Data-Driven Angular Jitter Estimator for Space Lidar
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Space-based 3D lidar systems face challenges in mitigating the blurring effect of random angular jitter, which degrades the cross-range resolution of data products, particularly due to the smaller ground sample distance compared to ground resolution distance and longer lever arm, making it difficult to eliminate large and expensive precise pointing mirrors.
Innovation Solution
The blind jitter estimation approach models the physics of lidar measurements using a Bayesian method to estimate jitter, allowing for precise compensation of pointing errors without additional sensors, thereby improving angular resolution and reducing the size, weight, and power requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large and expensive precise pointing mirrors are used to eliminate angular jitter, then pointing precision is improved, but device complexity, size, weight, and cost increase
Solution Approach 1:
The patent replaces the mechanical pointing mirror system with a computational approach. Instead of using physical mirrors to actively correct jitter, the system uses a blur kernel derived from jitter statistics to deblur the final image through computational methods, eliminating complex mechanical components while achieving equivalent or superior pointing precision
Solution Approach 2:
The patent changes the approach from physically controlling pointing parameters in real-time to statistically characterizing and compensating for jitter parameters in post-processing. By modeling jitter as a stochastic process and deriving a blur kernel from its statistical properties, the system transforms the problem from active control to passive computational correction
2Measurement precision
If active image correlation is used to determine jitter, then jitter estimation is achieved, but integration time increases and update rate decreases
Solution Approach 1:
The patent performs preliminary characterization of jitter statistics during the integration period itself. By accumulating photon arrivals and using them to build the blur kernel, the system prepares the compensation data structure during normal operation rather than requiring separate calibration periods, enabling real-time jitter compensation at the full frame rate
Solution Approach 2:
The patent makes the imaging system self-sufficient by using its own photon arrival data to characterize and compensate for jitter. The same photons used to form the image also provide the statistical information needed to derive the blur kernel, eliminating the need for separate auxiliary sensors or calibration procedures
3Measurement precision
If auxiliary passive imaging system is used for jitter estimation, then jitter measurement is achieved, but additional sensors increase device complexity and update rate is limited
Solution Approach 1:
The patent makes the lidar system multi-functional by having it simultaneously perform imaging and jitter characterization. The same optical path and detector array used for capturing the image also collect the temporal photon arrival data needed to derive jitter statistics, allowing one system to serve multiple purposes without adding auxiliary sensors
Solution Approach 2:
The imaging system characterizes its own jitter using its own measurement data. By analyzing the temporal distribution of photon arrivals at each pixel, the system extracts jitter information from its normal operational data stream, making it self-sufficient and eliminating dependence on external reference systems
4Measurement precision
If higher performance IMU/INS and star tracker are used to measure precise pointing, then pointing measurement accuracy is improved, but size, weight, and cost increase
Solution Approach 1:
The patent replaces heavy mechanical inertial measurement systems with a computational approach that derives pointing information from photon arrival statistics. Instead of using physical gyroscopes and accelerometers to track platform motion, the system infers jitter characteristics from the temporal distribution of detected photons, achieving comparable or superior accuracy without the weight penalty
Solution Approach 2:
The patent creates a computational model (blur kernel) that represents the effect of jitter on the image. This virtual model serves as a substitute for physical sensing systems, allowing the software to compensate for pointing errors without requiring additional hardware sensors or heavy mechanical stabilization systems
Data Source
AI summary
Space-based ground-imaging lidar has become increasingly feasible with recent advances in compact fiber lasers and single-photon-sensitive Geiger-mode detector arrays. A challenge with such a system is imperfect pointing knowledge caused by angular jitter, exacerbated by long distances between the satellite and the ground. Unless mitigated, angular jitter blurs the 3D lidar data. Unfortunately, using mechanical isolation, advanced IMUs, star trackers, or auxiliary passive optical sensors to reduce the error in pointing knowledge increases size, weight, power, and/or cost. Here, the 2-axis jitter time series is estimated from the lidar data. Simultaneously, a single-surface model of the ground is estimated as nuisance parameters. Expectation Maximization separates signal and background detections, while maximizing the joint posterior probability density of the jitter and surface states. The resulting estimated jitter, when used in coincidence processing or image reconstruction, can reduce the blurring effect of jitter to an amount comparable to the optical diffraction limit.