A necessary condition for effective performance of the multiplemodel adaptive estimator

Effective adaptive estimation for a general linear system driven by an input modeled by a randomly switching Gaussian process is considered. The performance of the multiple model adaptive estimator (MMAE) is, in some cases, unexpectedly hampered by a necessary condition not satisfied by the linear system. This key dependency for effective MMAE performance is based on a particular property of the DC gain of the linear system     

Enhanced motion and sizing of bank in moving-bank MMAE

The focus of this research is to provide methods for generating precise parameter estimates in the face of potentially significant parameter variations such as system component failures. The standard multiple model adaptive estimation (MMAE) algorithm uses a bank of Kalman filters, each based on a different model of the system. Parameter discretization within the MMAE refers to selection of the parameter values assumed by the elemental Kalman filters, and dynamically redeclaring such discretization yields a moving-bank MMAE. A new online parameter discretization method is developed based on the probabilities associated with the generalized chi-squared random variables formed by residual information from the elemental Kalman filters within the MMAE. This new algorithm is validated through computer simulation of an aircraft navigation system subjected to interference/jamming while attempting a successful precision landing of the aircraft.     

Multiple-model adaptive estimation using a residual correlationKalman filter bank

We propose a modified multiple model adaptive estimation (MMAE) algorithm that uses the time correlation of the Kalman filter residuals, in place of their scaled magnitude, to assign conditional probabilities for each of the modeled hypotheses. This modified algorithm, denoted the residual correlation Kalman filter bank (RCKFB), uses the magnitude of an estimate of the correlation of the residual with a slightly modified version of the usual MMAE hypothesis testing algorithm to assign the conditional probabilities to the various hypotheses that are modeled in the Kalman filter bank within the MMAE. This concept is used to detect flight control actuator failures, where the existence of a single frequency sinusoid (which is highly time correlated) in the residual of an elemental filter within an MMAE is indicative of that filter having the wrong actuator failure status hypothesis. This technique results in a delay in detecting the flight control actuator failure because several samples of the residual must be collected before the residual correlation can be estimated. However, it allows a significant reduction of the amplitude of the required system inputs for exciting the various system modes to enhance identifiability, to the point where they may possibly be subliminal, so as not to be objectionable to the pilot and passengers     

Second-order acceleration models for an MMAE target tracker

The performance of a multiple model adaptive estimator (MMAE) for an enhanced correlator/forward-looking-infrared tracker for airborne targets is analyzed in order to improve its performance. Performance evaluation is based on elemental filter selection and MMAE estimation error sizes and trends. The elemental filters are based on either first or second-order acceleration models. Improved filter selection is achieved by using acceleration models that separate the frequency content of acceleration power spectral densities into non-overlapping regions with second-order models versus the more traditional overlapping regions with first-order models. A revised tuning method is presented. The maximum a posteriori (MAP) versus the Bayesian MMAE is investigated. The calculation of the hypothesis probability calculation is altered to see how performance is affected. The impact of the ad hoc selection of a lower bound on the elemental filter probability calculation to prevent filter lockout is evaluated. Parameter space discretization is investigated     

Control of a large space structure using MMAE/MMAC techniques

The application of moving-bank multiple model adaptive estimation and control (MMAE/MMAC) algorithms to an actual spade structure (Space Integrated Controls Experiment (SPICE)) being examined at Phillips Laboratory at Kirtland AFB, NM, is presented. The structure consists of a large platform and a smaller platform connected by three legs in a tripod fashion. Kalman filtering and LQG (linear system, quadratic cost, Gaussian noise) control techniques are utilized as the primary design tools for the components of the MMAE/MMAC. Implementing a bank of filters or controllers increases the robustness of the algorithms when uncertainties exist in the system model, whereas the moving bank is utilized to reduce the computational load. Several reduced-order models are developed from the truth model using modal analysis and modal cost analysis. The MMAE/MMAC design with a substantially reduced-order filter model provides an excellent method to estimate a wide range of parameter variations and to quell oscillations in the structure.     

