06-07-2012, 11:42 AM
NEW APPROACHES TO PULSE COMPRESSION TECHNIQUES OF PHASE-CODED WAVEFORMS IN RADAR
[attachment=26943]
Acknowledgment
I would like to express my deep sense of respect and gratitude towards my advisor
and guide Prof. Ajit Kumar Sahoo, who has been the guiding force behind this work. I want
to thank him for introducing me to the field of Signal Processing and giving me the
opportunity to work under him. I extend my sincere thanks and respects to Prof. G.Panda for
his inspiration, tremendous help, advice and encouragement. I consider it my good fortune to
have got an opportunity to work with such a wonderful person.
I express my respects to Prof. S.K. Patra, Prof. K.K. Mahapatra, Prof. G. S. Rath,
Prof. S. Meher, Prof. S.K.Behera, Prof. Poonam Singh, Prof. D.P.Acharya, for teaching me
and also helping me how to learn. They have been great sources of inspiration to me and I
thank them from the bottom of my heart.
I would like to thank all faculty members and staff of the Department of Electronics
and Communication Engineering, N.I.T. Rourkela for their generous help in various ways for
the completion of this thesis.
Iam very thankful to my senior Vikas Baghel, who helped me a lot during my
research work. I would like to thank all the Ph.D. scholars in DSP lab, my seniors and my
friends especially Sunayana, Kranthi, Maitrayee, Sheema, Suresh, Chandu, Hanuma, Bharat,
Shreeshail, Gyan, and Pyagyan for their help during the course of this work. I am also
thankful to my classmates for all the thoughtful and mind stimulating discussions we had,
which prompted us to think beyond the obvious.
I am especially indebted to my parents (Mr. A. Kuppi Reddy, Mrs E. Amaravathi),
uncle, sisters and brothers-in-law for their love, sacrifice, and support. They are my first
teachers after I came to this world and have set great examples for me about how to live,
study, and work.
ABSTRACT
The present thesis aims to make an in-depth study of Radar pulse compression, Neural
Networks and Phase Coded pulse compression codes. Pulse compression is a method which
combines the high energy of a longer pulse width with the high resolution of a narrow pulse
width. The major aspects that are considered for a pulse compression technique are signal to
sidelobe ratio (SSR) performance, noise performance and Doppler shift performance.
Matched filtering of biphase coded radar signals create unwanted sidelobes which may mask
important information. The adaptive filtering techniques like Least Mean Square (LMS),
Recursive Least Squares (RLS), and modified RLS algorithms are used for pulse radar
detection and the results are compared.
In this thesis, a novel approach for pulse compression using Recurrent Neural
Network (RNN) is proposed. The 13-bit and 35-bit barker codes are used as signal codes to
RNN and results are compared with Multilayer Perceptron (MLP) network. RNN yields
better signal-to-sidelobe ratio (SSR), error convergence speed, noise performance, range
resolution ability and doppler shift performance than neural network (NN) and some
traditional algorithms like auto correlation function(ACF) algorithm. But the SSR obtained
from RNN is less for most of the applications. Hence a Radial Basis Function (RBF) neural
network is implemented which yields better convergence speed, higher SSRs in adverse
situations of noise and better robustness in Doppler shift tolerance than MLP and ACF
algorithm. There is a scope of further improvement in performance in terms of SSR, error
convergence speed, and doppler shift. A novel approach using Recurrent RBF is proposed for
pulse radar detection, and the results are compared with RBF, MLP and ACF. Biphase codes,
namely barker codes are used as inputs to all these neural networks. The disadvantages of
biphase codes include high sidelobes and poor Doppler tolerance.
The Golay complementary codes have zero sidelobes but they are poor Doppler
tolerant as that of biphase codes. The polyphase codes have low sidelobes and are more
Doppler tolerant than biphase codes. The polyphase codes namely Frank, P1, P2, P3, P4
codes are described in detail and autocorrelation outputs, phase values and their Doppler
properties are discussed and compared. The sidelobe reduction techniques such as single Two
Sample Sliding Window Adder(TSSWA) and double TSSWA after the autocorrelator output
are discussed and their performances for P4 code are presented and compared. Weighting
vi
techniques can also be applied to substantially reduce the range time sidelobes. The
weighting functions such as Kaiser-Bessel amplitude weighting function and classical
amplitude weighting functions (i.e. Hamming window) are described and are applied to the
receiver waveform of 100 element P4 code and the autocorrelation outputs, Peak Sidelobe
Level (PSL), Integrated Sidelobe Level (ISL) values are compared with that of rectangular
window. The effects of weighting on the Doppler performance of the P4 code are presented
and compared.
