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NASA NTRS1994

Application of DFT Filter Banks and Cosine Modulated Filter Banks in Filtering

None given. This is a proposal for a paper to be presented at APCCAS '94 in Taipei, Taiwan. (From outline): This work is organized as follows: Sec. II is devoted to the construction of the new 2m channel under-decimated DFT filter bank. Implementation and complexity of this DFT filter bank are discussed therein. IN a similar manner, the new 2m channel cosine modulated filter bank is discussed in Sec. III. Design examples are given in Sec. IV.

filter bank subband coding coding block filtering ↗

NASA NTRS1997

Root-Raised Cosine Filter Implementation That Uses Canonical Signed Digits for High-Speed Digital Filter Applications

NASA Lewis Research Center's Space Communications Division has been investigating high-speed digital filters that can operate at a higher speed than those in current use for a digital modulator and demodulator (modem). Using the Canonical Signed Digits (CSD) number representation for filter coefficients is a very effective way to increase the filter's speed while reducing complexity in the digital filter hardware design. This approach is a good alternative to using an expensive parallel-processing design technique or custom, application-specific integrated circuits. Such integrated circuits may not be suitable for applications that require filter speeds faster than what application-specific integrated circuits digital signal processors can offer for a dedicated channel. When a communication channel is a dedicated, multiplication process--a costly, time-consuming process--it can be greatly simplified by a replacement of the filter coefficients with CSD numbers. A computer code written with the MATLAB software package runs the program and generates CSD-represented filter coefficients that are based on minimizing minimum mean square errors. Also, the Alta Group of Cadence's Signal Processing Workstation is used to simulate and analyze the CSD filter responses. The impulse response of the root-raised cosine filter that is used as a base model is defined. From this filter, a set of coefficients is sampled and stored in a file. For the all coefficients, the optimal CSD number for each coefficient is searched on the basis of the minimum-mean-square-errors criterion. Because the distribution of CSD numbers is not uniform, quantization errors tend to be bigger for coefficients greater than 1/2. To offset errors that occur in a region of coefficients between 1/2 to 1 and to better represent fractions with CSD numbers, an extra nonzero digit is allowed for any coefficients exceeding 1/2. This will greatly improve frequency response as well as intersymbol interference at the receiver. The frequency response of a set of collected CSD-represented filter coefficients was compared with the same filter that was conventionally implemented. Analyses show CSD-implemented filters perform as well as conventional filters. Comparison of eye diagrams and bit-error-rate curves between CSD filters and traditionally implemented filters are almost indistinguishable. However, filter complexity was reduced from almost 3.5 to 1 for CSD filters. Complete computer simulation results are available. In the near future, work will focus on building actual working digital filter hardware in a field programmable gate array (FPGA).

Kim, Heechul↗

NASA NTRS1986

A recursive solution for a fading memory filter derived from Kalman filter theory

A simple recursive solution for a class of fading memory tracking filters is presented. A fading memory filter provides estimates of filter states based on past measurements, similar to a traditional Kalman filter. Unlike a Kalman filter, an exponentially decaying weight is applied to older measurements, discounting their effect on present state estimates. It is shown that Kalman filters and fading memory filters are closely related solutions to a general least squares estimator problem. Closed form filter transfer functions are derived for a time invariant, steady state, fading memory filter. These can be applied in loop filter implementation of the Deep Space Network (DSN) Advanced Receiver carrier phase locked loop (PLL).

Statman, J. I.↗

NASA NTRS2007

Comparing Consider-Covariance Analysis with Sigma-Point Consider Filter and Linear-Theory Consider Filter Formulations

