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NASA NTRS2011

Guidance, Navigation, and Control Program

The Rendezvous and Proximity Operations Program (RPOP) is real-time guidance, navigation, and control (GN&C) domain piloting-aid software that provides 3D Orbiter graphics and runs on the Space Shuttle's Criticality-3 Payload and General Support Computer (PGSC) in the crew cockpit. This software provides the crew with Situational Awareness during the rendezvous and proximity operations phases of flight. RPOP can be configured from flight to flight, accounting for mission-specific flight scenarios and target vehicles, via initialization load (I-load) data files. The software provides real-time, automated, closed-loop guidance recommendations and the capability to integrate the crew s manual backup techniques. The software can bring all relative navigation sensor data, including the Orbiter's GPC (general purpose computer) data, into one central application to provide comprehensive situational awareness of the rendezvous and proximity operations trajectory. RPOP also can separately maintain trajectory estimates (past, current, and predicted) based on certain data types and co-plot them, in order to show how the various navigation solutions compare. RPOP s best estimate of the relative trajectory is determined by a relative Kalman filter processing data provided by the sensor suite s most accurate sensor, the trajectory control sensor (TCS). Integrated with the Kalman filter is an algorithm that identifies the reflector that the TCS is tracking. Because RPOP runs on PC laptop computers, the development and certification lifecycles are more agile, flexible, and cheaper than those that govern the Orbiter FSW (flight software) that runs in the GPC. New releases of RPOP can be turned around on a 3- to 6-month template, from new Change Request (CR) to certification, depending on the complexity of the changes.

Hinkel, Heather↗

NASA NTRS2012

Heading Toward Launch with the Integrated Multi-Satellite Retrievals for GPM (IMERG)

The Day-l algorithm for computing combined precipitation estimates in GPM is the Integrated Multi-satellitE Retrievals for GPM (IMERG). We plan for the period of record to encompass both the TRMM and GPM eras, and the coverage to extend to fully global as experience is gained in the difficult high-latitude environment. IMERG is being developed as a unified U.S. algorithm that takes advantage of strengths in the three groups that are contributing expertise: 1) the TRMM Multi-satellite Precipitation Analysis (TMPA), which addresses inter-satellite calibration of precipitation estimates and monthly scale combination of satellite and gauge analyses; 2) the CPC Morphing algorithm with Kalman Filtering (KF-CMORPH), which provides quality-weighted time interpolation of precipitation patterns following cloud motion; and 3) the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks using a Cloud Classification System (PERSIANN-CCS), which provides a neural-network-based scheme for generating microwave-calibrated precipitation estimates from geosynchronous infrared brightness temperatures. In this talk we summarize the major building blocks and important design issues driven by user needs and practical data issues. One concept being pioneered by the IMERG team is that the code system should produce estimates for the same time period but at different latencies to support the requirements of different groups of users. Another user requirement is that all these runs must be reprocessed as new IMERG versions are introduced. IMERG's status at meeting time will be summarized, and the processing scenario in the transition from TRMM to GPM will be laid out. Initially, IMERG will be run with TRMM-based calibration, and then a conversion to a GPM-based calibration will be employed after the GPM sensor products are validated. A complete reprocessing will be computed, which will complete the transition from TMPA.

Huffman, George J.↗

NASA NTRS2009

Neural Network Target Identification System for False Alarm Reduction

A multi-stage automated target recognition (ATR) system has been designed to perform computer vision tasks with adequate proficiency in mimicking human vision. The system is able to detect, identify, and track targets of interest. Potential regions of interest (ROIs) are first identified by the detection stage using an Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter combined with a wavelet transform. False positives are then eliminated by the verification stage using feature extraction methods in conjunction with neural networks. Feature extraction transforms the ROIs using filtering and binning algorithms to create feature vectors. A feed forward back propagation neural network (NN) is then trained to classify each feature vector and remove false positives. This paper discusses the test of the system performance and parameter optimizations process which adapts the system to various targets and datasets. The test results show that the system was successful in substantially reducing the false positive rate when tested on a sonar image dataset.

