Reconstruction of long horizontal-path images under anisoplanatic conditions using multiframe blind deconvolution

All optical systems that operate in or through the atmosphere suffer from turbulence induced image blur. Both military and civilian surveillance, gun sighting, and target identification systems are interested in terrestrial imaging over very long horizontal paths, but atmospheric turbulence can blur the resulting images beyond usefulness. This work explores the mean square error (MSE) performance of a multiframe blind deconvolution (MFBD) technique applied under anisoplanatic conditions for both Gaussian and Poisson noise model assumptions. The technique is evaluated for use in reconstructing images of scenes corrupted by turbulence in long horizontal-path imaging scenarios. Performance is evaluated via the reconstruction of a common object from three sets of simulated turbulence degraded imagery representing low, moderate, and severe turbulence conditions. Each set consisted of 1000 simulated turbulence degraded images. The MSE performance of the estimator is evaluated as a function of the number of images, and the number of Zernike polynomial terms used to characterize the point spread function. A Gaussian noise model-based MFBD algorithm reconstructs objects that showed as much as 40% improvement in MSE with as few as 14 frames and 30 Zernike coefficients used in the reconstruction, despite the presence of anisoplanatism in the data. An MFBD algorithm based on the Poisson noise model required a minimum of 50 frames to achieve significant improvement over the average MSE for the data set. Reconstructed objects show as much as 38% improvement in MSE using 175 frames and 30 Zernike coefficients in the reconstruction

Target localization and tracking by fusing doppler differentials from cellular emanations with a multi-spectral video tracker

We present an algorithm for fusing data from a constellation of RF sensors detecting cellular emanations with the output of a multi-spectral video tracker to localize and track a target with a specific cell phone. The RF sensors measure the Doppler shift caused by the moving cellular emanation and then Doppler differentials between all sensor pairs are calculated. The multi-spectral video tracker uses a Gaussian mixture model to detect foreground targets and SIFT features to track targets through the video sequence. The data is fused by associating the Doppler differential from the RF sensors with the theoretical Doppler differential computed from the multi-spectral tracker output. The absolute difference and the root-mean-square difference are computed to associate the Doppler differentials from the two sensor systems. Performance of the algorithm was evaluated using synthetically generated datasets of an urban scene with multiple moving vehicles. The presented fusion algorithm correctly associates the cellular emanation with the corresponding video target for low measurement uncertainty and in the presence of favorable motion patterns. For nearly all objects the fusion algorithm has high confidence in associating the emanation with the correct multi-spectral target from the most probable background target

Wave front correction and post-detection image reconstruction under anisoplanatic conditions with a scene-based wave front sensor

© 2016 OSA. Improving images measured through anisoplanatic turbulence conditions for horizontal and downlooking imaging applications remains a challenge in image science. We have found that on-axis adaptive optics correction of images measured under anisoplanatic conditions followed by multi-frame blind deconvoution (MFBD)-based image reconstruction provides superior final images compared to the raw data or MFBD without predection compensation. The major challenge to on-axis compensation has been the lack of a beacon for wave front sensing. In this paper we examine the utility of the scenebased wave front sensor developed in the field of solar astronomy for using an extended object as the beacon for wave front sensing

Method for determining the spatial correlation width of a Gaussian-Schell beam to optimize the scintillation index over long horizontal turbulent paths

We describe a method for controlling the spatial correlation width of a propagating Gaussian-Schell model (GSM) beam in an unknown turbulent atmosphere to optimize the scintillation index (SI). In our approach we slowly change a simulated GSM beam from incoherent to coherent by controlling the coherence through the spatial correlation width. The goal is to use statistics to determine the point just before a GSM beam becomes too coherent and starts to exhibit the statistics of a fully coherent source. We define this value of the spatial correlation width as the upper-coherence limit. We show that there is a straightforward method of calculating the upper-coherence limit in a closed channel regardless of turbulence strength or aperture size. © 2012 Society of Photo-Optical Instrumentation Engineers (SPIE)

Fundamental considerations for wave front sensing with extended random beacons

Extending adaptive optics to laser beam control over long turbulent paths where there is no cooperative beacon present requires examination of wave front sensor performance in new regimes. In some scenarios of interest it is necessary to create beacon by sending an illuminator laser through the atmosphere to scatter from the target, which, in general, must be considered to be optically rough. Scattered light returned to the laser transmitter aperture is referred to as the beacon field, and this light may be used for wave front sensing. Physical effects on the beacon field which reduce wave front sensing accuracy include turbulence induced beam broadening and speckle on the outgoing beam, and coherent laser speckle effects in the scattered field. In this paper we analyze the impact of these phenomena on some of the measurements available for tracking and wave front sensing

Performance analysis of Kalman Filter and Minimum Variance controllers for Multi Conjugate Adaptive Optics

In the framework of zonal approach for Multi Conjugate Adaptive Optics (multiple-mirror, multiple-guide star) we investigate a predictive Kalman Filter (KF) based controller and a non-predictive classical Minimum Variance (MV) algorithm. The main goal of this work is to compare phase estimation performance achievable by the computationally more expensive Kalman filter approach, which explicitly accounts for the atmospheric turbulence temporal behavior through a first order autoregressive evolution model, and a simpler MV algorithm with and without temporal prediction. For representative examples of the Palomar 5.1 meter telescope single conjugate and Gemini-South 8 meter telescope multi conjugate adaptive optics systems the performance of KF and MV controllers has been compared with respect to their turbulence estimation capability. We have found that the KF algorithm, showing superior performance for single conjugate adaptive optics systems, is less effective in multi conjugate case. It has also been shown that MV algorithm with a temporal prediction added to it can work nearly as good as KF

