Defence Technology
RADAR
About Product
The AI Enabled Radar Training System is a real, safe-to-use pulse + FMCW radar platform designed for close-range object detection, SAR imaging, Doppler studies, and AI-based radar data classification. It supports multiple concurrent users and provides real-time radar signals for training, research, and machine-learning applications.
It offers 8 GHz pulse radar with <11 cm resolution and FMCW radar with Doppler, MTI, and SAR capabilities.
Shares real radar data with several pupils via a huge display controller. Users can apply filters, signals, FFT, MTI, and processing in real time.
AI module enables CNN-based gesture recognition, dataset creation, labeling, and radar-based classification using TensorFlow/Keras. Supports Python and MATLAB integration.
Web-based software with A-Scope, B-Scope, PPI, RCS estimation, CFAR, MTI, filters, window functions, and Doppler tools. Reads I/Q data and interfaces with MATLAB.
Includes linear step-motor conveyor for SAR imaging and high-resolution radar mapping. Produces advanced radar images using time-multiplexed data.
Wireless mobile vehicle carries different RCS targets, moves freely in the lab, and rotates reflectors for dynamic radar testing.
8 GHz radar with 0.5 ns pulse width, 25 m range, <11 cm resolution, and >100 fps processing. Features 23.3 GS/s sampling and variable PRF, gain, and power.
24 GHz FMCW radar with 1 GHz bandwidth, 15 cm resolution, >50 m range (cars), and 50 fps sampling. Includes patch antenna with high gain and low spurious emissions.
Motorized linear axis (1.8 m) for SAR scanning, driven by high-precision step motor. Produces high-resolution SAR imagery with integrated software and DSP.
Linux rack server with Intel Xeon CPU, 16 GB RAM, 240 GB SSD, enabling >100 concurrent student connections. Supports virtualized radar resources.
Battery-powered, remote-controlled vehicle carrying multiple RCS targets including corner reflectors and spheres. Allows free movement and rotation for radar testing.
Study pulse emission, PRF, Doppler shift, FFT, and moving target detection using real radar signals. Evaluate clutter, noise, and sensitivity settings.
Perform FMCW range-Doppler analysis and generate SAR images using linear motion. Understand sweep time, bandwidth effects, and high-resolution imaging.
Use A-scope, B-scope, and PPI for visualization; adjust STC, CFAR, gain, and thresholds to refine target visibility. Explore noise reduction and filtering.
Measure Radar Cross Section of different objects and analyze detectability based on material, angle, size, and distance.
Track moving targets using the wireless vehicle, study reflections, Doppler responses, and clutter cancellation.
Train CNN models on radar data for gesture classification, raw-data labeling, signal filtering, and neural-network-based pattern recognition.
With 35 years of legacy in Technical Education, Akademika extends its expertise to Defence and Skill development, building Innovative, Reliable Training Systems for the next generation of Learners.
Partner with Akademika
Join leading institutions that trust our engineering to Advance Learning and Research.
