Project H.A.W.K.

Human Activity Detection Through Walls Using Microwave Kinetics

A FreeRTOS-powered through-wall life detection radar with real-time vital sign extraction and a tactical LAN dashboard.

Overview

Project H.A.W.K. is an RTOS-based embedded system engineered for non-line-of-sight (NLOS) human detection using microwave Doppler radar. The system penetrates solid obstacles — drywall, rubble, wood, and smoke — to detect physiological micro-movements: breathing (0.2–0.6 Hz) and heartbeat (1.0–2.5 Hz).

Built on an ESP32 dual-core processor running FreeRTOS, the architecture guarantees deterministic timing and real-time preemptive multitasking across four concurrent tasks. A 1024-point FFT pipeline with parabolic interpolation provides sub-bin frequency accuracy for vital sign extraction. A 15-second auto-calibration sequence dynamically learns the environmental noise floor on every boot, and all telemetry is streamed over WebSocket to a tactical browser-based dashboard accessible from any device on the local network.

Key Applications

Domain	Use Case
Tactical & Defense	Urban warfare situational awareness — detect human presence behind walls before room entry, hostage rescue, and tactical reconnaissance.
Search & Rescue	Locate survivors trapped under earthquake debris or collapsed structures by detecting faint vital signs through rubble.
Advanced Security	Covert perimeter monitoring and intrusion detection in zero-visibility environments (smoke, darkness, fog).
Medical Monitoring	Non-contact vital sign monitoring in isolation wards or hazardous environments.

System Architecture

+-----------------------------------------------------------------------------+
|                     PROJECT H.A.W.K. -- SYSTEM OVERVIEW                     |
+-----------------------------------------------------------------------------+
|                                                                             |
|  +--------------+    +--------------+    +----------------------------+     |
|  | HB100/CDM324 |    | Active BPF   |    |    ESP32 (Dual-Core)       |     |
|  | Doppler      |--->| 0.1-3.0 Hz   |--->|     FreeRTOS Kernel        |     |
|  | Radar        | IF | Op-Amp       | ADC|                            |     |
|  | 10.525 GHz   |    | Circuit      |    | +------------------------+ |     |
|  +--------------+    +--------------+    | | T1: Radar Sampling     | |     |
|                                          | |     250 Hz (P4)        | |     |
|                                          | +------------------------+ |     |
|                                          | | T2: 1024-pt FFT        | |     |
|  +--------------------------------------+| |     Pipeline (P3)      | |     |
|  |     TACTICAL LAN DASHBOARD           || +------------------------+ |     |
|  | +--------+ +--------+ +------------+ || | T3: Detection &        | |     |
|  | | Vital  | | Confid.| | Event      | |<-|     Calibration (P2)   | |     |
|  | | Charts | | Gauge  | | Log        | || +------------------------+ |     |
|  | +--------+ +--------+ +------------+ || | T4: WebSocket &        | |     |
|  | WebSocket on ws://<ESP32_IP>:81      || |     Alarms (P1)        | |     |
|  +--------------------------------------+| +------------------------+ |     |
|                                          +----------------------------+     |
+-----------------------------------------------------------------------------+

Hardware Architecture

Component	Specification	Purpose
Microcontroller	ESP32 DevKit (32-bit, Dual-Core Xtensa LX6, 240 MHz)	FreeRTOS host, ADC sampling, WiFi/WebSocket server
Radar Sensor	HB100 (10.525 GHz) / CDM324 (24 GHz) Microwave Doppler	Emits CW microwave; IF output encodes target micro-motion
Signal Conditioning	Custom 2-stage Active Bandpass Filter (Op-Amp)	Passband: 0.1–3.0 Hz — isolates human vitals, rejects mechanical noise
ADC Interface	ESP32 12-bit SAR ADC (GPIO 34)	0–4095 counts at 250 Hz deterministic sampling
Alert Output	Active Buzzer (GPIO 25) + LED Indicator (GPIO 2)	1.5-second local hardware alarm on confirmed detection
Dashboard	Any browser on the LAN	Real-time tactical display via WebSocket (`ws://<IP>:81`)

Pin Map

Signal	GPIO	Direction
Radar ADC (Bandpass Filter Output)	`34`	Input (Analog)
Active Buzzer	`25`	Output (Digital)
Status LED	`2`	Output (Digital)

FreeRTOS Task Architecture

The system employs a preemptive priority-based RTOS scheduler with four concurrent tasks communicating through FreeRTOS Queues and Semaphores. The Arduino loop() is immediately deleted to free CPU cycles for the RTOS kernel.