ATR↗

NASA NTRS2018

Attitude Control System for a Balloon Based Telescope

The Balloon Experimental Twin Telescope for Infrared Interferometry (BETTII) is an 8-meter interferometer which operates on a high-altitude balloon. BETTII had its first successful engineering flight in June 2017. In this paper we discuss the design of the control system for BETTII, which includes the coarse pointing loop and the estimator controls algorithm (Extended Kalman Filter) implemented in FPGA. We will also discuss the different system modes that we defined in the controls system loop, which are used in different phases of the flight and are activated in order to acquire a target star in the science detector. The pointing loop uses different sensors and actuators in each phase to keep pointing at the desired target. The main sensors aregyroscopes, star cameras, and auxiliary sensors such as high-altitude GPS and magnetometers. The azimuth control is achieved with Compensated Controlled Moment Gyros (CCMG) and a Momentum Dump motor. For the elevation control, high-precision motors are used, which change the elevation of the siderostat mirrors. The combination of these instruments keep the baseline oriented within few arcseconds from the target star.In this paper, we will also present the software architecture relevant to the control system. This includes the description of the two flight computers present on the payload and the different control loops that are executed on them. Similarly, we will explain the importance of synchronization between all the sensors and actuators, which have to be referenced to a single master clock in order to obtain science data.

Vila Hernandez De Lorenzo, Jordi↗

NASA NTRSDate not supplied

Adaptive Multi-Sensor Localization Information Fusion for Autonomous Urban Air Mobility Operations

An adaptive method is developed to iteratively fuse the information provided by multiple sensors to enable autonomous urban air mobility type operations. First, noisy and bias corrupted IMU readings are processed as soon as they arrive using kinematic equations represented in the vehicle's body frame. To correct the systems drift resulting from the integration, an information content measure is introduced to decide on the environment. For the cluttered environment the information provided by environmental sensors is counted as reliable and the drift correction as accurate. For the open space, the GPS data is counted as reliable, and the drift correction is done based on the GPS readings. The measurement noise effects are minimize using Iterated Extended Kalman Filter framework. The algorithm is implemented in the in-house developed FlightDeckz simulation environment using an IMU model, simulated video recorded from a camera mounted on the vehicle (for the purpose of this study, outside scenery was generated with XPlane), which flies in an urban environment, and GPS data generated from the environment's digital map.

Onboard Perception↗

NASA NTRS1994

Progress in navigation filter estimate fusion and its application to spacecraft rendezvous

A new derivation of an algorithm which fuses the outputs of two Kalman filters is presented within the context of previous research in this field. Unlike other works, this derivation clearly shows the combination of estimates to be optimal, minimizing the trace of the fused covariance matrix. The algorithm assumes that the filters use identical models, and are stable and operating optimally with respect to their own local measurements. Evidence is presented which indicates that the error ellipsoid derived from the covariance of the optimally fused estimate is contained within the intersections of the error ellipsoids of the two filters being fused. Modifications which reduce the algorithm's data transmission requirements are also presented, including a scalar gain approximation, a cross-covariance update formula which employs only the two contributing filters' autocovariances, and a form of the algorithm which can be used to reinitialize the two Kalman filters. A sufficient condition for using the optimally fused estimates to periodically reinitialize the Kalman filters in this fashion is presented and proved as a theorem. When these results are applied to an optimal spacecraft rendezvous problem, simulated performance results indicate that the use of optimally fused data leads to significantly improved robustness to initial target vehicle state errors. The following applications of estimate fusion methods to spacecraft rendezvous are also described: state vector differencing, and redundancy management.

Carpenter, J. Russell↗

NASA NTRS2012

Recursive Implementations of the Consider Filter

One method to account for parameters errors in the Kalman filter is to consider their effect in the so-called Schmidt-Kalman filter. This work addresses issues that arise when implementing a consider Kalman filter as a real-time, recursive algorithm. A favorite implementation of the Kalman filter as an onboard navigation subsystem is the UDU formulation. A new way to implement a UDU consider filter is proposed. The non-optimality of the recursive consider filter is also analyzed, and a modified algorithm is proposed to overcome this limitation.

