Radar Final

8/4/2019 Radar Final

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ACKNOWLEDGEMENT

With immense pleasure, I would like to present this report for Siemens Ltd. It has

been an enriching experience for me to undergo my summer training at SIEMENS,

which would not have possible without the goodwill and support of the people

around. As a student of Amity School of Engineering and Technology, I would

like to express my sincere thanks to all those who helped me during my training

period. I would like to give my heartily thanks to __________________who

permitted me to get training at Siemens.

As we know learning something new needs hard work, keen insight and long

patience with scholarly vision based on content operation hence it becomes a

humble duty to express my sincere gratitude to my supervisors_________

Atlast but not least my grateful thanks to all my staff members, especially

________________________________for the proper guidance and assistance

extended by them. I am also grateful to my parents and friends for encouraging me

& giving me moral support.

However, I accept the sole responsibility for any possible error of omission and

would be extremely grateful to the readers of this project report if they bring such

mistakes to my notice.


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ABSTRACT

In this report we have discussed about RADARS. Firstly we discussed about the

interference in the radio signal sent by the radar these may suffer from theobjective that come in between the radar and the target. Their r clutters present

which degrades the signal power in between and hence cause degradation of the

required info. To remove this many techniques can be used as using high pitch of a

signal or by increasing its bandwidth.Secondaly we have two types of radars such

as the search and the tracking radar. These both radars play an important role in

finding and destroying its enenmy.search radar searches for its enemy where as the

tracking radar tracks the location of its enemy and destroys it when required. Then

we moved on to multiple tracking radars which track more than one target. Which

led to some problem of allocation of the measurement taken by radars and hence

we use different data association techniques to resolve this problem of MTT.we

discussed five of the techniques which helps us in allocating the desired

measurement to the corresponding target. These techniques are divided into nearest

neighbor and all neighbor .in the nearest neighbor technique only one value gets

allotted in the end. Whereas all neighbor techniques allowed all possible values to

the target after further calculations.


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COMPANY PROFILE

Introduction

DRDO has constituted four research boards to nurture and harness talent inacademic institutions, universities, R&D centres and industry. The organization

provides necessary facilities for promoting basic research and to catalyse cross-

fertilization of ideas with R&D agencies in other sectors for expanding and

enriching the knowledge base in their respective areas. The boards provide grants-

in-aid for collaborative defence-related futuristic frontline research having

application in the new world class systems to be developed by DRDO.

Vision

Make India prosperous by establishing world-class science and technology base

and provide our Defense Services decisive edge by equipping them with

internationally competitive systems and solutions.

Mission

Design, develop and lead to production state-of-the-art sensors, weaponsystems, platforms and allied equipment for our Defence Services.

Provide technological solutions to the Defence Services to optimize combateffectiveness and to promote well-being of the troops.

Develop infrastructure and committed quality manpower and build strongtechnology base.


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Core Competence

Deptt of Defence Research and Development (R&D) is working forindigenous development of weapons, sensors & platforms required by the

three wings of the Armed Forces. To fulfill this mandate, Deptt of DefenceResearch and Development (R&D), is closely working with academic

institutions, Research and Development (R&D) Centres and production

agencies of Science and Technology (S&T) Ministries/Depts. in Public &

Civil Sector including Defence Public Sector Undertakings & Ordnance

Factories

Defence Research &Development services

Recruitment and selection of right people with desired competencies form

the base of building an effective organization. Defence Research &Development Organization recruit/select scientists and engineers through an

annual competitive examination at national level called Scientist Entry Test

(SET) through open advertisement. In addition to this, talent search through

campus interviews, scholarship scheme through Aeronautics Research &

Development Board (ARDB) and fresh Ph.D scholars under Registration ofStudents with Scholastic Aptitude (ROSSA) is also launched.

Institute for Systems Studies and Analyses (ISSA)

Historical Background

The origin of Institute for Systems Studies and Analyses (ISSA) dates back to 1959

as Weapon Evaluation Group (WEG) of DRDO. As the activities of WEG

increased, it was renamed as Scientific Evaluation Group (SEG) in 1963, and, later

from the year 1968 it started functioning as Directorate of Scientific Evaluation

(DSE) of DRDO Headquarters.

Consequent to the reorganization of System Analysis activities within DRDO, in

the year 1980, DSE was reorganized into ISSA located at Delhi with a Centre for

Aeronautical Systems Studies and Analysis (CASSA) at Bangalore. CASSA was

merged with ISSA during 2003.


