Reactive Jammer Detection via Bayesian Signal History Matching
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Solution Overview
Problem
Current methods for detecting and characterizing reactive jamming attacks in wireless communications are inaccurate and prone to errors, particularly when using blind estimation techniques, and are not effective in identifying channel-aware jamming strategies.
Innovation Solution
A system and method that channelizes signals of interest and interferer signals, uses Bayesian thresholds to identify frequency support, compares detection map histories, and determines a percent match to reliably detect reactive jamming, estimating the jammer's listening interval and distinguishing between reactive and anticipatory jamming behaviors, while being insensitive to jammer modulation and signal type.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If blind estimation methods are used to detect reactive jamming, then the detection process is simple to implement, but the accuracy and reliability of detection deteriorates
Solution Approach 1:
The patent segments the detection process into multiple stages: channelization of signals, identification of frequency support using Bayesian thresholds, comparison of detection map histories, and calculation of percent match. This segmentation allows each stage to be optimized independently, improving overall reliability while maintaining implementation clarity.
Solution Approach 2:
The patent introduces an intermediary detection map history comparison mechanism that mediates between the raw signal processing and the final detection decision. By comparing detection map histories and calculating percent match, the system achieves more accurate detection without requiring complex direct analysis methods.
2Reliability
If reactive jamming detection requires analysis of signal interactions and correlations, then detection reliability improves, but the complexity of the detection system increases
Solution Approach 1:
The patent segments the complex signal analysis into discrete manageable steps: channelization, Bayesian threshold application, detection map generation, history comparison, and percent match calculation. This segmentation reduces the perceived complexity by breaking down the analysis process into clear, sequential operations.
Solution Approach 2:
The patent transforms the detection problem by changing parameters - using Bayesian thresholds instead of direct correlation analysis, and using detection map histories instead of raw signal interactions. This parameter transformation simplifies the implementation while maintaining detection reliability.
3Measurement precision
If the system analyzes jammer behavior to determine listening interval and reaction delay, then characterization accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary channelization and detection map generation during normal signal processing, so that when jamming detection is needed, the data is already prepared and ready for rapid analysis. This preliminary action reduces the processing time required for jammer characterization.
Solution Approach 2:
The patent analyzes only the necessary portions of the signal - specifically the frequency support and detection map histories relevant to jamming detection - rather than processing the entire signal in detail. This partial action achieves sufficient characterization precision without excessive processing time.
Data Source
AI summary
A method and system of reliably detecting a reactive jamming attack and estimating the jammer's listening interval for exploitation by a communication system comprises channelizing one or more signals of interest (SOI), channelizing one or more signals of unknown origin (SUO), identifying frequency support patterns for the SOI and SUO using Bayes thresholds, comparing SOI and SUO detection map histories, and determining a percent match, where a match percentage above a specified minimum indicates a reactive attack. Edge detection can be used to enhance jammer support. Embodiments further detect reactive jammer adaptation to changes in the SOI's frequency support. Embodiments include detectors that are insensitive to jammer modulation and/or signal type. A jammer reaction delay and/or size and periodicity of receive window can be detected. Embodiments determine if a jammer is copying and retransmitting the SOI's waveform(s), and/or if the jammer is anticipatory.