Performance enhancement of a multiple model adaptive estimator

We describe performance improvement techniques for a multiple model adaptive estimator (MMAE) used to detect and identify control surface and sensor failures on an unmanned flight vehicle. Initially failure identification was accomplished within 4 s of onset, but by removing the “β dominance” effects, bounding the hypothesis conditional probabilities, retuning the Kalman filters, increasing the penalty for measurement residuals, decreasing the probability smoothing, and increasing residual propagation, the identification time was reduced to 2 s     

Detection of interference/jamming and spoofing in a DGPS-aidedinertial system

Previous research at the Air Force Institute of Technology (AFIT) has resulted in the design of a differential Global Positioning System (DGPS) aided INS-based (inertial navigation system) precision landing system (PLS) capable of meeting the FAA precision requirements for instrument landings. The susceptibility of DGPS transmissions to both intentional and nonintentional interference/jamming and spoofing must be addressed before DGPS may be safely used as a major component of such a critical navigational device. This research applies multiple model adaptive estimation (MMAE) techniques to the problem of detecting and identifying interference/jamming and spoofing in the DGPS signal. Such an MMAE is composed of a bank of parallel filters, each hypothesizing a different failure status, along with an evaluation of the current probability of each hypothesis being correct, to form a probability-weighted average state estimate as an output. For interference/jamming degradation represented as increased measurement noise variance, simulation results show that, because of the good failure detection and isolation (FDI) performance using MMAE, the blended navigation performance is essentially that of a single extended Kalman filter (EKF) artificially informed of the actual interference noise variance. However, a standard MMAE is completely unable to detect spoofing failures (modeled as a bias or ramp offset signal directly added to the measurement). This work describes a moving-bank pseudoresidual MMAE (PRMMAE) to detect and identify such spoofing. Using the PRMMAE algorithm, spoofing is very effectively detected and isolated; the resulting navigation performance is equivalent to that of an EKF operating in an environment without spoofing     

MMAE-Based Control with Space-Time Point Process Observations

A multiple model adaptive estimator (MMAE) has been formulated to estimate the state of a dynamic system modeled by a linear stochastic differential equation, from which measurements, described as a noise-corrupted space-time point process functionally related to that state, are extracted. Assumed certainty equivalence is used to combine such an estimator with the LQ full-state feedback controller to synthesize a practical, implementable controller. Performance of the estimator and resultant controller characteristics are investigated via simulation as a function of approximation method used to limit the full-scale estimator to finite dimensionality and also as a function of important parameters defining the dynamics and observation processes.     

Interrelationship of single-filter and multiple-model adaptivealgorithms

An equivalent filter bank structure for multiple model adaptive estimation (MMAE) is developed that uses the residual and state estimates from a single Kalman filter and linear transforms to produce equivalent residuals of a complete Kalman filter bank. The linear transforms, which are a function of the differences between the system models used by the various Kalman filters, are developed for modeling differences in the system input matrix, the output matrix, and the state transition matrix. The computational cost of this new structure is compared with the cost of the standard Kalman filter bank (SKFB) for each of these modeling differences. This structure is quite similar to the generalized likelihood ratio (GLR) structure, where the linear transforms can be used to compute the matched filters used in the GLR approach. This approach produces the best matched filters in the sense that they truly represent the time history of the residuals caused by a physically motivated failure model     

Multipath-adaptive GPS/INS receiver

Multipath interference is one of the contributing sources of errors in precise global positioning system (GPS) position determination. This paper identifies key parameters of a multipath signal, focusing on estimating them accurately in order to mitigate multipath effects. Multiple model adaptive estimation (MMAE) techniques are applied to an inertial navigation system (INS)-coupled GPS receiver, based on a federated (distributed) Kalman filter design, to estimate the desired multipath parameters. The system configuration is one in which a GPS receiver and an INS are integrated together at the level of the in-phase and quadrature phase (I and Q) signals, rather than at the level of pseudo-range signals or navigation solutions. The system model of the MMAE is presented and the elemental Kalman filter design is examined. Different parameter search spaces are examined for accurate multipath parameter identification. The resulting GPS/INS receiver designs are validated through computer simulation of a user receiving signals from GPS satellites with multipath signal interference present The designed adaptive receiver provides pseudo-range estimates that are corrected for the effects of multipath interference, resulting in an integrated system that performs well with or without multipath interference present.     

