Modulated Radar Sensory Network for Object Tracking
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Solution Overview
Problem
Current radar systems face challenges in accurately detecting and tracking multiple closely spaced moving objects, especially in noisy conditions, due to high false positive and false negative errors, and low signal-to-noise ratios, which are exacerbated by distance and power limitations.
Innovation Solution
A sensory network system using modulated radar with wireless transmission, combined with acoustic and vibration sensors, and a computer processing system for data analysis, to detect and track objects over a wide area, minimizing false positives and negatives through multi-modal data integration and intelligent decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If radar is used to scan the region and generate discrete images, then tracking capability is provided, but false positives and false negatives increase due to noise and low signal-to-noise ratio
Solution Approach 1:
The patent combines multiple sensor types (radar, acoustic, vibration, infrared) into an integrated sensory network. By merging different sensing modalities, the system cross-validates detections and reduces false positives/negatives through multi-modal confirmation, directly addressing the reliability-precision contradiction in noisy conditions.
Solution Approach 2:
The system implements feedback mechanisms where detection results from multiple sensors are continuously analyzed and used to adjust tracking algorithms. The computer processing system uses feedback from the sensory network to refine object identification and reduce errors, improving both reliability and measurement precision over time.
2Measurement precision
If radar power is increased to improve signal-to-noise ratio, then detection accuracy improves, but energy consumption increases
Solution Approach 1:
The patent combines multiple low-power sensor types (acoustic, vibration, infrared) with radar to create a sensory network. This merging allows the system to achieve better overall detection accuracy without relying solely on high-power radar, thus improving signal-to-noise ratio while controlling energy consumption through distributed sensing.
Solution Approach 2:
The sensory network nodes are designed with multi-functionality, using a single platform to deploy multiple sensor types (radar, acoustic, vibration, infrared). This universal approach allows the system to leverage different sensing modalities for complementary detection, reducing the need for high-power radar operation and thereby lowering energy consumption while maintaining detection precision.
3Reliability
If multiple sensors are integrated to reduce false positives and negatives, then detection reliability improves, but system complexity increases
Solution Approach 1:
The patent segments the sensory network into distributed nodes, each containing a subset of sensors (radar, acoustic, vibration, infrared). This segmentation allows the system to achieve high detection reliability through multi-sensor integration while managing complexity by distributing functions across multiple independent units rather than concentrating all sensors in a single complex system.
Solution Approach 2:
Each sensory network node is designed to be self-contained with local processing capabilities. The nodes autonomously perform initial data processing and filtering before transmitting information to the base station, reducing the processing burden on central systems and thereby managing overall system complexity while maintaining high detection reliability through distributed intelligence.
4Reliability
If radar scans continuously to track multiple objects, then tracking coverage is improved, but false positives increase due to noise accumulation
Solution Approach 1:
The patent merges data from multiple sensor modalities (radar, acoustic, vibration, infrared) to achieve comprehensive tracking coverage. By combining independent detection channels, the system maintains wide coverage while reducing false positives through cross-validation, as an object must be detected by multiple sensor types to be confirmed, thereby resolving the contradiction between coverage and false positive rate.
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
The system effectively detects, identifies, and tracks objects with improved accuracy and reliability, even in noisy conditions, by integrating radar with complementary sensors and advanced data processing, reducing false positives and negatives and enhancing classification accuracy.
Implementation Method 1
providing a sensory network having at least one sensory device using modulated radar for detecting an object
Implementation Method 2
the at least one sensory device may further include a Gunn diode oscillator transmitting a continuous radar wave
Data Source
AI summary
A system for detecting object movement including a sensory network having at least one sensory device using modulated radar for detecting an object in proximity to the sensory network. The sensory network including wireless transmission means and a base station having computer processing means located remote from the sensory network and including wireless transmission means to communicate with the sensory network. The base station having computer readable program code means for causing the computer processing means to analyze data received from the sensory network to determine motion characteristics of the object in proximity to the sensory network.