FMCW LIDAR Signal Subband Classification for Resolution Sensitivity Trade-off
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Solution Overview
Problem
Conventional LIDAR systems face challenges in simultaneously achieving high detection sensitivity for distant targets and high angular and range resolution for close-range targets, as signal processing methods that enhance detection of weak signals from distant targets often degrade other metrics.
Innovation Solution
The proposed LIDAR system selectively processes subbands in the time and frequency domains by classifying them into different types based on criteria such as peak signal energy, average signal-to-noise ratio, and target characteristics, and then applying specific processing parameters to improve detection probability and resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If signal processing methods are used to enhance detection of weak signals from distant targets, then detection sensitivity for distant targets is improved, but angular resolution and range resolution for close-range targets deteriorate
Solution Approach 1:
The patent segments the frequency spectrum into multiple subbands (e.g., low frequency subbands for close-range targets and high frequency subbands for distant targets) and applies different processing parameters to each subband. This allows the system to optimize detection sensitivity for distant targets in high frequency subbands while maintaining angular resolution for close-range targets in low frequency subbands, thereby resolving the contradiction between detection sensitivity and angular resolution.
Solution Approach 2:
The patent applies different processing parameters (such as integration time, DFT length, and filter coefficients) to different frequency subbands based on their local characteristics. Low frequency subbands receive parameters optimized for angular resolution while high frequency subbands receive parameters optimized for detection sensitivity, enabling the system to achieve both high detection sensitivity for distant targets and high angular resolution for close-range targets simultaneously.
2Reliability
If signal processing methods are used to enhance detection of weak signals from distant targets, then detection sensitivity for distant targets is improved, but range resolution for close-range targets deteriorates
Solution Approach 1:
The patent divides the frequency spectrum into multiple subbands and applies different processing parameters to each. By segmenting the signal processing into distinct low frequency and high frequency pathways, the system can optimize range resolution for close-range targets in low frequency subbands while maintaining detection sensitivity for distant targets in high frequency subbands, thus resolving the contradiction between detection sensitivity and range resolution.
Solution Approach 2:
The patent applies locally optimized processing parameters to different frequency subbands. Low frequency subbands use parameters that maximize range resolution for close-range targets, while high frequency subbands use parameters that maximize detection sensitivity for distant targets. This local optimization allows simultaneous achievement of high detection sensitivity and high range resolution without compromise.
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 allows for a high probability of detecting distant targets while maintaining or improving the angular and range resolution for close-range targets, effectively addressing the limitations of conventional systems.
Implementation Method 1
Frequency-Modulated Continuous-Wave (FMCW) LIDAR systems use tunable lasers for frequency-chirped illumination of targets
Implementation Method 2
coherent receivers for detection of backscattered or reflected light from the targets that are combined with a local copy of the transmitted signal
Implementation Method 3
Mixing the local copy with the return signal, delayed by the round trip time to the target and back, generates a beat frequency at the receiver
Implementation Method 4
perform a discrete Fourier transform (DFT) on the time-domain sample block with a DFT processor to generate subbands in the frequency domain
Data Source
AI summary
A frequency modulated continuous wave (FMCW) light detection and ranging (LIDAR) system includes a processor and a memory. The memory stores instructions that, when executed by the processor, cause the system to: generate subbands in a frequency domain based on a range-dependent time domain baseband signal, classify each subband into a subband type, select processing parameters for each subband based on the respective subband type, and process each of the subbands using the selected processing parameters for the subband.