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A model for the Faraday anomalous dispersion optical filter is presented. The model predicts a bandwidth of 0.6 GHz and a transmission peak of 0.98 for a filter operating on the Cs (D2) line. The model includes hyperfine effects and is valid for arbitrary magnetic fields.
The spectral resolution and statistical significance of a harmonic analysis obtained by low-order MEM can be improved by subjecting the data to an adaptive filter. This adaptive filter consists of projecting the data onto the leading temporal empirical orthogonal functions obtained from singular spectrum analysis (SSA). The combined SSA-MEM method is applied both to a synthetic time series and a time series of AAM data. The procedure is very effective when the background noise is white and less so when the background noise is red. The latter case obtains in the AAM data. Nevertheless, reliable evidence for intraseasonal and interannual oscillations in AAM is detected. The interannual periods include a quasi-biennial one and an LF one, of 5 years, both related to the El Nino/Southern Oscillation. In the intraseasonal band, separate oscillations of about 48.5 and 51 days are ascertained.
The K passband (central wavelength 2.2 microns, FWHM 0.4 micron) is the longest wavelength standard near-IR passband through which deep ground-based imaging is possible. Thermal emission from telescope, instrument, and sky limits the depth to which such imaging can reach by producing strongly temperature-dependent backgrounds in the range 11-13.5 mag/sq arcsec. This paper describes how a passband, which is denoted as K-prime, located slightly shortward of the standard K passband (central wavelength 2.1 microns), yet still within the same atmospheric window, leads to a significantly lower thermal component of the background, reducing the background surface brightness by up to 0.9 mag sq arcsec, and thereby allowing deeper imaging to be obtained in the same integration time. The photometric differences between the K-prime filter and the standard K filter are discussed.
Limitations associated with the binary phase-only filter often used in optical correlators are presently circumvented in the writing of complex-valued data on a gray-scale spatial light modulator through the use of a computer-generated hologram (CGH) algorithm. The CGH encodes complex-valued data into nonnegative real CGH data in such a way that it may be encoded in any of the available gray-scale spatial light modulators. A CdS liquid-crystal light valve is used for the complex-valued CGH encoding; computer simulations and experimental results are compared, and the use of such a CGH filter as the synapse hologram in a holographic optical neural net is discussed.
A comparison is made of the performances of two detection strategies that are based on different data fusion techniques. The strategies detect changes in a linear system. One detection strategy involves combining the estimates and error covariance matrices of distributed Kalman filters, generating a residual from the used estimates, comparing this residual to a threshold, and making a decision. The other detection strategy involves a distributed decision process in which estimates from distributed Kalman filters are used to generate distributed residuals which are compared locally to a threshold. Local decisions are made and these decisions are then fused into a global decision. The performances of each of these detection schemes are compared, and it is concluded that better performance is achieved when local decisions are made and then fused into a global decision.
Distributed-effect sensors, which respond to spatially distributed inputs over a significant gauge length, encompass piezoelectric laminate films, modal-domain optical fiber sensors, and holographic sensors; they can be fabricated with spatially varying sensitivity to a distributed measurand for spatial filtering. Such spatial filters are configurable to extract various structural parameters from distributed measurements that cannot be directly measured by sensors. A modeling is presently conducted for distributed-effect sensors' integration into state-space structural models, noting the effects of fabrication errors on sensor operation.
A novel method is presented for identifying a state-space model and a state estimator for linear stochastic systems from input and output data. The method is primarily based on the relationship between the state-space model and the finite-difference model of linear stochastic systems derived through projection filters. It is proved that least-squares identification of a finite difference model converges to the model derived from the projection filters. System pulse response samples are computed from the coefficients of the finite difference model.
A remote sensing multispectral imaging instrument is being developed that uses a high resolution, fast programmable acoustooptic tunable filter (AOTF) as the spectral bandpass filter. A compact and fully computer controllable AOTF-based imaging spectrometer that operates in the visible wavelength range (0.5-0.8 microns) has been built and tested with success. A second imaging spectrometer operating in the near-infrared wavelength range (1.2-2.4 microns) is also under experimental investigation. The design criteria meeting various system issues, such as imaging quality, spectral response, and field of view (FOV), are discussed. An experiment using this AOTF imaging spectrometer breadboard is described.
A novel approach to the reduction of scattered, interfering signals that corrupt measurements of the signal backscattered from radar targets of interest is being developed. It is being explored with sphere measurements in an indoor microwave radar range. This method is based on the concept of Wiener filtering (which minimizes the difference between the signal plus noise and the desired signal in the time domain). In contrast to the traditional Wiener filter, in which the time domain error between two sequences are minimized, the approach reported uses the frequency domain phasor amplitudes of a swept frequency signal. It minimizes the difference (least-mean-square-magnitude) between the signal-plus-noise and the signal complex phasors, across the entire spectrum.
