Autonomic Subsystems for Cognition in Passive Coherent Location

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Autonomic Subsystems for Cognition in Passive

Coherent Location

Michael Inggs,Gunther Lange, Yoann Paichard

Department of Electrical Engineering, University of Cape Town,Rondebosch 7701, South Africa

Email: [email protected]

AbstractIn a previous paper [1] we mentioned that Pas-sive Coherent Location (PCL) can be thought of as CognitiveRadar[2]. The deployment of PCL systems (also known asPassive Bistatic RadarPBR) is fraught with difficulty, even inthe situation of a spatially static network of transmitters andreceivers. It is well known that PCL systems have to take intoaccount the strong, direct signals of cooperative and opportunistictransmitters used, and try to use terrain or antenna nulls[3]to mitigate the receiver dynamic range requirements. Receiverposition in the terrain also influences the coverage. This results

in a complex planning environment requiring propagation pre-diction tools to assist in selection of the best site [4]. The situationbecomes worse when the network of transmitter and receiversbecomes dynamic. In this paper, we discuss the cognition andnetworking requirements for PCL systems consisting of movingtransmitters and receivers, forming a cognitive, sensor network.We show that a sensible approach would use the structure ofhuman intelligence, which consists of a higher level integratingfunction, together with autonomic[5], lower level, subsystems.

I. INTRODUCTION

PCL type radars utilise electromagnetic emissions from

transmitters of opportunity or commission. This is similar

to the Cognitive Radio concept [6]. In cognitive radio, for

example, it was postulated that handsets could cooperate inrelaying of signals between peers, forming ad hoc networks,

that would rendezvous in clear spots in the spectrum in which

the network was immersed. In addition, the network of closer

spaced peers would use considerably less power using peer

to peer interconnection and relaying. Handsets would require

significant cognition capability to carry out this dynamic

organisational adaption.

Cognitive Radar[2] has been discussed by Haykin in terms

of improving the performance of radar systems, especially

in the area of tracking performance. In our paper, we are

taking a more fundamental look at the architecture of a PCL

radar system and seeing how the organisation of the human

nervous system might have useful analogues with the design

of these systems. We note that the growing field of Networked

Radar sensors, and existing systems such as Air Traffic Control

(ATC) have much in common with this proposed paradigm.

In our previous paper [1] we argued that a spatially dis-

tributed network of transmitters and receivers attempting to

provide target detection over a region of interest would need

to be cognitive i.e.decide which transmitters and receivers

would provide optimum coverage of an area of interest. The

fixed nature of the network considered in this earlier work,

however, means that the cognition was limited to adaption

i.e. dealing with the loss of transmitters and / or receiver

(due to maintenance or to enemy action), and, for the tracking

problem, deciding on the optimum input data to the tracking

filter. Most importantly, the planning of the network was

once-off operation, followed by command and control from

the central node, to obtain target plots for tracking purposes

In this paper, we discuss in more depth, the networking

issues of such a spatially distributed, moving, PCL systemand the more comprehensive adaption that will be required i

the transmitters and receivers are no longer stationary. In thi

case, even the interconnection will become complex, due to

line of site changes with time. It will be seen that emulation

of the autonomic subsystems of living organisms would b

a good model i.e. the effective operation of the system i

split between a higher level intelligence, and a lower level

autonomic systems, that are self-organising.

In Section IV we investigate the key elements of a Cognitive

PCL system, depicted in the cartoon system block diagram

(Figure 1). In this section we indicate the important an

variable parameters of these components, targets and th

propagation environment in which they operate. We allow thesubsystems to operate autonomically, supplying higher leve

information to the cognition system, rather than lower leve

data (e.g. raw plots).

Section IV discusses the cognition process that would need

to take place at node level versus system level. The analogy

here is with a higher order living organism, where autonomic

subsystems are responsible for lower level, detailed operation

and the brain coordinates at a higher level. We need to review

the current understanding of intelligence, and this is the subjec

of Section II. We show that the capabilities of the human mind

are partially understood, and are a summation and overlaying

of primordial nervous systems.

