Satellite Navigation Deception Detection via Statistical Classifier
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Solution Overview
Problem
Satellite navigation systems (SNS) are vulnerable to deception schemes that cause receivers to calculate erroneous locations, and existing detection methods are unreliable, often requiring external observations of inconsistent position, heading, or speed.
Innovation Solution
A deception detector using a non-linear statistical classifier processes receiver measurement data, such as position, gain, carrier-to-noise ratio, Doppler shift, and pseudorange, to detect deviations from a statistically normal operating state, allowing the receiver to identify and reduce the effects of deceptive signals without requiring external data or a stable clock.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If external observations are used to detect deceptive signals, then detection reliability improves, but device complexity increases
Solution Approach 1:
The system uses the receiver's own measurement data (pseudorange, carrier phase, Doppler) to detect deception, eliminating the need for external observation systems. The statistical classifier processes internally available data to determine whether deceptive signals are present, making the system self-sufficient and reducing overall complexity.
Solution Approach 2:
A statistical classifier is introduced as an intermediary component that processes receiver measurement data and determines the presence of deception. This classifier acts as a mediator between the raw measurement data and the final detection decision, improving reliability through statistical analysis without requiring complex external systems.
2Measurement precision
If a stable clock is required for deception detection, then measurement precision improves, but ease of operation deteriorates
Solution Approach 1:
The system uses the receiver's internal clock and its associated errors consistently across all measurements. By forming statistical relationships among measurements taken with the same imperfect clock, the system eliminates the need for external stable time references while maintaining detection accuracy.
Solution Approach 2:
The system changes the approach from requiring absolute time precision to using relative time relationships. By analyzing statistical patterns in measurement data that are invariant to clock errors, the system achieves deception detection without needing to correct for or eliminate clock instability.
3Reliability
If multiple measurement types are processed, then detection reliability improves, but device complexity increases
Solution Approach 1:
The statistical classifier is designed to process multiple types of receiver measurements (pseudorange, carrier phase, Doppler) using a unified approach. This multi-functional classifier can handle different measurement types simultaneously, improving detection reliability through diverse data inputs without requiring separate processing systems for each measurement type.
Data Source
AI summary
A method and device for detecting the presence of a deception scheme intended to thwart a satellite navigation system (SNS). A detector receives “receiver measurements” of various types from the SNS receiver. It conditions these values, so that they may be readily used by a process that determines whether the receiver is operating in a statistically normal state.