MIMO Radar Ghost Object Elimination via Two-Stage Detection
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Solution Overview
Problem
Multipath reflections in MIMO radar systems degrade performance by creating ghost objects that are misinterpreted as real, leading to inaccurate object detection and potential incorrect autonomous vehicle operations.
Innovation Solution
A method involving two stages of detection using range-Doppler maps and synthetic matrix processing to differentiate between real and ghost objects, where the first stage identifies potential objects and the second stage eliminates those caused by multipath reflections by analyzing beamforming results and ratios of transmit and receive angles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If MIMO radar system uses multiple transmit and receive antenna elements to increase angular resolution, then measurement precision is improved, but multipath reflections create ghost objects that degrade detection reliability
Solution Approach 1:
The detection process is divided into two distinct stages: a first stage that identifies potential objects using conventional beamforming, and a second stage that specifically targets and eliminates ghost objects caused by multipath reflections. This segmentation allows the system to maintain high angular resolution while systematically addressing the reliability issue through staged processing.
Solution Approach 2:
The patent introduces an intermediary verification process between initial object detection and final detection output. The second stage acts as a mediator that cross-checks potential objects by analyzing multipath reflection patterns and eliminating false detections, thereby preserving the high resolution capability while improving reliability.
2Device complexity
If conventional beamforming is used to detect objects, then detection process is simple, but ghost objects from multipath reflections cannot be distinguished from real objects
Solution Approach 1:
The system performs preliminary identification of potential objects in the first stage using conventional beamforming methods, establishing a baseline detection set. This preliminary action allows the subsequent second stage to focus computational resources specifically on eliminating ghost objects from this pre-identified set, rather than processing all possible detections.
Solution Approach 2:
The patent adds a new dimension to the detection process by introducing a second stage that operates on the results of the first stage. This dimensional addition transforms the detection workflow from a single-pass beamforming approach to a multi-stage process that incorporates ghost elimination logic, thereby improving identification accuracy without completely redesigning the base system.
3Measurement precision
If two stages of detection are implemented to eliminate ghost objects, then object detection accuracy is improved, but processing time and computational complexity increase
Solution Approach 1:
The second stage of detection applies partial action by focusing computational effort only on potential objects identified in the first stage, rather than re-processing all possible detections. The ghost elimination logic selectively analyzes specific candidates based on their likelihood of being multipath reflections, performing sufficient verification to eliminate ghosts without exhaustive processing of all detection possibilities.
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 eliminates multipath reflections, enhancing the accuracy of object detection and preventing incorrect autonomous vehicle maneuvers by distinguishing real from ghost objects, thereby improving the reliability of MIMO radar systems.
Implementation Method 1
A MIMO radar system with multiple transmit antenna elements and receive antenna elements increases angular resolution
Implementation Method 2
the processing the reflections includes performing a first fast Fourier transform (FFT) along range values and performing a second FFT on a result of the first FFT
Data Source
AI summary
Systems and methods include transmitting transmit signals from transmit elements, and receiving reflections resulting from the transmit signals at receive elements. The reflections are processed to obtain range-Doppler maps. Each range-Doppler map corresponds with one combination of the transmit elements and the receive elements. The range-Doppler map includes complex values that indicate intensity over a set of range values and a set of relative velocity values. A synthetic matrix of synthetic vectors of array response combinations is generated for transmit angles and receive angles. Each array response combination is a combination of a transmit response for one of the transmit angles and a receive response for one of the receive angles. Two stages of detection are performed. A first stage identifies potential objects and a second stage eliminates the potential objects that are ghost objects. The potential objects remaining after the second stage are the real objects.