Skip to main content

Hilbert Transform Explained


The Hilbert transform of a signal x(t) is

x^ (t) = H {x(t)} = 1π PV ∫ −∞ ∞ x(τ) t−τ dτ \boxed{\hat{x}(t)=\mathcal H\{x(t)\} =\frac{1}{\pi}\operatorname{PV}\int_{-\infty}^{\infty} \frac{x(\tau)}{t-\tau}\,d\tau}

where PV means the Cauchy principal value, because the kernel 1t−τ is singular at τ=t .

Convolving a time-domain signal f(t)f(t) with the Hilbert transform kernel 1πt\frac{1}{\pi t} produces a 90° phase shift in its frequency-domain representation.

In the frequency domain:

F { x^ (t)} = −j sgn (ω) X(ω) \boxed{\mathcal F\{\hat{x}(t)\} =-j\,\operatorname{sgn}(\omega)X(\omega)}

So, intuitively, the Hilbert transform shifts each positive-frequency component by −90∘ and each negative-frequency component by +90∘ , without changing its magnitude.

Why do we need it?

A very important application is creating the analytic signal:

z(t) = x(t) + j H {x(t)}

For example, if

x(t) = cos(ωt)

then

z(t) = cos(ωt) + jsin(ωt)

and from Euler's formula,

z(t) = e jωt

This is extremely useful for finding the instantaneous amplitude (envelope) and instantaneous phase of signals.

1. Start with Euler's formula

ejθ = cosθ + jsinθ

So we can write:

cosθ = ejθ + e−jθ 2

and

sinθ = ejθ − e−jθ 2j

The important point is that a real sine wave contains two frequencies:

  • +ω
  • −ω

2. What does the Hilbert transform do?

In frequency domain, the Hilbert transform multiplies the spectrum by

− j sgn (ω)

That means:

For positive frequency +ω :

−j

For negative frequency −ω :

+j

And multiplying by j means a +90∘ phase shift, while multiplying by −j means a −90∘ phase shift.


3. Apply it to sine

We have

sin(ωt) = ejωt − e −jωt 2j

Now apply the Hilbert transform.

For ejωt , the frequency is positive, so multiply by −j :

ejωt → −j ejωt

For e −jωt , the frequency is negative, so multiply by +j :

e −jωt → +j e −jωt

Therefore,

H { sin(ωt) } = −j ejωt +j e −jωt 2j

Cancel j:

= − ejωt + e −jωt 2

Rearrange:

= − ejωt + e −jωt 2

But we know

ejωt + e −jωt 2 = cos(ωt)

Therefore:

H { sin(ωt) } = − cos(ωt)


The minus sign comes from treating positive and negative frequencies differently:

+ω:−j −ω:+j

That's the fundamental reason.

And similarly:

H { cos(ωt) } = sin(ωt)

So don't memorize the two formulas separately. Remember the frequency-domain rule:

X(ω) → −j sgn (ω) X(ω)

Everything else follows from that.


Conventional Carrier (Textbook Representation) vs. Real Passband Signal

A real cosine carrier is simply

c(t) = cos( ωct )

This is a real physical waveform.


Why does jsin appear?

We sometimes create a complex version of the carrier:

e jωct = cos(ωct) + j sin(ωct)

This is Euler's formula.

There is nothing mysterious here.

It is just a convenient mathematical package containing:

cos(ωct)

and its 90°-shifted version

sin(ωct)

Think:

Complex carrier

       cosine
          ↓
e^(jωct) = cos(ωct) + j sin(ωct)
                         ↑
                       sine

The j tells us that the sine component is the quadrature/90° component.


Now suppose your information is m(t)

If you simply multiply it by the cosine carrier:

s(t) = m(t) cos( ωct )

you get ordinary AM-type modulation.

No Hilbert transform is needed.


What if we have I and Q?

