📍 PositionMe

Indoor & Outdoor Positioning System

PositionMe is a mobile Android application for real-time indoor/outdoor positioning. It fuses WiFi, GNSS, and IMU (PDR) data to provide accurate, robust user localization within university buildings like The Nucleus and The Noreen and Kenneth Murray Library.

🧠 Core Features

• 🔄 Real-Time Sensor Fusion: Combines PDR, GNSS, and WiFi using Kalman and Particle filters.
• 🧭 Interactive Map Interface: Live visualization of position, tagging, and sensor info.
• 📱 Sensor Dashboard: View all IMU, GNSS, and WiFi specs.
• 🧪 Manual Tagging System: Add calibrated anchor points for ML training.
• 📼 Replay Mode: Revisit recorded sessions with map visualization.
• 🧠 AI-based WiFi Prediction: Predicts location using a TFLite ML model (offline-trained).
• 🧰 Developer-Friendly Architecture: Modular components, extensible map utilities, and offline training pipelines.

📦 APK Installation (For End-Users)

1. Install APK on Android (Android 9+ recommended).
2. Enable location, Wi-Fi, and sensor access.
3. Use bottom nav bar to switch between Explore, Replay, Device, Settings.

🚀 Getting Started (Developers)

✅ Prerequisites

Tool	Version
Android Studio	Arctic Fox (2020.3.1) or newer
Android SDK	API Level 34
Gradle	8.0+
Java JDK	17+
Git	Latest stable

Device Requirements:

• Physical Android device with GNSS, WiFi, and IMU sensors.
• Developer mode enabled.
• Internet connection for OpenPositioning API.

🛠️ Setup Instructions

1. Clone Repository

   git clone https://github.com/Bol-C14/PositionMe_EWireless
   cd PositionMe

2. Open Project

   • Launch Android Studio → "Open existing project"
   • Select project root → wait for Gradle sync

3. API Key Setup Create secrets.properties in project root:

   MAPS_API_KEY=your_google_maps_key
   OPENPOSITIONING_API_KEY=your_openpositioning_key
   OPENPOSITIONING_MASTER_KEY=your_openpositioning_master_key
   

   ⚠️ Add secrets.properties to .gitignore to avoid leaking keys.

4. Build and Run

   • Connect Android device via USB
   • Enable USB Debugging
   • Click Run > Run 'app' in Android Studio
   • Grant permissions when prompted

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📁 Project Structure

com.openpositioning.PositionMe/
├── data/                # File handling & API comms
├── domain/              # Core algorithm logic
├── presentation/        # UI components
├── sensors/             # Sensor reading & fusion
├── utils/               # Utilities
└── Traj.java            # Trajectory data structure

⚙️ Core Functionalities

🔴 Real-Time Tracking (Explore)

• Live fused positioning using GNSS, WiFi, and IMU
• Toggle modes: GNSS / PDR / WiFi / Fused
• Tagging support for indoor calibration
• Fragment: RecordingFragment.java

🧭 Calibration

• Manually anchor ground truth positions
• Saved for ML training or evaluation
• Fragment: CalibrationFragment.java

📊 Sensor Fusion

Implemented in SensorFusion.java using:

• EKF.java – Extended Kalman Filter
• ParticleFilter.java – Sampling-based estimator
• Fallback ML model: SensorDataPredictor.java (TensorFlow Lite)

🔁 Replay

• Visualize past paths with interactive playback
• Reposition start point manually
• Fragment: ReplayFragment.java

🧠 Fusion & AI Modules

1. SensorFusion.java

• Central controller for EKF, PF, and fallback models.

2. EKF.java

• Tracks state [x, y, z, θ].
• Updates from PDR, GNSS, WiFi, and optional barometer.

3. ParticleFilter.java

• Monte Carlo sampling-based backup.
• Used to control drift when EKF fails.

4. SensorDataPredictor.java

• TFLite fallback ML model using top-20 BSSID+RSSI fingerprints.

🗺️ Map Visualization Modules

File	Purpose
TrajectoryPlotter.java	Draw real-time paths (raw, fusion, etc.)
TrajectoryMapWall.java	Render wall outlines
BuildingPolygonPlotter	Draw building boundaries
TrajectoryMapMaker	Add markers (e.g., toilet, lifts)

Tip: Add new building or floor support by editing coordinate lists.

🧪 Machine Learning Pipeline (Offline)

All scripts located in:

/projectSourceFolder/machine_learning/

🧭 Alignment – alignment.py

• Uses piecewise Procrustes alignment between anchor points
• Corrects PDR drift using user-tagged positions

🤖 Training – embedding_train_LSTM.py

1. Parse .json logs (recorded via app)
2. Align PDR segments
3. Extract WiFi top-20 BSSID-RSSI features
4. Use aligned positions as labels
5. Train and visualize prediction performance

📱 User Interface Guide

🌍 Explore Tab

• Switch between GNSS / WiFi / PDR / Fused modes
• Live sensor panel (accelerometer, gyroscope, etc.)
• Tagging panel for collecting ground truth
• Save and upload trajectories automatically

🔁 Replay Tab

• Download and view previous routes
• Manual replay with adjustable start points

📟 Device Tab

• List all sensors (type, vendor, resolution, power)

⚙️ Settings Tab

• Manual stride length
• Floor height setting
• Data sync over WiFi/mobile
• Edit advanced constants

🧩 Common Issues & Fixes

Issue	Fix
App crashes on launch	Check permissions, enable location services
API keys not working	Confirm secrets.properties setup
GNSS/WiFi data missing	Use a physical device and allow all permissions
Old WiFi data	Disable WiFi scan throttling in Developer Options