Changelog

All notable changes to this project will be documented in this file.

[2.6.1] - in proogress - CSI Normalization and Test Coverage Update

Fixed

• Extended CSI normalization paths (ESPHome/C++ and Micro-ESPectre/Python): runtime now consistently handles 256->128 (double HT-LTF), 228->114, and 114->128 remap paths before HT20 processing, reducing packet drops on short/double CSI payload variants (#93).
• Cross-stack alignment for CSI length handling: Micro-ESPectre normalization behavior and runtime logs are now aligned with ESPHome component behavior.

Added

• Unit tests for new CSI payload scenarios: added coverage for 114-byte and 228-byte CSI handling in C++ (test_csi_manager) and dedicated Python tests for 128/256/114/228 normalization paths (micro-espectre/tests/test_utils.py).

Documentation

• Troubleshooting update: added explicit wrong SC count normalization notes in SETUP.md for 256, 228, and 114 payload cases.

[2.6.0] - 2026-03-08 - ESP32-C5 Support, Context-Aware Calibration, and Stricter Validation Targets

Highlights

• More robust runtime on modern chips (ESP32-C5/C6): WiFi lifecycle handling is hardened, dual-band protocol/bandwidth APIs are used correctly (with safe fallback), C5 is forced to 2.4 GHz, and C5 CSI 114-byte payloads are normalized to HT20 128-byte internal layout
• Safer calibration and detector state transitions: calibration start failures are now handled explicitly, detector buffers are cold-cleared after calibration/channel switches, and NBVI input/band-size validation is hardened
• Stricter quality bar for motion validation: Python and C++ performance targets are now unified to Recall >95% and FP <5% for both MVS and ML, with docs updated accordingly (PERFORMANCE.md, test/README.md)

Reliability and Runtime Fixes

• Threshold handling unified across stacks: validation is aligned to 0.0-10.0 across ESPHome/C++ and Micro-ESPectre/Python (HA number, Serial, MQTT, detector setters); MQTT now propagates detector rejection correctly; factory_reset restores ML threshold to 5.0
• Serial command parsing hardening: T:<value> now uses validated strtof parsing (endptr, finite/range checks)
• Startup fail-fast behavior: setup now marks the component failed when WiFi initialization/handler registration fails
• Auxiliary task stability: DNS task always clears running_ on early exits, avoiding stale "already running" states
• Safety guards in diagnostics utilities: progress-bar width/marker bounds are now clamped to prevent fixed-buffer edge cases

Calibration, ML, and Dataset Pipeline

• NBVI hot-path optimization: reduced allocations, enforced memory-bounded chunked validation reads (avoids std::bad_alloc/abort() on low-heap targets), and replaced O(window) shifts with ring buffer + running statistics
• Context-aware grid-search metadata workflow: micro-espectre/tools/11_refresh_gridsearch_metadata.py introduced and then simplified to a single C++-aligned evaluation path (legacy hardcoded subcarrier override removed)
• Metadata consistency cleanup: gain_locked is now the single source of truth in .npz and dataset_info.json; deprecated use_cv_normalization and label_id metadata removed
• ML pipeline alignment (training + inference): both stacks now use [12, 14, 16, 18, 20, 24, 28, 36, 40, 44, 48, 52]; models were retrained/re-exported with validated seed, and feature extraction was simplified to the selected 12 runtime features

Tooling and Developer Experience

• Micro-ESPectre deploy diagnostics improved: ./me deploy now performs a REPL health-check and reports explicit remediation for ROM boot-loop (invalid header) with ./me flash --erase
• C5 support in me CLI expanded: C5 auto/manual selection, --chip c5, correct esp32c5 target mapping, and C5 firmware artifact selection (ESP32_CSI_C5.bin)
• Flash mapping hardening: per-chip offsets aligned with MicroPython board deploy options (including C5 0x2000)
• Optional BSSID lock in Micro-ESPectre: WIFI_BSSID support added in src/main.py
• ESP-IDF mock alignment: WiFi mock headers updated to modern protocol bitmasks, band-mode enums, and dual-band API signatures
• General cleanup: removed unused BaseDetector amplitude getters and refreshed stale comments/documentation (including C5 tested status in setup/examples, S2 still experimental)
• ESPHome 2026.2.4 validation: project configuration was re-validated after upgrading ESPHome from 2026.2.0 to 2026.2.4 (esphome config examples/espectre-c6-dev.yaml), with full Python and C++ test suites passing