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ISSA has grown to be the nodal System Analysis Laboratory of DRDO

specializing in System Analysis, Modeling and Simulation of Defence Systems as

well as development of Computer Wagrams, Performance Evaluation and

Systems Reliability Studies.

Vision

To be a leader in Systems Analysis, Modeling and Simulation of defence systems.

Mission

To Develop expertise and software for application in Decision Support Systems,

Computer Wagrams and Weapon Systems Analyses.

Institute for Systems Studies and Analyses (ISSA)

Achievements

-Performance Evaluation of defence systems in simulated combat environment

-Analysis of tactical plans by Modeling & Simulation of Combat Dynamics

-Combat Simulation and War gaming Software Development

-Decision Support Systems using multidimensional databases

-Reliability Evaluation and Life Data Analysis of Systems developed by sister labs


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LIST OF CONTENTS

SECTION TOPIC PAGE

Certificate 2

Abstract 3

Companys profilr 4

1.1 RADAR-introduction 9

1.2 Limiting factors

1. 2.1 interferrance1.2.2 clutter

10

1.3 Types of radar

1.3.1search radar1.3.2 tracking radar

11

1.4 Target tracking radar(TTR) 14

1.5 Multiple target tracking(MTT) 15

1.6 Problems faced by MTT 16

1.7 Techniques used 17

1.8 Distances 17

1.9 Explanation of the techniques

1.9.1 multi hypothesis

18-23


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technique(MHT)

1.9.2 probalistic (MHT)

1.9.3 prabolistic data

association(PDA)

1.9.4 joint (PDA)

1.9.5 nearest neighbor

1.10 Explanation of distances

1.10.1 mahalobian distance

1.10.2 Euclidean distance

24-25

1.11 comparision 26-27


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RADAR

1.1 Introduction

Radar is an object-detection system which uses electromagnetic wavesspecifically radio wavesto determine the range, altitude, direction, or speed of

both moving and fixed objects such as aircraft, ships, spacecraft, guided

missiles, motor vehicles, weather formations, and terrain. The radar dish, or

antenna, transmits pulses of radio waves or microwaves which bounce off any

object in their path. The object returns a tiny part of the wave's energy to a dish or

antenna which is usually located at the same site as the transmitter


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Practical radar was developed in secrecy during World War II by Britain and other

nations. The termRADAR was coined in 1940 by the U.S. Navy as an

acronym forradiodetectionandranging. The term radarhas since entered the

English and other languages as the common noun radar, losing all capitalization.

In the United Kingdom, the technology was initially called RDF (range anddirection finding), using the same initials used for radio direction finding to

conceal its ranging capability.

1.2 Limiting factors

1.2.1 Interference

Radar systems must overcome unwanted signals in order to focus only on the

actual targets of interest. These unwanted signals may originate from internal and

external sources, both passive and active. The ability of the radar system to

overcome these unwanted signals defines its signal-to-noise ratio (SNR). SNR is

defined as the ratio of a signal power to the noise power within the desired signal.

In less technical terms, SNR compares the level of a desired signal (such as targets)

to the level of background noise. The higher a system's SNR, the better it is inisolating actual targets from the surrounding noise signals.

1.2.2 Clutter


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Clutter refers to radio frequency (RF) echoes returned from targets which are

uninteresting to the radar operators. Such targets include natural objects such as

ground, sea, precipitation (such as rain, snow or hail), sand storms, animals

(especially birds), atmospheric turbulence, and other atmospheric effects, such

as ionosphere reflections.

PPI DISPLAY

Some clutter may also be caused by a long radar waveguide between the radar

transceiver and the antenna. In a typical plan position indicator (PPI) radar with a

rotating antenna, this will usually be seen as a "sun" or "sunburst" in the centre of

the display as the receiver responds to echoes from dust particles and misguided

RF in the waveguide. Adjusting the timing between when the transmitter sends a

pulse and when the receiver stage is enabled will generally reduce the sunburst

without affecting the accuracy of the range, since most sunburst is caused by a

diffused transmit pulse reflected before it leaves the antenna.

While some clutter sources may be undesirable for some radar applications (such

as storm clouds for air-defence radars), they may be desirable for others. Clutter is

considered a passive interference source, since it only appears in response to radarsignals sent by the radar.

1.3 Types Of Radar


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1. Search radar

2. Tracking radar

1.3.1 SEARCH RADAR

The Search Radar, as the name says SEARCH. Hence it searches the object around

itself. The main operation of the search radar is to make and update its search table.

