Radar Swaying Object Filtering via Doppler Sign Spectrum
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Solution Overview
Problem
Existing radar systems struggle to differentiate between moving objects like cars or people and swaying objects such as grass, trees, and poles, which are not of interest, leading to unnecessary processing and potential user distraction.
Innovation Solution
A method and device that analyze range-Doppler maps to identify swaying objects by calculating differences in energy values for positive and negative velocities, determining a frequency spectrum, and filtering out detections based on frequency and velocity thresholds to distinguish swaying objects from other moving objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If radar systems detect all moving objects, then detection coverage is improved, but false detections from swaying objects increase
Solution Approach 1:
The patent transforms the detection problem by changing parameters from raw radar data to processed features including velocity sign distribution, energy values, and frequency spectrum characteristics. By analyzing the frequency spectrum of velocity sign changes and comparing energy values across different velocity intervals, the system identifies swaying objects through their characteristic periodic motion patterns rather than treating all moving objects equally.
Solution Approach 2:
The patent introduces an intermediary processing layer between radar data collection and final detection. This intermediary layer includes range-Doppler map generation, velocity sign analysis, energy value calculation, and frequency spectrum determination. These intermediate processing steps filter out swaying objects before final detection, preventing false alarms while maintaining legitimate detections.
2Object-generated harmful factors
If swaying objects are filtered out, then false detections are reduced, but computational complexity increases
Solution Approach 1:
The patent segments the radar data processing into distinct functional stages: range-Doppler map generation, velocity interval classification, energy value calculation, frequency spectrum determination, and detection filtering. Each stage processes specific features independently, making the complex filtering task manageable and optimized at each step rather than attempting to solve everything simultaneously.
Solution Approach 2:
The patent applies partial processing by focusing computational resources on specific velocity intervals and frequency ranges most likely to contain swaying object signatures. Rather than processing all possible velocity ranges equally, the system identifies and analyzes only the relevant portions of the data where swaying objects are most likely to appear, reducing overall computational burden.
3Productivity
If all detections are processed further, then detection completeness is maintained, but processing time increases
Solution Approach 1:
The patent performs preliminary identification and classification of swaying objects before the main detection and tracking processing. By pre-filtering out objects identified as swaying through frequency spectrum analysis and energy value comparison, the system reduces the number of objects requiring full processing attention, thereby decreasing overall processing time while maintaining detection completeness for non-swaying objects.
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
Effectively identifies and filters out swaying objects, reducing unnecessary processing and clutter in radar surveillance systems, enhancing computational efficiency and user focus on relevant targets.
Implementation Method 1
a problem in prior art is that differentiation is not made between moving objects such as cars or people and objects such as grass, trees, and poles which are swaying due to wind
Data Source
AI summary
A method determines swaying objects for a plurality of range intervals based on range-Doppler maps provided by a radar system. Each range-Doppler map corresponds to a time interval of a sequence of time intervals and comprises a respective energy value for a plurality of velocity intervals for each range interval. A sequence of differences is calculated for the time intervals. For each time interval, a difference between a statistical measure of energy values for a set of velocity intervals with positive velocities for the range interval and the statistical measure of energy values for a set of velocity intervals with negative velocities for the range interval is calculated. A frequency spectrum is then determined. On condition that there is a peak in the frequency spectrum for a frequency above a frequency threshold, it is determined that there are one or more swaying objects at the range interval.