SEARCH · Engineering Papers

Results for “Filter error”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4

All sources NASA NTRS DOE OSTI

NASA NTRS1980

A parametric study of aliasing error for a narrow field of view scanning radiometer

Starting from the general measurement equation, it is shown that a NFOV scanner can be approximated by a spatially invariant system whose point spread function depends on the detector shape and angular characteristics and electrical filter transfer function for given patches at the top of the atmosphere. The radiometer is modeled by a detector, electrical filter, analog to digital converter followed by a reconstruction filter. The errors introduced by aliasing and blurring into a reconstruction of the input radiant exitance are modeled and analyzed for various detector shapes, sampling intervals, electrical filters and scan types. Quantitative results on the errors introduced are presented showing the various tradeoffs between design parameters. The results indicate that proper selection of detector shape coupled with electrical filter can reduce aliasing errors significantly.

Halyo, N.↗

NASA NTRSDate not supplied

Sequential Filtering in the Presence of Uniform Measurement Errors

This paper presents a sequential filtering strategy using observations corrupted with uniform measurement noise. While the Kalman filter remains the best linear estimator of the state, other filtering techniques provide minimum variance optimal estimates, a trait only enjoyed by the Kalman filter when the underlying noises are, in fact, Gaussian. This work develops a new approximate optimal estimator for uniform measurement noises. The resulting recursion requires just slightly more computational time to complete a measurement update than the Kalman filter, which generally cannot be claimed by other optimal strategies such as the particle or Gaussian mixture filters.

James S. McCabe↗

NASA NTRSDate not supplied

Sequential Filtering in the Presence of Uniform Measurement Errors

James S McCabe↗

NASA NTRS1984

GP-B error modeling and analysis

The analysis and modeling for the Gravity Probe B (GP-B) experiment is reported. The finite-wordlength induced errors in Kalman filtering computation were refined. Errors in the crude result were corrected, improved derivation steps are taken, and better justifications are given. The errors associated with the suppression of the 1-noise were analyzed by rolling the spacecraft and then performing a derolling operation by computation.

Source record↗

NASA NTRS2004

Adaptive Numerical Dissipative Control in High Order Schemes for Multi-D Non-Ideal MHD

The goal is to extend our adaptive numerical dissipation control in high order filter schemes and our new divergence-free methods for ideal MHD to non-ideal MHD that include viscosity and resistivity. The key idea consists of automatic detection of different flow features as distinct sensors to signal the appropriate type and amount of numerical dissipation/filter where needed and leave the rest of the region free of numerical dissipation contamination. These scheme-independent detectors are capable of distinguishing shocks/shears, flame sheets, turbulent fluctuations and spurious high-frequency oscillations. The detection algorithm is based on an artificial compression method (ACM) (for shocks/shears), and redundant multi-resolution wavelets (WAV) (for the above types of flow feature). These filter approaches also provide a natural and efficient way for the minimization of Div(B) numerical error. The filter scheme consists of spatially sixth order or higher non-dissipative spatial difference operators as the base scheme for the inviscid flux derivatives. If necessary, a small amount of high order linear dissipation is used to remove spurious high frequency oscillations. For example, an eighth-order centered linear dissipation (AD8) might be included in conjunction with a spatially sixth-order base scheme. The inviscid difference operator is applied twice for the viscous flux derivatives. After the completion of a full time step of the base scheme step, the solution is adaptively filtered by the product of a 'flow detector' and the 'nonlinear dissipative portion' of a high-resolution shock-capturing scheme. In addition, the scheme independent wavelet flow detector can be used in conjunction with spatially compact, spectral or spectral element type of base schemes. The ACM and wavelet filter schemes using the dissipative portion of a second-order shock-capturing scheme with sixth-order spatial central base scheme for both the inviscid and viscous MHD flux derivatives and a fourth-order Runge-Kutta method are denoted.

Yee, H. C.↗

NASA NTRS2013

Optimized tuner selection for engine performance estimation

A methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented. This technique specifically addresses the underdetermined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multi-variable iterative search routine which seeks to minimize the theoretical mean-squared estimation error. Theoretical Kalman filter estimation error bias and variance values are derived at steady-state operating conditions, and the tuner selection routine is applied to minimize these values. The new methodology yields an improvement in on-line engine performance estimation accuracy.