By the time it found a object it reports to the tracking radar (explained below).the

search radar doesnt recognize whether it has detected its enemy or friend .it just

sends its information to the tracking radar .its sensor gets activated after every

allotted time to search for the object around. As it has a long range it can easily

detect an object or target from a long distance. But it is unable to decide whether

its a moving orstationary .hence it informs tracking radar for further details.


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1.3.2TRACKING RADARS

Tracking radars have a pencil beam to receive echoes from a single target and track

the target in angle, range, and/or Doppler. Its resolution celldefined by its

antenna beam width, transmitter pulse length (effective pulse length may be shorter

with pulse compression), and/or Doppler bandwidthis usually small compared

with that of a search radar and is used to exclude undesired echoes or signals from

othertargets, clutter, and countermeasures. Electronic beam-scanning phased arrayradars may track multiple targets by sequentially dwelling upon and measuring

each target while excluding other echo or signal sources.

Because of its narrow beam width, typically from a fraction of 1 to 1 or 2,

tracking radars usually depend upon information from a surveillance radar or other


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source of target location to acquire the target, i.e., to place its beam on or in the

vicinity of the target before initiating a track. Scanning of the beam within a

limited angle sector maybe needed to acquire the target within its capture beam and

center the range-tracking gates on the echo pulse prior to locking on the target or

closing the tracking loops. The gate acts like a fast-acting on-off switch that turnsthe receiver on at the leading edge of the target echo pulse and off at the end of

the target echo pulse to eliminate undesired echoes. There present a missile fight

path which allows the target beam not to scatter and hence helps in locating the

target x at any desired range.

1.4. TARGET TRACKING RADAR (TTR)

1. PURPOSE. The target ranging radar (TRR) Furnishes target range data to the

computer when enemy approaches. It actually ensures every moment of the enemy

and makes it data base updated. It firstly determines the range and then keeps a

track so that it can destroy its enemy when required. Hence it provides safety to the

environment for which it is working.

2 . CAPABILITIES. The TTR uses the designated target position data

(supplied by whichever acquisition radar is in use) t o automatically slew

the target track antenna to the range and azimuth of the target. The operator,


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using manual antenna controls, searches forthe target in elevation. The. TTR

tracks the target, supplying accurate elevation, azimuth, and slant range data to

the computer system in the trailer mounted director s t a t ion. The TTR system

is synchronized with the selection acquisition radar system to permit simultaneous

display of acquisition and target track information and to permit rapid transferof the target position data t o the TTR system. Transfer of target azimuth and

range data is accomplished by automatic , antenna azimuth and range slewing

after operating the DESIGNATE switch in the battery control van and the

ACQUIRE switch in the radar control van. Provision is made for manual,

aided, or automatic tracking of the target in azimuth and elevation. Four

modes of target tracking in range are provided: manual, acquisition aid, track

aid, and automatic. Once acquired, the TTR system continues t o track the

target and supply the computer with position data until the target is abandoned.

1.5 MULTIPLETARGET TRACKING

MULTITARGET tracking (MTT) deals with the state estimation of an unknown

number of moving targets. Available measurements may both arise from the

targets, if they are detected, and from clutter. Clutter is generally considered to be a

model describing false alarms. Its statistical properties are quite different from

those of the target, which makes the extraction of target tracks from clutter

possible. To perform multimarket tracking, the observer has at his disposal a huge


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amount of data, possibly collected on multiple receivers. Elementary

measurements are receiver outputs, e.g., bearings, ranges, time-delays, Doppler,

etc.

1.6 PROBLEMS

Radar can suffer from problem too. The main difficulty, however, comes from the

assignment of a given measurement to a target model. These assignments are

generally unknown, as are the true target models. Taking an example if thetracking radar is used for tracking 4 objects simultaneously it will measures its

components (either x,y,z or R,Q,,v).where the symbols have their usual

meanings. For the time T1 it takes the reading of all the components of all four

objects and maintains a data sheet to store the result. At time T2 when again it

measures the value of the components it will not able to judge which component is

associated with which object as it is not mandatory that it wil select 1st

object for

1st

reading. Hence am ambiguous situation occurs where it cannot judge the

required component and its object. To remove this problem MTT uses dataassociation techniques. These techniques are being widely used in present

Some of the techniques are listed below

1.7 TECHIQUES

1. Multi-hypothesis tracking (MHT)

2. ProbabilisticMHT (PMHT)

3. Joint probabilistic data association (JPDA)

4. Probabilistic data association


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5. nearest neighbor

1.8 DISTANCES

1. mahalanobis distance

2. Euclidean distance

1.9 EXPLANATION OF VARIOUS TECNIQUES

1.9.1Multi-hypothesis tracking (MHT)

Multi-hypothesis tracking technique is one of the data association technique .Multi

means many and hypothesis means assumption. Here user makes lots of

hypothetical possibilities which can match to the final estimate result. as the

tracking radar tracks the multi objects, before getting to the estimated result user

predicts its possible result depending upon the previous observation. Then if the

result matches or comes nearby to the one measured, the values are accordingly

allotted.

