radar project pulse compression radar by: hamdi m. joudeh and yousef al-yazji supervisor: dr....
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Radar Project
Pulse Compression Radar
By: Hamdi M. Joudeh and Yousef Al-YazjiSupervisor: Dr. Mohamed Ouda
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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
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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.
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Basic Concepts:
Range ambiguity:
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Basic Concepts: Range resolution:
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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.
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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.
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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.
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Methods of implementation:
Active generation and processing:
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Methods of implementation:
Passive generation and processing:
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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.
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Linear FM: Disadvantages
Range-doppler cross coupling.
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Types of pulse compression:Linear FM: The process
LFM the transmitted pulse.
Receiver: matched filter.
compression ratio is given by B*T13
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Linear FM: Up and Down Chirp
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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.
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Linear FM: Example
Overlapped received waveforms:
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Linear FM: Example
Detected pulses (output of matched filter)
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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).
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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.
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Phase Coded: Codes used
Must have a minimum possible side-lobe peak of the aperiodic autocorrelation function.
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Phase Coded: Barker code
Optimal binary sequence, pseudo-random.
Pseudo-random = deterministic .Pseudo-random has the statistical
properties of a sampled white noise.
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Phase Coded: Auto correlation function of the Barker sequencePeak = N, 2Δt wide at base.
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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.
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Phase Coded: Auto Correlation MATLAB example.Two un-coded overlapped long pulses.
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Phase Coded: MATLAB work
Two barker coded overlapped long pulses.
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Implementation of Biphase-Coded System Using MATLAB:
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Implementation of Biphase-Coded System Using MATLAB:Why I and Q detection?
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Software steps and approaches: Waveform Generation:Required inputs:
- Barker code sequence.
- Maximum Range. (to calc. IPP).
- Range resolution. (to calc. pulse width).
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Waveform Generation:
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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
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Path and Receiver losses:
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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
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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)
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Added Noise:
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Detection:
Matched filter, I and Q detection.
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Correlation:
Result:
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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%
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Observations:
- For 500m difference:ΔR = 520m.Error = 4%.
- ErrorΔR decreases, the error increases.Error due to noise and sampling time.
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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
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Thank You for your attention
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