I I . EVOLUTION AND OURU NDERSTANDING OFH UMAN

INTELLIGENCE

In this section we discuss (very briefly) our understanding

of the functioning of higher level intelligence, as embodied in

the human brain and its allied systems[7]. Clearly the under

standing of human intelligence is a current area of research

and the development of new brain scanning technology i

assisting with this. It is beyond the scope of the paper t

give a full description of the theories and conjecture of how

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Fig. 1. Cartoon description of a cognitive PCL system, consisting of multiple transmitters and receivers, spatially distributed, and operating over a widfrequency range. We see data links linking receivers to a remote controller. For simplicity we have left out the links directly between receivers, and alspossible controlled transmitters, which can be fixed or moving.

human intelligence became the pinnacle of living intelligence.

However, examination of lower orders gives and insight as to

how human intelligence evolved.

Simple organisms have a rudimentary intelligence, sufficient

for survival. These are largely hard-wired and inflexible,

providing enough control to ensure harvesting of food for

existence, growth and reproduction. As we move up the scaleof organism complexity, capabilities such as movement enter

the picture, and the rudimentary animal now has the capability

of sensing predators, and taking evasive action to avoid being

eaten. This action is instinctive and inflexible. The steps in the

response are genetically programmed.

Moving upwards in the hierarchy of intelligent animals,

the animal may have developed methods of retaliating, and

the nervous system must now make a decision whether to

stand and fight, the so-called, flight or fight decision. Again,

this can a largely be genetically programmed, or, become

deliberate and adaptive, as the animal represents a higher form

of intelligent life.

The complexity of responses may be difficult to ascribe

to mere genetic programming, but one must remember that

the principle of evolution is that only those animals with the

response most lively to ensure nutrition, growth, reproduction

and the ability to avoid destruction will survive. It is thought

that human intelligence has evolved in waves, and that the

higher level capabilities are wrappers over the primordial

systems[7], [8]. This is interesting in that it indicates in the

very powerful structure of human intelligence, the system

engineering has been evolutionary, and that certain basic

functions have carried through and form part of this mor

capable system, and that the powerful forces of evolution have

not eradicated them.

We should thus look carefully at the structure of human

(and lesser) intelligences, and when establishing the functiona

and performance requirements of new sensors, see what w

can learn from the distribution of processing in these evolvedintelligences. In the next section, we briefly discuss cognition

and our attempts to understand human intelligence. We then

move to look at one of the primordial subsystems of the human

nervous system i.e. the autonomic nervous system, which we

propose might have many lessons in the design of a rada

sensor, especially a network of such systems.

The understanding of human cognition has gone through

a number of evolutions, and the state of this understanding

(now known as cognitive science is not in a rosy state[9]

Dupuy[9] believes that the start of modern cognitive science

lies in a series of meetings (The Macy Meetings) held between

1948 and 1953 to discuss a topic known as cybernetics. Thes

meetings involved some of pre-eminent engineers, scientistand mathematicians of the time, and sought to understand

the mechanisation of human intelligence. Dupuy then paint

a picture of controversy and a lack of real delivery by th

field, which avoids its cybernetics origins and has moved on

to carry the cognitive title. Beyond the scope of this pape

is an interesting discourse by Dupuy on the achievements and

failures of cognitive science.

The message to be carried out of this is probably that rada

systems thinkers are not going to gain a large insight into how

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to implement of cognition in radar systems, but certainly, the

achievements of medical science in understanding the physical

architecture of the human physiology do provide us with

structural inspirations. How cognition is actually implemented

is the controversial issue.

In the next section we give a brief overview of autonomic

systems and then, the operation of a PCL system.

III. AUTONOMICS YSTEMS

An detailed description of the human brain and its subsys-

tems is beyond the scope of this paper, but a good overview is

given by Gibb[7]. The purpose of this paper is to lift out the

autonomic system of the brain, which we believe to be very

applicable to a network of PCL radar receivers, as described

in Section IV. Figure 2 is a schematic view of the human

autonomic system.