Enhancements to a multiple model adaptive estimator/image-tracker

Three major enhancements to a previously devised multiple model adaptive estimator (MMAE) for target image tracking are developed and analyzed. These are: allowing some of the elemental filters to have rectangular fields of view and to be tuned for target dynamics that are harsher in one direction than others; considering both Gauss-Markov acceleration models and constant turn-rate models for target dynamics; and devising an initial target acquisition algorithm to remove important biases in the estimated target template to be used in a correlator within the tracker. Particularly good adaptation responsiveness is demonstrated in the multiple model algorithm's ability to handle harsh maneuver onset, yielding performance essentially equivalent to that of the best artificially informed tracking algorithm     

Sensor/actuator failure detection in the Vista F-16 by multiplemodel adaptive estimation

Multiple model adaptive estimation (MMAE) is applied to the Variable-In-flight Stability Test Aircraft (VISTA) F-16 flight control system at a low dynamic pressure flight condition (0.4 M at 20000 ft). Single actuator and sensor failures are addressed first, followed by dual actuator and sensor failures. The system is evaluated for complete or “hard” failures, partial or, “soft” failures, and combinations of hard and soft actuator and sensor failures. Residual monitoring is discussed for single and dual failure scenarios. Performance is enhanced by the application of a modified Bayesian form of MMAE, scalar residual monitoring to reduce ambiguities, automatic dithering where advantageous, and purposeful commands     

Multiple model adaptive estimation with filter spawning

Multiple model adaptive estimation (MMAE) with filter spawning is used to detect and estimate partial actuator failures on the VISTA F-16. The truth model is a full six-degree-of-freedom simulation provided by Calspan and General Dynamics. The design models are chosen as 13-state linearized models, including first order actuator models. Actuator failures are incorporated into the truth model and design model assuming a "failure to free stream." Filter spawning is used to include additional filters with partial actuator failure hypotheses into the MMAE bank. The spawned filters are based on varying degrees of partial failures (in terms of effectiveness) associated with the complete-actuaton-failure hypothesis with the highest conditional probability of correctness at the current time. Thus, a blended estimate of the failure effectiveness is found using the filters' estimates based upon a no-failure hypothesis, a complete actuator failure hypothesis, and the spawned filters' partial-failure hypotheses. This yields substantial precision in effectiveness estimation, compared with what is possible without spawning additional filters, making partial failure adaptation a viable methodology.     

MMAE-based control redistribution applied to the VISTA F-16

A novel control technique, termed control redistribution, is presented and applied in conjunction with multiple model adaptive estimation (MMAE) to the variable in-night stability test aircraft (VISTA) F-16, to detect and compensate for sensor and/or actuator failures. This ad hoc method redistributes control commands (that would normally be sent to failed actuators) to the nonfailed actuators, accomplishing the same control action on the aircraft. Dither is considered to help disambiguate failures in the longitudinal and lateral-directional channels. Detection of both single-actuator and single-sensor failures is considered. Failures are demonstrated detectable in less than 1 s, with an aircraft output nearly identical to that anticipated from a fully functional aircraft in the same environment     

MMAE/MMAC control for bending with multiple uncertain parameters

A moving-bank multiple model estimator/controller (MMAE/MMAC) based on linear system, quadratic cost, and Gaussian noise (LQG) assumptions is used to quell unwanted vibrations in a simulated large flexible space structure. The structure, known as the Space Integrated Controls Experiment (SPICE), exists at Phillips Laboratory, Kirtland Air Force Base, New Mexico. The structure consists of a large platform and a smaller platform connected by a tripod of flexible legs. The purpose of the control system is to maintain a very precise line-of-sight (LOS) vector through the center of the spacecraft. Kalman filtering, used to estimate the position and velocity of the bending modes of the structure, and LQG control techniques are the primary design tools used in the MMAE/MMAC algorithms. Implementing a parallel bank of filters increases robustness when uncertainties exist in the system model, here specifically allowing adaptation to uncertain and changing undamped natural frequencies of the bending modes of the structure. A moving-bank algorithm is utilized to reduce the computational loading. The MMAE/MMAC design provides a well-suited method of estimating variations in the vector of undamped natural frequencies and quelling vibrations in the structure. The MMAE/MMAC was able to track numerous parameter changes and jumps while providing adequate control for the structure.     