[2.5.1] - 2026-02-23 - HT STBC Multi-Antenna Router Fix

Fixed

• ESP32-C5/C6 STBC multi-antenna router fix: Multi-antenna routers with STBC TX send two HT training fields per frame (HT-LTF1 + HT-LTF2), causing the CSI callback to receive 256-byte packets instead of the expected 128 bytes for HT20. On ESP32-C5/C6, wifi_csi_acquire_config_t has no field to disable HT STBC capture (unlike older chips). ESPectre now accepts these packets and takes the first 64 subcarriers (HT-LTF1), which is a valid channel estimate (#76, espressif/esp-csi#238)
• Micro-ESPectre NBVI calibration on ESP32-C3: Fixed OOM crashes during calibration caused by large in-memory allocations in the streaming NBVI computation phase; calibration now completes successfully on C3 with ~59 KB free heap
• ESPHome 2026.2.0+ compatibility: Ensure SPIFFS inclusion for newer ESPHome versions (#87)
• CI develop branch: Use local component configs instead of main branch for CI builds on develop

Changed

• Micro-ESPectre NBVI calibration speed: Packet collection rate improved ~3x on ESP32-C3 (28 → 80 pps) by skipping sqrt on guard band subcarriers (excluded from NBVI selection), caching math.sqrt locally, and using integer arithmetic in the magnitude loop

Added

• Performance documentation: RAM, Flash, and detection timing benchmarks for ESP32-C3 and ESP32-C6 in PERFORMANCE.md
• Performance logging: Lightweight DEBUG-level logging for heap usage (startup/post-calibration) and detection time (~10s interval)
• git_ref substitution: All example YAML files now use a git_ref substitution, making it easy to switch between branches, tags, or commits
• Snapshot builds: Automated pre-release builds on every push to main, providing pre-compiled firmware for testing fixes before official releases

Documentation

• Clarify that NBVI calibration applies only to MVS mode
• Add media section in README

[2.5.0] - 2026-02-15 - ML Detector, Training Pipeline & Pre-built Firmware

Highlights

• ML Detector (Experimental): Neural network-based motion detection with ~3s boot time (no calibration needed)
• Training Pipeline: Collect data, train, and export models for both platforms
• Pre-built Firmware: Ready-to-flash binaries for all 6 ESP32 variants via GitHub Releases
• PCA & P95 Removed: Simplified to MVS + ML detectors, NBVI-only calibration

ML Detector

First experimental release of a neural network-based motion detector, available in both C++ (ESPHome) and Python (Micro-ESPectre).

• Configuration: detection_algorithm: ml in YAML
• Performance: ~97-100% F1 score depending on chip
• Boot time: ~3 seconds (vs ~10s for MVS) — no band calibration needed
• Zero dependencies: Manual MLP inference, no TFLite required

espectre:
  detection_algorithm: ml

The pre-trained model shipped with this release was trained on a limited dataset collected in a single environment. It performs well in initial testing, but we need your help to make it better. If you try the ML detector, consider contributing baseline (empty room) and movement recordings from your environment — the more diverse the training data, the more robust the model becomes. See ML_DATA_COLLECTION.md for how to collect and submit data via pull request.

For architecture and feature details, see ALGORITHMS.md.

Training Pipeline

Collect labeled data, train a model, and export weights for both platforms:

./me collect --label <name> --duration <sec>   # Collect data
python tools/10_train_ml_model.py               # Train model

Exports ml_weights.py (Python), ml_weights.h (C++), and TFLite checkpoint. See ML_DATA_COLLECTION.md for the full workflow.

Pre-built Firmware & Easy Install

New release.yml workflow builds and publishes firmware for all supported chips (ESP32, S2, S3, C3, C5, C6) on every tagged release.

SETUP.md now offers two installation paths:

• Option A: Web Flash — Download from Releases, flash via ESPConnect in Chrome
• Option B: ESPHome CLI — Traditional esphome run workflow

Based on PR #77 by @WLaoDuo.

Removed

PCA Detection Algorithm: While PCA itself is a well-known statistical technique, our implementation was based on Espressif's open-source esp-radar library. Since Espressif has transitioned this library to closed source, we have removed our PCA implementation to ensure full compliance with our GPLv3 license.

MVS remains the recommended algorithm with excellent performance. Future development will focus on the ML detector, which shows very promising results in early testing and requires no initial calibration.