Zanetti, Renato↗

NASA NTRS1993

Identification of a second-order mechanical system model from a state space realization

Many system identification algorithms produce models in modern state variable form. When a mechanical system is identified one knows that there must be a model in second order form. In structural dynamics one is interested in either a modal model of the system, or in a model including a mass matrix, stiffness matrix and a damping matrix. In this paper, algorithms are developed that convert a modern state space realization into the above two representations of interest in mechanical systems. The algorithm can be used with any identification methods that produce a modern state variable representation such as the Eigensystem Realization Algorithm (ERA), its modified version by data correlation (ERA/DC), and combinations of these with Observer/Kalman Filter Identification (OKID). An algorithm is developed that allows one to identify the damping matrix in a model representation, so that one determines how near to modal the damping is in the system, and can therefore understand the degree to which the modes are coupled by the damping in the system. A second algorithm produced the mass, damping, and stiffness matrices, given the input and output matrices. It is assumed that the number of sensors (or the number of actuators) is greater than or equal to the number of modes in the system, and it is proved that this is a necessary condition to be able to uniquely identify these matrices. Experience with examples suggests that a singular value truncation involved in these algorithms can help one determine the true system order. This truncation has the benefit of knowledge of the correct form for a mechanical system model, which is not present in the modern control identification algorithm.

Tseng, Dong-Huei↗

NASA NTRS1991

Identification of observer/Kalman filter Markov parameters: Theory and experiments

An algorithm to compute Markov parameters of an observer or Kalman filter from experimental input and output data is discussed. The Markov parameters can then be used for identification of a state space representation, with associated Kalman gain or observer gain, for the purpose of controller design. The algorithm is a non-recursive matrix version of two recursive algorithms developed in previous works for different purposes. The relationship between these other algorithms is developed. The new matrix formulation here gives insight into the existence and uniqueness of solutions of certain equations and gives bounds on the proper choice of observer order. It is shown that if one uses data containing noise, and seeks the fastest possible deterministic observer, the deadbeat observer, one instead obtains the Kalman filter, which is the fastest possible observer in the stochastic environment. Results are demonstrated in numerical studies and in experiments on an ten-bay truss structure.

Juang, Jer-Nan↗

NASA NTRS1991

Identification of observer/Kalman filter Markov parameters - Theory and experiments

Juang, Jer-Nan↗

NASA NTRS1997

Predictive Attitude Estimation Using Global Positioning System Signals

In this paper, a new algorithm is developed for attitude estimation using Global Positioning System (GPS) signals. The new algorithm is based on a predictive filtering scheme designed for spacecraft without rate measuring devices. The major advantage of this new algorithm over traditional Kalman filter approaches is that the model error is not assumed to represented by an unbiased Gaussian noise process with known covariance, but instead is determined during the estimation process. This is achieved by simultaneously solving system optimality conditions and an output error constraint. This approach is well suited for GPS attitude estimation since some error sources that contribute to attitude inaccuracy, such as signal multipath, are known to be non-Gaussian processes. Also, the predictive filter scheme can use either GPS signals or vector observations or a combination of both for attitude estimation, so that performance characteristics can be maintained during periods of GPS attitude sensor outage. The performance of the new algorithm is tested using flight data from the REX-2 spacecraft. Results are shown using the predictive filter to estimate the attitude from both GPS signals and magnetometer measurements, and comparing that solution to a magnetometer-only based solution. Results using the new estimation algorithm indicate that GPS-based solutions are verified to within 2 degrees using the magnetometer cross-check for the REX-2 spacecraft. GPS attitude accuracy of better than 1 degree is expected per axis, but cannot be reliably proven due to inaccuracies in the magnetic field model.

Crassidis, John L.↗

NASA NTRS1988

Modal identification using single-mode projection filters and comparison with ERA and MLE results