Optimal polarimetric processing for enhanced target detection

The results of a study of several polarimetric target detection algorithms are summarized. The algorithms were tested using real target-in-clutter data collected by the Lincoln Laboratory 35 GHz synthetic aperture radar (SAR) sensor. Fully polarimetric measurements (HH, HV, VV) are processed into intensity imagery using adaptive and nonadaptive polarimetric whitening filters (PWFs). Then a two-parameter constant false alarm rate (CFAR) detector is run over the imagery to detect the targets. Nonadaptive PWF processed imagery is shown to provide better protection performance than either adaptive PWF processed imagery or single-polarimetric-channel HH imagery. In addition, nonadaptive PWF processed imagery is shown to be visually clearer than adaptive processed imagery     

Characterization of Kalman filter residuals in the presence ofmismodeling

The mean and covariance of a Kalman filter residual are computed for specific cases in which the Kalman filter model differs from a linear model that accurately represents the true system (the truth model). Multiple model adaptive estimation (MMAE) uses a bank of Kalman filters, each with a different internal model, and a hypothesis testing algorithm that uses the residuals from this bank of Kalman filters to estimate the true system model. At most, only one Kalman filter model will exactly match the truth model and will produce a residual whose mean and standard deviation have already been analyzed. All of the other filters use internal models that mismodel the true system. We compute the effects of a mismodeled input matrix, output matrix, and state transition matrix on these residuals. The computed mean and covariance are compared with simulation results of flight control failures that correspond to mismodeled input matrices and output matrices     

An MMAE failure detection system for the F-16

A multiple model adaptive estimation (MMAE) algorithm is implemented with the fully nonlinear six-degree-of-motion, Simulation Rapid-Prototyping facility (SRF) VISTA F-16 software simulation tool. The algorithm is composed of a bank of Kalman filters modeled to match particular hypotheses of the real world. Each presumes a single failure in one of the flight-critical actuators, or sensors, and one presumes no failure. For dual failures, a hierarchical structure is used to keep the number of on-line filters to a minimum. The algorithm is demonstrated to be capable of identifying flight-critical aircraft actuator and sensor failures at a low dynamic pressure (20,000 ft, 0.4 Mach). Research includes single and dual complete failures. Tuning methods for accommodating model mismatch, including addition of discrete dynamics pseudonoise and measurement pseudonoise, are discussed and demonstrated. Scalar residuals within each filter are also examined and characterized for possible use as an additional failure declaration voter. An investigation of algorithm performance off the nominal design conditions is accomplished as a first step towards full flight envelope coverage     

基于预警卫星系统的临近空间飞行器跟踪

系统地研究了如何对临近空间飞行器进行有效实时跟踪的问题,并提出了一种基于约束总体最小二乘与自适应交互式多模型（CTLS-AIMM）滤波相结合的实时跟踪滤波算法。首先考虑到临近空间飞行器的特点,选择使用红外预警卫星系统探测目标飞行器,并使用约束总体最小二乘算法（CTLS）对目标进行粗定位;然后在粗定位信息基础上,使用自适应交互式多模型滤波算法（AIMM）对目标飞行器进行实时跟踪。在AIMM中,根据临近空间飞行器机动特性,合理选择目标模型集,并使用迭代最小二乘算法对模型参数进行自适应调整。通过仿真,验证了该跟踪滤波算法的可行性。     

基于预警卫星系统的临近空间飞行器跟踪简

 系统地研究了如何对临近空间飞行器进行有效实时跟踪的问题,并提出了一种基于约束总体最小二乘与自适应交互式多模型（CTLS-AIMM）滤波相结合的实时跟踪滤波算法。首先考虑到临近空间飞行器的特点,选择使用红外预警卫星系统探测目标飞行器,并使用约束总体最小二乘算法（CTLS）对目标进行粗定位;然后在粗定位信息基础上,使用自适应交互式多模型滤波算法（AIMM）对目标飞行器进行实时跟踪。在AIMM中,根据临近空间飞行器机动特性,合理选择目标模型集,并使用迭代最小二乘算法对模型参数进行自适应调整。通过仿真,验证了该跟踪滤波算法的可行性。