P95 Calibrator: NBVI (Normalized Band Variance Index) is now the sole calibration algorithm. NBVI consistently outperforms P95 by selecting non-consecutive subcarriers for better spectral diversity and resilience to narrowband interference.

Improvements

• Gain lock: Median-based calibration (replaces mean), signed FFT gain fix, CV normalization when gain lock is skipped
• Bug fixes: Double amplitude calculation fix, stack allocation in Hampel filter

Micro-ESPectre (R&D Platform)

• Extended hardware support: me CLI now supports ESP32, C3, S3, C6 with auto-detection and SHA256 firmware verification
• ML detector filter support: ML detector now accepts low-pass and Hampel filter parameters, matching C++ implementation
• Import standardization: All src/ modules now use try/except ImportError pattern for MicroPython/CPython compatibility
• Bug fixes: Signed int8 CSI parsing, ESP32 flash offset corrected, LowPass default cutoff aligned to 11.0 Hz

For Contributors

[2.4.0] - 2026-01-24 - Live Recalibration, Adaptive Threshold & PCA

Highlights

• Two detection algorithms: Choose between MVS (default) and PCA (Principal Component Analysis)
• Recalibrate from Home Assistant: New switch to trigger recalibration without reflashing
• Adaptive threshold by default: No manual tuning needed - works out of the box

New Features

Dual Detection Algorithms

Two motion detection algorithms are now available:

Calibrate Switch

New Home Assistant switch for triggering recalibration without reflashing:

• switch.espectre_calibrate: Turn ON to recalibrate, auto-turns OFF when complete
• Useful after room layout changes or furniture moves

Adaptive Threshold

The segmentation_threshold parameter is now optional:

• Default: Adaptive threshold calculated as P95 × 1.1 during calibration
• Manual override: Specify value in YAML to use fixed threshold

Improvements

Dual Band Selection Algorithms

Two automatic subcarrier selection algorithms:

HT20-Only Mode

Simplified to WiFi 4 (802.11n) HT20 mode for stable 64 subcarriers across all ESP32 variants:

• Consistent performance on C3, C6, S3, and original ESP32
• Reduced memory footprint

Other Improvements

• Lower threshold minimum: 0.1 (was 0.5) for high-sensitivity applications
• ESP32-C3 dev config: Added espectre-c3-dev.yaml

Bug Fixes

• ESP32-C3 boot crash: Fixed duplicate register_component calls
• USB Serial JTAG: Correct ESPHome macro for detection
• CSI data overflow: Limit to 128 bytes to prevent overflow

Micro-ESPectre (R&D Platform)

• SHA256 firmware verification: New verify command detects outdated firmware
• CSI Stream Protocol v2: Auto-detected chip type, contributor tracking
• Refactored calibrators: Cleaner architecture with p95_calibrator.py, nbvi_calibrator.py, threshold.py

For Contributors

Documentation

• Roadmap update: Added 3D Localization in v4.x (30-50cm indoor tracking with phase-coherent antenna array)
• Added Part 2 Medium article link to README
• Updated bug report template with crash debug section
• Added Home Assistant dashboard screenshot to README

[2.3.0] - 2025-12-31 - End of Year Edition

ESPectre - The Game

As a thank you to the community, I'm closing the year with something fun: a browser-based reaction game where your physical movement controls the gameplay through WiFi sensing. No controller needed!

Beyond the fun, it's actually useful for threshold tuning, calibration verification, and coverage testing. Uses Web Serial API (Chrome/Edge) with real-time CSI streaming at ~100 Hz.

Play now: espectre.dev/game

Features

Sensor Entity Customization

Full control over exposed sensor entities with standard ESPHome options: internal, icon, filters, disabled_by_default.

espectre:
  movement_sensor:
    name: "Movement"
    internal: true
    filters:
      - multiply: 100
  motion_sensor:
    icon: "mdi:motion-sensor"

See #51.

External Traffic Mode

Support for multi-device deployments with reduced network overhead.

• traffic_generator_rate: 0: Disable internal traffic generator and rely on external WiFi traffic
• publish_interval: Control sensor update rate independently from traffic source
• UDP Listener: Opens port 5555 to receive external UDP packets for CSI generation
• 95% less network overhead: One broadcast source feeds all ESPectre devices

External traffic source: espectre_traffic_generator.py - standalone script with daemon mode and Home Assistant integration.

See #50.

