Iterative Histogram Binwidth Optimization for LiDAR Signal Detection
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Solution Overview
Problem
Conventional LIDAR systems face challenges in optimizing histogram bin width, leading to trade-offs between spatial resolution and signal detection, particularly affecting the detection of far objects and distinguishing between signal peaks and noise, with existing adaptive binning methods being sensitive to target reflectivity and limited by hard boundaries between sub-regions.
Innovation Solution
An iterative histogram bin width optimization algorithm that adjusts bin sizes based on detected signal metrics, allowing for optimal bin selection that balances resolution and detection probability, is developed, which is agnostic to target reflectivity and capable of distinguishing between echo and noise signals, using a controller to iteratively determine bin numbers and identify target bins within the LIDAR system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a small bin width size is selected, then spatial resolution is improved, but signal detection capability for far objects deteriorates
Solution Approach 1:
The patent implements dynamic bin width adjustment where the bin width is not fixed but adapts based on the detected signal characteristics. The controller iteratively adjusts the bin width between iterations to optimize both resolution and detection capability, transforming the static parameter into a dynamic one that responds to actual measurement conditions.
Solution Approach 2:
The patent changes the bin width parameter iteratively based on signal quality metrics. By modifying this critical parameter across multiple iterations and selecting the optimal bin width that maximizes detection probability while maintaining resolution, the system resolves the contradiction between these two opposing requirements.
2Reliability
If a large bin width size is selected, then signal detection capability is improved, but spatial resolution deteriorates
Solution Approach 1:
The system employs dynamic bin width adjustment where the bin width evolves across iterations based on detected signal characteristics. This transforms the static, fixed bin width into a dynamic parameter that adapts to balance detection capability and resolution for different scattering conditions.
Solution Approach 2:
The patent iteratively modifies the bin width parameter and evaluates detection probability. By changing this parameter across multiple iterations and selecting the optimal value, the system achieves both adequate signal detection and spatial resolution, resolving the contradiction between these two parameters.
3Adaptability or versatility
If conventional adaptive binning methods are used, then bin width adaptation is achieved, but sensitivity to target reflectivity and hard boundary limitations persist
Solution Approach 1:
The patent performs preliminary actions by iteratively adjusting the bin width before final peak detection. Multiple iterations of bin width optimization are conducted in advance to ensure that the final bin configuration is optimal for detecting peaks regardless of target reflectivity or position, preventing boundary-related detection errors.
Solution Approach 2:
The system implements feedback by using detected peak positions and signal characteristics from one iteration to inform bin width adjustments in subsequent iterations. This closed-loop approach continuously refines the bin width based on actual detection performance, eliminating sensitivity to target reflectivity and boundary effects.
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 algorithm effectively enhances the detection of multiple objects by optimizing bin widths, improving the signal-to-noise ratio and enabling accurate distance determination, even in the presence of sunlight noise, by iteratively refining bin configurations and differentiating between object echoes and noise.
Implementation Method 1
a light source for emitting signals
Implementation Method 2
Some LIDAR systems employ Single Photon Multiplier (SiPM) and/or Single Photon Avalanche Diode (SPAD) detectors. They are solid-state, high-gain radiation detectors that produce an output current pulse upon absorption of a photon.
Implementation Method 3
The position and distance of the object can be computed using inter alia Time of Flight (TOF) calculations of the emitted and detected light beam
Data Source
AI summary
A LIDAR system and a method for detecting objects are disclosed. The LIDAR system has a controller configured to acquire a plurality of data points representative of detected signals, and perform an iterative process. During the first iteration the controller is configured to determine a first number of bins based on the plurality of data points, and in response to the first number being above a threshold: (i) organize the plurality of data points into the first number of bins, (ii) identify a target bin amongst the first number of bins, (iii) determine the distance of a first object based on the target bin; and (iv) determine a reduced plurality of data points based on the plurality of data points, which excludes data points associated with the target bin. During a second iteration, the controller determines a distance of a second object based on the reduced plurality of data points.