An Algorithm to Improve Ground-Based Spatial ...

AIAA 2015-0656 AIAA SciTech 5-9 January 2015, Kissimmee, Florida AIAA Modeling and Simulation Technologies Conference

McGrath, B. J., Newman, M. C., Lawson, B. D., & Rupert, A. H. An Algorithm to Improve Ground-‐Based Spatial Disorientation Training. Proceedings of the American Institute of Aeronautics and Astronautics, 7 Jan., Virginia beach, 9 Pages..

An Algorithm to Improve Ground-­­Based Spatial Disorientation Training Braden J. McGrath1 University of Canberra, Canberra ACT 2602, Australia Michael C. Newman2 National Aerospace Training and Research Center (NASTAR), Southampton, PA 18966, USA and

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Ben D. Lawson3, Angus H. Rupert4 U.S. Army Aeromedical Research Laboratory (USAARL), Fort Rucker, AL 36362, USA The prevalence of major accidents involving spatial disorientation (SD) in commercial and military aircraft in the past 20 years highlights the fact that SD remains a significant hazard during air transport. For the past 18 years, the authors have responded to requests from military and civilian aviation accident investigation communities to conduct perceptual analyses of mishaps when SD is a possible cause. The spatial orientation models we have developed predict the orientation and motion perception of a pilot when subjected to the acceleration environment of flight. The algorithm has been developed based on groundbased acceleration experiments and in-flight experimental data and is able to predict the perceived attitude of the pilot, which is critical to the pilot’s mental model of the attitude of the aircraft and the location of the ground. The current spatial orientation perceptual model is being developed with a number of different capabilities, including: 1. A post mishap analysis mode to assist investigators determine mishap causal factors 2. A real-time mode for SD prediction in-flight 3. A ground-based mode to enhance simulator fidelity in reproducing realistic SD scenarios The current model can address many types of mishap scenarios, including predicting the occurrence of the common somatogravic illusion, so by incorporating the perceptual model in ground-based simulation, one can ensure better fidelity for simulators in reproducing realistic SD scenarios. SD training is a complex topic and our recommendations will allow simulator manufacturers to place aircrew in a situation where there is a high probability of becoming disoriented, and then train to know where they are in space, while simultaneously operating the aircraft. In essence, the concept is to rehearse high-risk profiles to improve the mental model and free up short-term memory during real flight.

Nomenclature ACT BEA DVC SD CNS OMS TSAS G HUD MFD HMD NASTAR USAARL

= = = = = = = = = = = = =

Australian Capital Territory Bureau d'Enquêtes et d'Analyses Deputy Vice Chancellor spatial disorientation central nervous system orientation modeling system Tactile Situational Awareness System gravitoinertial force heads up display multi-function display helmet mounted display National Aerospace Training and Research Center United States Army Aeromedical Research Laboratory

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Senior Advisor, Office of DVC-Research, University of Canberra, Australia, Member. Research Scientist, NASTAR, 125 James Way, Southampton PA 18966. Non-member. 3 Research Psychologist, USAARL, P.O. Box 620577, Fort Rucker, AL 36362. Non-member. 4 Research Scientist, USAARL. Senior member. 2

Copyright © 2015 by the American Institute of Aeronautics and Astronautics, Inc. The U.S. Government has a royalty-free license to exercise all rights under the copyright claimed herein for Governmental purposes.

I. Introduction

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ircraft upset and the subsequent loss of control are key concerns of the aviation community, presently accounting for 29-46% of aviation accidents1. A recent study of air carrier flights from 1981 to 2010 indicated that among all incidents and accidents involving loss of control, Spatial Disorientation (SD) produced the secondgreatest number of fatalities, exceeded only by aerodynamic stalling2. Further, SD and stalling were not mutually exclusive - SD also occurred during or following 6.3% of aerodynamic stalling events and 36.8% of faulty recovery events.

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Pilots attempt to avoid SD in flight by centrally integrating a sufficient amount of sensory information and then executing an appropriate muscular response to control the aircraft. In the normal terrestrial environment, information is provided by three relatively independent and redundant visual, vestibular, and somatosensory (skinmuscles-joint) sensory systems, as shown in Figure 1, adapted from Correia & Guedry3

Visual

Somatosensory Vestibular

On Earth, these redundant systems are so reliable that when disorientation occurs it can usually be attributed to sensorimotor pathology. In aerospace flight, all of these systems function; however, the vestibular and somatosensory systems can only detect the resultant gravito-inertial force which the Central Nervous System (CNS) (based on all previous earth-bound experience) incorrectly interprets3 as the direction of the gravity vector as shown in Figure 2. In addition, flying generates angular motion outside normal terrestrial frequencies, resulting in inaccurate information from the vestibular angular rate sensors. Thus, acquisition of orientation information is left to the visual system that can also experience inadequacies or illusions in the aerial environment, as explained below.

