Beamforming Map Reflection Classification for Radar Ghost Targets
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Solution Overview
Problem
Radar systems often mischaracterize physical reflection points due to high-intensity localized amplitudes (main lobes) and less-intense satellite amplitudes (side lobes), leading to the detection of 'ghost' targets, which can cause unreliable environment characterization for vehicles, potentially resulting in operational issues or risks.
Innovation Solution
A method involving the generation of two beamforming maps using different response functions to distinguish between physical and apparent reflection points by calculating amplitude ratios and applying optimization techniques to preserve unit gain, allowing for the classification and removal of false reflection points from datasets used in vehicle control systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If beamforming is applied to detect reflection points, then reflection points can be detected, but side lobes cause false ghost targets to be detected
Solution Approach 1:
The system uses feedback by comparing amplitudes from two different beamforming maps (first and second maps) to identify and eliminate ghost targets. The amplitude comparison creates a feedback mechanism that validates whether detected reflection points are genuine or artifacts of side lobes.
Solution Approach 2:
The invention changes parameters by using two different response functions to generate two separate beamforming maps, then comparing amplitude parameters at the same spatial locations. This parameter comparison approach distinguishes genuine reflection points from ghost targets caused by side lobes.
2Reliability
If side lobes are suppressed to eliminate ghost targets, then false detections are reduced, but detection sensitivity may decrease
Solution Approach 1:
The second beamforming map acts as an intermediary tool to validate detections from the first map. By introducing this intermediate validation step through amplitude comparison, the system reliably distinguishes genuine targets from ghost targets without directly suppressing side lobes in the primary detection process.
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 effectively differentiates physical from apparent reflection points, reducing the risk of mischaracterization and enhancing the reliability of environment detection for autonomous or other vehicles by accurately classifying reflection points and updating datasets for improved operational control.
Implementation Method 1
sensing systems that probe reflected electromagnetic radiation from a surrounding environment
Data Source
AI summary
Systems, vehicles, and techniques are provided to classify reflection detection points in a sensing system. A reflection detection point can be classified as an apparent reflection or a physical reflection. In some embodiments, a beamforming map can be generated using a response function of an antenna array and data representative of electromagnetic signals received at the antenna array. Multiple reflection detection points can be detected using at least the beamforming map. A second beamforming distribution map also can be generated, using at least the data and a second response function of the array of antennas. The second response function includes minima at respective reflection points. A ratio between (i) a first amplitude of a reflection detection point in the second beamforming map and (ii) a second amplitude of the reflection point in the first beamforming map permits classifying the reflection detection point as an apparent reflection or a physical reflection.