Sensor Signal Analysis for Intrusion Event Differentiation
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Solution Overview
Problem
Current fence monitoring systems face challenges in differentiating between true intrusion events and false alarms, particularly in distinguishing between an intruder climbing, cutting the fence, and wind-induced disturbances, due to their limited sensitivity and ability to suppress false positives.
Innovation Solution
A method involving a sensor that generates output signals indicative of medium changes, analyzed in both time and frequency domains using algorithms to distinguish between event types based on amplitude, frequency, duration, repetition rate, and presence of a time domain step function, allowing for the suppression of false alarms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the sensor sensitivity is increased to detect all intrusion events, then the detection capability is improved, but the false alarm rate increases
Solution Approach 1:
The frequency spectrum is segmented into multiple bands (e.g., 2-10 Hz, 10-20 Hz, 20-50 Hz, 50-100 Hz, 100-200 Hz, 200-500 Hz, 500-1000 Hz) to analyze different event types separately. Each frequency band corresponds to specific intrusion characteristics, allowing the system to distinguish between climbing, cutting, and wind events by examining the distribution of energy across these segmented frequency ranges
Solution Approach 2:
The system changes the analysis parameter from simple amplitude thresholding to frequency-domain analysis using Fast Fourier Transform (FFT). By transforming the time-domain signal into the frequency domain and analyzing the spectral distribution, the system can identify characteristic frequency signatures of different intrusion types, thereby maintaining high detection sensitivity while reducing false alarms through parameter-based differentiation
2Measurement precision
If the monitoring system analyzes all signal characteristics in detail, then the event differentiation capability is improved, but the processing complexity increases
Solution Approach 1:
The complex signal analysis is segmented into distinct frequency bands, each processed independently through FFT. This segmentation allows the system to focus computational resources on specific frequency ranges associated with particular intrusion types, reducing overall processing complexity while maintaining comprehensive event differentiation capability
Solution Approach 2:
Different frequency bands are assigned different analytical weights and thresholds based on their association with specific event types. For example, lower frequency bands (2-20 Hz) are weighted for climbing detection while higher frequency bands (50-1000 Hz) are weighted for cutting detection. This local quality approach optimizes processing efficiency by applying tailored analysis criteria to each frequency segment rather than uniform processing
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Effectively differentiates between intrusion events and false alarms by analyzing signal characteristics, reducing false positives and accurately identifying the type of intrusion, enhancing security system responsiveness.
Implementation Method 1
This cable is typically optimized for sensitivity to the piezo electric affect, and is monitored by electronics that are intended to detect motion, vibration, and deflection of the sensor wire or cable caused by piezo-electric currents in the cable
Implementation Method 2
Optical monitoring and detection typically requires stringing and fastening an optical cable, that is, a cable containing fiber optic fibers, along the length of the fence. This cable is typically optimized for sensitivity to affecting one of the following optical parameters
Data Source
AI summary
A method of detecting intrusion events including at least two different event types which have different characteristics of frequency and time comprises providing a sensor responsive changes in a medium generated by a potential intrusion event with the sensor generating an output signal indicative of the changes in the medium, analyzing the signal to determine changes in amplitude so as to detect the change in amplitude of the detection signal as a function of time, and performing at least one of: (i) in the frequency domain, carrying out a frequency analysis of the signal from the sensor and dividing the frequency analysis into separate sections which are selected so as to correspond to the characteristic frequencies for each event type, or (ii) the algorithm requiring the presence or absence of a time domain step function.