AI Radial Velocity Interval Selection for Radar Ambiguity
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Solution Overview
Problem
Radar systems face limitations in accurately determining radial velocities due to ambiguities and trade-offs between velocity resolution and unambiguous velocity intervals, leading to discrepancies in measured velocities.
Innovation Solution
A computer-implemented method using an AI engine, such as a deep learning model, to map measured radial velocities to multiple intervals and determine probability values based on supplemental data like reflection angle and environmental context, allowing for precise selection of the actual radial velocity interval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the sampling frequency and chirp duration are increased to improve velocity resolution, then the maximum unambiguous velocity interval decreases
Solution Approach 1:
The patent segments the velocity measurement problem into multiple intervals. Instead of attempting to measure all velocities unambiguously in a single wide interval, the method divides the velocity space into multiple smaller intervals, each with its own unambiguous range. This allows the system to achieve high velocity resolution in each interval while maintaining reliability through the segmented approach.
Solution Approach 2:
The patent introduces an additional dimension to resolve velocity ambiguities by utilizing range information. When multiple velocity intervals are possible for a target, the system uses range data to determine which interval is correct, effectively adding a spatial dimension to the velocity measurement problem to disambiguate between multiple possible velocity values.
2Reliability
If the maximum unambiguous velocity interval is increased to cover higher velocities, then the velocity resolution decreases
Solution Approach 1:
The patent segments the velocity measurement problem into multiple intervals. Instead of attempting to measure all velocities unambiguously in a single wide interval, the method divides the velocity space into multiple smaller intervals, each with its own unambiguous range. This allows the system to achieve high velocity resolution in each interval while maintaining reliability through the segmented approach.
3Measurement precision
If system parameters are modified to optimize velocity resolution, then the overall radar system performance cannot be optimized simultaneously
Solution Approach 1:
The patent implements a dynamic approach to velocity measurement by selecting different velocity intervals based on the specific measurement scenario. Rather than being constrained by fixed system parameters, the system can adaptively choose appropriate intervals and use AI-based estimation to optimize performance for different target types and environmental conditions, making the overall system more versatile.
Solution Approach 2:
The patent changes the approach from modifying fixed system parameters to dynamically selecting and combining multiple velocity intervals through AI-based estimation. This allows the system to optimize performance by adapting parameter selection rather than being constrained by static parameter configurations.
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 radial velocity determination by reducing ambiguities and improving reliability, enabling more precise velocity estimation and reducing discrepancies between measured and actual velocities.
Implementation Method 1
radial velocity is obtained from a Fast Fourier transformation (FFT) to measure the phase shift of the intermediate signal across N FMCW chirps
Implementation Method 2
using radar sensors has seen a growth in popularity. One commonly used type of radar is a FMCW radar
Data Source
AI summary
The present disclosure relates to a computer-implemented method for determining a radial velocity of an object in a surrounding of a vehicle, the method comprising the steps of: obtaining measurement data from a radar, the measurement data comprising signal data indicative of a measured radial velocity of the object, mapping the measured radial velocity of the object to a plurality of radial velocity intervals, determining, using an artificial intelligence (AI) engine, a probability value for each interval of the plurality of radial velocity intervals based on supplemental measurement data, and determining the radial velocity of the object by selecting an interval of the plurality of radial velocity intervals based on the probability value. The disclosure further relates to a corresponding apparatus, computer program and vehicle.