The autonomic nervous system of many organisms (ANS

or visceral nervous system) consists of sensory (afferent) and

motor (efferent) subsystems[5]. These functions are largely

autonomous. For example, the spine of most animals is re-

sponsible for much of the detailed motion of limbs, receivingonly high level triggers from the brain itself. Similarly, the

stomach carries out digestion largely unsupervised, releasing

complicated sequences of digestive chemicals and informing

the brain of needs and problems.

In the human nervous system, the autonomic system is

connected to the hypothalamus, and represents the primordial,

instinctive nature of humans. Only high level commands from

the hypothalamus are connected through the amygdala to the

brain (to simplify the real situation). In a cognitive PCL,

the sensors themselves would be considered as afferent, and

we do not seem to have any significant examples of efferent

subsystems, unless the transmitters and receivers used their

platforms to make changes in position to improve sensing.For example, a standoff transmitter could move itself in space

or frequency to improve coverage. This thinking (i.e. mobile

PCL) is beginning to be interesting.

Work that has much in common with the discussion around

cognition in PCL is the topic of autonomic computing pro-

posed by IBM in 20031. They define the concept through eight

characteristics2 which seem to be discussing the cognitive

behaviour of the system through autonomic operation. Some

of the important characteristics of the computing system using

autonomic subsystems relate to:

1) The system must know about its elements, and what

resources can be marshalled.2) The system must be able to reconfigure itself to deal

with changing environments, but optimising the use of

its elements.

3) It should be able to heal itself by replacing or recon-

figuring remaining resources after a loss of resources (in

our case, transmitters and receivers).

4) It should try and protect its assets.

1http://www.research.ibm.com/autonomic/index.html2http://www.research.ibm.com/autonomic/overview/elements.html

Fig. 2. Schematic view of the human autonomic system, showing the sensoand motor subsystems and their connection to the brain. (adapted from GrayAnatomy)

We now move on to discuss a PCL system in terms o

the functionality of autonomic systems. This will mean tha

the autonomic PCL system will be responsible for adapting

itself to obtain optimum information for the higher leve

systems. This is especially important in terms of minimising

data transfer between individual nodes and the higher orde

command and control system.

IV. PCL NETWORKD ESCRIPTION ANDL INK TO

AUTONOMICS YSTEMS

To restrict the scope of this paper, we consider PCL

networks tasked with tracking airborne targets. It the widecontext, the discussion could concern any network of sensor

detecting objects of interest and reporting to a higher level in

the observation system. However, we postulate that the PCL

sensor network autonomously adapts itself to provide optimum

data to the cognitive, higher level, system.

The functions that could be combined to form autonomic

subsystems are described in the following subsections. W

also describe some of the capabilities that the subsystems tha

would be needed.

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A. Predicting Sensor Performance

Transmitters and receivers need to be correctly placed, or,

to decide which other sensors with which to network. In

a previous work [10], [4], and a paper in this conference

[3], we detail how it is possible to predict quantitatively the

coverage of a transmitter / receiver pair. The method needs

to know either the position of a target, or, the volume /

surface that defines its movement. The method uses terrainmaps and propagation modelling to measure this. Using this

method, each receiver can decide which transmitter to utilise,

at which frequency, and at which position. Similarly, mobile,

cooperative transmitter could move to maximise area coverage

for a give network of receivers and other transmitters.

The important point arising from this capability is that a

sensor has criteria it can use to optimise itself to be most

useful to the sensor network.

B. Transmitters

These will either be uncooperative members of other in-

frastructure (e.g. broadcast transmitters), or, transmitters con-

trolled by the cognitive system. A PCL system set up forair traffic monitoring will use fixed, terrestrial transmitters,

usually part of broadcast systems. A military, ground based

air defence system would, however, want to have a backup of

its own transmitters, as civilian resources would not be pre-

dictable or reliable. The utilisation of the transmitter resource

is potentially a difficult task.

