detection, identification & classification of intra pulse ...€” digital receiver, dsp,...

International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459, Volume 2, Issue 9, September 2012)

433

AK Singh1, Dr. K.Subba Rao

2

1Scientist in Defence Electronics Research Laboratory (DLRL), Hyderabad, India.

2Professor in ECE Department, CBIT, Hyderabad, India

[email protected]

Abstract—The modern day Electronic Warfare(EW)

receivers are required to match with the current day radar

technologies like the Low Probability of Intercept(LPI)

radars with various intra pulse modulations such as Chirp,

Barker, Frank, Poly-phase and Poly time codes. This paper

presents the state of the art single board Digital Receiver

solution for intercepting and analyzing complex radar

signals. Also the pre and post processing methodologies have

been discussed from both the algorithmic as well as hardware

point of view.

Keywords— Digital Receiver, DSP, EW-Electronic Warfare,

FPGA, Intra Pulse Modulation, LPI-Low Probability of

Intercept, Time Frequency Algorithms.

I. INTRODUCTION

Modern electronic intercept systems must perform the

tasks of detection, classification, identification and

exploitation in a complex environment of high noise,

interference and multiple signals. Some waveforms are

intentionally designed to make the detection process nearly

impossible. Such signals are referred to as Low Probability

of Intercept (LPI) waveforms [1]

. Parameters such as carrier

frequency, modulation type, data rate and time or angle-of-

arrival are just a few of the fundamental features that

distinguish one signal from another. The sorting and

cataloging of signals leads to the process of identification.

The task of classification requires sorting into groups

having similar characteristics. Each of these initial

processes: detection, Classification, identification and

exploitation require advanced signal processing

techniques. A combination of FPGA (pre processing) and

DSP processor (post processing) is being used to extract all

the parameters of LPI radar. The complete information of a

pulse is embedded in the form of a Pulse Descriptor Word

which is further processed to display the parameters of an

emitter on ESM display.

II. LPI RADAR SIGNALS

LPI Radars use continuous wave (CW), wide bandwidth

low power signals of the order of a few watts making its

detection difficult. There are many modulation techniques

that provide a wideband LPI CW transmit waveform. For

the intercept receiver to demodulate the waveform, the

particular modulation technique must be known (which is

typically not the case)

The most common modulation techniques available to

provide LPI features are [2]

:

i. Frequency modulation (Linear & Non Linear FM)

ii. Phase modulations (Barker, Frank, P1- P4, T1-T4

codes).

A. Frequency Modulation: Most of the LPI radars use

FMCW which is frequency modulation employing pulse

compression technique. The advantage of FMCW radars

are their extremely high time bandwidth product which

makes them very resistant to interception by ES systems.

Large modulation bandwidth provides very good range

resolution. Stepped, Ramp and Triangular frequency

modulations come under Linear FM while Sinusoidal and

Square FM comes under Non linear FM.

B. Phase Modulation: The phase modulated radar signal

can be expressed as

(2 )

( ) c kj f ts t Ae

----- (1)

Where s(t) is the transmitted signal, A is amplitude, fc is

the angular frequency of the carrier and k is the phase

modulation function that is shifted in time. By varying k

different phase coded signals such as Barker, Frank, P1,

Detection, Identification & Classification of Intra Pulse

Modulated LPI Radar Signal using Digital Receiver



434

P2, P3, P4 codes are generated. Pulse radar which uses

barker codes will achieve a high range resolution while

transmitting low peak power. Further, by increasing the

number of elements or phase values in the sequence allows

the construction of longer sequences, resulting in still

higher range resolution waveform with greater processing

gain in the receiver or equivalently a large compression

ratio. Detection, identification and classification of these

kinds of complex modulations are a major challenge for

the EW receiver. However, with the advances in Analog to

Digital Converters (ADCs) and real time Signal Processing

using FPGA and high end DSP processor, it became

possible to process these signals.

III. DETECTION, IDENTIFICATION AND CLASSIFICATION OF

LPI RADAR SIGNALS

Detection of LPI radar signals requires a large

processing gain because of the wideband nature of the LPI

radar. LPI radars are assumed to be low power, high duty

cycle signals with phase coding or digital frequency

modulations. As the coding is unknown and can be

complex, and assuming the frequency is also unknown,

then coherent detection is not possible and non-coherent

detection must be performed first[3]

. Detection involves the

extraction of parameters while Classification requires

sorting the signal into groups having similar parameters.