1.1. Background
RADAR is an acronym of Radio Detection And Ranging. There was a rapid growth in
radar technology and systems during world war II. In the recent years, there were many
accomplishments in radar technology. The major areas of radar applications includes
military, remote sensing, air traffic control, law enforcement and highway safety, aircraft
safety and navigation, ship safety and space [1.1, 1.2].
The rapid advances in digital technology made many theoretical capabilities practical
with digital signal processing and digital data processing. Radar signal processing is defined
as the manipulation of the received signal, represented in digital format, to extract the desired
information whilst rejecting unwanted signals. Pulse compression allowed the use of long
waveforms to obtain high energy simultaneously achieve the resolution of a short pulse by
internal modulation of the long pulse. The resolution is the ability of radar to distinguish
targets that are closely spaced together in either range or bearing. The internal modulation
may be binary phase coding, polyphase coding, frequency modulation, and frequency
stepping. There are many advantages of using pulse compression techniques in the radar
field. They include reduction of peak power, relevant reduction of high voltages in radar
transmitter, protection against detection by radar detectors, significant improvement of range
resolution, relevant reduction in clutter troubles and protection against jamming coming from
spread spectrum action [1.3].
In pulse compression technique, the transmitted signal is frequency or phase
modulated (but not amplitude modulated) and the received signal is processed in the receiver,
into a specific filter called "matched filter". In 1950-60, the practical realization of radars
using pulse compression have taken place. At the starting, the realization of matched filters
was difficult using traverse filters because of lack of delay line with enough bandwidth. Later
matched filters have been realized by using dispersive networks made with lumped-constant
filters. In recent years, instead of matched filters, many sophisticated filters are in use.
Barker code is the binary phase-coded sequence of 0, π values that result in equal
side-lobes after passes through the matched filter. J.S.Fu and Xin wu proposed adaptive
filtering techniques using LMS and RLS algorithms to suppress the sidelobes of barker code
of length 13 [1.4]. The SSR and doppler performance of this type of filters are very poor.
B.Zrnic et,al. proposed a self –clutter suppression filter design using modified RLS
algorithm that gave better performance compared to iterative RLS and ACF algorithms [1.5].
CHAPTER 1: INTRODUCTION
3
A multilayered neural network approach using back propagation algorithm which
yielded better SSR than basic ACF approach was presented by Kwan and Lee [1.6]. Khairnar
et,al. [1.7] proposed a RBFN for pulse compression that yielded high SSRs in different
adverse situations of noise, with misalignment of clock. This approach also has better range
resolution and robustness in doppler shift interference. Frank proposed a polyphase code
called as Frank code which is more Doppler tolerant and has lower sidelobes than binary
codes [1.8]. Kretschmer and Lewis have presented the variants of Frank polyphase codes,
namely P1, P2, P3, and P4 that have better properties than Frank code [1.9, 1.10].
1.2. Motivation
The pulse compression in radar has major applications in the recent years. For better
pulse compression, peak signal to sidelobe ratio should be as high as possible so that the
unwanted clutter gets suppressed and should be very tolerant under Doppler shift conditions.
Many pulse compression techniques have come into existence including neural networks. The
recurrent networks have inherent memory for dynamics that makes them suitable for dynamic
system modelling. They provide better stability, more robust to estimation errors and good
performance with more past information relevant to prediction. Hence the recurrent
connections are applied to the MLP and RBF networks for pulse radar detection to achieve
overall better performance. The study of polyphase codes and their sidelobe reduction
techniques are carried out since the polyphase codes have low sidelobes and are better
Doppler tolerant and better tolerant to precompression bandlimiting.
1.3. Thesis Organization
Chapter-1 Introduction
Chapter-2 Adaptive Filtering Techniques for Pulse radar Detection
The concept of pulse compression in radar is described in detail. The adaptive
filtering techniques using LMS, RLS and modified RLS algorithms are discussed for pulse
compression and the results are compared.
Chapter-3 Recurrent Neural Network Approach for Pulse Radar Detection
This chapter presents a novel recurrent neural network based pulse radar detection.
The simulation results are compared with that of MLP and ACF algorithms.
Chapter-4 Recurrent RBF Approach for Pulse Radar Detection
This chapter proposes a novel recurrent RBF network based pulse radar detection
technique which provides significant improvement in convergence rate, noisy conditions and
under Doppler conditions. The proposed network is compared with the other networks like
RNN, MLP and ACF.
Chapter-5 A Study of Polyphase Codes and their Sidelobe Reduction techniques
This chapter deals with the different polyphase codes such as Frank, P1, P2, P3, P4
and complementary codes namely Golay complementary codes. The study of these codes and
their properties, sidelobe reduction techniques are carried out.