Aviators are instructed to avoid SD by relying upon visual orientation cues to the exclusion of all other sensory cues4. When pilots have a clear view of the horizon, peripheral vision provides visual orientation cues through normal neural pathways. However, without a clear view of the horizon, visual orientation cues are obtained through focal vision of the attitude indicator, and as a result of training and experience are integrated cognitively to maintain spatial orientation. Pilots have learned to interpret the symbolic focal visual information on the attitude indicator and other flight instruments to develop a concept of where they are, what they are doing, and where they are going. As described by Wolfgang Langewiesche5, this complex talent must be developed through extensive training and maintained through practice; and it is the fragility of this concept that makes SD such a hazard.

No visual cues

centrifugal acceleration

Apparent

In 2013, the French authority for safety investigations in civil aviation (BEA) published a report on pilot orientation awareness during go-around operations6. Similar to a number of mishaps investigated by the authors, the BEA identified that the somatogravic illusion was a contributing factor to many accidents and serious incidents, primarily due to the absence of external visual cues. The typical somatogravic mishap occurs during a missed approach or goaround segment of a flight when visual attention is distracted (for example, increased workload, cockpit emergencies, transitions between visual and meteorological conditions, reduced visibility). Speed is slow, power is rapidly applied, and the aircraft then accelerates rapidly. In the absence of helpful visual cues, this generates a strong ‘tilt back’ sensation that pilots interpret (incorrectly) as a rapid pitching up sensation. Despite this perception,

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the aircraft may actually be in a level attitude or even a descent. This is the somatogravic illusion. Pilots may then push forward on the control column to control this (imaginary) climb, thinking they are lowering the aircraft nose back to level flight, when in actual fact they are lowering the nose into a dive. As the aircraft nose lowers, the aircraft continues to accelerate, generating additional pitch up sensations, causing pilots to lower the nose even further.

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Most SD mishaps are not due to radical maneuvers. While there are many situations that contribute to SD, the most common is when a pilot looks away from the aircraft’s orientation instruments and the horizon. When a pilot looks away from the horizon (loss of focal and peripheral visual cues), or looks away from his artificial horizon in instrument weather (loss of focal visual cues), the CNS computes spatial orientation with the remaining information at its disposal: vestibular and somatosensory. This vestibular and somatosensory information is redundant but frequently incorrect in the altered force environment of flight. In such circumstances, it is a physiologically normal response to experience SD. From a regulatory perspective, it is acceptable for a pilot to conduct passenger transport flights without ever having knowingly experienced:  Somatogravic illusions during training  Time pressure associated with changes in procedures and the application of full thrust with all engines operative in a ‘go-around’ scenario The BEA survey identified a number of pilot accounts detailing difficulties in the performance of their first actual go-around during a check flight and the following recommendation was made: “…ensure that manufacturers of simulators in cooperation with aircraft manufacturers improve simulator fidelity with respect to the phenomena of somatogravic illusions, especially during go-arounds…”. The present paper is focused on the somatogravic illusion (“state awareness”) associated with go-around and why the perception of pitch up magnitude (as determined by the model) should be presented to pilots in motion-based simulators vice the actual aircraft orientation, as is the current practice. During ground-based simulator training, the difference between the perceived pitch and the true pitch that characterizes somatogravic illusions is not properly simulated, so by incorporating the perceptual model in ground-based simulation, one can ensure better fidelity for simulators in reproducing realistic scenarios of abnormal situations.