Suppose we have two independent signals:

I(t)

and

Q(t)

Then we can form the complex baseband signal

z(t) = I(t) + j Q(t)

and use the complex carrier

e jωct

So:

z(t) e jωct

means

[I(t) +jQ(t)] [ cos(ωct) + jsin(ωct) ]


Where does the minus sign come from?

Expand it:

[I+jQ] [cos+jsin]

= Icos +jIsin +jQcos +j2Qsin

And because

j2=−1

we get

= Icos −Qsin + j[Isin +Qcos]

So:

z(t) e jωct = [ Icos(ωct) − Qsin(ωct) ] + j[ Isin(ωct) + Qcos(ωct) ]

The real part is therefore

s(t) = I(t) cos(ωct) − Q(t) sin(ωct)

That's where the minus sign came from.


Where does Hilbert transform enter?

Only now do we introduce it.

Suppose you have one real signal m(t) , but you want to construct a complex/analytic representation.

You calculate its Hilbert transform:

m^ (t) = H {m(t)}

and form

ma (t) = m(t) + j m^ (t)

So in this particular case:

I(t)=m(t)

and

Q(t) = m^ (t)

Then the real passband signal becomes

s(t) = m(t) cos(ωct) − m^ (t) sin(ωct)


The whole picture

Keep this diagram:

                    REAL SIGNAL
                        m(t)
                         │
                         │ Hilbert transform
                         ▼
                       m̂(t)
                         │
                         │
              ┌──────────┴──────────┐
              │                     │
              ▼                     ▼
             I(t)                  Q(t)
             m(t)                 m̂(t)
              │                     │
              │ × cos              │ × sin
              ▼                     ▼
       I cos(ωct)             Q sin(ωct)
              │                     │
              └──────────┬──────────┘
                         │
                         ▼
             I cos(ωct) - Q sin(ωct)
                         │
                         ▼
                  REAL PASSBAND
                     SIGNAL

And separately:

Complex representation:

        z(t) = I(t) + jQ(t)

Carrier:

        e^(jωct)
        = cos(ωct) + j sin(ωct)

Together:

        z(t)e^(jωct)

The three equations you should remember

Complex carrier:

e jωct = cos(ωct) + j sin(ωct)

Analytic signal:

ma (t) = m(t) + j m^ (t)

Real passband signal:

s(t) = m(t) cos(ωct) − m^ (t) sin(ωct)



Contact Us

Name

Email *

Message *

Popular Posts

LDPC Encoding and Decoding Techniques

Low Density Parity Check (LDPC) Guide Comprehensive analysis of linear error-correcting block codes, Tanner graphs, and 5G-NR implementations. 📘 Overview 🧮 Encoding 🧩 Decoding 📚 Resources Theory Encoding Tech Tanner Graph 5G Encoding Decoding 'LDPC' is the abbreviation for 'low density parity check'. LDPC code H matrix contains very few amount of 1's and mostly zeroes. LDPC codes are error correcting code. Using LDPC codes, channel capacities that are close to the theoretical Shannon limit can be achieved. Low density parity check (LDPC) codes are linear error-correcting block code suitable for error correction in a large block sizes transmi...

Flat vs Frequency Selective Online Simulator

Flat vs Frequency Selective Online Simulator Channel Type Without Fading Flat Fading Multipaths Nakagami m SNR(dB) Run Simulation Input Signal Signal After Fading Constellation Diagram BER vs SNR Explore Advanced Flat vs Frequency-Selective Fading Simulator Want to see these equations in action? Visualize it. Launch Simulator Tool Return to DSP Simulations Main Page →

Online Simulator for ASK, FSK, and PSK Signal Generation

Interactive Digital Signal Processing (DSP) Tutorial and Simulator for ASK, FSK, and BPSK modulation techniques. Try our new Digital Signal Processing Simulator!   •   Interactive ASK, FSK, and BPSK tools updated for 2025. Start Now Digital Modulation Visualizer: ASK, FSK, & BPSK Simulator Learn and visualize binary modulation techniques (ASK, FSK, BPSK) in real-time with adjustable carrier and sampling parameters. Perfect for DSP students and engineers. 📡 ASK Simulator 📶 FSK Simulator 🎚️ BPSK Simulator 📚 More Topics ASK Modulator FSK Modulator BPSK Modulator Demodulation More Topics 1. ASK (Ampli...