This technique can be best explained by an example as follows

For an instant time T1 let we get three readings of the range of three targets. And

in time T2 we get four readings of the same three targerts, hence it has been

estimated that the extra reading we have got is because of a clutter or some other

target which is useless to us. Hence in order to estimate which reading corresponds

to which target we make possible assumptions.

Let the three readings that v got in time T1 are r1, r2 and r3, that time it wasestimated that r1 corresponds to target 1 ,r2 to target2 and re to target3 .but at

instant T2 we get r1,r2,r3,r4.where it was not necessary that they arrive in the same

order as earlier .hence v have to estimate the value to the respective targets .for this

the required assumptions are


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T1 T2 T3 Clutter

R1 R2 R3 R4

R1 R2 R4 R3

R1 R3 R4 R2

R1 R3 R2 R4R1 R4 R3 R2

R1 R4 R2 R3

R2 R1 R3 R4

R2 R1 R4 R3

R2 R3 R1 R4

R2 R3 R4 R1

R2 R4 R1 R3

R2 R4 R3 R1

R3 R4 R1 R2

R3 R4 R2 R1

R3 R2 R1 R4

R3 R2 R4 R1

And hence many more assumptions can me made using these orders. these are all

called hypothetical assumptions which later get checked and allotted to the desired

target.


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Here presents a figure which shows the multiple hypothesis of a target

Here T represents a targetNT represents a new target

H represents a hypotheses

FA is the false alarm

1.9.2 ProbabilisticMHT (PMHT)

Probabilistic Multi-Hypothesis Tracking (PMHT) is an algorithm for

tracking multiple targets when measurement-to-target assignments areunknown and must be estimated jointly with the target tracks. The drawback

of MHT was it can allow only one measurement of a target, whereas here

PMHT allows more than one measurement of the target to be observed with

the measurements being independent of each other. An important


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modification of PMHT to utilize echo amplitude information in addition to

range and bearing measurements.

For example

As in the above example only range of the target was calculated. But here

with the range we can determine the velocity or angle .though same

hypothetical assumptions are to be made for different readings. But all these

readings are independent from each other

If for time T1 we wish to get range as well as the velocity of three targets

we will get 6 different readings R1,R2,R3 and V1,V2,V3.where R represents

range and V represents velocity, where R and V are independent of each

other. A separate table for every measurement is to be maintained. With the

help of the table we can estimate the correct value of the respective targets.

1.9.3 nearest neighbor

Nearest neighbor is the oldest technique for solving the problem of multiple targetdata association. In this technique the most closest match to the predicted and the

obtained value is being allotted to the respective target. There are many possible

and many close values are being obtained during the process and the nearest

neighbor hence ambiguity occurs which is removed by calculating the distance

between all the obtained and predicted values nearby and hence the best one gets

allotted.

Taking an example

As seen in the figure below.

let T1 ,T2 and T3 are the predicted reading of three targets present and x1 to x9

are the obtained reading that we get back after tracking, as there are only 3 targets


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6+3+3=12

Hence 11 is the smallest sum therefore

Target 1 gets the value of x1

Target 2 gets x3

And target 3 gets the value of x8 depending upon the predicted value T1,T2,T3.

All neighbor techniques

Given below PDA and JPDA techniques are also called all neighbor technique as it

allots all the possible neighbor of the given value and doesnt work upon the past

observations.

1.9.4 Probabilistic data association (PDA)

PDA is the data association technique which ponders upon only on the single target

tracking. It includes more than one measurement tracker technique like PMHT but

only for a single target. Hence this has been less used now. Here the approach

starts as when at time T1 r,q,v of the target has been read then it may be possible

that at time T2 more than one reading of r that is range gets detected this problem

hence been solved as follows

Firstly the possible value being written depending upon the value at T1 then the

observed 2 values of r being read and checked which one is the closest as a

window get formed on the basis of the predicted value. If the observed values are

within the window then the values are considered and hence an average has been

taken out of the predicted as well as the observed value which is hence called the

estimated value of the target. Hence the problem of the value estimation can be

easily done.

