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An electrical filter for removing noise from a voice communications signal is reported; seven sample values of the signal are obtained continuously, updated and subjected to filtering. Filtering is accomplished by adding balanced, with respect to a mid-point sample, spaced pairs of the sampled values, and then multiplying each pair by a selected filter constant. The signal products thus obtained are summed to provide a filtered version of the original signal.
An analysis of the use of ultraviolet narrow-band interference filters for total ozone determination is given with reference to the New Zealand filter spectrophotometer under the headings of filter monochromaticity, temperature dependence, orientation dependence, aging, and specification tolerances and nonuniformity. Quantitative details of each problem are given, together with the means used to overcome them in the New Zealand instrument. The tuning of the instrument's filter center wavelengths to a common set of values by tilting the filters is also described, along with a simple calibration method used to adjust and set these center wavelengths.
Unbalanced quadriphase-shift-keying (QPSK) is an attractive means for transmitting two digital data streams which in general have different average powers, data rates, and data formats. Previous analyses of the tracking performance of Costas loop demodulators of unbalanced QPSK have accounted only for the filtering effect produced by the loop's two arm filters on the equivalent additive noise perturbing the loop. When the bandwidth of these filters is selected on the basis of the order of the data rate, as is typical of optimum Costas loop design, the filtering degradations of the data modulations themselves and the cross-modulation noise produced by their multiplication in the loop often cannot be neglected. The purpose of this paper is to incorporate these additional filtering effects into the analysis. Many of the results obtained herein are in the form of closed-form expressions which can easily be evaluated numerically for design and performance prediction purposes.
The performance of five digital range rate filters is evaluated. A range rate filter receives an input of range data from a radar unit and produces an output of smoothed range data and its estimated derivative range rate. The filters are compared through simulation on an IBM 370. Two of the filter designs are implemented on a 6800 microprocessor-based system. Comparisons are made on the bases of noise variance reduction ratios and convergence times of the filters in response to simulated range signals.
This invention was an improvement in aircraft control systems that utilized feedback motion sensors to generate a signal to control the aircraft. The improvement consisted essentially of a complementary filter comprising a simplified model of the aircraft, a high pass filter, a low pass filter and a summing amplifier. The control signal was applied to the simplified model of the aircraft which attempted to compute the vehicle response to the signal. This computed response was then fed into the high pass filter to eliminate long term errors in the calculated response, with the result that a good estimate of the high frequency content of the aircraft motion was obtained. In order to obtain a good estimate of the low frequency content of the motion, a rate gyro signal was fed through the low pass filter that eliminates all of the offending noise.
Image reduction used to simulate increase in altitude of an acquisition platform is equivalent to data smoothing, and can be achieved either by neighborhood averaging or by filtering techniques. The averaging approach is limited for accurate simulation. A filtering method is described which was based on the hypothesis that all changes due to altitude increase can be represented by a point spread function. Determination of the scale function and factor are discussed as well as implementation of the filtering. Filtering can be either in the spatial or frequency domain. In the spatial domain, filtering consists of the convolution of the image with the weights mask, and then of the declination of the points according to the appropriates scale factor. A simulation of an aircraft day image in the infrared channel is examined.
There has been strong motivation to produce numerically stable formulations of the Kalman filter algorithms because it has long been known that the original discrete-time Kalman formulas are numerically unreliable. Numerical instability can be avoided by propagating certain factors of the estimate error covariance matrix rather than the covariance matrix itself. This paper documents filter algorithms that correspond to the covariance factorization P = UDU(T), where U is a unit upper triangular matrix and D is diagonal. Emphasis is on computational efficiency and numerical stability, since these properties are of key importance in real-time filter applications. The history of square-root and U-D covariance filters is reviewed. Simple examples are given to illustrate the numerical inadequacy of the Kalman covariance filter algorithms; these examples show how factorization techniques can give improved computational reliability.
Remote sensing data from satellites typically have three dimensions: scan position, spacecraft position, and wavelength. Inversion of the radiometric data to infer geophysical parameters is a filtering problem in which the dimension of wavelength (or channel number) is transformed into a dimension of geophysical parameters, and the most general solution is a three-dimensional filter. Linear filters have the advantages of computational speed and easily described transfer functions; but often the measurements are nonlinear functions of the parameters to be inferred. To the extent that the nonlinear inversion problem is overdetermined, it can be modeled by a critically determined linear problem. As an example, inversion of Scanning Multichannel Microwave Radiometer (SMMR) data by means of a three-dimensional Wiener Filter is described. Atmospheric water vapor content, rain liquid water content, surface wind speed and surface temperature are the parameters inferred from the measurements. Nonprecipitating liquid water and water vapor scale height are also modeled but not retrieved. The a priori statistics on which the filter is trained have the effect of governing the selection of a trade-off point of noise as a function of resolution (in all three retrieval dimensions).
The application of various Fourier filtering techniques in a coarse resolution grid point model and the poor simulation of the nonlinear effects responsible for change in the mean zonal flow is discussed. The poor simulation results in spurious energy and momentum transfers. An operational filter, a second order filter on a staggered grid, a fourth order filter on a nonstaggered grid, and a biharmonic diffusion filter were considered.
A 3 x 3 pixel spatial filter for postclassification was used for wheat classification to evaluate the effects of this procedure on the accuracy of area estimation using LANDSAT digital data obtained from a single pass. Quantitative analyses were carried out in five test sites (approx 40 sq km each) and t tests showed that filtering with threshold values significantly decreased errors of commission and omission. In area estimation filtering improved the overestimate of 4.5% to 2.7% and the root-mean-square error decreased from 126.18 ha to 107.02 ha. Extrapolating the same procedure of automatic classification using spatial filtering for postclassification to the whole study area, the accuracy in area estimate was improved from the overestimate of 10.9% to 9.7%. It is concluded that when single pass LANDSAT data is used for crop identification and area estimation the postclassification procedure using a spatial filter provides a more accurate area estimate by reducing classification errors.
