Object Tracking Device Velocity Estimation via Adaptive Folding Range Segmentation
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Solution Overview
Problem
Existing radar systems face ambiguity in velocity detection due to phase folding, leading to increased computational load and potential failure in tracking objects when the range of foldings is expanded or narrowed, affecting estimation accuracy.
Innovation Solution
An object tracking device with a signal acquirer, detector, connection determiner, candidate generator, and velocity determiner is configured to set an appropriate range of foldings based on observation angles, calculating velocity estimates and likelihoods to improve estimation accuracy while reducing computational load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the range of foldings is expanded to improve velocity detection coverage, then the likelihood of capturing the actual velocity increases, but the computational load increases significantly
Solution Approach 1:
The patent segments the velocity detection process by dividing the range of foldings into multiple subsets based on observation angles. Instead of processing all foldings uniformly, the system divides them into first and second ranges, processes them separately, and combines results. This segmentation reduces the computational complexity of handling the full range while maintaining detection coverage.
Solution Approach 2:
The patent dynamically adjusts the range of foldings based on observation angles. The system determines different first and second ranges of foldings according to the specific observation angle, making the processing scope adaptive rather than fixed. This dynamic adjustment optimizes computational resources by processing only relevant folding ranges for each detection scenario.
2Productivity
If the range of foldings is narrowed to reduce computational load, then processing speed improves, but velocity estimation accuracy deteriorates
Solution Approach 1:
The patent segments velocity estimates into multiple candidates corresponding to different folding ranges. By generating multiple velocity estimate candidates from divided folding ranges and selecting the most likely one, the system maintains accuracy while reducing the computational burden of processing the entire folding range at once.
Solution Approach 2:
The patent processes folding ranges in partial steps rather than all at once. It divides the full range into subsets, processes each subset to generate velocity estimates, and combines results. This partial processing approach reduces computational load while still achieving accurate velocity estimation through multiple candidate evaluation.
3Reliability
If multiple velocity estimates are calculated to resolve phase folding ambiguity, then velocity detection reliability improves, but computational complexity increases
Solution Approach 1:
The patent segments the calculation of velocity estimates by dividing folding ranges into subsets. Instead of calculating all possible velocity estimates from the full folding range simultaneously, it processes divided ranges separately, generating multiple candidates that are then evaluated and combined, reducing overall computational complexity.
Solution Approach 2:
The patent dynamically determines which velocity estimate candidates to pursue based on observation angles and folding range divisions. The system adaptively selects and processes candidates from different folding ranges, optimizing computational resources while maintaining reliable velocity detection through multiple candidate evaluation.
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 solution effectively suppresses the computational load and enhances velocity estimation accuracy by setting an appropriate range of foldings based on observation angles, ensuring accurate tracking of objects relative to a moving vehicle.
Implementation Method 1
a radar device that is mounted to a moving object and uses a modulation scheme for detecting a velocity from a phase change of the signal
Implementation Method 2
measures a distance to a target and a velocity of the target by using chirp signals whose frequencies continuously increase or decrease as radar signals and applying a two-dimensional FFT to a beat signal generated from transmitted and received chirp signals
Implementation Method 3
chirp signals whose frequencies continuously increase or decrease as radar signals
Data Source
AI summary
In an object tracking device, a candidate generator is configured to, given P=Kmax−Kmin+1 that defines a range of foldings of velocity by phase rotation from Kminth to Kmaxth foldings, calculate P velocity estimates for each of initial observation points. The candidate generator sets the number of foldings Kmin and the number of foldings Kmax such that Kmin<0 and |Kmin|>|Kmax| when an absolute value of an observation angle representing a direction of the observation point is equal to or less than a first threshold value, and Kmax>0 and |Kmin|<|Kmax| when the absolute value of the observation angle is greater than a second threshold. A velocity determiner is configured to, for each set of candidate targets, select one of the candidate targets belonging to the set of candidate targets, thereby determining the velocity of a target associated with the initial observation point.