SpDly Studios / Project documentation

Adaptive Signal-Driven FIR Filter System

Independent static recordProject archive

Status: Completed

Category: Digital Signal Processing · Embedded Systems · Real-Time Systems · Signal Acquisition · Embedded Firmware · Instrumentation

Project Type: Embedded Systems Engineering Project

Overview

The Adaptive Signal-Driven FIR Filter System is a real-time DSP platform that samples analog signals, analyzes their frequency content, and adapts its filtering behavior on the fly.

The goal was to show that meaningful digital signal processing can run on low-cost embedded hardware without a dedicated DSP chip. The system handles acquisition, frequency estimation, filter selection, and output reconstruction with minimal latency.

Unlike a fixed FIR implementation, this project adjusts the filter settings to match the incoming signal, while still allowing manual override when needed.

What it demonstrates

Adaptive signal processing on constrained hardware, with deterministic timing and low processor overhead.

Details

Objectives

  • Design a real-time signal acquisition system.
  • Implement interrupt-driven Analog-to-Digital Conversion.
  • Achieve high sampling frequency on an Arduino Uno.
  • Develop an adaptive FIR filtering system.
  • Automatically estimate the dominant input frequency.
  • Dynamically configure filter parameters.
  • Maintain continuous non-blocking signal processing.
  • Optimize processor utilization and minimize processing latency.
  • Reconstruct filtered analog output in real time.
  • Problem Statement

    Embedded microcontrollers often have limited processing power, memory, and computational resources, making real-time digital signal processing challenging. The objective was to determine whether a resource-constrained microcontroller could continuously sample analog signals, perform adaptive FIR filtering, and generate filtered outputs while maintaining deterministic timing and low processor utilization.

    System Architecture

    
    Input Signal
    
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    Analog Signal Acquisition
    
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    ADC Sampling (Interrupt-Driven, 40 kHz)
    
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    Circular Buffer
    
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    Frequency Analysis
    
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    Adaptive Filter Configuration
    
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    FIR Filtering
    
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    Output Reconstruction
    
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    Serial Monitoring (1 Mbps)
    
    

    Hardware Components

    Software Components

    Embedded Firmware Responsibilities: ADC configuration · Interrupt handling · Circular buffer management · Frequency estimation · FIR coefficient selection · Signal filtering · Output reconstruction · Serial communication

    Signal Acquisition

    Continuously samples incoming analog signals using the internal ADC.

  • Interrupt-driven sampling — operates independently of main application logic.
  • Register-level optimization — direct register manipulation instead of standard Arduino functions.
  • Continuous acquisition — non-blocking, deterministic timing.
  • Sampling Frequency: 40 kHz

    This sampling rate enables the system to process relatively high-frequency analog signals while maintaining stable timing.

    Interrupt-Driven Design

    Advantages over polling: precise sampling intervals · reduced timing jitter · deterministic execution · efficient CPU utilization · continuous operation.

    Circular Buffer

    Incoming samples stored using a circular buffer. Enables simultaneous sampling and processing without interrupting data acquisition. Provides continuous storage, non-blocking operation, and efficient memory utilization.

    Adaptive Filter Configuration

    Unlike conventional FIR filters with fixed coefficients, this system automatically adapts filter characteristics based on the detected input signal.

    Responsibilities: Estimate dominant frequency · Select cutoff frequency · Generate filter configuration · Allow manual override when necessary.

    FIR Filter

    A Finite Impulse Response digital filter with: linear phase response · stable operation · configurable order · adaptive cutoff frequency.

    The lower-order configuration provides an optimal balance between filtering performance and computational efficiency.

    Output Reconstruction

    After filtering, the processed signal is reconstructed and transmitted as an analog-equivalent output with real-time generation and minimal latency.

    Serial Communication

    Baud Rate: 1 Mbps

    Purpose: Parameter monitoring · Debugging · Signal visualization · Performance evaluation

    Implementation Summary

    Acquisition and timing

    Signal capture is interrupt-driven so sampling stays deterministic.

    Key choices: ADC sampling at 40 kHz · register-level optimization · circular buffering

    Adaptive filtering

    The system estimates the incoming frequency, selects a cutoff, and applies an FIR filter that fits the signal instead of using one fixed configuration.

    Key choices: automated cutoff selection · manual override · FIR filtering for stability and linear phase

    Output and monitoring

    After filtering, the signal is reconstructed for output and monitored over serial at 1 Mbps.

    Key choices: low-latency reconstruction · serial debugging · continuous non-blocking processing

    Results

    The implementation demonstrates real-time performance while keeping processor usage relatively low.

    Strengths: deterministic timing · adaptive operation · continuous processing · modular firmware

    Constraints: limited memory on Arduino Uno · filter order bound by available processing power · frequency estimation depends on input quality · output resolution limited by PWM

    Takeaways

    Engineering lessons: interrupt handling is critical for deterministic real-time systems · circular buffering avoids blocking · register-level programming improves performance · adaptive filtering improves usability · low-resource DSP requires careful tradeoffs.

    Applications

    Embedded DSP education · Audio signal processing · Sensor signal conditioning · Industrial instrumentation · Biomedical signal processing · Data acquisition systems · Communication systems · Real-time monitoring

    Lessons Learned

  • Efficient interrupt handling is essential for deterministic real-time systems.
  • Register-level optimization can significantly improve embedded performance.
  • Circular buffering enables continuous processing without blocking.
  • Adaptive filtering provides greater flexibility than fixed filter implementations.
  • Memory optimization is critical on low-resource microcontrollers.
  • DSP algorithms must be carefully balanced against available computational resources.
  • Technologies Used

    Hardware: Arduino Uno

    Software: Embedded C/C++ · Arduino IDE

    Engineering Concepts: Digital Signal Processing · Finite Impulse Response Filters · Adaptive Filtering · Interrupt Programming · Register-Level Programming · Circular Buffers · Analog-to-Digital Conversion · Embedded Systems · Real-Time Systems · Signal Processing

    Future Improvements

  • FFT-based automatic frequency estimation
  • Dynamic coefficient generation
  • Higher-order adaptive filters and IIR filter support
  • ARM Cortex-M or DSP processor implementation
  • DMA-based sampling
  • Graphical visualization software
  • Multi-channel signal processing
  • Hardware DAC output
  • Project Legacy

    The Adaptive Signal-Driven FIR Filter System showed that sophisticated DSP can be made practical on low-cost hardware. It strengthened experience in embedded firmware, interrupt-driven programming, real-time design, and low-level optimization, and became a foundation for later signal-processing work.