

0 50 100 150

5

6

7

2

1

8

Time (sec)

Ta

sk

Nu

mb

er

Job Schedule for 6 Tasks - Actual Utilization: NaN

0 50 100 150

5

6

7

2

1

8

Time (sec)

Ta

sk

Nu

mb

er

Job Schedule for 6 Tasks - Actual Utilization: NaN

Baseline Schedule

No Electronic Interference

No Cognitive Mitigations

Cognitively Enhanced Schedule

Electronic Interference

Cognitive Mitigations Implemented

Performance Retained, All Tasks Completed

Average Power Expenditure Increased


23 23

OUTLINE





23


Findings

■ A cognitive capability can be readily added to advanced radars

– A cognitive cycle can be readily constructed

■ Use existing hardware capabilities common to modern radars

■ But implement new drivers: cognitive software overlay cycle linking advanced near-real-time analytic techniques

– Cost of adding cognitive overlay is relatively low

■ Open Systems Architecture greatly facilitate addition of cognitive software overlay

■ Fruitful area to which to apply cognitive radar concepts include

– Spectrum sharing

– Windmill effects mitigation

– Non-homogenous clutter mitigation

– Electronic protection

■ The goal: maximize radar performance

24

The Cognitive Radar paradigm merits further investigation


Perceptive Receiver

Adaptive Transmitter

Feedback Adaptive Front End

Q: What Makes A Radar Cognitive?

26

A: It can learn from what it experiences…

and apply those lessons to new situations


Adaptive Front End: Cognitive Radar Enabling Technology

Generate, amplify, direct, transmit, receive, and condition optimized

waveform(s)

Radiating

Element

Controller

Transmit/Receive

(T/R) Module

Digital

Receiver/

Exciter(s)

(DREXs)

Power

Analog

Front

End

Digital

Front

End


Elements of Cognitive Radar

28

Transmit Radar

Waveforms

Database

Transmit

Antenna

Configurations

Database

Target

Models

Database

Receive

Antenna

Config.

Database

Environmental

Dynamic

Database

Multi-layered

Scheduler and

Executive Memory

Environmental

Scene Actuator

Working

Memory

Multi-layered

Perceptual Memory

Environmental

Scene Analyzer

Adaptive Agile Electronically Scanned Array Front End

Radar Environment

Transmitted

Signals Received

Signals

…

Arbitrary Waveform

Generator(s)

Target

Detections

and

Estimates

Adaptive Transmitter Perceptive Receiver/Signal Processor

Radar

Service

Requests

Primary

Radar

Products

E

n

t

e

r

p

r

i

s

e

S

e

r

v

i

c

e

B

u

s

Cognitive Radar

Elements: • Adaptive Transmitter

• Perceptive Receiver

• Adaptive AESA

• Multiple Interlocking

Feedback Paths

Cognition

Spans: • Antenna

• Transmitter

• Exciter

• Receiver

• Signal Processor

• Data Processor

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