DSP APPLICATIONS

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There has been an explosive growth in Digital Signal Processing theory and applications
over the years. This seminar report explores the applications of digital signal processing in
Radar. A survey on applications in Digital Signal Processing in Radar from a wide variety of
areas is carried out. A review is done on basic approaching models and techniques of signal
processing for different parameters and extracting information from the received signal. The
various techniques adopted at different stages of radar to obtain the target’s signature, is also
briefed.
Introduction
Flexibility and versatility of digital techniques grew in the front-end signal processing and
with the advent of integrated digital circuitry, high speed signal processors were developed
and realized. Radar continued to grow in the recent years by keeping the future developments
in mind and with better digital capability. Significant contributions in DSP in Radar have
been in MTI processing, Automatic Detection and extraction of signal, Image reconstruction,
etc. A case study on Radar Synthetic Vision System for Adverse Weather Aircraft landing is
discussed. In this report an effort is made to identify the contribution of DSP in the
advancement of Radars.
I. Modern RADAR
RADAR transmits radio signals at distant objects and analyzes the reflections. Data gathered
can include the position and movement of the object, also radar can identify the object
through its “signature” – the distinct reflection it generates. There are many forms of RADAR
– such as continuous, CW, Doppler, ground penetrating or synthetic aperture; and they’re used
in many applications, from air traffic control to weather prediction.
In the modern Radar systems digital signal processing (DSP) is used extensively. At the
transmitter end, it generates and shapes the transmission pulses, controls the antenna beam
pattern while at the receiver, DSP performs many complex tasks, including STAP (space time
adaptive processing) – the removal of clutter, and beamforming (electronic guidance of
direction).
The front end of the receiver for RADAR is still often analog due the high frequencies
involved. With fast ADC convertors- often multiple channel, complex IF signals are digitized.
However, digital technology is coming closer to the antenna. We may also require fast digital
interfaces to detect antenna position, or control other hardware.
The main task of a radar’s signal processor is to make decisions. After a signal has been
transmitted, the receiver starts receiving return signals, with those originating from near
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objects arriving first because time of arrival translates into target range. The signal processor
places a raster of range bins over the whole period of time, and now it has to make a decision
for each of the range bins as to whether it contains an object or not.
This decision-making is severely hampered by noise. Atmospheric noise enters into the
system through the antenna, and all the electronics in the radar’s signal path produces noise
too.
A. Major blocks of modern radar system
The major components of modern radar are the antenna, the tracking computer and the signal
generator. The tracking computer in the modern radar does all the functions. By scheduling
the appropriate antenna positions and transmitted signals as a function of time, keeps track of
targets and running the display system.
Fig 1. Block Diagram of a Modern Radar system “adopted from [6]”
Even if atmospheric attenuation can be neglected, the return from a distant object is incredibly
weak. Target returns often are no stronger than twice the average noise level, sometimes even
buried under it. It is quite difficult to define a threshold for the decision whether a given peak
is noise or a real target. If the threshold is too high then existing targets are suppressed, that is,
the probability of detection (PD) will drop. If the threshold is too low then noise peaks will be
reported as targets, that is, the probability of false alarms (PFA) will rise. A common
compromise is to have some 90% probability of detection and a false alarm rate of 10-6.
It maintains a given PFA known as CFAR, for Constant False Alarm Rate. Rather than keeping
the threshold at a fixed point, CFAR circuitry inspects one range bin after the other and
compares the signal level found there with the signal levels found in its neighboring bins. If
the noise level is rather high in all of these (eg, because of precipitation) then the CFAR
circuit will raise the threshold accordingly.
Antenna
System
Txr
Threshold
Detection
Reciever A/D
Convertor
Signal
Generator
Matched
Filters
Tracking &
Scheduling
Control
Clutter
mapping,
discrimination,
Range
marking,
Interpolation,
Monopulse
computations
Display
Processing
Control paths
Processed Tracking Computer
Radar Returns
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Further tasks of the signal processor are:
· Combining information: Secondary surveillance radars like those located on
airports can ask an aircraft’s transponder for information like height, flight number or
fuel state. Pilots may also issue a distress signal via the transponder. The ground
radar’s signal processor combinest his data with its own measurements of range and
angular direction and plots them all together on the appropriate spot on the scope.
· Forming tracks: By correlating the data sets which were obtained in successive scan
cycles, the radar can calculate a flight vector which indicates an aircraft’s speed and
expected position for the next scan period. Airport radars are capable of tracking
hundreds of targets simultaneously, and flight safety depends heavily on their
reliability. Military tracking radars use this information for gun laying or guiding
missiles into some calculated collision point.