Task Overview

#	Task	Priority	Stack	Function	IPC Mechanism
T1	Radar Acquisition	4 (Highest)	2 KB	250 Hz deterministic ADC sampling via `vTaskDelayUntil()`	Produces → `rawDataQueue` (2048 × `uint16_t`)
T2	Signal Processing	3	8 KB	1024-point FFT (DC removal → Hamming window → FFT → Magnitude → Sub-band peak detection)	Consumes `rawDataQueue` → Produces → `processedDataQueue` (8 × `VitalSignData`)
T3	Detection & Calibration	2	4 KB	Auto-calibration (15 s) + confidence ramp-up/decay algorithm	Consumes `processedDataQueue` → Produces → `dashboardQueue` + `detectionSemaphore`
T4	Communications & UI	1 (Lowest)	8 KB	WebSocket broadcast at ~20 Hz loop, buzzer/LED alarm with 1.5s auto-reset	Consumes `dashboardQueue` / `detectionSemaphore`

Inter-Task Data Flow

  rawDataQueue            processedDataQueue          dashboardQueue
   (2048 slots)              (8 slots)                  (4 slots)
       |                         |                          |
T1 --->|--> T2 ----------------->|--> T3 ------------------>|--> T4 --> WebSocket
(250 Hz)    (1024-pt FFT)             (Calibration +             (Broadcast +
                                       Confidence)                Buzzer/LED)
                                          |
                                          v
                                 detectionSemaphore
                                      (Binary)

Signal Processing Pipeline

Each FFT cycle processes 1024 samples collected over ~4.1 seconds at 250 Hz, yielding a frequency resolution of 0.2441 Hz/bin.

Pipeline Stages

DC Removal — Strips the ADC bias offset to center the waveform at zero.
Hamming Windowing — Reduces spectral leakage at bin boundaries.
1024-Point FFT — Full complex FFT using float (not double) to leverage the ESP32's hardware single-precision FPU (~30 ms vs ~300 ms on double).
Magnitude Extraction — complexToMagnitude() converts complex bins to real magnitudes.
Dual Sub-Band Peak Detection — Independent peak search with parabolic interpolation for sub-bin frequency accuracy:

Vital Sign	Frequency Band	FFT Bin Range	Resolution
Breathing	0.20 – 0.60 Hz	Bins 1–2	±0.12 Hz
Heartbeat	1.00 – 2.50 Hz	Bins 4–10	±0.12 Hz

Key Design Decisions

float over double: The ESP32's FPU is single-precision only. Using double forces software emulation at ~10x the compute cost — unacceptable for a real-time system.
static arrays: The 8 KB FFT buffers (vReal[1024], vImag[1024]) are declared static to place them in BSS rather than on the task stack, preventing stack overflow.
DC-safe bin clamping: Bin 0 (DC component) is always excluded from peak search, and parabolic interpolation results are clamped to the safe band range to prevent negative-frequency artifacts.

Dynamic Auto-Calibration

Instead of a hardcoded noise threshold, the system performs a 15-second environmental calibration on every boot.

Calibration Sequence

BOOT --> STATE_CALIBRATING (15s) --> STATE_ACTIVE
              |                            |
              | Track peak FFT magnitude   | Use dynamic threshold:
              | across both vital bands    |   activeThreshold =
              | (no detection logic runs)  |     peakNoise x 1.5
              |                            |
              | Telemetry still streams    | Confidence algorithm
              | to dashboard (state flag)  | fully operational
              |                            |
              v                            v
         Dashboard shows             Dashboard shows
         amber CALIBRATION           full tactical view
         overlay with                with live charts
         progress bar

Parameter	Value	Purpose
`CALIBRATION_DURATION_MS`	15000 ms	Window for noise floor measurement
`SAFETY_MARGIN`	1.5x	Multiplier applied to peak noise magnitude
`FALLBACK_THRESHOLD`	50.0	Used if calibration sees zero signal (dead ADC)
`REQUIRED_CONFIDENCE`	5	Consecutive positive readings needed for alert

Non-Blocking Design

The calibration phase uses xTaskGetTickCount() comparisons — no delay() calls — so the Detection Task continues consuming the processedDataQueue and feeding telemetry to the dashboard throughout the entire calibration window.

Tactical LAN Dashboard

A military-themed, browser-based monitoring console that connects to the ESP32 via WebSocket on port 81. No server, no cloud — just open index.html from any device on the same LAN.