The following two sections outline the implementation

methodology from an algorithmic and hardware point of

view for a modern ESM Receiver to detect, classify and

identify the various frequencies and phase coded radar

signals.

The identification of the extracted parameters of an

emitter is being implemented using a power PC. Here the

results obtained using pre processing and post processing

from FPGA and DSP Processors being used to identify the

various modulations based on the basic parameters of the

radar such as Frequency, Amplitude, Pulse Width, Time of

Arrival, Direction of Arrival and advanced parameters like

Modulation bandwidth, Modulation period, Code period

and time for an LPI Radar. These are the parameters that

distinguish one LPI radar signal from another and they are

required for effective exploitation.

IV. PROCESSING ALGORITHMS

The processing of signals in the hardware is done in two

levels i.e. pre processing using FPGA and post processing

using DSP Processor. These two mechanisms are

explained below.

A. Pre-Processing:

The present day Radar signals can be processed in real-

time if the time taken by the system to process signals is

equal to the time taken by the system to acquire the data,

especially if a radar signal is converted to digital data by

using high speed ADC with sampling frequency of the

order of GHz. In order to get real-time response, the rate at

which the data is processed should match the ADC data

rate[4]

. Achieving this task is very difficult unless some

special DSP algorithms are implemented. A Real-Time

FPGA based FFT algorithm has been implemented as the

initial detection algorithm to extract the basic parameters

of the Radar signal [5]

. The amplitude and phase spectrums

of the Fourier Transform give an idea of whether the

intercepted signal contains any frequency or phase

modulations apart from the basic parameters like

frequency, Amplitude, Pulse Width and Time of Arrival.

Decision logic is then used to decide whether the signal is

of LPI nature or not. The core logic of decision making is

comparison of phase and frequency of successive samples

obtained using initial detection algorithm i.e. FFT. The

presence of frequency modulation in the signal is indicated

by the spread of the frequency spectrum from the start to

the stop frequency [6]

. The detection of the Phase Coded

Signals with the Windowed Fourier Transform is based on

an important observation that in the FFT phase spectrum,

the phase of the signal varies by only few degrees from

sample to sample in case of signals where there is no phase

changes in the input signal, but, the phase variation is very

high in the case of a phase coded signal [7]

. Some of the

results obtained for detection of Frequency modulated and

Barker coded signals are mentioned in the test results in

figures 3 to 7.

B. Post-Processing:

As the present day Radar signals are modulated with

complex modulations, detection and analysis of these

signals using only FFT is very difficult. Hence, FFT is

used as initial detection algorithm to extract the basic

parameters of Radar and decide the nature of the signal [6]

.

If the signal is of LPI nature and also the pulse width of the

signal is >10us (as is the case with most of the LPI radars)

the data being processed using a post processing technique.

Otherwise all the basic pulse parameters like frequency,

amplitude, pulse width and time of arrival of the signal can

be extracted in real-time with high accuracy at the pre

processing stage itself.



435

The advanced time frequency algorithms like the Choi

Williams Distribution and Quadrature Mirror Filter Bank

algorithms [1]

are being implemented on DSP processor to

extract the advanced parameters of an LPI Radar. The time

frequency algorithms mentioned are highly mathematically

intensive and implementation of these algorithms in FPGA

is very difficult. TS-201 DSP processor is chosen to meet

this requirement of post processing algorithms. Hence, a

combination of FPGA and DSP processor being used to

extract the complete parameters of an LPI Radar and

classify them. The brief description of the post processing

algorithms WVD, CWD and QMFB has been explained in

the following sections.

i. Wigner Ville Distribution (WVD):

The Wigner Ville Distribution (WVD) is a two-

dimension function describing the frequency content of a

signal as a function of time. Using the WVD, frequency

and time changes in most of the LPI radar signals can be

identified.

The WVD of discrete input signal x (t) is defined as

1 2

1( , ) 2 ( ) ( ) ( ) ( )

N j wn

n Nw l w x l n x l n w n w n e

-------- (2)

where x(n) is an input signal

l is the time variable,

ω is the angular frequency variable,

* indicates the complex conjugate

w (n) is a length of 2N-1 real window

function with w (0) =1.

The above WVD algorithm is simulated in MATLAB

for different signals and it is observed that for multi

component signals the cross terms (Oscillatory positive

and negative peaks, due to interference between spectral

components) are present in the Wigner-Ville distribution.