This paper discusses recent activities of Jet Propulsion Laboratory in the development of a new type of remote sensing multispectral imaging instruments using acousto-optic tunable filter (AOTF) as programmable bandpass filter. This remote sensor provides real-time operation; observational flexibility; measurements of spectral, spatial, and polarization information using a single instrument; and compact, solid state structure without moving parts. Two microcomputer-controlled AOTF imaging spectrometer breadboard systems were designed and built. One operates in the wavelength range of 0.48-0.76 micron and the other in the range of 1.2-2.5 micron. Experiments were performed using these two systems to observe geological and botanical objects in laboratory and outdoor environment. Results have demonstrated the feasibility of using the AOTF multispectral imaging system as a real-time versatile remote sensor with operational flexibility for future Army tactical applications.
The paper describes a new operatorial approach to the study of turbulence, based on the general algebraic properties of the filtered representations of a turbulence field at different levels. The main results of this analysis is the averaging invariance of the filtered Navier-Stokes eaquations in terms of the generalized central moments, and an algebraic identity that relates the turbulent stresses at different levels. The resolved turbulence is defined, the algebraic consistency rules that relate these resolved quantities to the turbulent stresses at different levels are derived, and their possible uses in subgrid modeling is discussed.
Kalman-filter "observer" algorithm used in estimating unmeasured output variables of turbofan engine. Unmeasured output variables needed along with measured output variables as feedback data to characterize state of aircraft-and-engine system for control purposes. Subsystem or processor implementing Kalman-filter algorithm incorporated into adaptive digital control system, called "performance-seeking" control system, developed to compute engine-control settings to optimize performance of aircraft under given pseudo-steady-state cruise conditions. Basic concept of algorithm not restricted to aircraft-engine application and can be extended to internal-combustion engines in land vehicles.
Relatively simple low-pass-filtering algorithm suppresses high-frequency noise in digitally sampled signal. Algorithm derived from convolution of signal with causal exponential. Filter implemented by algorithm does not introduce phase shift. Algorithm applies forward convolution in time followed by backward convolution in time, and phase shift in second convolution cancels phase shift in first convolution.
Report describes experimental and theoretical studies of filters in propellant streams of propulsion system in Galileo spacecraft. Studies contributed to base of information useful in optimizing design of filters in propulsion systems of future spacecraft.
By using traditional control concepts of gain root locus, the active suppression of a flutter mode of a flexible wing is examined. It is shown that the attraction of the unstable mode towards a critical system zero determines the degree to which the flutter mode can be stabilized. For control situations where the critical zero is adversely placed in the complex plane, a novel compensation scheme called a 'Dipole' filter is proposed. This filter ensures that the flutter mode is stabilized with acceptable control energy. The control strategy is illustrated by designing flutter suppression laws for an active flexible wing (AFW) wind-tunnel model, where minimal control effort solutions are mandated by control rate saturation problems caused by wind-tunnel turbulence.
A technique is developed which demonstrates how to interpret a large fully-populated filter gain matrix as a set of scalar gains. The inverse problem is also solved, namely, how to develop a large-order filter gain matrix from a specified set of scalar gains. Examples are given to illustrate the method.
This paper presents a unique scheme for reconstructing tethered satellite skiprope motion by ground processing satellite magnetometer measurements. The measurements are modified based on ground knowledge of the earth's magnetic field and passed through bandpass filters tuned to the skiprope frequency. Skiprope motion is then deduced from the steady-state outputs of the filters. Simulation results are presented which verify the scheme and show it to be robust. The concept is not just limited to tethered satellites. Indeed, it can be applied whenever there is a need to reconstruct the coning motion of a body about a known axis, given measurements of a known vector in body-fixed axes.
Kalman filter estimates of zenith nondispersive atmospheric path delays at Westford, Massachusetts, Fort Davis, Texas, and Mojave, California, were obtained from independent analyses of data collected during January and February 1988 using the GPS and VLBI. The apparent accuracy of the path delays is inferred by examining the estimates and covariances from both sets of data. The ability of the geodetic data to resolve zenith path delay fluctuations is determined by comparing further the GPS Kalman filter estimates with corresponding wet path delays derived from water vapor radiometric data available at Mojave over two 8-hour data spans within the comparison period. GPS and VLBI zenith path delay estimates agree well within one standard deviation formal uncertainties (from 10-20 mm for GPS and 3-15 mm for VLBI) in four out of the five possible comparisons, with maximum differences of 5 and 21 mm over 8- to 12-hour data spans.