mmWave Sensor People Count Damping for Indoor Radar
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Solution Overview
Problem
Indoor radar sensors face challenges in accurately detecting the number of people in a space due to intermittent false positive and false negative detections, leading to unreliable raw count values.
Innovation Solution
The system applies damping to the raw count value from the mmWave sensor to reduce volatility and identifies credible objects based on their lifespan and movement, distinguishing between real people and ghost objects, thereby deriving a more reliable people count.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If damping is applied to reduce volatility of people count signal, then reliability of detection is improved, but response time to actual changes decreases
Solution Approach 1:
The damping factor is dynamically adjusted based on the current state of detection. When false positives are identified, damping is increased to filter out noise. When credible objects are detected, damping is reduced to improve responsiveness. This dynamic adjustment allows the system to adapt its filtering strength in real-time, balancing reliability and response time.
Solution Approach 2:
The system changes the damping parameter based on object characteristics such as lifespan and movement patterns. Objects with longer lifespans and normal movement ranges receive less damping, while transient objects receive more damping. This parameter change strategy allows differential treatment of different object types, improving both reliability and response time for different scenarios.
2Reliability
If damping is applied more heavily when people count decreases, then false negatives are reduced, but detection sensitivity decreases
Solution Approach 1:
Different damping levels are applied to different objects based on their local characteristics. Objects exhibiting normal human movement patterns within a predetermined range receive lighter damping, while objects with anomalous characteristics receive heavier damping. This local quality approach allows the system to maintain high sensitivity for credible targets while filtering out false negatives through selective damping.
3Measurement precision
If object lifespan threshold is used to distinguish credible objects from false objects, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system pre-establishes threshold values for object lifespan and movement ranges before detection begins. During operation, detected objects are automatically compared against these pre-set thresholds to determine credibility. This preliminary action approach simplifies the detection process by replacing complex real-time analysis with straightforward threshold comparisons, maintaining precision while reducing algorithmic complexity.
Data Source
AI summary
A computer assisted method for processing output from a mmWave sensor to derive a more reliable count of people in a room, zone or space being monitored by the sensor. In some examples, damping is applied to a varying “people count” signal from the sensor. The damping reduces volatility of the people count and avoids counting anomalous false positive detections. When the people count value decreases, damping may be applied more heavily to disregard intermittent false negatives where the sensor momentarily fails to detect an actual person. In some examples, the mmWave sensor provides point clouds representing the approximate shape and location of detected apparent objects, some of which may be people. Some example methods define digital targets corresponding to the point clouds. The targets are deemed to represent real people if the objects and their corresponding targets have sufficient lifespan and exhibit movement within a predetermined normal range.