Through-the-obstacle Radar Motion Detection via Histogram Analysis
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Solution Overview
Problem
Through-the-obstacle radar systems for ISR applications face challenges in effectively recording and processing signals over time to generate informative patterns for motion detection and scene imaging, often resulting in inaccurate or incomplete data analysis.
Innovation Solution
A through-the-obstacle radar system with a recording unit and processor that collects and processes signals over a substantial monitoring period to generate patterns, such as histograms and two-dimensional activity maps, which can be compared to normative patterns to detect unusual activity and calculate motion probabilities, facilitating improved motion detection and scene understanding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If signals are collected and processed over a substantial monitoring period to generate informative patterns, then measurement precision and reliability of motion detection are improved, but loss of time and productivity are worsened due to extended monitoring requirements
Solution Approach 1:
The system performs preliminary signal collection and storage during a substantial monitoring period before pattern generation. Raw radar signals are accumulated in a recording unit during phase one, then processed in phase two to generate activity patterns. This preliminary action allows the system to build up sufficient data for accurate pattern recognition without requiring continuous real-time processing, thereby improving measurement precision while managing time loss through structured phased operation.
Solution Approach 2:
The monitoring and processing operation is divided into distinct phases: signal collection phase where raw signals are recorded over time, and pattern generation phase where processed signals are analyzed to create activity patterns. This segmentation allows the system to separate data accumulation from data analysis, enabling accurate motion detection through extended monitoring while organizing the time-consuming process into manageable stages that can be optimized independently.
2Reliability
If signals are collected and processed over a substantial monitoring period to generate informative patterns, then reliability of ISR applications is improved, but productivity is worsened due to extended data collection requirements
Solution Approach 1:
The system performs preliminary signal collection and storage during a substantial monitoring period before pattern generation. Raw radar signals are accumulated in a recording unit during phase one, then processed in phase two to generate activity patterns. This preliminary action allows the system to build up sufficient data for accurate pattern recognition without requiring continuous real-time processing, thereby improving measurement precision while managing time loss through structured phased operation.
Solution Approach 2:
The monitoring and processing operation is divided into distinct phases: signal collection phase where raw signals are recorded over time, and pattern generation phase where processed signals are analyzed to create activity patterns. This segmentation allows the system to separate data accumulation from data analysis, enabling accurate motion detection through extended monitoring while organizing the time-consuming process into manageable stages that can be optimized independently.
3Measurement precision
If complex signal processing is performed to generate activity patterns and detect unusual behavior, then measurement precision is improved, but device complexity is worsened
Solution Approach 1:
The signal processing is divided into distinct operational phases: first phase collects and stores raw radar signals in a recording unit without immediate processing, second phase processes the stored signals to generate activity patterns, and third phase compares patterns against normative data to detect unusual behavior. This temporal and functional segmentation reduces instantaneous processing complexity while maintaining overall measurement precision through systematic multi-stage analysis.
Solution Approach 2:
The system performs preliminary signal collection and storage during a substantial monitoring period before pattern generation. Raw radar signals are accumulated in a recording unit during phase one, then processed in phase two to generate activity patterns. This preliminary action allows the system to build up sufficient data for accurate pattern recognition without requiring continuous real-time processing, thereby improving measurement precision while managing time loss through structured phased operation.
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 enables the generation of valuable information on building layouts, behavior patterns, and activity levels, reducing false alarms and enhancing the effectiveness of ISR applications by providing real-time and near-real-time data analysis.
Implementation Method 1
Through-the-obstacle radar systems enable gathering information through obstacles such as walls, doors, ground, smoke, vegetation and other visually obstructing substances
Data Source
AI summary
There are provided a through-the-obstacle radar system and method of operating thereof comprising recording signals and/or derivatives thereof collected during a certain substantial monitoring period, and using the recorded information for generating patterns informative of a monitoring scene. There are further provided a method of motion detection based on through-the-obstacle radar and the system thereof. The method comprises collecting signals and/or derivatives thereof acquired by the radar system during a certain substantial monitoring period and accommodating respective records, said records comprising information characterizing the signals and/or derivatives thereof and information indicative, at least, of the time the signals were obtained; processing the accommodated records and generating at least one histogram characterizing a normative motion level at different time intervals; comparing an actual motion level with the level in the normative histogram corresponding to the same time intervals; and recording the motion as detected if its actual level fits a certain relationship with the corresponding level in the normative histogram.