Laser Beacon Identification Using Extended Kalman Filter
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Solution Overview
Problem
Establishing and maintaining reliable laser communication between distant nodes is challenging due to the need for precise identification and alignment of laser signals, which are affected by atmospheric effects and mechanical instabilities, particularly in satellite communications where nodes are well-separated and subject to frequency and angular shifts.
Innovation Solution
A method using two overlapping, co-linear beam components where one component is a beacon modulated with a square wave amplitude modulation pattern, and an extended Kalman filter is applied to verify the beacon's modulation frequency, enabling rapid detection and differentiation from other light sources, and compensating for jitter using a focal plane array.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If laser communication beams are used for point-to-point communication over great distances, then data transfer speed and security are improved, but precise identification and alignment of laser signals become more difficult due to atmospheric effects and mechanical instabilities
Solution Approach 1:
The laser communication signal is divided into two separate components: a beacon component at a first wavelength and a data component at a second wavelength. This segmentation allows the beacon to be independently detected and used for alignment purposes, while the data component carries the actual communication payload. The separation of functions resolves the contradiction by making identification (via beacon) independent from data transfer (via data component).
Solution Approach 2:
The beacon component acts as an intermediary between the transmitting and receiving nodes. It provides a reference signal that enables the receiving node to identify and align with the incoming laser beam before the actual data communication begins. This intermediary beacon resolves the alignment difficulty by providing a distinct, easily detectable target for acquisition and tracking systems.
2Area of stationary object
If nodes are well-separated in satellite communications, then geographic coverage is improved, but frequency and angular shifts increase making signal verification more challenging
Solution Approach 1:
The system utilizes different wavelengths (parameters) for the beacon and data components. The beacon at the first wavelength is optimized for detection and alignment, while the data component at the second wavelength carries the communication payload. This parameter change allows the system to maintain precise measurement capability despite large geographic separations, as the beacon wavelength can be specifically chosen for optimal atmospheric transmission and detection over long distances.
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 rapid and reliable verification of laser communication signals, ensuring accurate alignment and maintaining communication integrity despite atmospheric and mechanical disturbances, enhancing the security and efficiency of laser communication systems.
Implementation Method 1
one component is a beacon modulated with a square wave amplitude modulation pattern, and an extended Kalman filter is applied to verify the beacon's modulation frequency
Implementation Method 2
compensating for jitter using a focal plane array
Data Source
AI summary
A disclosed apparatus and method verify whether a hot spot within a scene is a valid laser-com signal, and further identify laser-com signals of interest. An extended Kalman filter (EKF) is applied to a received signal or beacon to detect whether it is modulated according to a square wave or other expected amplitude modulation pattern. Embodiments further determine whether the modulation frequency matches a signal or beacon of interest. In embodiments, a plurality of indexed EKF's are "tuned" to different modulation frequencies, and are used to identify a beacon's modulation frequency index. Where each EKF is required to compute amplitude and phase partial derivatives of the observation vector, the amplitude partials can be calculated according to the expected modulation pattern, and the phase partials can be calculated using a truncated Fourier or step-wise approximation of the expected modulation pattern. Embodiments further assume a time-invariant covariance matrix.