Example for better analyzing

Let at time T1 the range of a target comes out to be 169.at time T2 ,firstly the

predicted value gets estimated that is 170 which comes out depending upon the

value at T1.the observed values comes out to be 167,168,171,172 ,187.hence a

window has been formed between 167-173.therefore values outside the window


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gets discarded. Like 187 in this case now the analysis is between the four readings,

now this value is multiplied by a weighted quantity (w) like in this case if we let

that the w-distance between the observed and predicted value. Hence in this PDA

only the present values are being used ,there is no need to store the past

observation.

167 would have w as 3

168 -2

171-1

172-3

Hence by the formula

Value = o1*w1 +o2*w2/w1+w2

Hence the value comes out to be 170.5

Where o1- observation 1

And w1- weight alloted 1

1.9.5 Joint probabilistic data association (JPDA)

This technique is the extension of PDA described above .the extension lies as this

can work on multiple target tracking. In this technique one or more measurements

of one or more target can me estimated. This is the most frequent technique used

nowadays. Here a table is maintained for different measurements of different

target.

This technique can be best explained via an example.

As this technique deals with one or more target let we have to find out the

velocity(V) and the distance(D) of 2 targets .At time T1 the V and D taken out to

be 15m/s,20m/s and 120m,60m respectively of the 2 targets.now at the time T2 the

velocity and the distance predicted and observed of the two targets were

TIME PREDICTED OBSERVED

VELOCITY DISTANCE VELOCITY DISTANCE

T2 15 121 17,20,19,25,15,14 125,60,55,54,122,62

21 61


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As now from the table it can be seen that there are only two targets therefore

predicted readings are just 2 but the observed readings are more than 2 hence there

must be clutter around .therefore it is required to estimate the possible value of the

given 2 targets. This estimation required further calculations as stated below

Now the first thing to do is form a window

VELOCITY WINDOW DISTANCE WINDOW

TARGET 1 13-17 119-122

TARGET2 19-22 55-65

Now the readings outside this window are discarded. Hence the possible readings

left are

VELOCITY DISTANCE

TARGET1 14,15,17 122

TARGET2 19,20 55,60,62

Now we choose the closest value to the predicted one

VELOCITY WEIGHT ALLOTED

TARGET1

14 2

15 1

17 4

DISTANCE

122 1

TARGET2

VELOCITY

19 2

20 1

DISTANCE

55 6

60 1

62 1

Now for the estimation of the values of the targets we use the following formula

Value = o1*w1 +o2*w2/w1+w2

Where o1- observation 1

And w1- weight alloted 1


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hence the estimate values of the 2 targets are

VELOCITY DISTANCETARGET1 15 121.5

TARGET2 20.75 60.75

1.10 DISTANCE USED INDETERMININ THE ESTIMATE VALUE

1.10.1 Mahalanobis distance

There is always a distance between the predicted and the observed value.this

distance is called the mahalanobis distance. The smaller the distance ,the morelikely it is to have originated from the target. The further predicted values also

depends upon this mahalanobis distance. Consider the case in which n geometric

features are being tracked and n measurements are found in the next image frame.

In principle, any measurement vector might have originated from any geometric

feature and there are 2^n possible combinations of assignments. The distance

measure is therefore needed to quantify this likelihood. Hence this distance plays a

vital role in estimation of the correct value.

Where x is the predicted value

= observed value


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S is the possible value matrix.

1.10.1Euclidean distance

This is the "ordinary" distance between two points that one would measure with a

ruler, and is given by the Pythagorean formula.

Here d is the Euclidean distance

P is the predicted value

q Is the observed value

This distance is also used for estimation of the value. The smaller the distance

between the predicted and the observed value, the better it is.

COMPARISION BETWEEN THE TECNIQUES

ADVANTAGE LIMITATION SCENERIO


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MHT It can automaticallycorrect the errors if

occurs.

It has tocompute

multiple

answers

Morememory

requirement

.

Best resultwith less

targets

And withless clutter

Air to airoperation

PMHT Can be used tomeasure multiple

targets

Morememory

space

Air to airoperation

PDA Recursive in nature No need to store

past observation

and multiple

hypothesis

Used foronly single

target

Noexplicitly

provision

for track

initiation

and

deletion.

JPDA Used for multipletarget

Independent ofclutter

No need to storepast observation

and multiple

hypothesis

Noexplicitly

provisionfor track

initiation

and

deletion.

High falsetarget

density Dominant

use in

SONAR

Air toground

operation

NEAREST

NEIGHBOR

Computationallyefficient

Less memoryrequirement

Misscorrelation

affectsadversely

Morepossibilities

of track

loss.

Used for theplaces where

there aremore targets

and clutter.


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