Anisoplanatic studies and Fried parameter estimation via multi-channel laser communication system

The knowledge of the turbulence conditions and the ability to describe its properties are the key aspects to improve performance and extend the range of optical communication systems. The developed multi-channel, outdoor 3.2 km, partially over water, turbulence measurement and monitoring communication platform is directed to collect significant amount of the experimental data with the goal of statistically describe atmospheric turbulence. The communication system described in this paper has two transmitters and two receivers. The transmitter side is equipped with the laser and the bank of 14 horizontally, in-line mounted LEDs. The receiver side consists of two channels for wave front sensor (WFS) and point spread function (PSF) measurements. Data collected via both channels is further used for Fried parameter estimation and anisoplanatic studies. In this article authors provide comprehensive analysis of the turbulence statistics extracted from the experimental data. Statistics of Freid parameter r0 is derived from 6 Tb of data collected through 40 days time interval, and under various day and night atmospheric conditions. These data collected from WFS and PSF channels are digitally post processed and results obtained from PSF measurements are compared with the ones derived from the WFS data. Consistent results obtained via both channels allows authors to conclude that the entire system performs reliably and generates trustworthy results. Results extracted from the data collected via both channels show significant fluctuations of r0 with the values ranging from 2mmand up to 20 cm. The data collected from the PSF channel is also used for measurements of anisoplanetic effects. Theoretically, the severe anisoplanatic conditions found in horizontally imaging scenarios can be approximated by a finite number of phase screens placed along the imaging path. However, comparison of adjacent PSFs generated in this manner reveals significant correlation at angles much larger than the predicted theoretical isoplanatic angle. In this article the effects of anisoplanatism on the optics point spread function via simulations and experimental approaches have also been studied. © 2013 IEEE

Penalized least-squares for imaging with hypertelescopes

Practical considerations such as cost constrain the aperture size of conventional telescopes, which, combined with atmospheric turbulence effects, even in the presence of adaptive optics, limit achievable angular resolution. Sparse aperture telescopes represent a viable alternative for achieving improved angular resolution by combining light collected from small apertures distributed over a wide spatial area either using amplitude interferometry or a direct imaging approach to beam-combining. The so-called densified hypertelescope imaging concept in particular provides a methodology for direct image formation from large sparse aperture arrays. The densification system suppresses wide-angle side lobes and concentrates that energy in the center of the focal plane, significantly improving the signal-to-noise ratio of the measurement. Even with densification, an inevitable consequence of sparse aperture sampling is that the point-spread function associated with the direct image contains an additional structure not present in full aperture imaging systems. Postdetection image reconstruction is performed here to compute a high-fidelity estimate of the measured object in the presence of noise. In this paper, we describe a penalized least-squares object-estimation approach and compare the results with the classical Richardson–Lucy deconvolution algorithm as it is applied to hypertelescope image formation. The parameters of the algorithm are selected based on a comprehensive simulation study using the structure similarity metric to assess reconstruction performance. We find that the penalized least-squares formulation with optimized parameters provides significantly improved reconstructions compared with the conventional Richardson–Lucy algorithm

Beaconless stochastic parallel gradient descent laser beam control: Numerical experiments

We apply a target-in-the-loop strategy to the case of adaptive optics beam control in the presence of strong atmospheric turbulence for air-to-ground directed energy laser applications. Using numerical simulations we show that in the absence of a cooperative beacon to probe the atmosphere it is possible to extract information suitable for effective beam control from images of the speckled and strongly turbulence degraded intensity distribution of the laser energy at the target. We use a closed-loop, single-deformable-mirror adaptive optics system driven by a target-in-the-loop stochastic parallel gradient descent optimization algorithm minimizing a mean-radius performance metric defined on the image of the laser beam intensity distribution formed at the receiver. We show that a relatively low order 25-channel zonal adaptive optical beam control system controlled in this way is capable of achieving a high degree of turbulence compensation with respect to energy concentration if the tilt can be corrected separately. © 2007 Optical Society of America

Pseudo-guide wave propagation in stratified, inverted temperature distributions in the atmosphere

We have developed a theoretical model and a simulation model of wave propagation through stratified, inverted temperature distributions in the atmosphere. These conditions are present when local weather conditions cause a warm layer of air to exist above a cool layer of air, rather than the more common situation at low altitude, where the air temperature gradually decreases with increasing altitude. In this situation a sort of guided wave propagation is possible for laser beams. The ray describing the beam propagation direction is found to follow a path through the inverted layer similar to the path a guided ray follows through a gradient-index optical fiber, but without the phase matching condition being satisfied in general, leading to our use of the term pseudo-guided wave for this propagation phenomenon. For the temperature inversion to exist, it is necessarily the case that the vertical component of the wind must be very weak, and we conjecture that as a result the turbulence is quite weak in this layer, and possibly anisotropic. In this paper we describe this phenomenon, and establish the realistic nature of the phenomenon by examining real vertical temperature profiles. Ray optics and wave optics propagators are used to show that propagation through these layers leads to much higher beam quality at a target plane than would be expected under more typical atmospheric conditions. We also use the measured vertical temperature profiles to show that these conditions are quite common. © 2014 IEEE