Critical analysis of radar data signal de noising by implementation of haar wavelet transformation

IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 04 Issue: 05 | May-2015, Available @ http://www.ijret.org 13
CRITICAL ANALYSIS OF RADAR DATA SIGNAL DE-NOISING BY
IMPLEMENTATION OF HAAR WAVELET TRANSFORMATION
Ananth Abhishek Jillepalli1
, Raghavendra Deeduvanu2
, Sreenath DeviReddy3
, Deepak Vangala4
, Ravi
Mathey5
1
IVth
Year, Department of CSE, Vidya Jyothi Institute of Technology, Hyderabad
2
IVth
3
IVth
4
IVth
5
Head of Department, Department of Computer Science and Engineering, Vidya Jyothi Institute of Technology,
Hyderabad
Abstract
Data signal de-noising is a crucial part of every transaction which involves capturing waves and processing them into
understandable format. Most essentially, this process of data signal de-noising is important in the field of military and defence.
Because of the rapid development in military communication systems, a limitation arises in the systems. The limitation being -
quantity of signals profoundly increases along with noise and other disturbances’ presence in the signal. In order to overcome
this drawback, batch de-noising techniques are implemented on the systems to produce clean signals. In this paper, the critical
analysis case of RADAR [RAdio Detection And Ranging] data de-noising by implementation of Haar Wavelet Transformation is
considered. A signal set simulation containing four signals procured from RADAR data is taken into consideration. The signals
are then subjected to de-noising in MATLAB and indigenously developed python tool using Haar Wavelet Transformations. The
ensuing results pertaining to accuracy and efficiency of output signals are then compared.
Keywords: RADAR data, MATLAB tool, Python, De-noising techniques, Haar Wavelet Transformation, Signal
Simulation.
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1. INTRODUCTION
The main focus of this paper is to contrast de-noising
methods available in two different tools and to determine
the most efficient and accurate output producing tool. The
potential conclusion for this analytical problem case will
also include the extent of memory consumption by the two
tools. To this point, the intention of this paper is to perform
de-noising of four signals MATLAB and Python, in order
to properly de-noise the signals, such that a military system
can precisely and memory efficiently process the signals
being received into understandable data. The substantial
inspiration for this work is that the problem of efficient and
accurate denoising targets considerable number of military
tracking systems.
The process of de-noising a captured RADAR data set
commences with the processing and selection of a single
relevant signal from the entire captured set. The signal
selection is of instrumental importance to the process of
cleaning the signal and subsequently will be applied to
entire set of signals. Our efforts enable us to optimise the
process through which signals can be cleaned out
efficiently without much data loss and precision, as
intended. A potential conclusion and a result like this one
will not only supplement military tracking and
communication systems in understanding the signal data
quickly and with agility, but it will also reduce system
maintenance costs as our research also enables us to find
out cost effective tool for de-noising. This paper will
concentrate on the critical analysis of data signal de-noising
from a signal set simulated from the data obtained from
RADAR transceivers.
2. BACKGROUND STUDY
The large number of signals received in transceiver systems
far outruns the modules deployed for denoising. We
conducted some initial experiments on internal equipment
and were able to deduce the most frequent category of
signals received. Prior research on this matter revealed the
four standard categories of signals being received
frequently:
 First, the most frequent category of signals being
received belongs to tracking type of data which
track the whereabouts of any device and report
back to the base.
 Second, the next frequent class of signals being
received belongs to communication data type
where in one base communicates with the other.
 Next, the signals being received in this category
belong to medical data type of the military
personnel which contain the medical records of the
staff and soldiers.
 Finally, the least category of signals received is
trash or pranks which are mostly useless.

