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Advanced Distributed Detection Architecture for Sensor Networks with Parallel Fusion Techniques

This project presents a detailed architecture for distributed detection within sensor networks, emphasizing parallel fusion strategies. The proposed system integrates various components, including data acquisition, preprocessing, and multiple classifiers to effectively identify and classify signal types. Utilizing both centralized and decentralized processing methods, our architecture ensures efficient tracking and localization of sources. By implementing advanced feature extraction and calibration processes, we enhance detection accuracy and system robustness. This comprehensive approach targets improved performance in real-world applications.

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Advanced Distributed Detection Architecture for Sensor Networks with Parallel Fusion Techniques

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  1. Distributed Detection with a Sensor Network Sensor Network Source z1 z2 detector 1 detector 2 Parallel Fusion Network Fusion Rule Fusion Center

  2. Source positions Proposed Functional Architecture no Grab data block Preprocess Detector Detection Fusion Signal detected? Grab data block Detector Preprocess yes BSS Feature Extraction Classifier signal types Classification Fusion Feature Extraction Classifier BSS Array Calibration Tracker Track Fusion 2-D Localize Legend Cluster 1 Centralized Processing Cluster 2 Centralized Processing Decentralized Processing Inferencing Products Tracker Array Calibration

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