AI-Powered Radar Intelligence · Ankara, Turkey

MORE DATA.
STRONGER
COMPETITION.

BESASEM Technology generates synthetic radar target signatures using advanced SBR algorithms — delivering high-accuracy training data for AI classification systems.

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SBR
Shooting & Bouncing Rays
RCS
Radar Cross Section
ISAR
Inverse SAR Imaging
HRRP
High Res Range Profile
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About Us

Engineering the Future of Radar AI

Founded in Ankara in 2025, BESASEM Technology is an engineering and consultancy company operating at the intersection of radar systems, electromagnetic analysis, and AI-supported signal processing.

Ankara, Turkey Est. 2025 Defense Tech AI / EM Analysis

We specialize in generating synthetic electromagnetic signatures that power AI training pipelines — bridging the gap between real-world test range limitations and the massive data requirements of modern machine learning.

  • Radar Cross Section (RCS) simulation and analysis
  • High Resolution Range Profile (HRRP) generation
  • Inverse Synthetic Aperture Radar (ISAR) imaging
  • Computational electromagnetic modeling
  • Expert engineering team with defense background
Services

The Data Factory

We produce millions of labeled synthetic radar signatures — enabling AI systems to detect, classify, and track targets with unprecedented accuracy.

01
Synthetic Data Generation

Generate massive datasets of labeled radar target signatures using the SBR computational electromagnetic method — tailored to any desired platform geometry.

02
Virtual Anechoic Chamber

A digital counterpart to physical test ranges. Analyze radar targets across every angle and frequency in a controlled simulation environment — at a fraction of real cost.

03
AI Training Datasets

Structured, high-accuracy training corpora for classification algorithms. Outputs include RCS, HRRP, and ISAR data ready for direct ingestion into ML pipelines.

04
EM Consultancy

Expert electromagnetic analysis for defense programs — precise modeling of target signatures, stealth assessment, and detection scenario evaluation.

05
CAD Model Integration

Specially triangulated 3D CAD models optimized for SBR computation. We process your platform geometry into simulation-ready meshes without loss of EM fidelity.

06
GPU-Accelerated Simulation

High-speed parallel computation on CPU and GPU architectures enables multi-scattering analyses at modern radar operating frequencies — results in hours, not weeks.

Technology

Advanced Target Signature Simulation

Radar signatures of any target platform are modeled with high accuracy using the proprietary SBR algorithm — then utilized directly for Artificial Intelligence training.

Digital twin vs real-world radar target
Fig. 1 — From Digital Twin to Real-World Deployment · SBR-Powered AI Training Pipeline
High-speed computation based on parallel CPU and GPU architectures for rapid scenario iteration
Multi-scattering analyses with efficient ray tracing — capturing complex electromagnetic interactions
Results at modern radar operating frequencies: L, S, C, X and higher bands
Outputs structured for direct AI algorithm training ingestion — RCS, HRRP, ISAR formats
User-friendly interface with parametric scenario configuration and batch processing
Specially triangulated CAD models preserving full electromagnetic fidelity at all aspect angles
Output Sample

ISAR Imagery Output

A real simulation output from BESASEM's SBR engine — an ISAR image of a turboprop aircraft at 3 GHz, alongside its CAD model. This is the kind of labeled data fed directly into AI classification pipelines.

ISAR Image Output — 3 GHz Turboprop Aircraft Simulation
ISAR Image · 3 GHz · BW: 0.40 GHz · Angle: 8.0° · Res: 37.5cm × 35.8cm
Simulation TypeInverse Synthetic Aperture Radar (ISAR) — 2D high-resolution image of target scattering centers in range and cross-range dimensions.
Frequency3 GHz (S-band) · Bandwidth: 0.40 GHz · Image resolution: 37.5 cm × 35.8 cm
Target PlatformTwin-engine turboprop transport aircraft — modeled from triangulated CAD geometry using the SBR electromagnetic method.
AI ApplicationEach output image is a labeled training sample for deep learning classifiers — millions of such images across frequencies, angles, and platform types constitute a full AI training dataset.
Color ScaleNormalized RCS in dB — white/yellow indicate dominant scattering centers; dark areas indicate low radar reflectivity.
Why BESASEM

Decisive Advantages

Our synthetic data generation capabilities deliver measurable improvements across every stage of radar AI development.

Millions of Labeled Data Points

Generate datasets at scale that would be physically impossible — or prohibitively expensive — to collect from real-world test ranges.

Significant Cost Reduction

Dramatically reduce radar target data acquisition costs by replacing physical chamber testing with validated computational models.

Speed & Accuracy

Accelerate classification model development with high-fidelity data that improves both training speed and final model accuracy.

Stealth Target Detection

Enhance capability to detect low-observable targets by training on synthetic signatures that include challenging edge-case geometries.

New Target Class Creation

Easily introduce new target classes into existing AI pipelines without waiting months for physical data collection campaigns.

Time Savings

Compress years of traditional test range operations into weeks of simulation — without sacrificing electromagnetic accuracy.

Get in Touch

Start a Conversation

Whether you're exploring synthetic data for an AI program or need electromagnetic consultancy — we'd like to hear from you.

Emailcontact@besasem.com
Websitewww.besasem.com
LocationÇankaya, Ankara — Turkey