Recent literature in applied estimation theory reflects growing interest in the sigma-point (also called unscented ) formulation for optimal sequential state estimation, often describing performance comparisons with extended Kalman filters as applied to specific dynamical problems [c.f. 1, 2, 3]. Favorable attributes of sigma-point filters are described as including a lower expected error for nonlinear even non-differentiable dynamical systems, and a straightforward formulation not requiring derivation or implementation of any partial derivative Jacobian matrices. These attributes are particularly attractive, e.g. in terms of enabling simplified code architecture and streamlined testing, in the formulation of estimators for nonlinear spaceflight mechanics systems, such as filter software onboard deep-space robotic spacecraft. As presented in [4], the Sigma-Point Consider Filter (SPCF) algorithm extends the sigma-point filter algorithm to the problem of consider covariance analysis. Considering parameters in a dynamical system, while estimating its state, provides an upper bound on the estimated state covariance, which is viewed as a conservative approach to designing estimators for problems of general guidance, navigation and control. This is because, whether a parameter in the system model is observable or not, error in the knowledge of the value of a non-estimated parameter will increase the actual uncertainty of the estimated state of the system beyond the level formally indicated by the covariance of an estimator that neglects errors or uncertainty in that parameter. The equations for SPCF covariance evolution are obtained in a fashion similar to the derivation approach taken with standard (i.e. linearized or extended) consider parameterized Kalman filters (c.f. [5]). While in [4] the SPCF and linear-theory consider filter (LTCF) were applied to an illustrative linear dynamics/linear measurement problem, in the present work examines the SPCF as applied to nonlinear sequential consider covariance analysis, i.e. in the presence of nonlinear dynamics and nonlinear measurements. A simple SPCF for orbit determination, exemplifying an algorithm hosted in the guidance, navigation and control (GN&C) computer processor of a hypothetical robotic spacecraft, was implemented, and compared with an identically-parameterized (standard) extended, consider-parameterized Kalman filter. The onboard filtering scenario examined is a hypothetical spacecraft orbit about a small natural body with imperfectly-known mass. The formulations, relative complexities, and performances of the filters are compared and discussed.

Lisano, Michael E.↗

NASA NTRS1976

Improvement in birefringent filters. IV - The alternate partial polarizer filter

The design and performance of a birefringent filter with alternate partial polarizers are analyzed. The properties of several filter configurations with imperfect intermediate polarizers are examined. It is shown that such filters have significant advantages in transmission and profile shape over both the standard Lyot and the contrast element Lyot filters. These theoretical advantages are demonstrated by the measured properties of an actual filter using the alternate partial polarizer (APP) design. This filter is a four-module eight-crystal APP device which has been built using Polaroid HN-38 for the perfect polarizers and two laminated sheets of HN-55 for the partial polarizers. The measured characteristics of the filter are found to be in good agreement with theory.

Title, A. M.↗

NASA NTRS1991

Estimation of optimal Kalman filter gain from non-optimal filter residuals

This paper presents a novel method of estimating the optimal steady state Kalman filter gain of a linear discrete time-invariant system from a non-optimal Kalman filter residual sequence. The relation between the optimal residual sequence and a signal derived from the non-optimal residual sequence is described by a Moving Average (MA) model whose coefficients are expressed in terms of the state space parameters and the optimal steady state Kalman filter gain. In order to identify the MA model, a whitening filter of the derived signal, which corresponds to an AutoRegressive (AR) model of the signal, is first identified using the least-squares method. Then the inverse filter of the whitening filter, which corresponds to the MA model, is calculated. From the coefficients of the identified MA model, the optimal steady state Kalman filter gain can be obtained. Numerical example is provided to illustrate the feasibility of this approach.

Chen, Chung-Wen↗

NASA NTRS1994

Enhanced orbit determination filter: Inclusion of ground system errors as filter parameters

The theoretical aspects of an orbit determination filter that incorporates ground-system error sources as model parameters for use in interplanetary navigation are presented in this article. This filter, which is derived from sequential filtering theory, allows a systematic treatment of errors in calibrations of transmission media, station locations, and earth orientation models associated with ground-based radio metric data, in addition to the modeling of the spacecraft dynamics. The discussion includes a mathematical description of the filter and an analytical comparison of its characteristics with more traditional filtering techniques used in this application. The analysis in this article shows that this filter has the potential to generate navigation products of substantially greater accuracy than more traditional filtering procedures.

Masters, W. C.↗

NASA NTRS1994

Reflective Filters Design for Self-Filtering Narrowband Ultraviolet Imaging Experiment Wide-Field Surveys (NUVIEWS) Project

We report the design of multilayer reflective filters for the self-filtering cameras of the NUVIEWS project. Wide angle self-filtering cameras were designed to image the C IV (154.9 nm) line emission, and H2 Lyman band fluorescence (centered at 161 nm) over a 20 deg x 30 deg field of view. A key element of the filter design includes the development of pi-multilayers optimized to provide maximum reflectance at 154.9 nm and 161 nm for the respective cameras without significant spectral sensitivity to the large cone angle of the incident radiation. We applied self-filtering concepts to design NUVIEWS telescope filters that are composed of three reflective mirrors and one folding mirror. The filters with narrowband widths of 6 and 8 rim at 154.9 and 161 nm, respectively, have net throughputs of more than 50 % with average blocking of out-of-band wavelengths better than 3 x 10(exp -4)%.