The cross terms cause interference that can obscure

physically relevant components of the LPI signal‟s

modulation [8]

. So, Choi Willams Distribution which has a

modified WVD kernel is used.

ii. Choi-Williams Distribution (CWD):

The Choi-Williams Distribution (CWD) is given by[9]

dddAetC tj

f ),(),(2

1),,( )(

-------- (3)

Where (ξ, τ) is a kernel function and

A (μ, τ) = x (μ + τ/2) x*(μ- τ/2) -------- (4)

and x (μ) is time signal and x*(μ) is its complex conjugate.

This equation represents a generalized class of bilinear

transformation that satisfies the marginal conditions and

has good resolution in both the time and frequency spaces.

The Wigner-Ville time-frequency distribution, is based on

(2) where the kernel function is (ξ, τ) = 1.

The Choi-Williams distribution uses an exponential

weighting kernel in order to reduce the cross-term

components of the distribution. The kernel function that

gives the Choi-Williams distribution is

e

/22

),(

------ (5)

Where σ (σ>0) is a scaling factor. By substituting the

above kernel in (5) into (3), the equation for the discrete

Choi- Williams distribution of the input signal x (n) with a

discrete time index and windowed for large data sample

sets shown below.

2

2

2),(2),(

Nn

Nn

nj

x enlSlCWD ------ (6)

Where

)()()(4

1

)(),( *2/

2/

4

)(

2

2

2

Wnxnxe

nnWnlSM

M

l

Here l is the time variable, ω is the angular frequency

variable, σ is a positive-valued scaling factor, and *

indicates the complex conjugate. and W(n) is a

symmetrical window (such as Hamming) which has

nonzero values on the interval -N / 2 to N / 2 and W(μ) is a

uniform rectangular window that has a value of one for the

range of -M / 2 and M / 2 .



436

The choices of N and M on these windows respectively

determine the frequency resolution of the CWD and the

range at which the function will be defined.

The discrete CWD can be modified to fit the standard

discrete Fourier Transform (DFT) by setting ω=πk/2N.

The final equation can be written as

12

0

/2' ),(2)2

,(Nn

n

Nknj

x enlSN

klCWD

----------- (7)

Where the kernel function S‟ (l, n) is defined as

121)2,(

0

10),(),('

NnNNnlS

Nn

NnnlSnlS

--------- (8)

The results of the CWD for a given frank coded signal to

extract the Frequency, Time period and bandwidth are

given in test results in Figure 8. The frequency of the

signal can be found out from the peak of the mesh plot at

view angle of 00

elevation and 900

in azimuth. From

Figure 10, we can observe that the frequency

corresponding to the peak occurs at 999.3MHz.The

horizontal distance between the successive strips on the

mesh plot gives the total code duration (T) of the signal.

From figure 10, it can be observed as 75.19-44.07=31.12

ns. The bandwidth of the signal can be found out from the

vertical distance between successive spectrogram strips.

From figure 10, the bandwidth is calculated as 1277-

784=493MHz. Once these parameters are known, the sub

code duration (tb) can be calculated as the inverse of

Bandwidth. The number of cycles of the carrier per phase

(cpp) i.e. within each code duration, is given by the sub

code duration (tb) multiplied by the frequency of the signal

(fc) and converting the result to nearest integer. The no of

phase changes are determined by dividing the total code

duration (T) by the sub code duration (tb) and converting to

nearest integer. As a result of this property, the CWD is

often thought as a signal‟s energy distribution in the time-

frequency domain.

iii. Quadrature Mirror Filter Bank(QMFB):

The architecture of the QMFB tree is illustrated in Fig 1.

Each QMF pair divides a digital input waveform into its

high frequency and low frequency components with a

transition centered at π/2. Since each filter output signal

has half the bandwidth, only half the samples are required

to meet the Nyquist criteria, therefore these sequences are

then down sampled by two. The same number of output

samples, as were input is returned. The square of each

element of the input waveform represents the waveform‟s

energy for that sample and each element represents the

energy contained in the corresponding tile in the left most

time-frequency diagram as shown in Fig 1. Similarly, the

outputs from each layer of the tree form a matrix whose

elements, when squared, approximately represent the

energy contained in the tiles of the corresponding time-

frequency diagrams as shown in Fig. 1[1]

.

When the waveform consists entirely of White

Gaussian Noise, the tile‟s energy will have random values

with a Chi-squared probability distribution. When a

deterministic signal is added, tiles containing energy from

the signal will have probability distribution that is Chi-

squared with non-centrality parameters and will, therefore,

tend to have larger mean values and thus make threshold

detection a possibility.