Dashboard Features

Feature	Description
Connection Overlay	Full-screen animated radar sweep with live reconnect countdown
Calibration Overlay	Amber warning overlay with progress bar and countdown timer during the 15s noise floor analysis
Threat Assessment	Real-time status panel — transitions from green `MONITORING` to red-alert `HUMAN DETECTED` with flashing vignette
Confidence Gauge	5-bar visual gauge (green to amber to red) with labels: NO SIGNAL, WEAK, TRACKING, ACQUIRING, LOCKED, CONFIRMED
Vital Sign Charts	Dual Chart.js line graphs — Breathing (0.2-0.6 Hz, cyan) and Heartbeat (1.0-2.5 Hz, magenta) — scrolling 30-point window
System Metrics	Total alerts, current breathing Hz, current heartbeat Hz, last alert timestamp
Event Log	Scrolling console with timestamped telemetry, system events, and alerts (max 150 entries)
Audio Alert	Web Audio API tactical 3-tone square-wave alarm on human detection
Auto-Reconnect	Automatic WebSocket recovery with visual countdown on connection loss

Tech Stack

Layer	Technology
Structure	Semantic HTML5
Styling	Vanilla CSS — glassmorphism, CSS custom properties, CRT scan-line overlay
Typography	Orbitron (display) + JetBrains Mono (data) via Google Fonts
Charts	Chart.js 4.4.7 (CDN)
Data Transport	Native WebSocket API, `ws://<ESP32_IP>:81`

Communication Protocol

All telemetry is transmitted exclusively over WebSocket (Serial output is disabled in production firmware to eliminate UART jitter in the time-critical 250 Hz sampling loop).

$HAWK Protocol

Message Type	Format	Direction
Handshake	`$HAWK,STATUS,CONNECTED`	ESP32 to Dashboard
Telemetry	`$HAWK,DATA,<breathFreq>,<heartFreq>,<breathMag>,<heartMag>,<confidence>,<maxConf>,<timestamp>,<state>`	ESP32 to Dashboard
Alert	`$HAWK,ALERT,HUMAN_DETECTED,TIME:<ms_timestamp>`	ESP32 to Dashboard

Telemetry Fields

Field	Type	Description
`breathFreq`	`float`	Dominant frequency in breathing band (Hz)
`heartFreq`	`float`	Dominant frequency in heartbeat band (Hz)
`breathMag`	`float`	FFT magnitude of breathing peak (signal strength)
`heartMag`	`float`	FFT magnitude of heartbeat peak (signal strength)
`confidence`	`int`	Current detection confidence (0 to `maxConf`)
`maxConf`	`int`	Required confidence threshold for alert trigger
`timestamp`	`ulong`	ESP32 `millis()` uptime
`state`	`string`	`CALIBRATING` or `ACTIVE`

Repository Structure

H.A.W.K/
|
+-- ESP32/                             # Embedded firmware (Arduino/FreeRTOS)
|   +-- ESP32.ino                      # Entry point -- Queue/Semaphore creation, WiFi init, task launch
|   +-- globals.h                      # Pin definitions, SystemState enum, VitalSignData struct, RTOS handles
|   +-- radar_sensor.cpp / .h          # T1: 250 Hz deterministic ADC sampling task
|   +-- signal_processing.cpp / .h     # T2: 1024-pt FFT pipeline with dual sub-band peak detection
|   +-- detection.cpp / .h             # T3: Auto-calibration + confidence algorithm + alert logic
|   +-- comms_ui.cpp / .h              # T4: WiFi/WebSocket server, $HAWK protocol, buzzer/LED alarms
|
+-- Dashboard/                         # Browser-based tactical monitoring console
|   +-- index.html                     # Semantic HTML5 structure with all overlay layers
|   +-- styles.css                     # Full design system -- dark theme, glassmorphism, animations
|   +-- app.js                         # WebSocket client, Chart.js graphs, calibration/alert UI logic
|
+-- LTspice/                           # Analog circuit simulation files
|   +-- HB100-ESP32.asc                # LTspice schematic -- active bandpass filter design
|   +-- HB100-ESP32.raw / .log         # Simulation output data
|
+-- Oscilloscope/                      # Hardware debug utility
|   +-- Oscilloscope.ino               # ESP32 Serial Plotter sketch -- verify ADC signal & DC bias
|
+-- Abstract.txt                       # Project abstract
+-- LICENSE                            # MIT License
+-- README.md                          # This file