· Resolving ambiguities in range or Doppler measurements: Depending on the
radar’sp ulse repetition frequency (PRF), the readings for range, Doppler or even both
are ambiguous. The signal processor is aware of this and selects a different PRF when
the object in question is measured again. With a suitable set of PRFs, ambiguities can
be eliminated and the true target position can be determined.
· Ground Clutter Mapping: Clutter is the collective term for all unwanted blips on a
radar screen. Ground clutter originates from buildings, cars, mountains etc, and a
clutter map serves to raise the decision threshold in areas where known clutter
sources are located.
· Time and power management: Within a window of some 60°x40°, phased array
radars can instantly switch their beam position to any position in azimuth and
elevation. When the radar is tasked with surveying its sector and tracking dozens of
targets, there’s a danger of eithe r neglecting part of the search sector or losing a target
if the corresponding track record isn’t updated in time. Time management serves to
maintain a priority queue of all the tasks and to produce a schedule for the beam
steering device. Power management is necessary if the transmitter circuitry runs the
danger of overheating. If there’s no backup hardware then the only way of continuing
regular operation is to use less power when less power is required, say, for track
confirmation.
· Countering interference: Interference can be a) natural, or b) man-made. Natural
interference can be heavy rain or hail storms, but also varied propagation conditions.
Man-made interference, if created on purpose, is also called jamming and is one of
the means of electronic countermeasures.
B. Detection of Signals
Detection is the process by which the presence of the target is sensed in the presence of
competing indications which arise from background echoes (clutter), atmospheric noise, or
noise generated in the radar receiver. The noise power present at the output of the radar
receiver can be minimized by using filter, whose frequency response function maximizes the
output peak-signal to mean-noise (power) ratio is called matched filter. we shall discuss the
application of digital filtering to matched filters.
C. Fast Convolution Filter implementation[5]
a. Dual pipeline FFT matched Filter
In this system, FFTs are pipelined and both the forward and reverse radix-r FFTs are
implemented in hardware. Initial recording of the data is done using input buffer (IB) memory
and it takes ‘N/r’ clock pulses to read N data points and ‘r’ input rails. The amount of time
‘N/r’ is called as one epoch. It requires three epochs for the first data to be completely
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filtered, and is delivered by one epoch thereafter. In the dual FFT systems arbitrary data is
filtered sequentially with arbitrary reference functions selected from reference memory.
Drawback
In many applications the same data set be filtered with several different filters, in this case
only one forward transform is performed followed by several inverse transforms, it is possible
to eliminate one of the pipeline FFTs. This is desirable since it would save a large amount of
hardware.
b. Single forward FFT matched Filter
The data is first transformed and the result stored in the temporary storage memory [TSM].
The data is then multiplied by the filter function and inverse transformed. This allows
multiple readouts of the forward transformed data from the TSM and multiple filtering of the
same data set; the output of each filter will appear sequentially.
Drawback
The data at the output of the forward FFT are in digit reverse order, it is then corrected by
reading the data out of the TSM in digit reversed order. The second FFT is performed the
output is placed into an output buffer, and to be read in a bit reversed order from the output
buffer.
It requires five epochs for the first data to be completely filtered, and is delivered by one
epoch thereafter.
c. Single inverse FFT matched Filter
A single inverse FFT is employed and the data is read from the input buffer in digit reverse
order. The data is transformed and stored in TSM in normal order, and then read out in bit
reverse order.
Drawback
Complex conjugation must be performed after each transform and stored. The digit reversed
access to the IB is required and the IB may quite large compared to TSM and may be difficult
to implement.
d. Reconfigurable FFT Matched Filter
The FFT subsystem switches the interstage delay lines to realize both forward and reverse
transforms.
Forward Transform: By routing the data through the interstage delay memories [IDMs] in
decreasing order
Inverse Transform: by sending data through the IDMs in the increased order of size.
The total memory of each stage is the number of delay lines memories.
Comparison of 4 matched Filter systems
The relative performance of Dual Pipeline matched Filter is fastest but requires two complete
pipeline FFTs hence more hardware is required.
Single inverse transform matched Filter has better performance over Single forward transform
matched Filter and also doesn’t require a double buffered output memory.
Reconfigurable FFT matched filter is preferred and chosen most of the times, it doesn’t
require digit reversed to the IB and also doesn’t require digital recording of TSM.
II. Doppler processing[5]
Doppler processing is used to filter out clutter and thereby reveal fast moving targets. Such
filters are implemented digitally, FFT or a set of transversal filters. Cancellers and few
optimized methods are some of the Clutter rejection techniques:
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A. Cancellers
Clutter rejection filter amounts to the design of FIR digital filter with stopbands to reject the
clutter frequency component. A simple filter is a two-pulse cancellor.
A two-pulse canceller is used if the clutter component [assuming DC only] remains constant
in a given range bin and can be eliminated by subtracting the output from two successive
pulses. The transfer function of two-pulse canceller is equal to 1- z-1 . And is equivalent to
FIR digital filter with magnitude responsesin (w 2) .
