At a glance

II-VI's approach to compensating the atmospheric effects combines advanced wavefront sensors with an optimization algorithm called SPGD, for "stochastic parallel gradient descent." This approach has many applications including:

  • Remote sensing
  • Laser communications
  • Beam combining
  • Beam shaping
  • Directed energy
  • Laser target tracking

Optical applications such as astronomy, remote sensing, and laser communications are adversely affected by atmospheric effects such as turbulence. A fairly common approach to compensating for these atmospheric effects is to use a standard statistical model, such as the Kolmogorov model, to calculate the parameters needed to build compensating systems based on wavefront sensors and phase conjugation by means of deformable mirrors. However, the performance of such adaptive optics systems is often disappointing, as the real-world atmosphere does not necessarily conform to a standard statistical model.

Deformable mirror WACO DM-25 (front and back views)

SPGD control of WACO DM via GUI Software

The SPGD algorithm starts with an essentially random set of control parameters and rapidly adapts to the fluctuations in the atmosphere based on the measurement of some metric, such as reflected optical signal strength. II-IV's Chief Scientist, Dr. Vorontsov, has been a major developer of this algorithm for quite some time. Because of its high rate of convergence, it is especially suited to be used with our high-speed fiber positioners (for tip-tilt control) and phase shifters (for phase control.) This approach is much more effective than traditional adaptive optics at coping with combinations of effects such as turbulence, scintillation, and aerosol diffusion over significant distances (> 1 km.) in the atmosphere. We can also combine these methods with traditional adaptive optics, but using advanced wavefront sensing techniques such as our Multi-Aperture (MAPR) wavefront sensor and WACO deformable mirrors, to correct larger amplitude and static aberrations, such as defects in the optical system itself.

We use SPGD in our 2-channel and 24-channel SPGD fiber positioner controllers, and in the controller for the WACO DM. Other components, such as the MAPR wavefront sensor, are in development.

SPGD is "stochastic" in the sense that it uses a starting set of values that are more or less random. It is "parallel" because it optimizes all of the control values at the same time. "Gradient" refers to the fact that the algorithm only operates on a function (or region of a function) that is everywhere differentiable. In fact, a naïve implementation of SPGD would be subject to finding false "local" minima or maxima of the function. Finally, the process is a "descent" as it should ideally yield smaller and smaller differences between the current and updated values of the control variables.

More information about SPGD controllers...

A flow-chart diagram of the algorithm appears in the next figure. Optonicus has developed several key modifications and enhancements to this algorithm, to improve its speed and to cope with problems such as local function minima and delays in the control loop. As a result, SPGD provides a key ingredient to several of our hardware and software products.

SPGD flowchart. u represents the set of control values being optimized, and J is the "metric" output of the function. γ is a control parameter, commonly called "gain", and k is the iteration number (initially zero.)