If the illuminators and receivers are carried by unmanned

airborne vehicles (UAVs), then the positioning can be decided

autonomically, without requiring system level intervention.

C. Receivers

The receivers of the cognitive PCL system are currently

thought of as having the most autonomic functionality i.e. theywill continuously adapt themselves to the coverage require-

ments and the changing interconnectivity. They will probably

have sophisticated, multiple null steering antennas to minimise

dynamic range requirements[11].

The receiver will sense the electromagnetic (EM) environ-

ment. Potential transmitters will be assessed again the sophis-

ticated propagation modelling mentioned in Section IV-A, and

the best transmitters used. This modelling will need to have

transmitter location, and this information will come from the

higher level, or, from triangulation measurements with which

a number of receivers will need to cooperate.

The receiver will generally need to extract doppler-delay

information from the targets, and plot extraction after detec-

tion. Receivers interconnected with high quality data links may

transmit coherent data to an integration processor, higher in

the network.

D. Networks of Data Links

The interconnection of the nodes of a PCL system is

essential for enhanced performance that is likely from a

cognitive system. The autonomic sensor receivers need to

receive commands from the central intelligence, and also have

access to peer nodes for configuration data. The network

available will vary enormously in capacity and Quality o

Service (QoS), due to technology, covertness and propagation

paths available. These data links will determine the strategie

available to the autonomic receivers.

The data links themselves can be part of a Cognitiv

Radio network. Such a network would be able to utilised the

minimum amount of energy to provide robust, peer to peer

relaying of data.

E. Terrain

Terrain knowledge can be utilised at many levels. Most ob

vious is the prediction of coverage. The illumination provided

by available transmitters can be accurately predicted. Mobile

transmitters, under system control, can be deployed to provid

optimum coverage. Fortunately, good terrain maps are now

available for most of the earths surface.

As is well known, PCL receivers must be protected from

direct illumination, to reduce dynamic range requirements

Terrain models can be used to either chose the best receive

sites, or, allow a receiver to determine which transmitters touse, including the best frequency, optimised for best direc

signal suppression, and best coverage.

F. Detection

For most PCL systems, extraction of range (low resolution

and doppler (high resolution) is possible, but positioning o

targets will require further processing, as discussed in the nex

section.

It might be possible, however, if a large inter-node band

width is available, to attempt coherent radar processing before

detection. Nodes will have to share a large volume of data to

an integrator node to effect this. Then, with improved SNR

track-before-detect and other technologies can be deployed toimplement target tracking.

G. Tracking

For tracking implemented at a particular receiver site, the

simplest is doppler / delay tracking, which once initiated based

on detections, will yield three dimensional state vector. W

have demonstrated this with doppler and bearing tracking

[12]. However, input of geolocated state vectors from peers

once translated to the local coordinate system, will make the

tracking process more robust during local loss of detections.

The track state vectors can be fed upwards through th

network to assist other notes, and inform the higher level o

intelligence, which will make decisions on these. The highe

level may also prioritise areas of interest, since nodes migh

be compute limited in their signal and data processing. The

command to prioritise certain areas can also be used to bias

node level decisions as to which transmitters to utilise, once

the propagation assessment has been done.

H. High Level Functions

This probably the most difficult area of the proposed system

since it is not clear what sort of artificial intelligence wil

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be needed i.e. the mechanisation, as well as the learning,

adaption and proper match to the human operator, or, higher

level intelligence.

In the human autonomic nervous system, the hypothalamus

orchestrates the interaction of the subsystems, linked to it

by the network of nerves (the autonomic nervous system).

The subsystems, as indicated above, will carry out signifi-

cant preprocessing, to reduce traffic. The implementation of

the marshalling intelligence of the hypothalamus will need

significant development, since the implementation of human

cognition is somewhat open to dispute. Some believe that

intelligence requires the running of a computer program, but

this is disputed [9] by others.

