Adaptive Threshold Millimeter Wave Radar Target Position Estimation
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Solution Overview
Problem
Existing methods for target object position estimation, such as those using FMCW radar and cameras, face challenges in accurately determining the position of targets that change their position between processing cycles, leading to noise interference and inefficient peak extraction.
Innovation Solution
A method employing millimeter wave radar that performs FFT and Doppler FFT on digital signals to generate intensity distributions, predicts target types using image data, and adjusts threshold values based on predicted target types to accurately estimate target positions, sizes, orientations, and velocities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a fixed threshold value is used for peak extraction in target detection, then the device complexity is reduced, but the measurement precision of target position deteriorates when target position changes between processing cycles
Solution Approach 1:
The patent applies dynamics by making the threshold value adaptive rather than fixed. The threshold is dynamically adjusted based on the predicted position of the target object. When the target position changes between processing cycles, the threshold automatically adapts to the new position, maintaining high measurement precision without increasing device complexity.
Solution Approach 2:
The patent changes the parameter of threshold value from a static fixed value to a dynamic value that varies according to predicted target position. This parameter change allows the system to maintain optimal detection sensitivity across different target positions without requiring complex device modifications.
2Measurement precision
If the threshold value is adjusted based on predicted target position, then the measurement precision of target position is improved, but the device complexity increases due to additional prediction processing
Solution Approach 1:
The patent applies preliminary action by performing target position prediction in advance of the actual detection process. The predicted position from the previous processing cycle is used to set the threshold for the current cycle, allowing the system to prepare optimal detection parameters before actual measurement occurs.
Solution Approach 2:
The patent implements feedback by using the predicted target position information from previous cycles to inform and adjust the threshold setting in current cycles. This feedback mechanism creates a closed-loop system where detection results continuously improve based on prior information without requiring complex additional processing.
3Reliability
If noise interference is reduced through multiple processing cycles, then the reliability of target detection is improved, but the loss of time increases due to repeated processing
Solution Approach 1:
The patent applies preliminary action by using prediction results from previous processing cycles to pre-set optimal thresholds for current detection. This allows noise reduction and reliable target detection to be achieved more efficiently, reducing the number of repeated processing cycles needed and thereby reducing time loss.
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 enhances the accuracy of target position estimation by reducing noise interference and improving peak extraction, enabling precise detection of target characteristics regardless of position changes.
Implementation Method 1
reception signals obtained based on reception of reflected waves of modulated transmission waves transmitted from a millimeter wave radar
Implementation Method 2
obtaining an intensity distribution representing an intensity on a cell of a combination of a distance and a velocity
Implementation Method 3
obtaining frequency distributions each representing a frequency and an intensity for at least two successive transmission waves by performing FFT
Data Source
AI summary
A target object position estimation method includes: obtaining frequency distributions representing frequency and intensity for two successive transmission waves; obtaining an intensity distribution representing an intensity on a cell of a combination of a distance relative to a position of a millimeter wave radar and a velocity; generating at least one ranging point by extracting at least one cell having a greater intensity than a predetermined threshold value in the intensity distribution; predicting a type of the target object to which the ranging point belongs; setting a change threshold value in a target area; generating a plurality of ranging points representing positions relative to the position of the millimeter wave radar based on a plurality of cells extracted from the target area and representing greater intensities than the change threshold value; and estimating the position of the target object relative to the position of the millimeter wave radar.