_______________________________________________________________________________________
2.1 Signal Overview
Signals have many instances and forms depending upon the
category of the data being transmitted. The overview of
signal forms is as follows:
Overview
A very frequently found type of signal wave is the sine
wave. These signals are used almost for every type of data
transmission. Sine waves are also easier to de-noise since
there many functions developed for sine owing to the huge
potential of applications.
A not so frequently used type of signal wave is the Doppler
wave. These signals are not used unless the data
transmission must be very secure. Doppler waves are hard
and slow to transmit and so, anyone who uses them should
sacrifice speed for security.
A very rare type of signals wave used are the Chebyshev
filter waves. The infrastructure needed to generate these
waves is so complex that these waves were generated only
one in entire human history till now. They are fast, secure
and reliable but complex to generate.
3. CONTEMPORARY USAGE OF DE-NOISING
IN VARIOUS SYTEMS
The conventional process of eliminating unnecessary
disturbances and any unwanted resources from an entity
has been coined into a single term, known as de-noising.
From then on, this phenomenon of de-noising an entity has
been implemented on various fields and for various
applications.
Technically, de-noising is taking any variable or entity as
input and removing unwanted data, called “noise” with the
use of some algorithms. The various implementations of
de-noising functionality is as follows:
3.1 Image Refining using De-Noising
Unclear and obfuscated images are often not desirable for
obvious reasons of lacking clarity. As such, to make these
images clearer, there have been many attempts at
developing image de-noising software. Some successful
attempts resulted in software like Topaz Image Cleaner,
Ninja Noise Remove and Photoshop Image processor.
3.2 Data Refinement using De-noising
Refinement of data, also known as data cleaning or rarely
also known as data classification is the process of
eliminating opaque data item sets from relevant material in
a large collection of data items, like Data Warehouses and
Data Bases.
Due to the numerous successful attempts of data denoising,
made in the last decade, there are a couple of tools in
market available, which can efficiently refine data without
much data loss and with precision. Most popular ones are
WEKA tool and CITRUS development environment which
includes internally built data de-noising module.
3.3 Signal Refining using De-noising
Partly as a result of gigantic expansion of signal usage for
data transmission in the last few decades, there has been an
urgent need for some techniques which would be capable of
eliminating unnecessary data and unwanted disturbances
from the signals.
As such, there have been countless attempts made at
developing algorithms and subsequently developing tools
which can de-noise signals of any format and containing
any data formats. As a result, there have arisen numerous
tools for de-noising signals universally, though most of
them are inefficient and even incompetent to some extent.
There are presently three tools which, we can classify under
“working as intended” category. They are: MATLAB,
OriginPro and Sony WavePad (for audio signals only). Of
these three, only MATLAB and OriginPro are capable of
de-noising signals of any format and type. And of those
two, our experiments clearly showed that MATLAB
produces accurate and efficient results.
3.4 Proposed Tool
The tool that we propose has been built from scratch using
Python, VB Script and minor C++ functionality only. No
external modules or third party software was used in the
indigenous development of our tool. In our internal
experiments, the python tool was able to de-noise signals of
all formats and any data type. As such, we move forward to
compare MATLAB and our tool on the basis of efficiency,
accuracy, compatibility and memory costs.
4. OBSERVATION AND ANALYSIS
We have generated four signals containing RADAR data
simulation. Three of which relate to tracking and
communication type of data and one of them is a medical
record type of data. The Sequence of our Observation and
subsequent analysis is as follows:
 MATLAB Observation
 Python Tool Observations
 Comparative Analysis
4.1 MATLAB Observations
All the MATLAB Observations have been conducted
through the use of a specialised module toolbox known as
Wavelet Toolbox. The Signal Graphs shown below are only
screenshots of MATLAB Wavelet toolbox results.
4.1.1 Signal #1
Signal #1 contains information which pertains to the data
sent by trackers. Trackers are basic identifying devices
which can be used to track any object.

_______________________________________________________________________________________
Original Signal
Fig 1: – Original Signal #1 (MATLAB)
Frequency – 100 cycles / 1 sec
Standard Deviation – 20 metres
De-Noised Signal
Fig 2:– De-Noised Signal #1 (MATLAB)
Signal Accuracy – 89%
Noise Removed – 92%
4.1.2 Signal #2
sent by locators. Locators are the devices which can locate
entities that are either lost or not cannot be tracked with
Trackers.
Original Signal
Fig 3 – Original Signal #2 (MATLAB)
De-Noised Signal
A Decomposition Level – 2 has been used with Haar
Wavelet Discrete Transformation and Inverse Discrete
Transformation.
4.1.3 Signal #3
sent by communicators. These signals carry very private
information and so, the signals are secured with algorithms.

_______________________________________________________________________________________
Original Signal
De-Noised Signal
Transformation.
4.1.4 Signal #4
Signal #4 contains information which pertains to the
medical data of one staff member recorded internally.
Original Signal
De-Noised Signal
Transformation.
4.2 Python Tool Observations
All the Observations using Python Tool have been
conducted through the use of a custom, specially developed
and designed UI module. The Signal Graphs shown below
are only screenshots of Python Tool signal processing
results.

_______________________________________________________________________________________
4.2.1 Signal #1
sent by trackers. Trackers are basic identifying devices
which can be used to track any object.
Original Signal
Fig 9 – Original Signal #1 (PYTHON)
De-Noised Signal
Transformation.
Fig 10 – De-Noised Signal #1 (PYTHON)
4.2.2 Signal #2
sent by locators. Locators are the devices which can locate
entities that are either lost or not cannot be tracked with
Trackers.
Original Signal
De-Noised Signal
Transformation.
4.2.3 Signal #3
sent by communicators. These signals carry very private
information and so, the signals are secured with algorithms.

