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Journals / EMO Bilimsel Dergi / 2026 / Cilt: 16 Sayı: 3

Adaptive Signal Source Estimation Using Eigenvalue Analysis and Optimized Dynamic Thresholding

Uyarlamalı Sinyal Kaynağı Tahmini: Özdeğer Analizi ve Optimize Edilmiş Dinamik Eşikleme Yaklaşımı

Gökhan Yüksek(BATMAN ÜNİVERSİTESİ)0000-0002-6832-8622
Timur Lale(BATMAN ÜNİVERSİTESİ)0000-0002-6958-5057
Pages
49–60
Publication Date
2026-09-30
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Results show that all methods perform well under low-noise conditions, but the proposed method excels when noise is high or sources are closely located.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study presents a method for identifying signal sources using adaptive thresholding, crucial in radar, wireless communication, and sensor networks.
📊
Key Findings & Data:Three approaches—Minimum Description Length (MDL), Akaike Information Criterion (AIC), and a novel eigenvalue-based adaptive thresholding—are compared through simulations on ultra-wideband (UWB) sensor arrays designed for noisy, low-range conditions with closely spaced sources.
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Conclusion & Impact:Results show that all methods perform well under low-noise conditions, but the proposed method excels when noise is high or sources are closely located.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Uyarlamalı Eşikleme · Kaynak Tahmini · Özdeğer Analizi

Abstract

This study presents a method for identifying signal sources using adaptive thresholding, crucial in radar, wireless communication, and sensor networks. Three approaches—Minimum Description Length (MDL), Akaike Information Criterion (AIC), and a novel eigenvalue-based adaptive thresholding—are compared through simulations on ultra-wideband (UWB) sensor arrays designed for noisy, low-range conditions with closely spaced sources. MDL and AIC employ covariance matrix eigenvalues to balance model complexity and data fit, while the proposed adaptive thresholding dynamically adjusts the noise floor using eigenvalue distribution, improving robustness in high-noise scenarios. Results show that all methods perform well under low-noise conditions, but the proposed method excels when noise is high or sources are closely located. Additionally, sensitivity parameters in the threshold function are optimized via a lightweight search, providing stable performance across varying noise and source configurations.

Özet

Bu çalışma, radar, kablosuz iletişim ve sensör ağlarında çok önemli olan uyarlanabilir eşiklemeyi kullanarak sinyal kaynaklarını tanımlama yöntemini sunmaktadır. Minimum Tanım Uzunluğu (MDL), Akaike Bilgi Kriteri (AIC) ve yeni bir özdeğer tabanlı uyarlanabilir eşikleme olmak üzere üç yaklaşım, gürültülü, düşük menzilli koşullar ve birbirine yakın kaynaklar için tasarlanmış ultra geniş bant (UWB) sensör dizileri üzerinde simülasyonlar yoluyla karşılaştırılmıştır. MDL ve AIC, model karmaşıklığı ve veri uyumunu dengelemek için kovaryans matrisi özdeğerlerini kullanırken, önerilen uyarlanabilir eşikleme, özdeğer dağılımını kullanarak gürültü tabanını dinamik olarak ayarlar ve yüksek gürültülü senaryolarda sağlamlığı artırır. Sonuçlar, tüm yöntemlerin düşük gürültülü koşullarda iyi performans gösterdiğini, ancak önerilen yöntemin gürültünün yüksek olduğu veya kaynakların birbirine yakın olduğu durumlarda daha üstün olduğunu göstermektedir. Ek olarak, eşik fonksiyonundaki duyarlılık parametreleri, hafif bir arama yoluyla optimize edilerek, değişen gürültü ve kaynak yapılandırmaları arasında istikrarlı bir performans sağlar.

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Cite This Article

Yüksek, G., Lale, T. (2026). Adaptive Signal Source Estimation Using Eigenvalue Analysis and Optimized Dynamic Thresholding. EMO Bilimsel Dergi, Cilt: 16 Sayı: 3, 49–60. https://ajindex.com/articles/1390243

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