Park, Jung- Ho↗

NASA NTRS2017

International Space Station (ISS) Bacterial Filter Elements (BFEs): Filter Efficiency and Pressure Testing of Returned Units

The air revitalization system aboard the International Space Station (ISS) provides the vital function of maintaining a clean cabin environment for the crew and the hardware. This becomes a serious challenge in pressurized space compartments since no outside air ventilation is possible, and a larger particulate load is imposed on the filtration system due to lack of sedimentation due to the microgravity environment in Low Earth Orbit (LEO). The ISS Environmental Control and Life Support (ECLS) system architecture in the U.S. Segment uses a distributed particulate filtration approach consisting of traditional High-Efficiency Particulate Adsorption (HEPA) media filters deployed at multiple locations in each U.S. Segment module; these filters are referred to as Bacterial Filter Elements, or BFEs. These filters see a replacement interval, as part of maintenance, of 2-5 years dependent on location in the ISS. In this work, we present particulate removal efficiency, pressure drop, and leak test results for a sample set of 8 BFEs returned from the ISS after filter replacement. The results can potentially be utilized by the ISS Program to ascertain whether the present replacement interval can be maintained or extended to balance the on-ground filter inventory with extension of the lifetime of ISS beyond 2024. These results can also provide meaningful guidance for particulate filter designs under consideration for future deep space exploration missions.

Filtration↗

NASA NTRS1993

Exact reconstruction analysis/synthesis filter banks with time-varying filters

This paper examines some of the analysis/synthesis issues associated with FIR time-varying filter banks where the filter bank coefficients are allowed to change in response to the input signal. Several issues are identified as being important in order to realize performance gains from time-varying filter banks in image coding applications. These issues relate to the behavior of the filters as transition from one set of filter banks to another occurs. Lattice structure formulations for the time varying filter bank problem are introduced and discussed in terms of their properties and transition characteristics.

Arrowood, J. L., Jr.↗

NASA NTRS1977

Optimal filtering and filter stability of linear stochastic delay systems

Optimal filtering equations are obtained for very general linear stochastic delay systems. Stability of the optimal filter is studied in the case where there are no delays in the observations. Using the duality between linear filtering and control, asymptotic stability of the optimal filter is proved. Finally, the cascade of the optimal filter and the deterministic optimal quadratic control system is shown to be asymptotically stable as well.

Kwong, R. H.-S.↗

NASA NTRS2016

Filter Efficiency and Leak Testing of Returned ISS Bacterial Filter Elements After 2.5 Years of Continuous Operation

The atmosphere revitalization equipment aboard the International Space Station (ISS) and future deep space exploration vehicles provides the vital functions of maintaining a habitable environment for the crew as well as protecting the hardware from fouling by suspended particulate matter. Providing these functions are challenging in pressurized spacecraft cabins because no outside air ventilation is possible and a larger particulate load is imposed on the filtration system due to lack of sedimentation in reduced gravity conditions. The ISS Environmental Control and Life Support (ECLS) system architecture in the U.S. Segment uses a distributed particulate filtration approach consisting of traditional High-Efficiency Particulate Adsorption (HEPA) filters deployed at multiple locations in each module. These filters are referred to as Bacteria Filter Elements (BFEs). As more experience has been gained with ISS operations, the BFE service life, which was initially one year, has been extended to two to five years, dependent on the location in the U.S. Segment. In previous work we developed a test facility and test protocol for leak testing the ISS BFEs. For this work, we present results of leak testing a sample set of returned BFEs with a service life of 2.5 years, along with particulate removal efficiency and pressure drop measurements. The results can potentially be utilized by the ISS Program to ascertain whether the present replacement interval can be maintained or extended to balance the on-ground filter inventory with extension of the lifetime of ISS to 2024. These results can also provide meaningful guidance for particulate filter designs under consideration for future deep space exploration missions.