Traffic Generator Ping Mode

New traffic_generator_mode: ping option using ICMP echo requests instead of DNS queries. Use when DNS mode has low packet rates (~18 pps instead of ~100 pps) due to routers not responding to root domain queries.

See #48.

Gain Lock Mode

New gain_lock option to control AGC/FFT gain locking behavior:

• auto (default): Enable gain lock but skip if signal too strong (AGC < 30)
• enabled: Always force gain lock (may freeze if too close to AP)
• disabled: Never lock gain (less stable CSI but works at any distance)

Solves the issue where devices too close to the AP (RSSI > -40 dB) would freeze during calibration. See TUNING.md for AGC threshold details.

Improvements

WiFi Channel Change Detection

Automatic detection and buffer reset when AP switches channel (auto-channel, roaming), preventing false positives from CSI data spikes.

Fixes #46.

Multi-Window NBVI Calibration

Optimized subcarrier selection with multi-window validation, gain lock phase exclusion (first 300 packets), and updated noise gate percentile (10% → 25%).

Calibration Fallback with Normalization

When NBVI calibration fails, normalization is still calculated and default subcarriers [11-22] are used, preventing 2000%+ motion values from missing normalization.

Platform Support

ESP32-C3 Super Mini Tested

Added example configuration espectre-c3.yaml. Full feature support including gain lock and NBVI calibration.

ESP32 (Original/WROOM-32) Tested

Tested on ESP32-WROOM-32D Mini (CH340). Fixed NBVI calibration not starting on platforms without gain lock.

Known limitations (ESP32 original only): - AGC/FFT gain lock not available - External traffic generator must start after ESP32 connects to WiFi - Broadcast mode not supported; use unicast instead

See espressif/esp-csi#247.

[2.2.0] - 2025-12-19 - Gain Lock, Low-Pass Filter & ML Data Collection

Gain Lock (AGC/FFT Stabilization)

Automatic gain control locking for stable CSI measurements, based on Espressif esp-csi recommendations.

• Two-phase calibration: Gain Lock (3s, 300 pkt) → NBVI (~7.5s, 10 × window_size pkt)
• Gain lock happens BEFORE NBVI calibration to ensure clean data
• Eliminates amplitude variations caused by automatic gain control
• Supported on ESP32-S3, C3, C5, C6 (not available on ESP32, S2)
• New files: gain_controller.h, gain_controller.cpp

Baseline Variance Normalization

Automatic attenuation for consistent thresholds across devices and environments.

• Always enabled - no configuration needed
• During calibration, calculates baseline variance using selected subcarriers
• If baseline > 0.25: attenuate with scale = 0.25 / baseline_variance
• If baseline ≤ 0.25: no scaling needed (scale = 1.0)
• Prevents over-amplification of weak signals while taming strong ones
• Removed normalization_enabled and normalization_target parameters

Low-Pass Filter

New 1st order Butterworth IIR filter to reduce high-frequency RF noise.

• Cutoff frequency: 11 Hz (human movement: 0.5-10 Hz, RF noise: >15 Hz)
• Reduces false positives in noisy environments (51% → 2%)
• Disabled by default: enable with lowpass_enabled: true
• Processing pipeline: Normalization → Hampel → Low-Pass → Buffer

NBVI Improvements

Optimized parameters and restricted search range for better subcarrier selection:

Dynamic null subcarrier detection replaces hardcoded lists - adapts to local RF conditions.

Performance

Lazy Variance Evaluation: Moving variance calculated only at publish time. - ~99% CPU savings for variance calculation - New API: csi_processor_update_state() (C++), seg.update_state() (Python)

Automatic sdkconfig

The ESPHome component now auto-configures all required sdkconfig options: - CONFIG_ESP_WIFI_CSI_ENABLED, CONFIG_PM_ENABLE, AMPDU settings, buffer sizes, tick rate - YAML files only need platform-specific options (WiFi 6, CPU frequency, PSRAM)

ML Data Collection

New infrastructure for building labeled CSI datasets (groundwork for 3.x): - me collect CLI subcommand for recording labeled samples - .npz format for ML-ready datasets - csi_utils.py module with CSIReceiver, CSICollector, MVSDetector

Configuration Changes

Removed options (now automatic): - normalization_enabled, normalization_target, manual sdkconfig options

Default values: All filters disabled, normalization always active.

