Skip to main content

FIR vs IIR Digital Filters and Recursive vs Non Recursive Filters


Key Features

  • The higher the order of a filter, the sharper the stopband transition
  • The sharpness of FIR and IIR filters is very different for the same order
  • A FIR filter has an equal time delay at all frequencies, while the IIR filter's time delay varies with frequency. Usually, the biggest time delay in the IIR filter is at the filter's cutoff frequency.
  • The term 'IR' (impulse response) is in both FIR and IIR. The term 'impulse response' refers to the appearance of the filter in the time domain.

1. What Is the Difference Between an FIR and an IIR Filters?

The two major classifications of digital filters used for signal filtration are FIR and IIR. The primary distinction between FIR and IIR filters is that the FIR filter provides a finite period impulse response. In contrast, IIR is a type of filter that produces an infinite-duration impulse response for a dynamic system.

Mathematical representation of a filter equation:

A*y(t) = c1*x(t) + c2*x(t - t0) + c3*x(t - t1) + c4*x(t - t2) + . . . + cn*x(t – tn)
    

To make A equal 1, we change the values of the coefficients c1, c2, c3, etc., in the filter equation above. We carry out this to recover the original signal from various multipath (with different delay spreads).

We concentrate on taps and the corresponding weights when designing filters. The filter converges for some weightings of various taps. Some filters function quickly, while others function precisely. Applications determine uses. FIR filters have a limited number of taps and generate a finite amount of impulses. IIR filters, on the other hand, can generate an infinite number of impulse responses despite having a finite number of taps.

Why do we use filters?

The purpose of the use of different kinds of filters is different. But in general, they all smoothen the noisy signal.

MATLAB Code for FIR Filter

In this MATLAB code, we use a FIR filter of order 20 to remove high-frequency noise from a clean sinusoidal signal. The highest frequency component in the sinusoidal signal is 500 Hz. We set the cutoff frequency of the FIR filter to 1000 Hz.

clc;
clear;

% Sampling parameters
Fs = 8000; % Sampling Frequency (Hz)
t = 0:1/Fs:0.1;

% Create a noisy signal
f_clean = 500;
f_noise = 3000;
signal_clean = sin(2*pi*f_clean*t);
signal_noise = 0.5 * sin(2*pi*f_noise*t);
signal = signal_clean + signal_noise;

% FIR Filter Design
N = 20;
fc = 1000;
wn = fc / (Fs/2);
b = fir1(N, wn, 'low', hamming(N+1));

filtered_signal = filter(b, 1, signal);

% Plot
figure;
subplot(3,1,1); plot(t, signal); title('Noisy Signal');
subplot(3,1,2); plot(t, filtered_signal); title('Filtered Signal');
subplot(3,1,3); plot(t, signal_clean); title('Original Clean Signal');
    

Search related filters

Output

MATLAB FIR filter output showing noisy, filtered, and original signals

2. Difference between recursive and non-recursive filters

The output of a recursive filter is directly dependent on one or more of its previous outputs. In a non-recursive filter, the output is independent of previous outputs, such as a feed-forward system with no feedback.

3. Solve: The impulse response of a filter is defined as h[n] =

Impulse response filter question diagram

Now tell us this filter is a 1. Non-recursive IIR filter 2. Recursive IIR filter 3. Non-recursive FIR filter 4. Recursive FIR filter

Answer: Option 3

Generally, an FIR filter has a finite number of impulse responses and the output is independent of previous outputs. Therefore, the correct answer is Non-recursive FIR filter.

Next Page >>

Read more about

People are good at skipping over material they already know!

View Related Topics to







Contact Us

Name

Email *

Message *

Popular Posts

BER vs SNR for M-ary QAM, M-ary PSK, QPSK, BPSK, ...(MATLAB Code + Simulator)

📘 Overview of BER and SNR 🧮 Online Simulator for BER calculation of m-ary QAM and m-ary PSK 🧮 MATLAB Code for BER calculation of M-ary QAM, M-ary PSK, QPSK, BPSK, ... 📚 Further Reading 📂 View Other Topics on M-ary QAM, M-ary PSK, QPSK ... 🧮 Online Simulator for Constellation Diagram of m-ary QAM 🧮 Online Simulator for Constellation Diagram of m-ary PSK 🧮 MATLAB Code for BER calculation of ASK, FSK, and PSK 🧮 MATLAB Code for BER calculation of Alamouti Scheme 🧮 Different approaches to calculate BER vs SNR What is Bit Error Rate (BER)? The abbreviation BER stands for Bit Error Rate, which indicates how many corrupted bits are received (after the demodulation process) compared to the total number of bits sent in a communication process. BER = (number of bits received in error) / (total number of tran...

Theoretical BER vs SNR for binary ASK, FSK, and PSK with MATLAB Code + Simulator

📘 Overview & Theory 🧮 MATLAB Codes 📚 Further Reading Theoretical BER vs SNR for Amplitude Shift Keying (ASK) The theoretical Bit Error Rate (BER) for binary ASK depends on how binary bits are mapped to signal amplitudes. For typical cases: If bits are mapped to 1 and -1, the BER is: BER = Q(√(2 × SNR)) If bits are mapped to 0 and 1, the BER becomes: BER = Q(√(SNR / 2)) Where: Q(x) is the Q-function: Q(x) = 0.5 × erfc(x / √2) SNR : Signal-to-Noise Ratio N₀ : Noise Power Spectral Density Understanding the Q-Function and BER for ASK Bit '0' transmits noise only Bit '1' transmits signal (1 + noise) Receiver decision threshold is 0.5 BER is given by: P b = Q(0.5 / σ) , where σ = √(N₀ / 2) Using SNR = (0.5)² / N₀, we get: BER = Q(√(SNR / 2)) Theoretical BER vs ...