Simon, Donald L.↗

NASA NTRS1977

Modeling error analysis of stationary linear discrete-time filters

The performance of Kalman-type, linear, discrete-time filters in the presence of modeling errors is considered. The discussion is limited to stationary performance, and bounds are obtained for the performance index, the mean-squared error of estimates for suboptimal and optimal (Kalman) filters. The computation of these bounds requires information on only the model matrices and the range of errors for these matrices. Consequently, a design can easily compare the performance of a suboptimal filter with that of the optimal filter, when only the range of errors in the elements of the model matrices is available.

Patel, R.↗

NASA NTRS1987

Optimal application of Morrison's iterative noise removal for deconvolution. Appendices

Morrison's iterative method of noise removal, or Morrison's smoothing, is applied in a simulation to noise-added data sets of various noise levels to determine its optimum use. Morrison's smoothing is applied for noise removal alone, and for noise removal prior to deconvolution. For the latter, an accurate method is analyzed to provide confidence in the optimization. The method consists of convolving the data with an inverse filter calculated by taking the inverse discrete Fourier transform of the reciprocal of the transform of the response of the system. Various length filters are calculated for the narrow and wide Gaussian response functions used. Deconvolution of non-noisy data is performed, and the error in each deconvolution calculated. Plots are produced of error versus filter length; and from these plots the most accurate length filters determined. The statistical methodologies employed in the optimizations of Morrison's method are similar. A typical peak-type input is selected and convolved with the two response functions to produce the data sets to be analyzed. Both constant and ordinate-dependent Gaussian distributed noise is added to the data, where the noise levels of the data are characterized by their signal-to-noise ratios. The error measures employed in the optimizations are the L1 and L2 norms. Results of the optimizations for both Gaussians, both noise types, and both norms include figures of optimum iteration number and error improvement versus signal-to-noise ratio, and tables of results. The statistical variation of all quantities considered is also given.

Ioup, George E.↗

NASA NTRS1993

The Communication Link and Error ANalysis (CLEAN) simulator

During the period July 1, 1993 through December 30, 1993, significant developments to the Communication Link and Error ANalysis (CLEAN) simulator were completed and include: (1) Soft decision Viterbi decoding; (2) node synchronization for the Soft decision Viterbi decoder; (3) insertion/deletion error programs; (4) convolutional encoder; (5) programs to investigate new convolutional codes; (6) pseudo-noise sequence generator; (7) soft decision data generator; (8) RICE compression/decompression (integration of RICE code generated by Pen-Shu Yeh at Goddard Space Flight Center); (9) Markov Chain channel modeling; (10) percent complete indicator when a program is executed; (11) header documentation; and (12) help utility. The CLEAN simulation tool is now capable of simulating a very wide variety of satellite communication links including the TDRSS downlink with RFI. The RICE compression/decompression schemes allow studies to be performed on error effects on RICE decompressed data. The Markov Chain modeling programs allow channels with memory to be simulated. Memory results from filtering, forward error correction encoding/decoding, differential encoding/decoding, channel RFI, nonlinear transponders and from many other satellite system processes. Besides the development of the simulation, a study was performed to determine whether the PCI provides a performance improvement for the TDRSS downlink. There exist RFI with several duty cycles for the TDRSS downlink. We conclude that the PCI does not improve performance for any of these interferers except possibly one which occurs for the TDRS East. Therefore, the usefulness of the PCI is a function of the time spent transmitting data to the WSGT through the TDRS East transponder.

Ebel, William J.↗

NASA NTRS1988

A signal filter with zero phase lag

Rotorcraft and rotorcraft models are tested at the NASA Ames National Full-Scale Aerodynamics Complex (NFAC). The models tested in the NFAC wind tunnels are controlled by a remote control console located in a control room. Certain critical information must be displayed on the control console to monitor the rotor to insure that the rotor is within operational limits. The signal for these parameters is complex (with ac and dc components) and is derived from pitch and flapping transducers on the rotor head. The pitch and flapping cyclics are derived from resolver circuits that indicate the magnitude and phase of the cyclics. Conventional filter circuits used to separate the ac and dc components have a phase lag on the ac component, which will introduce an error in the cyclic vector. To overcome this error, a filter with zero phase lag needed to be developed. This paper discusses a rotorcraft circuit that was designed to have an ac/dc filter with zero phase lag over the frequency range.

Forsyth, T. J.↗

NASA NTRS1987

A further analysis for the minimum-variance deconvolution filter performance

Chi and Mendel (1984) analyzed the performance of minimum-variance deconvolution (MVD). In this correspondence, a further analysis of the performance of the MVD filter is presented. It is shown that the MVD filter performs like an inverse filter and a whitening filter as SNR goes to infinity, and like a matched filter as SNR goes to zero. The estimation error of the MVD filter is colored noise, but it becomes white when SNR goes to zero. This analysis also conects the error power-spectral density of the MVD filter with the spectrum of the causal-prediction error filter.