The Single-Mode Projection Filter (SPF) is a newly developed algorithm for eigensystem parameter identification from both analytical results and test data. The SPF is formulated with a single mode only and practical for parallel processing implementation. Explicit formulations of SPF are derived for the multi-input multi-output (MIMO) system by using the orthogonal matrices of the controllability and observability matrices in the general sense. The modal parameters of SPF are initially obtained from an analytical model in modal space. The experimental data are then processed through SPF to update its modal parameters and to minimize a cost function defined by the norm of an error matrix. The updated modal parameters represent the characteristics of the test data. A two-dimensional global minimum optimization algorithm is developed and applied for the filter update by using the interval analysis method. The SPF is developed based on a single-mode subsystem and identifies only one modal frequency and one modal damping within a specified region. For an n-modes structure, n SPF can be implemented for parallel processing to reduce the computational burden. The SPF is applied to analyze the simulated data for the MAST beam structure. The estimated modal parameters are comparable to those from the Eigensystem Realization Algorithm (ERA) and repeated modal frequencies are identified. The modal analysis of the Spacecraft Control Laboratory Experiment (SCOLE) data is also performed by using the ERA and the Maximum Likelihood Estimate (MLE). The result shows that the first five modal frequencies are very close from ERA and MLE. However, there are slight disparities in the damping rates and the computational burdens are quite different among these two algorithms.

Huang, Jen-Kuang↗

NASA NTRS1979

Flight test evaluation of a digital controller used in a VTOL automatic approach and landing system

As part of the NASA Langley Research Center's effort to develop technology for VTOL operation in the air transportation system in the late 1980's and beyond, research has been conducted aimed at developing digital controller design procedures. This paper describes the verification of one design procedure by the flight evaluation of an advanced digital control algorithm. The control algorithm, operating at 10 iterations per second, follows step guidance commands with zero steady state error and thus provides an autotrim capability for the nonlinear vehicle. Changes in vehicle dynamics are accounted for using a gain scheduling technique. This control algorithm is combined with sensor filters, a trajectory generator, and a closed loop guidance algorithm to form a VTOL autoland system. A CH-47 tandem rotor helicopter which contains a set of sensors, onboard digital flight computers and electro-hydraulic actuators is used in the evaluation. All software, except input-output routines, is coded in FORTRAN using floating point arithmetic and executed in the flight computer. This autoland system is exercised by automatically flying straight-in descending decelerating trajectories typical of VFR manual approaches to a predetermined landing pad.

Downing, D. R.↗

NASA NTRS1996

Ideas for Future GPS Timing Improvements

Having recently met stringent criteria for full operational capability (FOC) certification, the Global Positioning System (GPS) now has higher customer expectations than ever before. In order to maintain customer satisfaction, and the meet the even high customer demands of the future, the GPS Master Control Station (MCS) must play a critical role in the process of carefully refining the performance and integrity of the GPS constellation, particularly in the area of timing. This paper will present an operational perspective on several ideas for improving timing in GPS. These ideas include the desire for improving MCS - US Naval Observatory (USNO) data connectivity, an improved GPS-Coordinated Universal Time (UTC) prediction algorithm, a more robust Kalman Filter, and more features in the GPS reference time algorithm (the GPS composite clock), including frequency step resolution, a more explicit use of the basic time scale equation, and dynamic clock weighting. Current MCS software meets the exceptional challenge of managing an extremely complex constellation of 24 navigation satellites. The GPS community will, however, always seek to improve upon this performance and integrity.

Hutsell, Steven T.↗

NASA NTRS2002

Addition of random run FM noise to the KPW time scale algorithm

The KPW (Kalman plus weights) time scale algorithm uses a Kalman filter to provide frequency and drift information to a basic time scale equation. This paper extends the algorithm to three-state clocks nd gives results for a simulated eight-clock ensemble.

time scale clock ensemble clock noise↗

NASA NTRS1984

The evaluation of failure detection and isolation algorithms for restructurable control

Three failure detection and identification techniques were compared to determine their usefulness in detecting and isolating failures in an aircraft flight control system; excluding sensor and flight control computer failures. The algorithms considered were the detection filter, the Generalized Likelihood Ratio test and the Orthogonal Series Generalized Likelihood Ratio test. A modification to the basic detection filter is also considered which uses secondary filtering of the residuals to produce unidirectional failure signals. The algorithms were evaluated by testing their ability to detect and isolate control surface failures in a nonlinear simulation of a C-130 aircraft. It was found that failures of some aircraft controls are difficult to distinguish because they have a similar effect on the dynamics of the vehicle. Quantitative measures for evaluating the distinguishability of failures are considered. A system monitoring strategy for implementing the failure detection and identification techniques was considered. This strategy identified the mix of direct measurement of failures versus the computation of failure necessary for implementation of the technology in an aircraft system.

