Overlapping Radar Nodes for Small Object Detection
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Solution Overview
Problem
Existing surveillance systems struggle to reliably detect small objects entering secured areas due to their small radar cross-section and are hindered by perimeter fence topography and environmental obstructions, making it difficult to differentiate between contraband and natural clutter.
Innovation Solution
A network of radar nodes transmitting overlapping radar beams with distinct frequencies, using Doppler effect analysis and machine learning to classify moving objects, allowing for the detection and identification of small objects with low radar cross-sections across a secured perimeter.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Length of stationary object
If existing radar systems use high power to detect targets at long range, then detection range is improved, but the ability to detect small objects with low radar cross-section deteriorates
Solution Approach 1:
The system divides the detection task into multiple radar nodes, each covering a specific sector. Each node transmits radar beams in different directions and processes returns independently, then combines results to achieve comprehensive coverage with optimized detection for small objects in each zone
Solution Approach 2:
Each radar node is configured with specific beam patterns and power levels optimized for its local detection zone. The system adjusts transmission parameters locally at each node to maximize detection of small objects in its coverage area while maintaining overall system coordination
2Area of stationary object
If single scanning radar units use high power for long range detection, then coverage area is improved, but detection reliability of small objects deteriorates
Solution Approach 1:
The surveillance area is divided into multiple zones covered by different radar nodes. Each node provides focused coverage of its assigned area with optimized parameters, and the central processor integrates data from all nodes to achieve reliable detection across the entire coverage area
Solution Approach 2:
The system combines detection data from multiple radar nodes to achieve comprehensive coverage. By merging the detection capabilities of multiple nodes with overlapping beams, the system achieves both wide coverage area and high detection reliability through data fusion and correlation analysis
3Area of stationary object
If multiple unit surveillance systems transmit signals to form trip wires, then detection coverage is improved, but system complexity increases
Solution Approach 1:
Each radar node is designed as a multi-functional unit that can transmit multiple beam patterns, detect various object types, and operate in different modes. This universal design allows the system to achieve comprehensive coverage and detection capabilities while reducing the number of specialized components needed
Solution Approach 2:
The system dynamically adjusts radar transmission parameters such as frequency, power, and beam direction based on detection needs and environmental conditions. This parameter optimization allows the radar nodes to achieve effective coverage with reduced complexity by adapting to different scenarios rather than requiring multiple fixed-configuration units
4Shape
If radar systems are designed for slow moving large targets, then detection of high RCS targets is improved, but detection of small objects with low RCS deteriorates
Solution Approach 1:
The radar system adjusts transmission frequency, pulse duration, and signal processing parameters to optimize detection sensitivity for small objects. By changing operational parameters such as using higher frequencies and longer integration times, the system achieves detection capability for low RCS targets while maintaining coordination with nodes detecting larger targets
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 and classifies small moving objects, including those with low radar cross-sections, by processing Doppler characteristics and machine learning algorithms, enhancing security by reducing false positives and improving detection accuracy in complex environments.
Implementation Method 1
a transmitter configured to transmit a radar signal as a beam, and one or more receivers configured to receive a reflected radar signal
Implementation Method 2
process the digitised signal to detect characteristics of any Doppler effects created by the movement of an object through one or more of the radar beams
Data Source
AI summary
Object detection systems and methods are provided. An object detection system comprises a plurality of nodes, each node having a transmitter configured to transmit a radar signal as a beam, and one or more receivers configured to receive a reflected radar signal. The nodes and transmitters are arranged such that the radar beam of one transmitter at least partly overlaps with the radar beam from the transmitter at an adjacent one of the nodes. The object detection system comprises a processor configured to receive a digitised signal from each node, process the digitised signal to detect characteristics of any Doppler effects created by the movement of an object through one or more of the radar beams, compare the Doppler characteristics with Doppler signatures associated with known objects, and thereby classify the object.