Chi, Chong-Yung↗

NASA NTRS2021

The benefit of brightness temperature assimilation for the SMAP Level-4 surface and root-zone soil moisture analysis

The Soil Moisture Active Passive (SMAP) Level-4 (L4) product provides global estimates of surface soil moisture (SSM) and root-zone soil moisture (RZSM) via the assimilation of SMAP brightness temperature (Tb) observations into the NASA Catchment Land Surface Model (CLSM). Here, using in situ measurements from 2474 sites in China, we evaluate the performance of soil moisture estimates from the L4 data assimilation (DA) system and from a baseline “open-loop” (OL) simulation of CLSM without Tb assimilation. Using random forest regression, the efficiency of the L4 DA system (i.e., the performance improvement in DA relative to OL) is attributed to eight control factors related to the CLSM as well as τ–ω radiative transfer model (RTM) components of the L4 system. Results show that the Spearman rank correlation (R) for L4 SSM with in situ measurements increases for 77 % of the in situ measurement locations (relative to that of OL), with an average R increase of approximately 14 % (ΔR=0.056). RZSM skill is improved for about 74 % of the in situ measurement locations, but the average R increase for RZSM is only 7 % (ΔR=0.034). Results further show that the SSM DA skill improvement is most strongly related to the difference between the RTM-simulated Tb and the SMAP Tb observation, followed by the error in precipitation forcing data and estimated microwave soil roughness parameter h. For the RZSM DA skill improvement, these three dominant control factors remain the same, although the importance of soil roughness exceeds that of the Tb simulation error, as the soil roughness strongly affects the ingestion of DA increments and further propagation to the subsurface. For the skill of the L4 and OL estimates themselves, the top two control factors are the precipitation error and the SSM–RZSM coupling strength error, both of which are related to the CLSM component of the L4 system. Finally, we find that the L4 system can effectively filter out errors in precipitation. Therefore, future development of the L4 system should focus on improving the characterization of the SSM–RZSM coupling strength.

Jianxiu Qiu↗

NASA NTRS2023

Extended Kalman Filter Performance on the Artemis-1 Mission

The Artemis Program is NASA’s campaign to explore the Moon and beyond. Artemis-1, the uncrewed exoLEO test flight of the Orion spacecraft, was completed in 2022. There were four navigation Extended Kalman Filters (EKFs) that are part of the Orion navigation system. The Atmospheric Extended Kalman Filter (ATMEKF) estimates the vehicle position, velocity, and attitude (referred to as the vehicle state) during the ascent and entry phases of flight. Once Orion is outside of Earths atmosphere, the Earth Orbit Extended Kalman Filter (EOEKF) and CisLunar Extended Kalman Filter (CLEKF) estimate the translational states, depending on the phase of flight, while the Attitude Extended Kalman Filter (ATTEKF) estimates the rotational state of the vehicle. The Kalman filters propagate the vehicle state forward in time using a combination of dynamics models and the output data from the Inertial Measurement Unit (IMU). The filters update the vehicle states and associated uncertainties, in the form of the covariance matrix, using pseudorange measurements from GPS (in ATMEKF/EOEKF), optical navigation measurements of the Earth or Moon (in CLEKF), and star tracker measurements (in ATTEKF). Simultaneously, the Kalman filters estimate error sources in the sensors, which are included in the state vectors as Exponentially Correlated Random Variables (ECRVs). This paper will summarize the performance of these filters during the Artemis-1 mission.

Artemis-1↗

NASA NTRS2004

Generation of Higher Order Modes in a Rectangular Duct

Advanced noise control methodologies to reduce sound emission from aircraft engines take advantage of the modal structure of the noise in the duct. This noise is caused by the interaction of rotor wakes with downstream obstructions such as exit guide vanes. Mode synthesis has been accomplished in circular ducts and current active noise control work has made use of this capability to cancel fan noise. The goal of the current effort is to examine the fundamental process of higher order mode propagation through an acoustically treated, curved duct. The duct cross-section is rectangular to permit greater flexibility in representation of a range of duct curvatures. The work presented is the development of a feedforward control system to generate a user-specified modal pattern in the duct. The multiple-error, filtered-x LMS algorithm is used to determine the magnitude and phase of signal input to the loudspeakers to produce a desired modal pattern at a set of error microphones. Implementation issues, including loudspeaker placement and error microphone placement, are discussed. Preliminary results from a 9-3/8 inch by 21 inch duct, using 12 loudspeakers and 24 microphones, are presented. These results demonstrate the ability of the control system to generate a user-specified mode while suppressing undesired modes.