In practice, the clutter have a power spectrum that covers frequencies above DC. The twopulse
canceller will attenuate low frequency components but may not totally reject clutter. A
three-pulse canceller with its transform function equivalent to FIR filter is 0.5 + z-1 + 0.5z-2 .
This attenuates further the components near DC.
Other Optimum Design Methods
These are based on the assumption of the clutter and the desired signal.
a. Delong Hofstetter Technique
Under the constraint of the known clutter spectrum, in this technique the Signal-tointerference
ratio is maximized at a given Doppler frequency, if the clutter were white
Gaussian Noise, the matched filter would be optimum signal processor.
b. Another approach
Clutter is located in frequency bands disjoint from the band occupied by the signals of
moving targets. If the clutter is near DC, then a with an high pass filter the clutter frequencies
are within the stop band, and pass band passes the desired signals. These signals are then
passed through narrowband pass filters.
B.Implementation of Clutter Filter
The returns from the same range bin over several pulses are linearly combined to form the
output per IPP, each delay of D can be realized using shift register.
a. The direct implementation of the optimum linear processor with N points requires N
multiplications per output point. Since a different optimum processor is designed for each
Doppler channel, the filter tap weights are different for each channel.
b. A simpler Suboptimum Processor is obtained by cascading a three-pulse cancelor with a
bank of bandpass filters(implemented by a sliding FFT). N-point FFT requires 2 N log N
multiplications; only 2 log N multiplications are required per each Doppler Channel. Thus
significant hardware simplifications are possible with this scheme provided its performance is
adequate.
Eg:- moving Target Detector
MTI Signal Processing
A major task in moving target indicator (MTI) radar is to obtain a time-domain filter, with the
introduction of digital technology, these are achieved using digital transversal filters,
recursive filters and filter banks.
Delay
[IPP]
Input
Output
+

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III. Adaptive Thresholding and Automatic Detection[8]
Digital processing permitted the reference level to be generated/internally from the
observations themselves, thereby permitting more sensitive and faster thresholds. Most of the
Radars employ automatic detection circuits to maintain, ideally, a constant false alarm rate
[CFAR] by generating estimates of the receiver output. Automatic target detection for a
search radar can be achieved by comparing the processed voltage in each cell to
1. A fixed threshold level.
2. Threshold levels based upon the mean amplitude of the ambient interference.
3. A level computed on the basis of partial [a prior] knowledge of the interference
distribution.
4. A threshold level determined by distribution-free statistical hypothesis testing that
assumes no a priori knowledge of the statistical distribution of the interference.
In first case a detection decision is made if the processed signal o r , is equal or greater than a
present threshold. That is, if ro ³ Tp , a detection is declared.
The second and third cases represent adaptive threshold CFAR processors. In these
processors, estimates of the unknown parameters of the known distribution of the processed
interference are formed. In the second case can achieve CFAR when the distribution of the
processed interference is completely described by its mean level. The third case forms
estimates of the unknown parameters of the known [a priori] distribution.
The fourth case are called nonparametric CFAR processors. These distribution-free process
form a test variable whose statistics are independent of the distribution of the input
[nonprocessed] interference.
A. Adaptive Threshold CFAR processors
The Adaptive threshold CFAR processors is applicable to situations were the distribution of
the processed data [in the no-signal case] is known generally and unknown parameters
associated with the distribution can be estimated. It is often implemented as moving or sliding
window through which estimates of the unknown parameters of the interference are formed.
B. Distribution free CFAR processors
These provide CFAR characteristics when the background return has a unknown distribution.
These processors remain insensitive to variations in the distribution, and generally
experiences additional detection loss their CFAR properties make their application
advantageous.
1. Double Threshold Detector
2. Modified double threshold Detector
3. Rank order Detector
4. Rank-sum double quantizer Detector
C. SCANNING Radar Applications[8]
The optimum processor for a pulsed, noncoherent waveform on n pulses is a square law
detector followed by a n pulse noncoherent integrator that uses equal weighting of each
detected pulse.
The integrator must not only be realizable in practical sense but also
1. provide a small detection loss as possible
2. provide a means of minimizing losses associated with integration sample window and
scanning beam straddle of the target
3. In track-while scan applications, permit accurate measurements of the target angular
position.
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Fig. Square law detector “from [8]”
Integrators that are typically configured are sliding window. And this requires the storage of
data for n interpulse periods.
The single–loop processor requires storage of data for the single interpulse period. Of course,
if data memory is somehow restricted and if performance is acceptable the feedback approach
is preferred and single feedback loop is shown below.