UGC NET Electronic Science Previous Year Question Papers with Solutions

Download Papers and Solutions Exam Pattern Preparation Tips FAQs More Home / Engineering & Other Exams / UGC NET 2026 PYQ 📊 Exam Highlights: Electronic Science (88) Feature Details Junior Research Fellowship (JRF) ₹37,000 + HRA per month Eligibility M.Sc/M.Tech in Electronics (55%) Validity of Certificate JRF (3 Years) | Lectureship (Lifetime) 📥 Download UGC NET Electronics PDFs Complete collection of previous year question papers, answer keys and explanations for Subject Code 88. Start Downloading 📂 View All Question Papers June 2025 - Question Paper Download PDF June 2025 - Sol...

Gaussian minimum shift keying (GMSK)

📘 Overview & Theory 🧮 Simulator for GMSK 🧮 MSK and GMSK: Understanding the Relationship 🧮 MATLAB Code for GMSK 📚 Simulation Results for GMSK 📚 Q & A and Summary 📚 Further Reading Dive into the fascinating world of GMSK modulation, where continuous phase modulation and spectral efficiency come together for robust communication systems! Core Process of GMSK Modulation Phase Accumulation (Integration of Filtered Signal) After applying Gaussian filtering to the Non-Return-to-Zero (NRZ) signal, we integrate the smoothed signal to produce a continuous phase signal. For GMSK, the modulation index is $h=0.5$, meaning a bit '1' results in a phase shift of $\pi/2$: θ(t) = 2πh ∫ 0 t m filtered (τ) dτ This integration is crucial for avoiding abrupt phase transitions, ensuring smooth and continuous phase changes. Phase Mo...

Constellation Diagrams of ASK, PSK, and FSK (with MATLAB Code + Simulator)

Constellation Diagrams: ASK, FSK, and PSK Comprehensive guide to signal space representation, including interactive simulators and MATLAB implementations. 📘 Overview 🧮 Simulator ⚖️ Theory 📈 Q-function 📚 Resources BASK Modulation Transmits one of two signals: 0 or $\sqrt{E_b}$, representing binary 0 and 1. Simple but sensitive to noise. BFSK Modulation Transmits one of two signals: $\sqrt{E_b}$ on the Y-axis or $\sqrt{E_b}$ on the X-axis. These are orthogonal signals. BPSK Modulation Transmits $+\sqrt{E_b}$ or $-\sqrt{E_b}$ (antipodal signaling). Most efficient binary scheme. ...

1G to 5G Technology - Evolution of Wireless Generations

Cellular wireless evolution Generation Frequency band PHY features Data rate Spectral Eff. (bps/Hz) 1G 850 MHz FDMA, FM N/A N/A 2G 900 MHz, 1.8 GHz TDMA/CDMA, GMSK/QPSK, FEC, PC 10 Kbps < 1 3G 1.8–2.5 GHz CDMA, QAM 1–40 Mbps 1–8 4G 2–8 GHz OFDMA, SC-FDMA, QAM, MIMO-OFDM 100–600 Mbps 15 5G 1–6 GHz mm wave (26–28 GHz) < 1 GHz (massive IoT) visible light? massive MIMO, beamforming D2D, Full duplex, NOMA LDPC and Polar codes OFDM & variants (adapted to extremes?) multi-Gbps several tens Waveform design is the major change between the generations Mobile Wireless Generations Specifications  1G  Voice, Analog traffic, FDMA  2G  Voice, SMS, CS data ...