Gerhold, Carl H.↗

NASA NTRS1988

Gravity gradient measurements

It is shown that, for floated satellite gradiometers subject to very low levels of unmodeled forces and torques, dynamic estimation can dramatically reduce rotation correction errors. The filter structure is developed, and the value of factorization techniques is examined. Numerical examples are given for a few practical cases using plausible instrument ensembles. To achieve these early results, great simplifications have been made, and important error sources have been suppressed.

Sonnabend, D.↗

NASA NTRS2021

Assessment of M2020 Terrain Relative Landing Accuracy: Flight Performance vs Predicts

Terrain Relative Navigation (TRN) was a critical enabling Entry, Descent, and Landing (EDL) technology that enabled Mars 2020 mission Perseverance rover to land at Jezero crater. TRN pro-vides real-time, autonomous, map-relative position determination and generates a landing target based on a priori knowledge of hazards. The required performance for TRN was to land within 60m of the selected target. The required 60m was sub-allocated to various error sources in three major categories: targeting error, knowledge error, and control error. The targeting error is the error in selecting an appropriate landing target and the knowledge of the target on the surface. It includes the Lander Vision System (LVS) position localization with respect the ground, the synchronization between the Lander Vision System measurement and the main Navigation filter, and errors associated with the LVS Reference Map and Safe Target Selec-tion (STS). The knowledge error is the contribution of knowledge growth from the synchronization with LVS to touchdown. The control error encompasses how accurately the system could stay on the desired reference trajectory. The TRN error budget uses a combination of analysis, simulation, and hardware test-ing results to bound the various error contributions obtained during the verification and validation process. This paper first presents a description the TRN system, focusing on the architecture of LVS and STS. The paper then gives detailed overview of the TRN error budget, with a description of the major error contribu-tions in each of the three categories. Next, the paper gives the results for three versions of the error budget, pre-launch, in-flight pre-landing, and post-landing. The paper compares the pre-flight analysis, the pre-landing analysis using in-flight data during cruise, to the post-landing analysis of the TRN performance. Pre-landing analysis best estimate of the landing performance was 33m, compared to the 60m require-ment. Post-landing analysis estimated a landing accuracy of 8.53m or better, much better than the 33m pre-landing estimate. The actual post-landing imagery calculated the distance of the rover to the targeted location to be 5m. The post-landing analysis closely bounds the image-based assessment of landing accu-racy, indicating the success of the error budget architecture in bounding the landing accuracy, as well as the fidelity of the simulations used to model and predict performance.

Chen, Allen↗

NASA NTRS1975

Separation of man-made and natural patterns in high-altitude imagery of agricultural areas

A nonstationary linear digital filter is designed and implemented which extracts the natural features from high-altitude imagery of agricultural areas. Essentially, from an original image a new image is created which displays information related to soil properties, drainage patterns, crop disease, and other natural phenomena, and contains no information about crop type or row spacing. A model is developed to express the recorded brightness in a narrow-band image in terms of man-made and natural contributions and which describes statistically the spatial properties of each. The form of the minimum mean-square error linear filter for estimation of the natural component of the scene is derived and a suboptimal filter is implemented. Nonstationarity of the two-dimensional random processes contained in the model requires a unique technique for deriving the optimum filter. Finally, the filter depends on knowledge of field boundaries. An algorithm for boundary location is proposed, discussed, and implemented.

Samulon, A. S.↗

NASA NTRS2005

Nonlinear Attitude Filtering Methods

The extended Kalman filter (EKF) is the workhorse of real-time spacecraft attitude estimation. Since the group SO3 of rotation matrices has dimension three, most attitude determination EKFs use lower- dimensional attitude parameterizations than the nine-parameter attitude matrix itself. The fact that all three- parameter representations of SO3 are singular or discontinuous for certain attitudes has led to extended discussions of constraints and attitude representations in EKFs. The most successful EKF uses a nonsingular parameterization for the global attitude, which necessarily has more than three parameters, while employing a three-component representation for the attitude errors. This filter has become known as the Multiplicative Extended Kalman Filter. These issues are now well understood, however, and the EKF has performed admirably in the vast majority of attitude determination applications. Nevertheless, poor performance or even divergence arising from the linearization implicit in the EKF has led to the development of nonlinear filters, most recently sigma point or unscented filters and particle filters.

Markley, F. Landis↗