Automotive Radar Stationary Target Detection in Tunnel Clutter
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Solution Overview
Problem
Automotive radar systems face challenges in accurately detecting targets in environments with clutter, such as steel tunnels, where noise signals overpower target signals, leading to incomplete or inaccurate detection of front targets.
Innovation Solution
A target detection device and method utilizing a histogram processor, candidate area determiner, kernel density estimator, and target determiner to analyze radar reception signals, distinguish between clutter and stationary targets, and improve detection performance by selecting candidate areas and applying kernel density estimation for range data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional radar detection is used in steel tunnel environments, then the radar system can operate continuously, but the detection accuracy deteriorates due to clutter signals overpowering target signals
Solution Approach 1:
The patent segments the detection process into multiple stages: histogram generation from radar signals, candidate area determination based on histogram peaks, kernel density estimation for probability distribution, and threshold-based target identification. This multi-stage segmentation allows progressive refinement of detection accuracy while maintaining system reliability in cluttered environments like steel tunnels
Solution Approach 2:
The patent applies partial action by focusing computational resources only on candidate areas identified through histogram analysis, rather than processing all radar signals uniformly. By concentrating the kernel density estimation on specific range bins with high detection frequencies, the system achieves improved precision without proportionally increasing overall computational load
2Measurement precision
If noise filtering is applied to remove clutter signals, then detection precision may improve, but loss of target information occurs when noise signals are misidentified as targets
Solution Approach 1:
The patent implements feedback through iterative histogram updates and probability density accumulation. The system continuously refines its understanding of the environment by accumulating detection frequencies across multiple radar scans, allowing it to distinguish between persistent targets and transient clutter signals while preserving information about both
Solution Approach 2:
The patent changes parameters dynamically by adjusting the threshold probability density based on accumulated kernel density estimates. Rather than using a fixed noise threshold, the system adapts its discrimination criteria based on the statistical properties of the received signals, enabling accurate clutter rejection while preserving weak target signals
3Measurement precision
If complex signal processing algorithms are used to distinguish targets from clutter, then detection accuracy improves, but device complexity increases
Solution Approach 1:
The patent segments the complex signal processing into modular components: histogram processing, candidate area determination, kernel function generation, and probability density accumulation. Each module performs a specific function with well-defined inputs and outputs, making the overall complex system more manageable and implementable
Solution Approach 2:
The system performs self-service through automatic threshold adaptation and candidate area identification. The histogram processor automatically identifies regions of interest, and the kernel density estimator automatically determines appropriate probability thresholds, reducing the need for manual parameter tuning and external intervention
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
Enables precise identification of stationary targets amidst clutter, enhancing target detection accuracy and reliability in environments like steel tunnels, thereby improving vehicle safety systems.
Implementation Method 1
Automotive radar, which may be widely used in these technologies, may detect surrounding objects using reflected signals that are reflected after transmitting radar signals.
Data Source
AI summary
The present embodiments relate to a target detection device and method, and a radar device including the same. A target detection device according to an embodiment may determine a candidate area within a specific distance range from a host vehicle, create a kernel function for each of a plurality of range data included in the candidate area, determine an accumulated probability density by accumulating a plurality of kernel functions, and determines a final stationary target based on the accumulated probability density.