Enhanced logging: Movement logs now include WiFi channel and RSSI:

[I][espectre]: [######--|----] 43% | mvmt:0.43 thr:1.00 | IDLE | 101 pkt/s | ch:3 rssi:-47

Testing & Documentation

• 324 pytest tests with CI integration (test-python job)
• Python coverage uploaded to Codecov
• New micro-espectre/ALGORITHMS.md with scientific documentation of MVS, NBVI, Hampel filter

[2.1.0] - 2025-12-10 - Made for ESPHome Compliance

Made for ESPHome Compliance

All example configurations now meet "Made for ESPHome" requirements

WiFi Provisioning

• BLE provisioning: esp32_improv for easy setup via ESPHome/HA Companion app
• USB provisioning: improv_serial for web.esphome.io configuration (not yet supported on ESP32-C5)
• Captive Portal: Fallback AP "ESPectre Fallback" for WiFi configuration
• No hardcoded credentials: Removed YOUR_WIFI_SSID placeholders

Dashboard Adoption

• dashboard_import: One-click adoption from ESPHome Dashboard
• project metadata: Version tracking for firmware updates

Code Cleanup

• Renamed espectre_component.cpp/.h → espectre.cpp/.h
• Component ID standardized to espectre_csi
• Updated me CLI: erase_flash → erase-flash (esptool deprecation fix)

Performance Optimization

Unified variance algorithm and optimized Hampel filter across both platforms

This release focuses on code uniformity between MicroPython and C++ implementations, improving numerical stability and performance.

Algorithm Uniformity

• Two-pass variance: Both platforms now use the same numerically stable algorithm
• Formula: Var(X) = Σ(x - μ)² / n (more stable than E[X²] - E[X]²)
• Eliminates catastrophic cancellation risk with float32
• Identical behavior between MicroPython and C++

Hampel Filter Optimization

• C++ (ESPHome): Eliminated dynamic memory allocation
• Pre-allocated static buffers in hampel_turbulence_state_t
• Insertion sort replaces qsort() for small arrays (N=3-11)

• ~20-25μs saved per packet (no malloc/free overhead)

• MicroPython: Pre-allocated buffers and circular buffer

• Eliminated list creation per call
• Insertion sort for small arrays
• ~120μs saved per packet

Validation

• New test script 16_test_optimization_equivalence.py using real CSI data
• Verified with 2000 real CSI packets (baseline + movement)
• Maximum variance difference: 9.41e-14 (effectively zero)

Test Suite & Code Coverage

• 140 test cases (+72 from 2.0.0) with real CSI data
• Full device testing: All tests run on both native and ESP32-C6 via IWiFiCSI dependency injection
• Codecov integration: Coverage badge, PR comments, 80% threshold
• 84% line coverage, 94% function coverage
• Refactoring: Shared utilities in utils.h, configurable CalibrationManager

[2.0.0] - 2025-12-06 - ESPHome Native Integration

Major - ESPHome Native Integration

Complete platform migration from ESP-IDF to ESPHome

This release represents a major architectural shift from standalone ESP-IDF firmware to a native ESPHome component, enabling seamless Home Assistant integration.

⚠️ Note: Extensively tested on ESP32-S3 and ESP32-C6, but bugs may still exist. Community contributions, bug reports, and support for additional ESP32 variants are welcome!

Two-Platform Strategy

ESPectre now follows a dual-platform development model:

ESPectre focuses on core motion detection for Home Assistant integration. Micro-ESPectre provides features (variance, skewness, kurtosis, entropy, IQR, spatial_, temporal_) and advanced filters (Butterworth, Wavelet, Savitzky-Golay) for research/ML applications.

New Architecture: - Native ESPHome component: Full C++ implementation as ESPHome external component - Home Assistant auto-discovery: Automatic device and sensor registration via Native API - YAML configuration: All parameters configurable via simple YAML files - OTA updates: Wireless firmware updates via ESPHome

Implementation: - components/espectre/: Complete ESPHome component with Python config and C++ implementation - Modular C++ architecture: calibration_manager, csi_manager, sensor_publisher, etc. - Binary sensor for motion detection - Movement score sensor - Adjustable threshold (number entity) - controllable from Home Assistant

Micro-ESPectre

R&D Platform for Wi-Fi CSI Motion Detection - Pure Python implementation for MicroPython

Micro-ESPectre is the research and development platform of the ESPectre project, designed for rapid prototyping, algorithmic experimentation, and academic/industrial research. It implements motion detection algorithms in pure Python, enabling fast iterations without compilation overhead.

