1. Radar Project Pulse Compression Radar
By: Hamdi M. Joudeh and Yousef Al-Yazji
Supervisor: Dr. Mohamed Ouda

2. 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.

3. Basic Concepts: Target Range: R= cΔt / 2
Inter pulse period (IPP) and Pulse repetition frequency (PRF): PRF=fr=1/IPP
Duty Cycle = dt = t ⁄ T, Pav = Pt × dt.

4. Basic Concepts:
Range ambiguity:

5. Basic Concepts: Range resolution:

6. 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.

7. 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.

8. 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.

9. Methods of implementation:
Active generation and processing:

10. Methods of implementation:
Passive generation and processing:

11. 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.

12. Linear FM: Disadvantages
Range-doppler cross coupling.

13. Types of pulse compression: Linear FM: The process
LFM the transmitted pulse.
Receiver: matched filter.
compression ratio is given by B*T

14. Linear FM: Up and Down Chirp

15. 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.

16. Linear FM: Example
Overlapped received waveforms:

17. Linear FM: Example
Detected pulses (output of matched filter)

18. 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).

19. 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.

20. Phase Coded: Codes used
Must have a minimum possible side-lobe peak of the aperiodic autocorrelation function.

21. Phase Coded: Barker code
Optimal binary sequence, pseudo-random.
Pseudo-random = deterministic .
Pseudo-random has the statistical properties of a sampled white noise.

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

23. 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.

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

25. Phase Coded: MATLAB work
Two barker coded overlapped long pulses.

26. Implementation of Biphase-Coded System Using MATLAB:

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

28. Software steps and approaches: Waveform Generation:
Required inputs:
- Barker code sequence.
- Maximum Range. (to calc. IPP).
- Range resolution. (to calc. pulse width).

29. Waveform Generation:

30. 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

31. Path and Receiver losses:

32. Added Noise: Implementing AWGN, a major challenge.
We need the standard deviation, σ2 = No/2.
K=Boltzmann’s constant, and Te=effective noise temperature.

33. Added Noise: Calculate (SNR)I from Substitute in
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)

34. Added Noise:

35. Detection:
Matched filter, I and Q detection.

36. Correlation:
Result:

37. 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%

38. Observations: - For 500m difference: - Error ΔR = 520m. Error = 4%.
ΔR decreases, the error increases.
Error due to noise and sampling time.

39. 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.

40. Thank You for your attention