Elijah On the Move SW

EOTM
Platform

AI powered on the move radar processing that enables high performance target detection without hardware modification.

Static Radar

EOTM

Software

On the Move Radar

What is EOTM

Make Any Elijah Radar Mobile.

EOTM is an AI powered software platform that transforms a stationary radar into a mobile radar system.
Using NVIDIA CUDA-based edge computing and advanced AI signal processing,
EOTM compensates for platform motion in real time enabling accurate target detection while the vehicle is moving.

How it works

Mobile Platform

Vehicle / Data Source

Edge Computing Box

CUDA-Based High-Speed Parallel Processing

Advanced AI Processing (DCNN, TBD)

Reduced Latency

Parallel Signal Processing

Lightweight / Optimized

Real-Time Results Display

Low-Latency Target Detection

Core Technologies

01

Terrain-Adaptive AI MTI

Suppresses ground clutter by applying adaptive AI filtering
based on terrain and GPS information.

02

Dynamic LOS Compensation

Continuously updates the radar line-of-sight to compensate
for vehicle motion and yaw in real time.

03

AI Target Classification

DCNN-based AI accurately distinguishes targets
from clutter and noise using 3D tensor radar data.

04

CUDA Edge Computing

Optimized CUDA parallel processing delivers
ultra-low latency AI computation on edge devices.

How it works

Detection Performance

UP

Consistent performance
on the move

Processing Latency

DOWN

Real-time AI processing
with minimal latency

Classification Accuracy

HIGH

AI-Based precise
clutter / target separation

Hardware Modification

NO

Upgrade Elijah radar
with software only.

Research

The core technologies behind EOTM are based on proprietary research
independently developed and published by TORIS.

Related Research

Enhanced Radar False Alarm Mitigation in Low-RCS Target Detection Using Time-Varying Trajectorieson Range–Doppler Diagrams With DCNN

Jiyeon Choi,
Young-Hoon Chun,
Seok Chan Eom,
Daegun Oh,
and Youngwook Kim

IEEE Transactions on Instrumentation and Measurement,
Vol. 74, 2025

Request Full Paper

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