Acquiring Signal... 0%
SAURON
Intelligence From Noise
[WIFI + BLUETOOTH]
SIGNAL SCAN
ESP32 nodes continuously scanning WiFi RSSI and Bluetooth signal strength in real time.
[DISTRIBUTED MESH]
MULTI-NODE
Multiple sensing nodes collect signal data simultaneously for spatial inference.
[NOISE MITIGATION]
FILTERING
Temporal smoothing, statistical filtering, and probabilistic modeling extract meaning from noise.
[SENSOR FUSION]
ACQUIRED
Device identifiers, timestamps, and RSSI aggregated across the distributed mesh.
LEARNING
Modeling signal behavior dynamically
[KNN + RANDOM FOREST]
PREDICTIVE
ML models trained on real-world data estimate position from incomplete signals.
[REAL-TIME OUTPUT]
HEATMAP
Signal intensity heatmaps and estimated x,y coordinates rendered in real time.
[LOCALIZATION 100%]
LOCKED
Temporal tracking of device movement across the sensing grid complete.
SIGNALSENSE
Intelligence From Noise
How It Works.
Signal strength is inconsistent and unreliable in real-world environments. Instead of relying on traditional geometric assumptions, SignalSense AI applies machine learning to model signal behavior dynamically — extracting spatial intelligence from everyday wireless signals.
Distributed Sensing
Multiple ESP32-powered nodes continuously scan WiFi and Bluetooth signals, collecting RSSI values, device identifiers, and timestamps. Data streams to a central Raspberry Pi 4 for real-time analysis.
Adaptive Intelligence
K-Nearest Neighbors, Random Forest Regression, and optional neural network estimators learn how signals behave in specific environments — how obstacles affect readings and how to estimate position from noisy, incomplete data.
Spatial Visualization
The system produces estimated x,y device coordinates, signal intensity heatmaps, and temporal movement tracking — all rendered through a custom dashboard interface updated continuously.
The Architects.
The minds engineering the future of signal intelligence. Each operator brings a critical discipline to the SignalSense framework.
Pratheek Madderla
Chief Executive Officer
Architecting the vision behind Sauron's AI-driven localization framework. Overseeing system design, signal processing strategy, and full-stack deployment.
Harshith Chemudugunta
Chief Technology Officer
Leading embedded systems engineering and RF hardware integration. Responsible for the multi-modal sensor fusion pipeline across LoRa, WiFi, and BT arrays.
Chahel Paatur
Chief Operations Officer
Driving operational infrastructure and machine learning model deployment. Managing kinematic noise filtering and real-time RSSI heatmap generation.
Garvin Yu
Chief Hardware Architect
Engineering the physical node infrastructure and antenna array design. Overseeing PCB layout, power management, and field-deployable enclosure systems.