Fig. 1: Quadrature Mirror Filter Bank (QMFB) Tree.

Since the transform is linear, a fundamental limit on the

minimum area of each of the tiles exists. However, looking

at the figure, it can be noted that each layer outputs a

matrix of energy values for tiles that are twice as long (in

time) and half as tall (in frequency) as the tile in the

previous layer.



437

By properly comparing these matrices, it is possible to

find concentrations of energy and estimate their position

and sizes with high resolution in both time and frequency.

Using these techniques, a waveform can be decomposed

and the bandwidths, the time widths, and locations in the

time-frequency plane can be estimated. The algorithm is

being implemented at the post processing stage.

V. LPI DIGITAL RECEIVER HARDWARE

Keeping in view the above discussed aspects and the

complexity of the algorithms, Digital Receiver hardware

containing the state of the art data acquisition and

processing devices has been designed by adapting the

latest PCB board design practices. The block diagram of

the hardware is shown below (figure 2).

Fig. 2: Block Diagram of LPI Digital Receiver

The LPI Digital Receiver

Hardware will have two

modules namely IF & Clock synthesizer section and LPI

Digital Receiver Main board. LPI Detector and Analyzer

hardware receives IF input in the frequency range of either

160MHz±20MHz (140 to 180 MHz) or

1000MHz±250MHz (750 to 1250 MHz). The inputs are

amplified and filtered to eliminate any spurious signals

with a gain of 12dB to meet the input power level

requirements of ADC which is present on the main board.

These signals are fed to ADC which operates at 1350

MHZ sampling rate. The sampled data will be fed to

Virtex-6 SX-475 FPGA. The FPGA receives the sampled

data in real time for pre-processing and to compute/process

the signal parameters. After pre-processing, depending on

the pre defined logic criterion, the data is sent to two TS-

201 DSP processors for further processing. The processed

data along with parameters of the signal from DSPs and

FPGA will be given to a PowerPC for De-interleaving of

the signals. The output of the FPGA is fed through a 51 pin

„D‟ Connector and also through VME connectors to ESM

processor card for further processing.

VI. RESULTS ACHIEVED

Some of the following tests results obtained for various

modulations using pre processing are shown from figure 3

to 7 and post processing techniques are shown from figure

8 to 10.

i. Result 1: Real-Time ADC data Capture

Real-Time data Captured from an 8 Bit ADC with a

sampling frequency of 1.35 GHz using Virtex-4 FPGA is

shown in this figure 3. Top portion of the figure shows the

bus plot and the bottom portion of the figure shows

waveform plot using Chip Scope Pro Tool.

Fig. 3: Real Time Data Capture using ADC

ii. Result 2: FFT Spectrum on Real-Time Data

FFT Spectrum obtained for the ADC data captured in

Result1 is shown in the following figure 4. The plot shows

the FFT spectrum for 1us pulse width signal using 256

point FFT.

IF &

Clo

ck S

ynth

esiz

er

Sect

ion

ADC FPGA

FPGA

P

ow

er P

C

TS201

VM

E In

terf

ace

TS201

201

Time (ns)

AD

C D

ata



438

Fig. 4: Real Time FFT Magnitude Spectrum during pre-processing

iii. Result 3: FMCW detection using FFT

Frequency variation plot for an FMCW signal is shown

in the figure 5. The plot is obtained for a linear FM with

triangular modulation.

Fig.5: FWCW Detection using real time FFT

iv. Result 4: Detection of a Phase Modulated Barker Code

Figure 6 & 7 describes the magnitude and Phase

spectrum for a given signal without and with phase

modulation respectively. The variation of the phase

spectrum for a given 7 bit Barker code is shown in figure

7.

Fig. 6: FFT Spectrums of an unmodulated Signal

Fig. 7: FFT Spectrums for a phase coded Signal

v. Result 5: Effect of cross terms for a multi component

Signal

Figure 8 & 9 shows the output of both the CWD and

WVD mesh plots respectively when two frequency

components 750MHz and 1250MHz are present in the

signal.

Frequency Index

FFT

Mag

nit

ud

e Fr

equ

ency

Time



439

Fig. 8: CWD output with no cross terms.

Fig. 9: WVD output with cross term at 1GHz.

vi. Result 6: Parameter extraction of Phase modulated

signal with Frank Code.

Figure 10 describes the extraction of Time Period and

Bandwidth using CWD mesh plot for a given Frank Code.