A technique is described in which all data points along an arc of the orbit may be used in an optimal numerical filter for wide-field-of-view measurements of earth emitted radiation. The statistical filter design is derived whereby the filter is required to give a minimum variance estimate of the radiative exitance at discrete points along the ground track of the satellite. An equation for the optimal numerical filter is given by minimizing the estimate error variance equation with respect to the filter weights, resulting in a discrete form of the Wiener-Hopf equation. Finally, variances of the errors in the radiant exitance can be computed along the ground track and in the cross track directions.
Standard image processing techniques which are used to enhance noncoherent optically produced images are not applicable to radar images due to the coherent nature of the radar imaging process. A model for the radar imaging process is derived in this paper and a method for smoothing noisy radar images is also presented. The imaging model shows that the radar image is corrupted by multiplicative noise. The model leads to the functional form of an optimum (minimum MSE) filter for smoothing radar images. By using locally estimated parameter values the filter is made adaptive so that it provides minimum MSE estimates inside homogeneous areas of an image while preserving the edge structure. It is shown that the filter can be easily implemented in the spatial domain and is computationally efficient. The performance of the adaptive filter is compared (qualitatively and quantitatively) with several standard filters using real and simulated radar images.
Digital flight control systems are popular for their flexibility, reliability, and power; however, their use sometimes results in deficient handling qualities, including pilot-induced oscillation (PIO), which can require extensive redesign of the control system. When redesign is not immediately possible, temporary solutions, such as the PIO suppression (PIOS) filter developed for the Space Shuttle, have been proposed. To determine the effectiveness of such PIOS filters on more conventional, high-performance aircraft, three experiments were performed using the NASA F-8 digital fly-by-wire and USAF/Calspan NT-33 variable-stability aircraft. Two types of PIOS filters were evaluated, using high-gain, precision tasks (close formation, probe-and-drogue refueling, and precision touch-and-go landing) with a time delay or a first-order lag added to make the aircraft prone to PIO. Various configurations of the PIOS filter were evaluated in the flight programs, and most of the PIOS filter configurations reduced the occurrence of PIOs and improved the handling qualities of the PIO-prone aircraft. These experiments also confirmed the influence of high-gain tasks and excessive control system time delay in evoking pilot-induced oscillations.
Digital flight control systems are popular for their flexibility, reliability, and power; however, their use sometimes results in deficient handling qualities, including pilot-induced oscillation (PIO), which can require extensive redesign of the control system. When redesign is not immediately possible, temporary solutions, such as the PIO suppression (PIOS) filter developed for the Space Shuttle, have been proposed. To determine the effectiveness of such PIOS filters on more conventional, high-performance aircraft, three experiments were performed using the NASA F-8 digital fly-by-wire and USAF/Calspan NT-33 variable-stability aircraft. Two types of PIOS filters were evaluated, using high-gain, precision tasks (close formation, probe-and-drogue refueling, and precision touch-and-go landing) with a time delay or a first-order lag added to make the aircraft prone to PIO. Various configurations of the PIOS filter were evaluated in the flight programs, and most of the PIOS filter configurations reduced the occurrence of PIOs and improved the handling qualities of the PIO-prone aircraft. These experiments also confirmed the influence of high-gain tasks and excessive control system time delay in evoking pilot-induced oscillations.
The high latitude filtering techniques commonly employed in global grid point models to eliminate the high frequency waves associated with the convergence of meridians, can introduce serious distortions which ultimately affect the solution at all latitudes. Experiments completed so far with the 4 deg x 5 deg, 9-level GLAS Fourth Order Model indicate that the high latitude filter currently in operation affects only minimally its forecasting skill. In one case, however, the use of pressure gradient filter significantly improved the forecast. Three day forecasts with the pressure gradient and operational filters are compared as are 5-day forecasts with no filter.
An s/z hybrid model for a general phase locked loop is proposed. The impact of the loop filter on the stability, gain margin, noise equivalent bandwidth, steady state error and time response is investigated. A specific digital filter is selected which maximizes the overall gain margin of the loop. This filter can have any desired number of integrators. Three integrators are sufficient in order to track a phase jerk with zero steady state error at loop update instants. This filter has one zero near z = 1.0 for each integrator. The total number of poles of the filter is equal to the number of integrators plus two.
New manufacturing process produces filters strong, yet have small, precise dimensions and smooth surface finish essential for dichroic filtering at submillimeter wavelengths. Many filters, each one essentially wafer containing fine metal grid made at same time. Stacked square wires plated, fused, and etched to form arrays of holes. Grid of nickel and tin held in brass ring. Wall thickness, thickness of filter (hole depth) and lateral hole dimensions all depend upon operating frequency and filter characteristics.
Four design methodologies for loop filters for a class of digital phase-locked loops (DPLLs) are presented. The first design maps an optimum analog filter into the digital domain; the second approach designs a filter that minimizes in discrete time weighted combination of the variance of the phase error due to noise and the sum square of the deterministic phase error component; the third method uses Kalman filter estimation theory to design a filter composed of a least squares fading memory estimator and a predictor. The last design relies on classical theory, including rules for the design of compensators. Linear analysis is used throughout the article to compare different designs, and includes stability, steady state performance and transient behavior of the loops. Design methodology is not critical when the loop update rate can be made high relative to loop bandwidth, as the performance approaches that of continuous time. For low update rates, however, the miminization method is significantly superior to the other methods.