Chapter-6 Conclusion and Scope for Future Work
The concluding remarks for all the chapters is presented in this chapter. It also
contains some future research topics which need attention and further investigation.
[attachment=26943]
Acknowledgment
I would like to express my deep sense of respect and gratitude towards my advisor
and guide Prof. Ajit Kumar Sahoo, who has been the guiding force behind this work. I want
to thank him for introducing me to the field of Signal Processing and giving me the
opportunity to work under him. I extend my sincere thanks and respects to Prof. G.Panda for
his inspiration, tremendous help, advice and encouragement. I consider it my good fortune to
have got an opportunity to work with such a wonderful person.
I express my respects to Prof. S.K. Patra, Prof. K.K. Mahapatra, Prof. G. S. Rath,
Prof. S. Meher, Prof. S.K.Behera, Prof. Poonam Singh, Prof. D.P.Acharya, for teaching me
and also helping me how to learn. They have been great sources of inspiration to me and I
thank them from the bottom of my heart.
I would like to thank all faculty members and staff of the Department of Electronics
and Communication Engineering, N.I.T. Rourkela for their generous help in various ways for
the completion of this thesis.
Iam very thankful to my senior Vikas Baghel, who helped me a lot during my
research work. I would like to thank all the Ph.D. scholars in DSP lab, my seniors and my
friends especially Sunayana, Kranthi, Maitrayee, Sheema, Suresh, Chandu, Hanuma, Bharat,
Shreeshail, Gyan, and Pyagyan for their help during the course of this work. I am also
thankful to my classmates for all the thoughtful and mind stimulating discussions we had,
which prompted us to think beyond the obvious.
I am especially indebted to my parents (Mr. A. Kuppi Reddy, Mrs E. Amaravathi),
uncle, sisters and brothers-in-law for their love, sacrifice, and support. They are my first
teachers after I came to this world and have set great examples for me about how to live,
study, and work.
ABSTRACT
The present thesis aims to make an in-depth study of Radar pulse compression, Neural
Networks and Phase Coded pulse compression codes. Pulse compression is a method which
combines the high energy of a longer pulse width with the high resolution of a narrow pulse
width. The major aspects that are considered for a pulse compression technique are signal to
sidelobe ratio (SSR) performance, noise performance and Doppler shift performance.
Matched filtering of biphase coded radar signals create unwanted sidelobes which may mask
important information. The adaptive filtering techniques like Least Mean Square (LMS),
Recursive Least Squares (RLS), and modified RLS algorithms are used for pulse radar
detection and the results are compared.
In this thesis, a novel approach for pulse compression using Recurrent Neural
Network (RNN) is proposed. The 13-bit and 35-bit barker codes are used as signal codes to
RNN and results are compared with Multilayer Perceptron (MLP) network. RNN yields
better signal-to-sidelobe ratio (SSR), error convergence speed, noise performance, range
resolution ability and doppler shift performance than neural network (NN) and some
traditional algorithms like auto correlation function(ACF) algorithm. But the SSR obtained
from RNN is less for most of the applications. Hence a Radial Basis Function (RBF) neural
network is implemented which yields better convergence speed, higher SSRs in adverse
situations of noise and better robustness in Doppler shift tolerance than MLP and ACF
algorithm. There is a scope of further improvement in performance in terms of SSR, error
convergence speed, and doppler shift. A novel approach using Recurrent RBF is proposed for
pulse radar detection, and the results are compared with RBF, MLP and ACF. Biphase codes,
namely barker codes are used as inputs to all these neural networks. The disadvantages of
biphase codes include high sidelobes and poor Doppler tolerance.
The Golay complementary codes have zero sidelobes but they are poor Doppler
tolerant as that of biphase codes. The polyphase codes have low sidelobes and are more
Doppler tolerant than biphase codes. The polyphase codes namely Frank, P1, P2, P3, P4
codes are described in detail and autocorrelation outputs, phase values and their Doppler
properties are discussed and compared. The sidelobe reduction techniques such as single Two
Sample Sliding Window Adder(TSSWA) and double TSSWA after the autocorrelator output
are discussed and their performances for P4 code are presented and compared. Weighting
vi
techniques can also be applied to substantially reduce the range time sidelobes. The
weighting functions such as Kaiser-Bessel amplitude weighting function and classical
amplitude weighting functions (i.e. Hamming window) are described and are applied to the
receiver waveform of 100 element P4 code and the autocorrelation outputs, Peak Sidelobe
Level (PSL), Integrated Sidelobe Level (ISL) values are compared with that of rectangular
window. The effects of weighting on the Doppler performance of the P4 code are presented
and compared.
