Anomalous Object Detection Using Movement Parameter Feedback
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Solution Overview
Problem
Existing radar-based systems for detecting anomalous objects in an area of interest often face challenges in confirming the presence of such objects due to uncertain probability signals, which may not be high enough to trigger alarms but not low enough to ignore.
Innovation Solution
A system comprising sensors configured to detect movement, a computing device that receives and compares movement parameters from both controlled objects and detected movements, and the ability to adjust the movement parameters of controlled objects to enhance detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If radar-based sensors are used to detect movement in the area of interest, then the ability to detect potential anomalous objects is improved, but the reliability of confirmation is worsened due to uncertain probability signals
Solution Approach 1:
The system uses controlled objects with known movement parameters as feedback references. The computing device compares movement parameters from sensors against expected parameters from controlled objects, using this feedback loop to distinguish genuine anomalous objects from false detections and improve confirmation reliability
Solution Approach 2:
Controlled objects serve as intermediary elements between the radar sensors and the detection algorithm. These objects with predictable movement patterns act as mediators that help calibrate and validate the detection system, providing a reference framework for interpreting sensor data and reducing uncertainty
2Productivity
If the system generates alarms for potential anomalous objects, then the responsiveness to possible threats is improved, but the false positive rate worsens due to uncertain probability signals
Solution Approach 1:
The comparison mechanism provides feedback validation before alarm generation. By continuously comparing sensor-detected movement parameters against expected parameters from controlled objects, the system only triggers alarms when significant deviations are confirmed, reducing false positives while maintaining responsiveness to genuine threats
Solution Approach 2:
The system performs preliminary comparison and validation actions before generating alarms. Movement parameters are pre-computed for controlled objects, and sensor data is preliminarily filtered and compared against these expectations before triggering alarm conditions, ensuring that only confirmed anomalies generate false positives
3Measurement precision
If multiple movement parameters are monitored and compared, then the accuracy of anomalous object detection is improved, but the system complexity worsens
Solution Approach 1:
The computing device performs multiple functions using a unified comparison mechanism: it tracks controlled objects, extracts movement parameters from sensor data, compares parameters against expectations, and generates alarms. This multi-functional approach improves detection accuracy without proportionally increasing system complexity by consolidating operations into a single processing unit
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
This approach allows for more accurate detection of anomalous objects by comparing movement parameters and adjusting controlled object movements, thereby reducing false positives and improving the reliability of detection systems.
Implementation Method 1
radar units include any one or a combination of Doppler, frequency modulated continuous wave (FMCW), ultra-wide band (UWB) and pulse radar units
Implementation Method 2
If the object has a velocity relative to the radar unit, the frequency spectrum of reflected EM pulses may be shifted by the Doppler effect, relative the set of emitted EM pulses. The Doppler effect may be used to determine the relative velocity of the object
Data Source
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AI summary
A system (100) and method (400) for a detection of anomalous objects (194-1... 194-N) in an area of interest, AOI, (150) is disclosed. The method (400) includes receiving (402), by a computing device (200), from a control unit (182) associated with one or more objects (192-1... 192-N), a first set of movement parameters associated with movement of the one or more objects (192-1... 192-N) in the AOI (150). The method (400) further includes extracting (404), by the computing device (200), from output signals generated by the one or more sensors (102, 104), a second set of movement parameters associated with movements in the AOI (150). The method (400) further includes comparing (406), by the computing device (200), the first and second sets of movement parameters and determining (408), by the computing device (200), presence of one or more anomalous objects (194-1... 194-N) in the AOI (150) based on the comparison of the first and second movement parameters.