Motyka, P.↗

NASA NTRS2012

Accuracy Assessment of Aqua-MODIS Aerosol Optical Depth Over Coastal Regions: Importance of Quality Flag and Sea Surface Wind Speed

Coastal regions around the globe are a major source for anthropogenic aerosols in the atmosphere, but the underlying surface characteristics are not favorable for the Moderate Resolution Imaging Spectroradiometer (MODIS) algorithms designed for retrieval of aerosols over dark land or open-ocean surfaces. Using data collected from 62 coastal stations worldwide from the Aerosol Robotic Network (AERONET) from approximately 2002-2010, accuracy assessments are made for coastal aerosol optical depth (AOD) retrieved from MODIS aboard Aqua satellite. It is found that coastal AODs (at 550 nm) characterized respectively by the MODIS Dark Land (hereafter Land) surface algorithm, the Open-Ocean (hereafter Ocean) algorithm, and AERONET all exhibit a log-normal distribution. After filtering by quality flags, the MODIS AODs respectively retrieved from the Land and Ocean algorithms are highly correlated with AERONET (with R(sup 2) is approximately equal to 0.8), but only the Land algorithm AODs fall within the expected error envelope greater than 66% of the time. Furthermore, the MODIS AODs from the Land algorithm, Ocean algorithm, and combined Land and Ocean product show statistically significant discrepancies from their respective counterparts from AERONET in terms of mean, probability density function, and cumulative density function, which suggest a need for future improvement in retrieval algorithms. Without filtering with quality flag, the MODIS Land and Ocean AOD dataset can be degraded by 30-50% in terms of mean bias. Overall, the MODIS Ocean algorithm overestimates the AERONET coastal AOD by 0.021 for AOD less than 0.25 and underestimates it by 0.029 for AOD greater than 0.25. This dichotomy is shown to be related to the ocean surface wind speed and cloud contamination effects on the satellite aerosol retrieval. The Modern Era Retrospective-Analysis for Research and Applications (MERRA) reveals that wind speeds over the global coastal region 25 (with a mean and median value of 2.94 meters per second and 2.66 meters per second, respectively) are often slower than 6 meters per second assumed in the MODIS Ocean algorithm. As a result of high correlation (R(sup 2) greater than 0.98) between the bias in binned MODIS AOD and the corresponding binned wind speed over the coastal sea surface, an empirical scheme for correcting the bias of AOD retrieved from the MODIS Ocean algorithm is formulated and is shown to be effective over the majority of the coastal AERONET stations, and hence can be used in future analysis of AOD trend and MODIS AOD data assimilation.

Anderson, J. C.↗

NASA NTRS1998

Effects of magnetometer calibration and maneuvers on accuracies of magnetometer-only attitude-and-rate determination

Two different algorithms - a deterministic magnetic-field-only algorithm and a Kalman filter for gyroless spacecraft - are used to estimate the attitude and rates of the Rossi X-Ray Timing Explorer (RXTE) using only measurements from a three-axis magnetometer. The performance of these algorithms is examined using in-flight data from various scenarios. In particular, significant enhancements in accuracies are observed when' the telemetered magnetometer data are accurately calibrated using a recently developed calibration algorithm. Interesting features observed in these studies of the inertial-pointing RXTE include a remarkable sensitivity of the filter to the numerical values of the noise parameters and relatively long convergence time spans. By analogy, the accuracy of the deterministic scheme is noticeably lower as a result of reduced rates of change of the body-fixed geomagnetic field. Preliminary results show the filter-per-axis attitude accuracies ranging between 0.1 and 0.5 deg and rate accuracies between 0.001 deg/sec and 0.005 deg./sec, whereas the deterministic method needs a more sophisticated techniques for smoothing time derivatives of the measured geomagnetic field to clearly distinguish both attitude and rate solutions from the numerical noise. Also included is a new theoretical development in the deterministic algorithm: the transformation of a transcendental equation in the original theory into an 8th-order polynomial equation. It is shown that this 8th-order polynomial reduces to quadratic equations in the two limiting cases-infinitely high wheel momentum, and constant rates-discussed in previous publications.

Challa, M.↗