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

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.

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.

Basic Concepts: Range ambiguity:

Basic Concepts: Range resolution:

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.

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.

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.

Methods of implementation: Active generation and processing:

Methods of implementation: Passive generation and processing:

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.

Linear FM: Disadvantages Range-doppler cross coupling.

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

Linear FM: Up and Down Chirp

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.

Linear FM: Example Overlapped received waveforms:

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

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).

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.

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

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

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

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.

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

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

Implementation of Biphase-Coded System Using MATLAB:

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

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

Waveform Generation:

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

Path and Receiver losses:

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

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)

Added Noise:

Detection: Matched filter, I and Q detection.

Correlation: Result:

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%

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

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

Thank You for your attention