Distributed SDR Surveillance for RF Signal Analysis

The patent divides the centralized data analysis system into multiple distributed data analysis nodes that operate independently across the network. Each node processes local radio frequency signal data, performing extraction, filtering, and preliminary analysis locally. This segmentation reduces the burden on any single system component and distributes computational load, thereby maintaining high data processing capability while reducing overall system complexity and improving response time through localized decision-making.

The patent transitions from a traditional centralized hierarchical architecture to a distributed mesh network topology, adding spatial distribution as a new dimension to the system architecture. Multiple data analysis nodes are deployed across different geographic locations, each capable of independent operation and local data processing. This dimensional change enables parallel processing of radio frequency signals, improving productivity while reducing the complexity burden on any single node and enabling faster local response times.

The patent implements preliminary data processing and filtering functions directly within the radio frequency receiver hardware before data is transmitted to distributed analysis nodes. The receiver performs initial signal extraction, noise filtering, and feature detection, preparing data in advance for more efficient distributed analysis. This preliminary action reduces the computational burden on distributed nodes, enabling faster processing speeds while keeping individual device complexity manageable by pre-processing data locally.

Each distributed data analysis node operates autonomously, independently processing radio frequency signal data received from local sensors without requiring constant centralized coordination. Nodes perform self-diagnosis, self-configuration, and independent decision-making for local anomalies, reducing communication overhead and enabling faster local processing. This self-service capability improves processing speed while maintaining reasonable device complexity through modular, independent operation.

The patent applies machine learning algorithms selectively rather than uniformly across all data processing tasks. Simple radio frequency signal extraction and filtering use traditional deterministic algorithms, while machine learning is applied only to higher-level pattern recognition and criminal behavior detection where its superior accuracy is needed. This partial application of machine learning reduces overall computational time and resource requirements while maintaining high detection accuracy for critical functions, avoiding the excessive computational burden of applying ML to every processing stage.

The system performs preliminary data preprocessing, feature extraction, and filtering using traditional algorithms before feeding processed data to machine learning models. By preparing clean, pre-processed input data in advance, the machine learning algorithms require fewer iterations and less computational time to achieve accurate criminal behavior detection. This preliminary action significantly reduces the computational time burden while maintaining or improving detection accuracy through better-quality input data.