IV. Case Study – Radar Synthetic Vision System[1]
One of the solutions during the adverse weather landing is to use a radar imaging system. In
this section we will discuss the prototype section for aiding pilots to land in zero visibility
weather conditions. In this section we will describe a synthetic vision system (SVS). This
system provides a runway image to the allow pilots to see through fog and adverse weather.
The SVS system[9] consists of an electro mechanically scanned antenna, transmitter,
millimeter wave integrated circuit (MMIC) receiver, display processor and heads up display
(HUD).
Images are enhanced using beam sharpening, noise reduction, and motion compensation
processing techniques prior to the critical transformation from radar coordinates to true pilot
perspective for display on the HUD. In this system Versa module Eurocard (VME) platform
is used. Single antenna operation by using a circular and isolating Positive-Intrinsic-Negative
(PIN) diode switch in duplexer. The receiver system consists of a low noise GaAs Field-effect
Transistor (FET) MMIC receiver. The received and amplified signal is down converted twice.
The detected output is sent to display processor for digitization, signal processing and to the
display unit.
SVS Display Processor and Raw Data Recording
The raw digitizer radar video data recording obtains snapshots at 4s intervals with a total
recording time of 600s. During aircraft approach the control processor is interrupted by the
IRIG Time Code Slave every 4s, which signals, to the control processor that a raw digitizer
frame acquisition are to be performed. The control processor stores the next immediate frame
after antenna retraces.
A. Image Enhancement
Imagery that are obtained by scanning millimeter (MMW) radars suffer from multitude of
quality degradations. The solutions for the degradation such as poor contrast, angular loss and
motion induced distortion are considered. The source of resolution loss in the finite antenna
beamwidth. The processing done on the raw radar data to create the effects of the antenna
with smaller beamwidth is termed as Beam sharpening. Let us consider y to be the magnitude
of the received echoes, and Y to be the output of logarithmic amplifier.
The relation between y and Y is Y= 100 log10(y).
Let x be the reflected signal, and A be the antenna beam pattern and we can express as
Square law
Detector
A/D
Convertor
Single Pulse
Delay
ADDER
Scaling
Constant [kf]
Range gate
IF signal
To adaptive Threshold
CFAR
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y= Ax+n where n is the random noise. From y , we estimate the undistorted signal x with a
finite impulse (FIR) filter F such that: =
^x
Fy where F is a matrix and coefficients are to be
calculated. FIR filter was applied to the transformed radar data y. The output data is subjected
to nonlinear transformation to get back to original domain Final Image = 100 log10 (X). For
each of the data frames, the video signal was digitized every 50ns for a total of 570 samples in
range for each pulse. Multiple pulses were recorded at azimuth angle step; this allowed SNR
and image quality improvement.
The noise in the radar imagery is suppressed by spatial and temporal filters. In this Beam
sharpener approach FIR filter, it not only acts as a beam sharpener it also acts as a spatial
noise suppressor. For reducing scintillation effect due to sequential radar imagery temporal
filters are used. The other form of distortion in real time sequential processing is due to the
motion of platform. In this system distortions due to scanned latency are much smaller
compared to processing latency and the effect of the motion induced distortion is a smeared
image. The difference between the true angle and the measured angle is called angular error.
Using predictive filters these distortions are compensated.
The SVS Radar was tested and demonstrated successfully and the test data analysis indicated
that the system provide adequate contrast between runway and surrounding grass/terrain, in
all cases of weather, The system has an excellent performance under all conditions for which
the human eye was inadequate.
V. Signal Processing in Synthetic Aperture Radar(SAR)
Digital processing has also permitted increased capability for extracting target information
form the radar signal. High resolution SAR provides an image of a scene. Radars are used to
recognize one type of target from another, with the aid of digital processing, inverse SAR
(ISAR) produces an image of a target good enough to recognize from other classes of targets
by extracting the spectrum of an target echo signal. Interferometric SAR, which uses two
antennas spaced vertically with a common SAR system, can provide height information to
obtain 3D image of a scene. Greater flexibility and real-time operation suggests digital signal
processing in SAR.
SAR exploits the probability density of the clutter to detect man-made features by modeling
the clutter by a family of densities and picking the density that best describes the clutter on a
local basis.
Fourier based methods are used for detection of stationary and moving target detection and
identification in reconnaissance SAR. The computational time of the Time domain correlator
[TDC] is overcomed in the frequency domain. Here Digital spotlighting principle is used to
extract the target’s coherent SAR signature.
Step 1
Coherent matched-filtered SAR reconstruction of the scene in the presence of foliage is
developed by exploiting the angle and frequency from the target’s coherent SAR signature.
Step 2
With the help of Fourier-based method the three dimensional statistic representing the moving
target coordinates and speed is used for moving target detection.
A. Strip Mapping SAR
On the incoming

ork. A simplified block diagram of an ADSL system

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