1.1. Background
RADAR is an acronym of Radio Detection And Ranging. There was a rapid growth in
radar technology and systems during world war II. In the recent years, there were many
accomplishments in radar technology. The major areas of radar applications includes
military, remote sensing, air traffic control, law enforcement and highway safety, aircraft
safety and navigation, ship safety and space [1.1, 1.2].
The rapid advances in digital technology made many theoretical capabilities practical
with digital signal processing and digital data processing. Radar signal processing is defined
as the manipulation of the received signal, represented in digital format, to extract the desired
information whilst rejecting unwanted signals. Pulse compression allowed the use of long
waveforms to obtain high energy simultaneously achieve the resolution of a short pulse by
internal modulation of the long pulse. The resolution is the ability of radar to distinguish
targets that are closely spaced together in either range or bearing. The internal modulation
may be binary phase coding, polyphase coding, frequency modulation, and frequency
stepping. There are many advantages of using pulse compression techniques in the radar
field. They include reduction of peak power, relevant reduction of high voltages in radar
transmitter, protection against detection by radar detectors, significant improvement of range
resolution, relevant reduction in clutter troubles and protection against jamming coming from
spread spectrum action [1.3].
In pulse compression technique, the transmitted signal is frequency or phase
modulated (but not amplitude modulated) and the received signal is processed in the receiver,
into a specific filter called "matched filter". In 1950-60, the practical realization of radars
using pulse compression have taken place. At the starting, the realization of matched filters
was difficult using traverse filters because of lack of delay line with enough bandwidth. Later
matched filters have been realized by using dispersive networks made with lumped-constant
filters. In recent years, instead of matched filters, many sophisticated filters are in use.
Barker code is the binary phase-coded sequence of 0, π values that result in equal
side-lobes after passes through the matched filter. J.S.Fu and Xin wu proposed adaptive
filtering techniques using LMS and RLS algorithms to suppress the sidelobes of barker code
of length 13 [1.4]. The SSR and doppler performance of this type of filters are very poor.
B.Zrnic et,al. proposed a self –clutter suppression filter design using modified RLS
algorithm that gave better performance compared to iterative RLS and ACF algorithms [1.5].
CHAPTER 1: INTRODUCTION
3
A multilayered neural network approach using back propagation algorithm which
yielded better SSR than basic ACF approach was presented by Kwan and Lee [1.6]. Khairnar
et,al. [1.7] proposed a RBFN for pulse compression that yielded high SSRs in different
adverse situations of noise, with misalignment of clock. This approach also has better range
resolution and robustness in doppler shift interference. Frank proposed a polyphase code
called as Frank code which is more Doppler tolerant and has lower sidelobes than binary
codes [1.8]. Kretschmer and Lewis have presented the variants of Frank polyphase codes,
namely P1, P2, P3, and P4 that have better properties than Frank code [1.9, 1.10].
1.2. Motivation
The pulse compression in radar has major applications in the recent years. For better
pulse compression, peak signal to sidelobe ratio should be as high as possible so that the
unwanted clutter gets suppressed and should be very tolerant under Doppler shift conditions.
Many pulse compression techniques have come into existence including neural networks. The
recurrent networks have inherent memory for dynamics that makes them suitable for dynamic
system modelling. They provide better stability, more robust to estimation errors and good
performance with more past information relevant to prediction. Hence the recurrent
connections are applied to the MLP and RBF networks for pulse radar detection to achieve
overall better performance. The study of polyphase codes and their sidelobe reduction
techniques are carried out since the polyphase codes have low sidelobes and are better
Doppler tolerant and better tolerant to precompression bandlimiting.
1.3. Thesis Organization
Chapter-1 Introduction
Chapter-2 Adaptive Filtering Techniques for Pulse radar Detection
The concept of pulse compression in radar is described in detail. The adaptive
filtering techniques using LMS, RLS and modified RLS algorithms are discussed for pulse
compression and the results are compared.
Chapter-3 Recurrent Neural Network Approach for Pulse Radar Detection
This chapter presents a novel recurrent neural network based pulse radar detection.
The simulation results are compared with that of MLP and ACF algorithms.
Chapter-4 Recurrent RBF Approach for Pulse Radar Detection
This chapter proposes a novel recurrent RBF network based pulse radar detection
technique which provides significant improvement in convergence rate, noisy conditions and
under Doppler conditions. The proposed network is compared with the other networks like
RNN, MLP and ACF.
Chapter-5 A Study of Polyphase Codes and their Sidelobe Reduction techniques
This chapter deals with the different polyphase codes such as Frank, P1, P2, P3, P4
and complementary codes namely Golay complementary codes. The study of these codes and
their properties, sidelobe reduction techniques are carried out.
Chapter-6 Conclusion and Scope for Future Work
The concluding remarks for all the chapters is presented in this chapter. It also
contains some future research topics which need attention and further investigation.