Radar Project

Pulse Compression Radar

By: Hamdi M. Joudeh and Yousef Al-YazjiSupervisor: Dr. Mohamed Ouda

1

Introduction:

Radars can be classified according to the waveforms:

- Continuous Wave (CW) Radars.

- Pulsed Radars (PR).

We are concerned in Pulsed Radars:

- Train of pulsed waveforms.

- Transmitted periodically.2

Basic Concepts:

Target Range: R= cΔt / 2Inter pulse period (IPP) and Pulse repetition

frequency (PRF): PRF=fr=1/IPPDuty Cycle = dt = t ⁄ T, Pav = Pt × dt.

3

Basic Concepts:

Range ambiguity:

4

Basic Concepts: Range resolution:

5

Pulse Compression:

Short pulses are used to increase range resolution.

Short pulses = decreased average power.Decreased average power=Decreased

detection capability.Pulse compression = Increased average

power + Increased Range resolution.

6

Advantages of pulse compression:

Maintain the pulse repetition frequency (PRF) .

The avoidance of using high peak power.

Increases the interference immunity.Increases range resolution while

maintaining detection capability.

7

The concept of pulse compression:

1- Generation of a coded waveform: (various types).

2- Detection and processing of the echo: (achieved by a compression filter).

The actual compression process takes place in the receiver by the matched filter or a correlation process.

8

Methods of implementation:

Active generation and processing:

9

Methods of implementation:

Passive generation and processing:

10

Types of pulse compression:Linear FM: Advantages

Easiest to generate.The largest number of generation

and processing approaches.SNR is fairly insensitive to Doppler

shifts.

11

Linear FM: Disadvantages

Range-doppler cross coupling.

12

Types of pulse compression:Linear FM: The process

LFM the transmitted pulse.

Receiver: matched filter.

compression ratio is given by B*T13

Linear FM: Up and Down Chirp

14

Linear FM: Compression

Compression Ratio=T/t.∆R = C*t/2.Higher Compression Ratio = Better range

resolution.Compression Ratio=B*T .wideband LFM modulation = Higher

compression ratio.

15

Linear FM: Example

Overlapped received waveforms:

16

Linear FM: Example

Detected pulses (output of matched filter)

17

Phase Coded: Introduction

Long Pulse with duration(T) divided to (N) coded sub-pulses with duration(t).

Uncoded pulse (T), ∆R = C*T/2.Duration of compressed pulse = duration

of sub-pulse = t.Compression ratio = B*T = T/t.New ∆R = C*t/2 (better).

18

Phase Coded: Codes used

binary codes, sequence of either +1 or -1.Phase of sinusoidal carrier alternates

between 0° and 180° due to sub-pulse.

19

Phase Coded: Codes used

Must have a minimum possible side-lobe peak of the aperiodic autocorrelation function.

20

Phase Coded: Barker code

Optimal binary sequence, pseudo-random.

Pseudo-random = deterministic .Pseudo-random has the statistical

properties of a sampled white noise.

21

Phase Coded: Auto correlation function of the Barker sequencePeak = N, 2Δt wide at base.

22

Phase Coded: Detection and compression

compressed pulse is obtained in the receiver by correlation or matched filtering.

compression ratio = N = T/t.half-amplitude width = t = sub-pulse

width.∆R = C*t/2.

23

Phase Coded: Auto Correlation MATLAB example.Two un-coded overlapped long pulses.

24

Phase Coded: MATLAB work

Two barker coded overlapped long pulses.

25

Implementation of Biphase-Coded System Using MATLAB:

26

Implementation of Biphase-Coded System Using MATLAB:Why I and Q detection?

27

Software steps and approaches: Waveform Generation:Required inputs:

- Barker code sequence.

- Maximum Range. (to calc. IPP).

- Range resolution. (to calc. pulse width).

28

Waveform Generation:

29

Path and Receiver losses:

Radar equation:

Modified: L= Radar losses

RCS of 0.1 and 0.08 m2

Ranges = 60 and 61 Km F = 5.6 GHz, G = 45dB L= 6dB

30

Path and Receiver losses:

31

Added Noise:

Implementing AWGN, a major challenge.We need the standard deviation, σ2 = No/2.

K=Boltzmann’s constant, and Te=effective noise temperature.32

Added Noise:

Calculate (SNR)I from Te=290K, Pt=1.5 MW.

Substitute in

Using the actual E in MATLAB, sum(signal2). And Bt = #of subpulses.

MATLAB function randn().Noise = σ*randn(# of noise samples)

33

Added Noise:

34

Detection:

Matched filter, I and Q detection.

35

Correlation:

Result:

36

Observations:

Calculating the range difference:Between the two peeks 130 samples.Δt = samples*Ts. Where Ts= 5*10-8 sec.ΔR = Δt* C / 2, ΔR = 975m.Error of 2.5%

37

Observations:

- For 500m difference:ΔR = 520m.Error = 4%.

- ErrorΔR decreases, the error increases.Error due to noise and sampling time.

38

References:

Radar Handbook - 2nd Ed. - M. I. Skolnik.MATLAB Simulations for Radar Systems

Design, Bassem R. Mahafza and Atef Z. Elsherbeni.

Digital Communications - Fundamentals and Applications 2nd Edition - Bernard Sklar.

http://mathworld.wolfram.com/BarkerCode.html

39

Thank You for your attention

40