Online Simulator for ASK, FSK, and PSK

Try our new Digital Signal Processing Simulator!   Start Simulator for binary ASK Modulation Message Bits (e.g. 1,0,1,0) Carrier Frequency (Hz) Sampling Frequency (Hz) Run Simulation Simulator for binary FSK Modulation Input Bits (e.g. 1,0,1,0) Freq for '1' (Hz) Freq for '0' (Hz) Sampling Rate (Hz) Visualize FSK Signal Simulator for BPSK Modulation ...

How Windowing Affects Your Periodogram

The windowed periodogram is a widely used technique for estimating the Power Spectral Density (PSD) of a signal. It enhances the classical periodogram by mitigating spectral leakage through the application of a windowing function. This technique is essential in signal processing for accurate frequency-domain analysis.   Power Spectral Density (PSD) The PSD characterizes how the power of a signal is distributed across different frequency components. For a discrete-time signal, the PSD is defined as the Fourier Transform of the signal’s autocorrelation function: S x (f) = FT{R x (Ï„)} Here, R x (Ï„)}is the autocorrelation function. FT : Fourier Transform   Classical Periodogram The periodogram is a non-parametric PSD estimation method based on the Discrete Fourier Transform (DFT): P x (f) = \(\frac{1}{N}\) X(f) 2 Here: X(f): DFT of the signal x(n) N: Signal length However, the classical periodogram suffers from spectral leakage due to abrupt truncation of the ...

MATLAB code for Pulse Code Modulation (PCM) and Demodulation

📘 Overview & Theory 🧮 Quantization in Pulse Code Modulation (PCM) 🧮 MATLAB Code for Pulse Code Modulation (PCM) 🧮 MATLAB Code for Pulse Amplitude Modulation and Demodulation of Digital data 🧮 Other Pulse Modulation Techniques (e.g., PWM, PPM, DM, and PCM) 📚 Further Reading MATLAB Code for Pulse Code Modulation clc; close all; clear all; fm=input('Enter the message frequency (in Hz): '); fs=input('Enter the sampling frequency (in Hz): '); L=input('Enter the number of the quantization levels: '); n = log2(L); t=0:1/fs:1; % fs nuber of samples have tobe selected s=8*sin(2*pi*fm*t); subplot(3,1,1); t=0:1/(length(s)-1):1; plot(t,s); title('Analog Signal'); ylabel('Amplitude--->'); xlabel('Time--->'); subplot(3,1,2); stem(t,s);grid on; title('Sampled Sinal'); ylabel('Amplitude--->'); xlabel('Time--->'); % Quantization Process vmax=8; vmin=-vmax; %to quanti...

MATLAB Codes for Various types of beamforming | Beam Steering, Digital...

📘 How Beamforming Improves SNR 🧮 MATLAB Code 📚 Further Reading 📂 Other Topics on Beamforming in MATLAB ... MIMO / Massive MIMO Beamforming Techniques Beamforming Techniques MATLAB Codes for Beamforming... How Beamforming Improves SNR The mathematical [↗] and theoretical aspects of beamforming [↗] have already been covered. We'll talk about coding in MATLAB in this tutorial so that you may generate results for different beamforming approaches. Let's go right to the content of the article. In analog beamforming, certain codebooks are employed on the TX and RX sides to select the best beam pairs. Because of their beamforming gains, communication created through the strongest beams from both the TX and RX side enhances spectrum efficiency. Additionally, beamforming gain directly impacts SNR improvement. Wireless communication system capacity = bandwidth*log2(1+SNR)...

BER performance of QPSK with BPSK, 4-QAM, 16-QAM, 64-QAM, 256-QAM, etc (MATLAB + Simulator)

📘 Overview 📚 QPSK vs BPSK and QAM: A Comparison of Modulation Schemes in Wireless Communication 📚 Real-World Example 🧮 MATLAB Code 📚 Further Reading   QPSK provides twice the data rate compared to BPSK. However, the bit error rate (BER) is approximately the same as BPSK at low SNR values when gray coding is used. On the other hand, QPSK exhibits similar spectral efficiency to 4-QAM and 16-QAM under low SNR conditions. In very noisy channels, QPSK can sometimes achieve better spectral efficiency than 4-QAM or 16-QAM. In practical wireless communication scenarios, QPSK is commonly used along with QAM techniques, especially where adaptive modulation is applied. Modulation Bits/Symbol Points in Constellation Usage Notes BPSK 1 2 Very robust, used in weak signals QPSK 2 4 Balanced speed & reliability 4-QAM ...

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

📘 Overview of Energy per Bit (Eb / N0) 🧮 Online Simulator for constellation diagrams of ASK, FSK, and PSK 🧮 Theory behind Constellation Diagrams of ASK, FSK, and PSK 🧮 MATLAB Codes for Constellation Diagrams of ASK, FSK, and PSK 📚 Further Reading 📂 Other Topics on Constellation Diagrams of ASK, PSK, and FSK ... 🧮 Simulator for constellation diagrams of m-ary PSK 🧮 Simulator for constellation diagrams of m-ary QAM BASK (Binary ASK) Modulation: Transmits one of two signals: 0 or -√Eb, where Eb​ is the energy per bit. These signals represent binary 0 and 1.    BFSK (Binary FSK) Modulation: Transmits one of two signals: +√Eb​ ( On the y-axis, the phase shift of 90 degrees with respect to the x-axis, which is also termed phase offset ) or √Eb (on x-axis), where Eb​ is the energy per bit. These signals represent binary 0 and 1.  BPSK (Binary PSK) Modulation: Transmits one of two signals...