Coherent Lidar Peak Association via Shape Similarity
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Solution Overview
Problem
In multi-target scenarios, coherent LIDAR systems face challenges in accurately associating peaks corresponding to different targets, due to the complexity of combining up-chirp and down-chirp frequency signals.
Innovation Solution
The method involves transmitting optical beams with different frequency chirps, receiving return signals, and generating a baseband signal with peaks associated with up-chirp and down-chirp frequencies. Metrics such as peak shape, intensity, and frequency are computed and used to pair peaks from both sets, optimizing the association through algorithms that minimize a cost function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If FMCW LIDAR systems use up-chirp and down-chirp frequency sweeps to detect target range and velocity, then measurement capability is improved, but peak association complexity increases in multi-target scenarios
Solution Approach 1:
The patent segments the peak association problem by introducing an association cost metric that evaluates individual peak pairs independently. The total cost function decomposes into separable components (frequency difference, range difference, velocity difference, peak intensity ratio), allowing the complex multi-target association problem to be solved through iterative pairwise optimization rather than exhaustive combinatorial search.
Solution Approach 2:
The patent transforms the peak association problem from a complex combinatorial optimization into a parameter-based cost minimization problem. By defining association costs based on physical parameters (frequency difference, range difference, velocity difference) and optimizing these parameters iteratively, the system achieves efficient peak pairing without exhaustive search through all possible associations.
2Adaptability or versatility
If multiple targets are present in the field of view, then detection coverage is improved, but peak association accuracy deteriorates
Solution Approach 1:
The patent implements feedback through iterative optimization where the association cost is calculated for all peak pairs, the minimum cost pair is identified and associated, then the process repeats with updated peak sets. This feedback loop continuously refines peak associations by using the cost metric to guide successive improvements, ensuring accurate pairing even when multiple targets are present.
Solution Approach 2:
The patent performs preliminary action by pre-calculating association costs for all possible peak pairs before final association. The cost function incorporating frequency difference, range difference, velocity difference, and peak intensity ratio is computed in advance for each pair, allowing the system to efficiently identify correct associations without exhaustive real-time search during target detection.
3Measurement precision
If optimization algorithms are used to minimize cost function, then peak association accuracy is improved, but computational time increases
Solution Approach 1:
The patent applies partial action by performing optimization iteratively only on the necessary subset of peak pairs. Rather than exhaustively optimizing all possible associations simultaneously, the system performs successive passes identifying and associating minimum cost pairs, stopping when all peaks are associated. This partial optimization approach achieves sufficient accuracy without the computational burden of complete exhaustive search.
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 associates peaks in multi-target scenarios, improving the accuracy of target identification and range/velocity measurement in LIDAR systems by leveraging computed metrics and optimization algorithms.
Implementation Method 1
coherent receivers for detection of backscattered or reflected light from the targets that are combined with a local copy of the transmitted signal. Mixing the local copy with the return signal, delayed by the round-trip time to the target and back, generates signals at the receiver with frequencies that are proportional to the distance to each target
Implementation Method 2
Frequency-Modulated Continuous-Wave (FMCW) LIDAR systems use tunable, infrared lasers for frequency-chirped illumination of targets
Data Source
AI summary
A method includes transmitting a plurality of optical beams towards a plurality of targets, receiving a plurality of return signals based on reflections of the plurality of optical beams from the plurality of targets, and generating a first plurality of peaks each associated with a different up-chirp frequency of the plurality of optical beams and a second plurality of peaks each associated with a different down-chirp frequency of the plurality of optical beams. The method further includes determining peak shape similarities between each of the first plurality of peaks and the second plurality of peaks, pairing each peak of the first plurality of peaks with a peak of the second plurality of peaks based on the peak shape similarities, and identifying the plurality of targets based on the pairing.