Uploaded June 2026 | Updated September 2026, 2 weeks ago
This technical presentation explains how MATLAB® and Simulink® support the full passive direction-finding and geolocation signal chain for EMSO applications. Starting with electromagnetic signal–environment modeling, the talk demonstrates how to synthesize realistic IQ data at three fidelity levels: the power level for architecture trades and GDOP analysis, the measurement level for tracker design, and the signal level for full-waveform algorithm development.
The presentation covers AI and deep learning for automated signal detection and classification in a congested spectrum, including synthetic training data generation, spectrogram-based classification, and radar parameter estimation using YOLOX networks. These techniques address the labeled data scarcity problem by generating unlimited training examples for signals that have never been intercepted, including waveforms and frequency-agile emitters.
The presentation also thoroughly covers direction-finding techniques, including DOA estimation with six algorithms (MUSIC, MVDR, monopulse, beamscan, IAA, and deep learning), TOA, TDOA, and TSOA. The TDOA section explains why the time difference of arrival is the workhorse of passive geolocation: It requires no emitter cooperation—only GPS-disciplined oscillators synchronizing the receiver network. Explore how to use a USRP™ X310 software-defined radio with live RF hardware to validate direction-finding algorithms. The same MATLAB code can process both simulated and real over-the-air signals without modification. The geolocation section covers multilateration methods, GDOP analysis for optimizing sensor placement, and hybrid techniques combining AOA with TDOA or TSOA. To transform noisy intermittent detections into maintained emitter custody, you can track with Kalman filtering and data association. You can also track the emitter during platform maneuvers.
The talk concludes with the simulation-to-deployment workflow. HDL Coder™ generates synthesizable Verilog and VHDL for FPGA targets. MATLAB Coder™ generates C/C++ code for embedded processors, eliminating manual recoding between algorithm development and fielded systems.
Chapters:
0:00 Passive Direction-Finding and Geolocation with MATLAB and Simulink
1:52 Modeling the EMSO Signal Environment: IQ Data Synthesis and Scenario Generation
5:26 Low Probability of Intercept Radar Simulation for Passive Geolocation
6:33 Bistatic and Multi-Static Passive Radar Simulation and Processing in MATLAB
8:47 AI and Deep Learning for Signal Detection and Classification in Congested Spectrum
11:06 Synthetic Training Data Generation for AI-Based Signal Recognition and Spectrogram Analysis
14:34 Direction-Finding Algorithms: DOA, TOA, TDOA, and TSOA for Passive Geolocation
17:45 RF Hardware Validation of Direction Finding with USRP X310 and MATLAB
18:56 TDOA and Time-Based Techniques for Passive Emitter Geolocation
22:04 Emitter Geolocation, Multilateration, and GDOP Sensor Placement Optimization
25:08 Emitter Tracking, Data Association, and Passive Ranging Through Maneuvering
28:08 FPGA and Embedded Code Generation for Direction Finding and Tracking Algorithms
30:00 End-to-End EMSO System Architecture and Key Takeaways
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Get a free product trial: goo.gl/ZHFb5u
Learn more about MATLAB: goo.gl/8QV7ZZ
Learn more about Simulink: goo.gl/nqnbLe
See what's new in MATLAB and Simulink: goo.gl/pgGtod
© 2026 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc.
See mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
This technical presentation explains how MATLAB® and Simulink® support the full passive direction-finding and geolocation signal chain for EMSO applications. Starting with electromagnetic signal–environment modeling, the talk demonstrates how to synthesize realistic IQ data at three fidelity levels: the power level for architecture trades and GDOP analysis, the measurement level for tracker design, and the signal level for full-waveform algorithm development.
The presentation covers AI and deep learning for automated signal detection and classification in a congested spectrum, including synthetic training data generation, spectrogram-based classification, and radar parameter estimation using YOLOX networks. These techniques address the labeled data scarcity problem by generating unlimited training examples for signals that have never been intercepted, including waveforms and frequency-agile emitters.
The presentation also thoroughly covers direction-finding techniques, including DOA estimation with six algorithms (MUSIC, MVDR, monopulse, beamscan, IAA, and deep learning), TOA, TDOA, and TSOA. The TDOA section explains why the time difference of arrival is the workhorse of passive geolocation: It requires no emitter cooperation—only GPS-disciplined oscillators synchronizing the receiver network. Explore how to use a USRP™ X310 software-defined radio with live RF hardware to validate direction-finding algorithms. The same MATLAB code can process both simulated and real over-the-air signals without modification. The geolocation section covers multilateration methods, GDOP analysis for optimizing sensor placement, and hybrid techniques combining AOA with TDOA or TSOA. To transform noisy intermittent detections into maintained emitter custody, you can track with Kalman filtering and data association. You can also track the emitter during platform maneuvers.
The talk concludes with the simulation-to-deployment workflow. HDL Coder™ generates synthesizable Verilog and VHDL for FPGA targets. MATLAB Coder™ generates C/C++ code for embedded processors, eliminating manual recoding between algorithm development and fielded systems.
Chapters:
0:00 Passive Direction-Finding and Geolocation with MATLAB and Simulink
1:52 Modeling the EMSO Signal Environment: IQ Data Synthesis and Scenario Generation
5:26 Low Probability of Intercept Radar Simulation for Passive Geolocation
6:33 Bistatic and Multi-Static Passive Radar Simulation and Processing in MATLAB
8:47 AI and Deep Learning for Signal Detection and Classification in Congested Spectrum
11:06 Synthetic Training Data Generation for AI-Based Signal Recognition and Spectrogram Analysis
14:34 Direction-Finding Algorithms: DOA, TOA, TDOA, and TSOA for Passive Geolocation
17:45 RF Hardware Validation of Direction Finding with USRP X310 and MATLAB
18:56 TDOA and Time-Based Techniques for Passive Emitter Geolocation
22:04 Emitter Geolocation, Multilateration, and GDOP Sensor Placement Optimization
25:08 Emitter Tracking, Data Association, and Passive Ranging Through Maneuvering
28:08 FPGA and Embedded Code Generation for Direction Finding and Tracking Algorithms
30:00 End-to-End EMSO System Architecture and Key Takeaways
--------------------------------------------------------------------------------------------------------
Get a free product trial: goo.gl/ZHFb5u
Learn more about MATLAB: goo.gl/8QV7ZZ
Learn more about Simulink: goo.gl/nqnbLe
See what's new in MATLAB and Simulink: goo.gl/pgGtod
© 2026 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc.
See mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