A 4 bit Frank Code with carrier Frequency of 1GHz

having code duration of 32 ns has been considered in the

present case.

Fig. 10: CWD mesh plot for extraction of Time Period and

Bandwidth

Table 1 shows the comparison of critical parameters

obtained by CWD with the actual values of the parameters.

Table 1: Detection Effectiveness of CWD for a Frank Coded Signal

Parameter

Actual Value

Extracted Value

Error (%)

Frequency

1 GHz

999.3MHz

0.07

Total Code Duration

32 nsec

31.12 nsec

2.75

Sub Code Duration

2 nsec

2.0284nsec

1.42

No of Phases

16

16

0

No of Cycles of the

Carrier

2

2

0



440

VII. CONCLUSION

The paper outlined both the pre and post Digital Signal

processing methodology for Detection, Identification &

Classification of Intra Pulse Modulated LPI Radar Signal.

The pre-processing and post processing is achieved with a

combination of high end Virtex-6 FPGA, advanced Tiger

SHARC DSP processor and Power PC. The proposed

hardware along with algorithms discussed serves as a

single board solution to detect the various frequencies and

phase coded radar signals and identifies their characteristic

parameters.

REFERENCES

[1] Philip E Pace, Detecting And Classifying LPI Radar, Second

Edition,Artech House, Inc., Norwood, Massachusetts, 2009.

[2]. Bomer L, and Antwailer M, “Polyphase Barker Sequences”, IEEE

Electronics Letters, Vol 25, No. 23, 1989.

[3]. Aytuk Denk, “Detection and Jamming Low Probability of Intercept

(LPI) Radars” Naval Postgraduate School Master‟s Thesis, Sept.

2006.

[4] R. Pavan Kumar, A.K. Singh, Prof. K. Subba Rao, "High speed ADC

data synchronisation",National Conference on Signal Processing &

Communication Systems, (SPCOMS-2011), P.No. 37-39, 1st -

2nd Apr 2011, Andhra Pradesh, India.

[5]. James Tsui,Special Design topics in Wideband Digital Receivers,

Artech House,Inc., Norwood, Massachusetts, 2010,pp 321-346.

[6]. Griffithss H D, and Bradford W J, “Digital generation of high time

bandwidth product linear FM waveforms for radar altimeters”, IEE

Proc., Vol139,No. 2, P. No. 160-169, April 1992

[7]. VVSRN Raju, A.K. Singh, R. Rama Rao, Prof. K. Subba Rao,

"Detection of Barker coded LPI Radar signals using

windowed FFT", International Symposium on Microwaves-

2010(ISM-10), P.No. 229-233, 11th - 14th Dec 2010, Bangalore.

[8]. VVSRN Raju, A.K. Singh, G Mamata,,” Wigner-Ville Distribution

Algorithm for Identification Of Intra pulse Modulated Signals From

LPI Radar”, National Conference on Algorithms(NCA),2008,Mumbai,India.

[9]. L. Cohen, “Generalized phase-space distribution functions,

Math.Phys” vol.7, pp. 781-786, 1966.

BIO DATA OF AUTHORS

A.K. Singh was born in 1969 at

Ranchi (Dist) of Jharkhand State, India. He Graduated in Electronics

and Communication Engineering

(ECE) from Institution of Engineers (India), Calcutta in 1993

and M. E. in Digital System (ECE)

from Osmania University in 2003. He joined Defence Electronics

Research Laboratory (DLRL),

Hyderabad, India in 1996 after completing Electronics Fellowship

Course at Institute of Armament

Technology (IAT), Pune, India. He has worked on Frequency

Receivers for Radar EW systems.

Currently he is Scientist-„F‟ and leading a team of scientists involved in design & development of Real time Digital Receiver. He is also

carrying out his PhD research work under Prof. K Subba Rao on

Time- Frequency Analysis of Intra Pulse Modulated signals.

Dr. K Subba Rao has Graduated

from S V University, Tirupati, India. He obtained the Master‟s

and Ph.D degree from Osmania

University (O.U), Hyderabad, India. He Joined Osmania

University in 1981 as lecturer and

served as a headed the ECE department. Currently he is

working as professor in Chaitanya Bharathi Institute of Technology,

Hyderabad, India He has

published more than 150 research papers in national and

international journals/

conferences. His current interests include signal processing for

Radar & Spread spectrum

applications and Bio Medical signal processing.

detection, identification & classification of intra pulse ...€” digital receiver, dsp,...

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