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Detection of Multipath Propagation Interference in Pulsed Radar Signals in a Non-Coherent Receiver with Convolutional Neural Network Models
KTH, School of Electrical Engineering and Computer Science (EECS).
2025 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Faltande neurala nätverksmodeller för upptäckt av flervägsutbredningsinterferens i pulsade radarsignaler i en icke-koherent mottagare (Swedish)
Abstract [en]

This thesis researches if convolutional neural network models can be used to detect and classify multipath signal propagation in radar signals. Specifically, a convolutional neural network model is used to detect multipath propagation in a simulated frequency modulated pulsed radar signal. A combined determin- istic and stochastic signal propagation software model is used to generate a labeled data set that emulates pulses detected by radar warning system located on a vessel in a marine environment. The simulated data set is created and quantified with MATLAB and is represented in the form of in- phase and quadrature signals. The model is trained and evaluated on data that represent no multipath signal propagation, multipath signal propagation, and non-multipath interference (two separate emitters that interfere without multipath) in two separate scenarios for a wide range of signal-to-noise ratios. The machine learning model has an average accuracy of 91.78% on the test set and produces robust results for high signal-to-noise environments for the multipath classes.

Abstract [sv]

Denna masteruppsats undersöker om faltande neurala nätverksmodeller kan användas för att upptäcka och klassificera flervägsutbredning i radarsignaler. Ett neuralt nätverk med faltande filter används för att upptäcka flervägsutbredn- ing i en simulerad, pulsad, frekvensmodulerad radarsignal. En kombinerad deterministisk och stokastisk signalutbredningsmodell används för att genere- ra en märkt datamängd som emulerar pulser upptäckta av ett radarvarnarsystem placerat på en farkost i en marin miljö. Det simulerade datamängden är skapad med hjälp av MATLAB och representeras i kvadraturamplitudmodellering. Modellen är tränad och utvärderad på data som representerar fallen ingen flervägsutbredning, flervägsutbredning samt falsk flervägsutbredning (två sepa- rata radarkällor som interfererar utan flervägsutbredning) för en mängd signal- brusförhållanden i två olika scenarier. Maskininlärningsmodellen har en genomsnittlig prestanda på 91.78% och är robust även i svåra brusförhållanden för flervägsutbredningsklasserna.

Place, publisher, year, edition, pages
2025. , p. 82
Series
TRITA-EECS-EX ; 2025:46
Keywords [en]
Multipath Signal Processing, Multipath Detection, Machine Learning for Signal Processing, Convolutional Neural Networks, Electronic Warfare
Keywords [sv]
Signalbehandling vid flervägsutbredning, upptäckt av flervägsutbredning, maskininlärning för signalbehandling, faltande neurala nätverk, telekrig
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-361853OAI: oai:DiVA.org:kth-361853DiVA, id: diva2:1948997
External cooperation
Saab AB
Supervisors
Examiners
Available from: 2025-04-07 Created: 2025-04-01 Last updated: 2025-04-07Bibliographically approved

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CiteExportLink to record
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