The hypothalamus in turn, links via the amygdala to the

cortex, the seat of human intelligence and high level cognition.

The myriad of low level activity is filtered extensively by this

part of the brain, and necessary coordination carried out. The

amygdala link and activate emotive (emotion driven) responses

with the cortex. They take memories of scenarious of previous

experience and the primordial responses, and produce emotive

responses to the instinctive responses of the autonomic systemorchestrated by the hypothalamus.

The higher level functions of a radar system include the

interface to the command and control system, which at present

is implemented by a combination of displays and human

operators. Even here, we see the human operators in the

command and control system in a way acting the part of

the hypothalamus, combining the inputs from a number of

different sensors (e.g. primary and secondary radar, and to

come, PCL systems).

V. CONCLUSION

This paper has given a brief overview of how a PCL system

can use a combination of autonomic operations to support ahigher level intelligence in providing a robust (reliable and

sensitive) system for aircraft target tracking.

Further work is required in terms of the autonomic network

that must adapt to provide the system with a subsystem level

of intercommunications, similar to the autonomic intercon-

nections of the human system, and the coordinating, very

programmed function of the hypothalamus.

The functionality of the receivers is quite well defined, since

the receivers are able to predict their performance, and provide

filtered information to the higher level system, depending on

link QoS and capacity. The prediction tools mentioned in this

paper are of great assistance in this process.

The biggest uncertainty i.e. the are requiring the mos

work is the high level cognition system, that will take inputs

from the autonomic subsystems, to reconfigure, learn an

provide optimum information to the user. The implementation

of human-like cognition is still a very open question, and the

subject of a great deal of research in the field of cognitiv

science.

ACKNOWLEDGMENT

The authors would like to thank the South African Nationa

Defence Force for student funding.

REFERENCES

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[2] S. Haykin, Cognitive radar: a way of the future, Signal ProcessinMagazine, IEEE, vol. 23, no. 1, pp. 3040, 2006.[3] M. R. Inggs, G. E. Lange, and Y. Paichard, A quantitative method fo

mono- and multistatic radar coverage area prediction, in Proceedingof the IEEE Radar 2010 Conference, Aerospace Electronic SystemSociety. IEEE, May 2010.

[4] M. R. Inggs, Y. Paichard, and G. E. Lange, Passive coherent locatiosystem planning tool, in Proceedings of the Radar 2009 ConferenceOctober 2009.

[5] B. Kolb and I. Whishaw, An Introduction to Brain and Behaviour. 4Madison Avenue, New York 10010, USA: Worth Publishers, 2006.

[6] I. Mitola and J. Maguire, Cognitive radio: making software radios morpersonal, Personal Communications, IEEE, vol. 6, no. 4, pp. 1318August 1999.

[7] B. J. Gibb, The Rough Guide to the Brain, 1st ed. London: PenguiBooks, 2007.

[8] P. Qunilan and B. Dyson, Cognitive Psychology. Edinburgh Gate

Harlow, Essex, CM20 2JE, IK: Pearson Eduction Limited, 2008.[9] J.-P. Dupuy,On the Origins of Cognitive Science: the Mechanisation othe Mind. 55 Hayward St., Cambridge, MA 02142, USA: MIT Pres2009.

[10] G. E. Lange, Performance prediction and improvement of a bistatipassive coherent location radar, Masters thesis, University of CapTown, December 2009.

[11] K. Ebrahim, E. Tsai, G. E. Lange, Y. Paichard, and M. R. Inggs, Nuplacement in a circular antenna array for passive coherent location systems, inProceedings of the 2010 IEEE International Radar ConferenceIEEE AES. IEEE, May 2010.

[12] N. Morrison, R. T. Lord, and M. R. Inggs, The gauss-newton algorithmin passive aircraft tracking using doppler and bearings, in Proceedingof the IET International Conference on Radar Systems (RADAR 2007)Institute of Electrical and Electronic Engineers, October 2007.

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Autonomic Subsystems for Cognition in Passive Coherent Location

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