Key Features: - Instant Deploy: ~5 seconds to update code (no compilation) - MQTT Integration: Runtime configuration via MQTT commands - Auto Calibration Algorithm: Automatic subcarrier selection (F1=97.6%) - Analysis Tools: Complete suite for CSI analysis and algorithm optimization - Feature Extraction: Statistical features (variance, skewness, kurtosis, entropy, IQR) - Confidence Score: Experimental motion detection confidence estimation - NVS Persistence: Persistent configuration on filesystem

Advanced Applications (ML/DL ready): - People counting - Activity recognition (walking, falling, sitting, sleeping) - Localization and tracking - Gesture recognition

Dependencies: - micropython-esp32-csi - Custom MicroPython fork with native CSI support for ESP32 family - MQTT broker (e.g., Mosquitto)

Test Suite Refactoring

Migration from Unity (ESP-IDF) to PlatformIO Unity for ESPHome consistency

The test suite has been migrated from ESP-IDF's Unity framework to PlatformIO Unity, aligning with the ESPHome ecosystem and enabling native (desktop) test execution without hardware.

Complete test suite with 68 test cases organized in 5 suites and Memory leak detection:

# Run tests locally (native is the default environment)
cd test && pio test

CI/CD Pipeline

GitHub Actions integration for automated quality assurance

• Automated testing: Runs on push to main/develop and pull requests
• ESPHome build verification: Compiles espectre.yaml to validate component
• Status badge: Real-time CI status displayed in README
• Path filtering: Only triggers on changes to components/espectre/ or test/

[1.5.0] - 2025-12-03 - Automatic Subcarrier Selection

Automatic Subcarrier Selection

• Zero-configuration subcarrier selection using NBVI (Normalized Baseline Variability Index) algorithm.
• Auto-calibration at boot, re-calibration after factory_reset.
• Formula: NBVI = 0.3 × (σ/μ²) + 0.7 × (σ/μ).
• Achieves F1=97.6% (Recall 95.3%, Precision 100%, FP 0%).

[1.4.0] - 2025-11-28 - Major Refactoring & Technical Debt Reduction

Major Refactoring

• Feature extraction module: Extracted to csi_features.c/h, reduced csi_processor.c by 50%
• Configuration centralization: All defaults in espectre.h, validation in validation.h/c
• Two-pass variance: Numerically stable calculation
• Traffic generator: Max rate 1000 pps (was 50), default 100 pps
• CLI migration: Bash → Python (cross-platform)
• Wi-Fi Theremin: espectre-theremin.html for CSI sonification
• Removed: Redundant segmentation parameters (min_length, max_length, k_factor)

[1.3.0] - 2025-11-22 - ESP32-C6 Platform Support

ESP32-C6 Platform Support

• WiFi 6 (802.11ax) support with proper CSI configuration
• Runtime-configurable parameters: threshold, window_size via MQTT
• Web Monitor: espectre-monitor.html with real-time visualization
• System monitoring: CPU/RAM usage in stats command
• MQTT optimization: Simplified message format, removed segment tracking

[1.2.1] - 2025-11-17

Wi-Fi Optimization

ESP-IDF best practices: disabled power save (WIFI_PS_NONE), configurable country code, HT20 bandwidth.

[1.2.0] - 2025-11-16 - Simplified Architecture & MVS Segmentation

Simplified Architecture

• MVS algorithm: Moving Variance Segmentation with adaptive threshold
• Amplitude-based features: +151% separation improvement for skewness/kurtosis
• Traffic generator: ICMP ping-based (was UDP broadcast)
• 64 subcarriers: All available (was 52 filtered)
• 10 features: Added temporal_delta_mean, temporal_delta_variance

[1.1.0] - 2025-11-08

Auto-Calibration System

• Fisher's criterion: Automatic feature selection (4-6 from 8)
• Butterworth filter: Order 4, cutoff 8Hz
• Wavelet filter: Daubechies db4 for high-noise environments
• NVS persistence: Configuration survives reboots
• Modular architecture: Split into 10 specialized modules

[1.0.0] - 2025-11-01

Initial Release

CSI-based movement detection for ESP32-S3. Hampel + Savitzky-Golay filters, 15 features, 4-state detection (IDLE/MICRO/DETECTED/INTENSE), MQTT publishing, CLI tool. 10-100 pps, <50ms latency, 3-8m range.

License

GPLv3 - See LICENSE for details.