Getting Started

Prerequisites

Hardware

Component	Quantity	Notes
ESP32 Development Board	1	Any variant with ADC-capable GPIO 34
HB100 or CDM324 Radar Module	1	10.525 GHz or 24 GHz Doppler
Op-Amp (e.g., LM358 / MCP6002)	1–2	For active bandpass filter stages
Resistors & Capacitors	As per filter design	See LTspice schematic for values
Active Buzzer	1	3.3V compatible
LED	1	With appropriate current-limiting resistor

Software

Tool	Version
Arduino IDE	2.x+ (or PlatformIO)
ESP32 Board Package	Latest via Board Manager
`arduinoFFT` library	v2.0.0+ by Kosme
`WebSockets` library	By Markus Sattler (Links2004)

Installation

1. Clone the Repository

git clone https://github.com/chandansaipavanpadala/H.A.W.K-HumanActivityDetectionThroughWallsUsingMicrowaveKinetics.git
cd H.A.W.K-HumanActivityDetectionThroughWallsUsingMicrowaveKinetics

2. Hardware Wiring

Signal	Connect To	ESP32 GPIO
Bandpass Filter Output	`RADAR_ADC_PIN`	34
Active Buzzer (+)	`BUZZER_PIN`	25
Status LED (Anode)	`LED_PIN`	2

Tip: Use the LTspice/HB100-ESP32.asc schematic as a reference for the analog filter circuit design.

3. Configure WiFi Credentials

Open ESP32/comms_ui.h and update:

#define WIFI_SSID     "YourNetworkName"
#define WIFI_PASSWORD "YourPassword"

4. Compile and Flash

Open ESP32/ESP32.ino in Arduino IDE.
Select your ESP32 board model and COM port.
Install required libraries (arduinoFFT, WebSockets) via Library Manager.
Compile and upload the firmware.

5. Launch the Dashboard

After flashing, note the ESP32's IP address from your router's DHCP table.

Open Dashboard/app.js and update the WebSocket URL:

const WEBSOCKET_URL = 'ws://<YOUR_ESP32_IP>:81';

Open Dashboard/index.html in any modern browser on the same LAN.

6. System Validation

On boot, the dashboard will show a cyan radar-sweep loading overlay while connecting.
Once connected, the amber calibration overlay appears for 15 seconds — stand clear of the sensor.
After calibration, the full tactical dashboard activates with live charts.
Introduce a target subject in front of the radar — monitor the confidence gauge ramp from NO SIGNAL to CONFIRMED.
Upon confirmed detection: RED ALERT flashes, buzzer sounds, and the $HAWK,ALERT packet is broadcast.

Development Tools

Oscilloscope Utility

The Oscilloscope/Oscilloscope.ino sketch streams raw ADC values at 250 Hz to the Arduino Serial Plotter for hardware debugging:

Verify the 1.65V DC bias from the op-amp circuit
Confirm the ADC signal is centered and not clipping
Validate the radar module is producing a clean IF signal

Tools -> Serial Plotter -> 115200 baud

LTspice Simulation

The LTspice/ directory contains the complete active bandpass filter schematic (HB100-ESP32.asc). Use LTspice to simulate and verify the filter response before building the physical circuit.

License

This project is licensed under the MIT License — see the LICENSE file for details.

Built for the Real-Time Operating Systems course — Amrita Vishwa Vidyapeetham

Project H.A.W.K. — Because every second counts when lives are behind walls.

Name		Name	Last commit message	Last commit date
Latest commit History 19 Commits
.github/workflows		.github/workflows
Dashboard		Dashboard
ESP32		ESP32
LTspice		LTspice
Oscilloscope		Oscilloscope
LICENSE		LICENSE
README.md		README.md

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Project H.A.W.K.

Human Activity Detection Through Walls Using Microwave Kinetics

Table of Contents

Overview

Key Applications

System Architecture

Hardware Architecture

Pin Map

FreeRTOS Task Architecture

Task Overview

Inter-Task Data Flow

Signal Processing Pipeline

Pipeline Stages

Key Design Decisions

Dynamic Auto-Calibration

Calibration Sequence

Non-Blocking Design

Tactical LAN Dashboard

Dashboard Features

Tech Stack

Communication Protocol

$HAWK Protocol

Telemetry Fields

Repository Structure

Getting Started

Prerequisites

Installation

1. Clone the Repository

2. Hardware Wiring

3. Configure WiFi Credentials

4. Compile and Flash

5. Launch the Dashboard

6. System Validation

Development Tools

Oscilloscope Utility

LTspice Simulation

License

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