ISS Air Quality↗

NASA NTRS2017

Filter Efficiency and Pressure Testing of Returned ISS Bacterial Filter Elements (BFEs)

The air quality control equipment aboard the International Space Station (ISS) and future deep space exploration vehicles provide the vital function of maintaining a clean cabin environment for the crew and the hardware. This becomes a serious challenge in pressurized space compartments since no outside air ventilation is possible, and a larger particulate load is imposed on the filtration system due to lack of sedimentation. The ISS Environmental Control and Life Support (ECLS) system architecture in the U.S. Segment uses a distributed particulate filtration approach consisting of traditional High-Efficiency Particulate Air (HEPA) filters deployed at multiple locations in each U.S. Seg-ment module; these filters are referred to as Bacterial Filter Elements, or BFEs. In our previous work, we presented results of efficiency and pressure drop measurements for a sample set of two returned BFEs with a service life of 2.5 years. In this follow-on work, we present similar efficiency, pressure drop, and leak tests results for a larger sample set of six returned BFEs. The results of this work can aid the ISS Program in managing BFE logistics inventory through the stations planned lifetime as well as provide insight for managing filter element logistics for future exploration missions. These results also can provide meaningful guidance for particulate filter designs under consideration for future deep space exploration missions.

ISS Air Quality↗

NASA NTRS2017

International Space Station (ISS) Bacterial Filter Elements (BFEs): Filter Efficiency and Pressure Drop Testing of Returned Units

ISS Air Quality↗

NASA NTRS1975

Worst-error analysis of batch filter and sequential filter in navigation problems

This paper proposes a worst-error analysis for dealing with problems of estimation of spacecraft trajectories in deep space missions. Navigation filters in use assume either constant or stochastic (Markov) models for their estimated parameters. When the actual behavior of these parameters does not follow the pattern of the assumed model, the filters sometimes result in very poor performance. To prepare for such pathological cases, the worst errors of both batch and sequential filters are investigated based on the incremental sensitivity studies of these filters. By finding critical switching instances of non-gravitational accelerations, intensive tracking can be carried out around those instances. Also the worst errors in the target plane provide a measure in assignment of the propellant budget for trajectory corrections. Thus the worst-error study presents useful information as well as practical criteria in establishing the maneuver and tracking strategy of spacecraft's missions.

Nishimura, T.↗

NASA NTRS1990

Rocket noise filtering system using digital filters

A set of digital filters is designed to filter rocket noise to various bandwidths. The filters are designed to have constant group delay and are implemented in software on a general purpose computer. The Parks-McClellan algorithm is used. Preliminary tests are performed to verify the design and implementation. An analog filter which was previously employed is also simulated.

Mauritzen, David↗

NASA NTRS1991

Mass property identification - A comparison study between extended Kalman filter and neuro-filter approaches

Two algorithms, extended Kalman filter and neuro-filter, are formulated to perform mass property identification for the Space Station Freedom. Control moment gyros that are part of the Station's basic momentum management system are chosen to provide input excitation in the form of applied torques. These torques together with the measured angular body rate responses are supplied to the filters. From these data, both algorithms are shown to accurately identify the station mass properties when excitation levels are high and balanced between axes. The neuro-filter, however, is shown to be more robust and to perform well even with weakly persistent, unbalanced signals contaminated with noise.

Lam, Quang↗

NASA NTRS2017

Summary of Martian Dust Filtering Challenges and Current Filter Development

Traditional air particulate filtering in manned spaceflight (Apollo, Shuttle, ISS, etc.) has used cleanable or replaceable catch filters such as screens and High-Efficiency Particulate Arrestance (HEPA) filters. However, the human mission to Mars architecture will require a new approach. It is Martian dust that is the particulate of concern but the need also applies to particulates generated by crew. The Mars Exploration Program Analysis Group (MEPAG) high-lighted this concern in its Mars Science, Goals, Objectives, Investigations and Priorities document [7], by saying specifically that one high priority investigation will be to "Test ISRU atmospheric processing systems to measure resilience with respect to dust and other environmental challenge performance parameters that are critical to the design of a full-scale system." By stating this as high priority the MEPAG is acknowledging that developing and adequately verifying this capability is critical to success of a human mission to Mars. This architecture will require filtering capabilities that are highly reliable, will not restrict the flow path with clogging, and require little to no maintenance. This paper will summarize why this is the case, the general requirements for developing the technology, and the status of the progress made in this area.

O'Hara, William J., IV↗