LIDAR Analog Circular Buffer for Triggered Signal Digitization
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Solution Overview
Problem
Existing LIDAR systems face challenges in accurately detecting weaker returns from distant or highly light-absorbing objects and suffer from false triggers due to noise, while continuously digitizing the entire return signal is power-intensive and costly.
Innovation Solution
The system captures LIDAR return signal samples in an analog circular buffer and digitizes only the portions associated with the target object, using a dual approach of analog pulse detection and digital signal processing to enhance accuracy and reduce resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the threshold voltage is lowered to detect weaker returns from distant objects, then detection sensitivity is improved, but false triggers due to noise increase
Solution Approach 1:
The system performs preliminary analog processing and comparison operations before ADC conversion. The comparator evaluates the return signal against a reference threshold in the analog domain, pre-identifying potential target returns. This preliminary action allows the system to focus subsequent digital processing resources only on signals that have already been flagged as potential targets, rather than processing all signals uniformly.
Solution Approach 2:
The patent introduces an intermediary analog comparison stage between the photo-detector and the ADC. This intermediary comparator circuit acts as a mediator that pre-evaluates signals and triggers ADC conversion only when a threshold crossing is detected. This intermediary layer separates the low-level analog signal domain from the high-precision digital processing domain, allowing threshold adjustments without directly impacting the full digital processing chain.
2Loss of information
If continuous digitization of the entire return signal is performed, then complete signal analysis capability is improved, but power consumption and cost increase
Solution Approach 1:
The system extracts only the relevant portions of the return signal for digital processing. Instead of continuously digitizing the entire return signal, the comparator triggers the ADC to capture and digitize only those signal segments that exceed the threshold and represent potential target returns. This extraction principle removes unnecessary data from the digital processing pipeline, reducing power consumption and computational load while retaining all information needed for target detection.
Solution Approach 2:
The system performs partial digitization rather than complete digitization of the return signal. The ADC operates intermittently, converting to digital only the signal portions that are deemed relevant by the analog comparator. This partial action approach provides sufficient signal analysis capability for target detection while avoiding the excessive power consumption and cost associated with continuous full-signal digitization.
3Ease of manufacture
If lower-cost components and reduced power consumption are used, then device cost and energy usage are improved, but signal processing accuracy deteriorates
Solution Approach 1:
The signal processing chain is segmented into distinct functional stages: analog signal conditioning, analog comparison/threshold detection, triggered ADC conversion, and digital processing. Each segment performs a specific function with appropriate precision for that stage. The analog comparator uses lower-precision threshold comparison, while the ADC and digital processor handle only the triggered segments with high precision. This segmentation allows lower-cost components in non-critical paths while maintaining overall measurement accuracy.
Solution Approach 2:
The system changes the operational parameters of different components based on their function in the signal chain. The analog comparator operates with variable threshold levels adapted to detection requirements, the ADC converts at triggered intervals rather than continuously, and digital processing focuses computational resources on captured target segments. These parameter changes optimize the balance between component cost and processing accuracy, allowing lower-cost components where full precision is not required while maintaining high accuracy where it matters most.
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 method improves detection efficiency and accuracy by reducing false triggers and conserving ADC, processor, and power resources, enabling more precise distance calculations even under noisy conditions.
Implementation Method 1
The photo-detector 105 converts the light energy into an electrical signal that is amplified and otherwise analog processed
Data Source
AI summary
Samples of a light radar (“LIDAR”) return signal are stored in an analog circular buffer following the transmission of a LIDAR pulse. Sampling continues for a fixed period of time or number of samples during a post-trigger sampling period after the occurrence of a trigger signal from a trigger circuit. The trigger circuit indicates the receipt of a return pulse associated with a target object based upon one or more return signal characteristics. Following the post-trigger sampling period, the stored analog samples are sequentially read out and converted to digital sample values. The digital sample values may be analyzed in a digital processor to further confirm the validity of the returned LIDAR pulse, to determine a time of arrival of the LIDAR pulse, and to calculate a distance to the target object. Some versions include multiple circular buffers and capture clocks, enabling the capture of samples from multiple return pulses.