Defense / DRDO
KASAM — Multi-Sensor Situational Awareness System
An AI-enabled situational awareness platform integrating multi-sensor inputs, real-time dashboards, and autonomous decision support. Field-tested and validated by the Indian Army — earned a formal Armed Forces commendation.
Overview
Led development of an AI-enabled situational awareness platform integrating multi-sensor inputs, dashboards, and autonomous decision support. The system consolidates heterogeneous sensor streams into a unified operational picture and flags high-priority events for human operators.
The Problem
Operational users faced a fragmented view of the environment — multiple sensor feeds, no unified dashboard, and manual correlation of events. Response time and decision quality both suffered. KASAM was built to close that gap with an AI-first sensor fusion and decision-support layer.
My Role & Contribution
- Led end-to-end development of the AI stack and integration with sensor subsystems
- Designed the real-time dashboard and operator workflow
- Worked directly with end-users during field trials to iterate on the system
Approach
- Sensor fusion layer combining multiple input modalities into a unified event stream
- Computer vision models for automated detection and classification in real time
- Decision-support layer flagging high-priority events and suggesting actions
- Edge-deployable inference pipeline tuned for constrained operational hardware
Tech Stack
Python
PyTorch
OpenCV
YOLOv8
FastAPI
React.js
WebSockets
Docker
Linux / Edge
Results & Impact
- Field-tested and validated by the Indian Army during operational trials
- Received a formal commendation from the Armed Forces
Note: Certain implementation details are covered by DRDO confidentiality. The case study describes the approach at a level appropriate for public sharing. For deeper technical discussion, reach out directly.
// TODO: add diagrams / screenshots (architecture, dashboard, sensor stack)