_______________________________________________________________________________________
Original Signal
De-Noised Signal
Transformation.
4.2.4 Signal #4
Signal #4 contains information which pertains to the
medical data of one staff member recorded internally.
Original Signal
De-Noised Signal
Transformation.
4.3 Analysis
The results obtained from above observational cases are
recorded in the tables below for comparison purposes and
to determine the most accurate and efficient tool in data
signal de-noising. The comparative value tables are as
follows:
Attribute Signal
#1
Signal
#2
Signal
#3
Signal
#4
Frequenc
y
100 130 100 75
Stnd. Dev. 20 15 20 30

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Matlab Specifics
Signal Acc. 89% 93% 97% 99%
Noise Rem. 92% 91% 89% 89%
Observation – Very Consistent with colour indentation,
average efficiency but high cost and high memory
requirement.
Python Tool Specifics
Signal Acc. 90% 100% 90% 95%
Noise Rem. 80% 95% 80% 90%
Observation – Very Efficient, cost-effective and memory
friendly but no colour indentation and bad consistency.
Frequency – Number of cycles present in ONE second
time.
Stnd. Dev. – Standard Deviation (Average Deviation
between crusts).
Signal Acc. – Percentage of signal accuracy when
compared to original signal.
Noise Rem. – Percentage of Noise Removed from original
signal.
5. RESULTS and DISCUSSIONS
The most important purpose of these analytics is to guide
schemata of de-noising techniques available in two
different tools, which are: MATLAB and Python Tool. We
have given four same signals as input to the two tools and
we were able to ascertain their efficiency, obtain their
consistency, observe their performance, note their memory
taxation and finally, we were also able to see their relative
cost expenses. After the aforementioned comparative
critical analysis of all the involved components, we have
arrived at a conclusive result.
In all our experiments and testing with MATLAB using 4
signals, we have noticed that MATLAB is very consistent
and easy to understand due to colour indentation, but is also
costly both monetarily and memory wise. Efficiency in
denoising and retaining relevant data is found to be
average.
As of our experiments and testing with Python Tool using 4
signals, we have observed that our Python Tool is very
efficient, almost takes 25% of memory taken by MATLAB
and projected monetary cost at maintaining is also very
low. Consistency is a bit lacking feature in our Python tool
and it may be a bit harder to understand in first look due to
lack of specialised colour indentation.
Overall, if the client requirement is data signal de-noising
efficiency and maintenance cum memory cost
effectiveness, then our PYTHON TOOL is optimal. If the
client requirement is consistency and ease of
understanding, then MATLAB TOOL is optimal.
REFERENCES
(All the references were accessed during Feb-March 2015).
[1]. Introduction to Haar Wavelets – Portland State
University.
[2]. Besslich PhW, Trachtenberg LA. The sign transform:
an invertible non-linear transform with quantized
coefficients. In: Moraga C, editor. Theory and application
of spectral techniques. University Dortmund Press;
October 1988.
[3]. Beauchamp KG. Applications of Walsh and related
functions with an introduction to sequency theory. New
York: Academic Press and Publications p. 4-13; 1984.
[4]. Boiko LL. Generalized Fourier–Haar transform on
finite Abelian groups. In: Yaroslavsky LP, editor. Digital
signal processing and applications. Moscow: Nauka; 1981.
p. 12–22
[5]. Buron AM, Michell JA, Sloana M. Single chip fast
Haar transform at megahertz rates. In: Moraga C, editor.
Theory and application of spectral techniques. University
Dortmund Press and publications for open source; October
1988, p. 8–17.
[6]. Burrus CS, Gophinat RA, Guo H. Introduction to
wavelets and wavelet transforms. Englewood Cliffs:
Prentice Hall; 1988.
[7]. Castleman KR. Digital image processing. Englewood
Cliffs Press and publications p. 13-22: Prentice-Hall; 1996.
[8].https://en.wikipedia.org/wiki/Haar_Transformation_wa
velet
[9].https://en.wikipedia.org/wiki/Discrete_wavelet_transfor
m
BIOGRAPHIES
1. Ananth Abhishek Jillepalli, IVth
Year, Department of
CSE, Vidya Jyothi Institute of Technology,
Hyderabad.
E-Mail: ananth9328@gmail.com
2. Raghavendra Deeduvanu, IVth
Year, Department of
CSE, Vidya Jyothi Institute of Technology,
Hyderabad.
E-Mail: raghavendra065@gmail.com
3. Sreenath DeviReddy, IVth
Year, Department of CSE,
Vidya Jyothi Institute of Technology, Hyderabad.
E-Mail: sreenathreddy9194@gmail.com
4. Deepak Vangala IVth
Year, Department of CSE,
Vidya Jyothi Institute of Technology, Hyderabad.
E-Mail: deep.vangala21@gmail.com
5. Ravi Mathey, Head of Department, Department of
Computer Science and Engineering, Vidya Jyothi
Institute of Technology, Hyderabad.
E-Mail: ravimathey@gmail.com

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