II. Spatial Orientation System Modeling The modeling of the spatial orientation system represents a classic bioengineering problem, where the vestibular system must be combined with known dynamics and orientation percepts and be capable of predicting the final perceived six degree-of-freedom spatial orientation. The principal vestibular-based models can be grouped into two categories that are based on the underlying engineering formulation; the first is an “observer theory model” and the second is a “non-internal classical control model.” The observer theory model was introduced by Young7 and expanded by Oman8. These models are based on the hypothesis that the CNS includes internal models of sensory dynamics, body dynamics and physical relationships, and only differ in their mathematical implementation (Figure 5). These models were developed to explain how the CNS integrates sensory information from disparate sensory modalities to estimate body motion and spatial orientation. Three types of internal models have emerged as the state-of-art models for human spatial orientation perception:   

An optimal estimator approach using Kalman filter techniques A constant gain estimator A sensory weighting technique - where the central estimate of a physical variable is computed by a weighted averaging of multiple cues of available sensory information

The observer theory models have been shown to successfully model many aspects of human spatial orientation perception, and represent the majority of the modeling work in this field. Three types of observer theory models produce similar results; however, these models differ in the following areas:  The use of internal loops

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  

The implementation of the gravito-inertial force resolution mechanism The implementation of an idiotropic vector5 The use of visual inputs

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The constant gain estimator model developed by Merfeld and colleagues9 implements a gravito-inertial force resolution mechanism where the estimate of gravity is constrained to have a constant norm. The other models (Kalman filter and Sensory weighting) do not. The Kalman Filter observer models 10 11 12 13 14 15 use internal feedback loops that are defined by a gain followed by an integration term. This integration term originates from the formulation of the system model, because Gaussian noise is assumed to account for the uncertainties in the model regarding the variable to be estimated. Model result variation with driving noise may be used to account for pilot skill differences. The additional integration term leads to differences in optimal estimates compared to the sensory weighting model and Merfeld’s observer model. The sensory weighting model by Zupan16 includes an idiotropic vector that allows for the prediction of sinusoidal vertical and torsional components during yaw rotation about an Earth-horizontal axis. Models without an idiotropic vector do not predict the correct response for the yaw rotation about an Earth-horizontal axis experiments16. Refinements by Merfeld40 and others17 18 provided further development of the observer model approach. Several spatial orientation modeling systems have been developed based on various combinations of observer theory models. Kynor19 implemented a stable, extended Kalman filter (EKF) algorithm. He was able to successfully simulate SD during two fixed-wing jet accidents involving the A-10 Thunderbolt. Since both of the chosen A-10 accidents were the result of underestimation of roll rate or angle, the general utility of the Kynor model for high-G, supra- threshold, tactical-flight analysis still needs to be established. Additionally, the Kynor EKF model has limitations that may constrain the extent to which it can (without modification) provide accurate perceptual estimation during high otolith-canal conflict or in the presence of visual flow sensory input. Small et al. also developed an SD model 20 that included a user interface, rules for classification of classic orientation illusions, and a rudimentary model for sensory cue interaction. Attempts to incorporate a more comprehensive observer model of human perception into the model have proved difficult so far, due to the inherent differences between the two modeling approaches. Recently, Newman et al.21 22 developed an orientation perception model that incorporated six other models as well, to allow thorough examination of mishaps to determine whether pilot misperception may have contributed. The latest effort, named the Orientation Modeling System (OMS), permits the inclusion of additional visual flow and non-visual sensory information not present in previous models. The OMS incorporated a number of visual sensory paradigms including linear and circular vection, rotation in the light, and acceleration in the light. The second model category is the “non-internal model” that uses classical control theory to model the components of the vestibular system. Many authors have used this technique to describe eye movement responses of the vestibular system. None of these models explicitly include internal models of sensory dynamics, body dynamics, or physical relationships. Robinson 23 used a feedback loop to prolong the vestibular ocular reflex compared with the activity of the semicircular canal first-order afferent. Wearne 24 extended earlier models to implement the tendency of the eye movement rotation axis to shift toward alignment with gravity. These models were primarily designed to describe the vestibular ocular reflex responses, with less emphasis on perception. However, Mayne 25 proposed a framework that explains how the information from the vestibular system is processed to give subjective orientation. This framework was the basis of a spatial orientation model described by Grissett26 and implemented by McGrath 27. While the sensorimotor and perceptual counterparts of vestibular reactions frequently tell a similar story, one must be careful using models developed exclusively on the sensorimotor aspect because there are a number of situations in which perceptual and sensorimotor reactions diverge. In the majority of SD mishaps, the key assumption is that the pilot was not allocating attention to the primary flight display. There are a multitude of contributory factors that have been addressed by many human factors experts such as tunneling of attention, task saturation, novelty of events and so on. Currently, we can only assume the pilots were not looking at the flight displays. A critical improvement to the mishap analysis tool and thus the ability to model and predict SD is the addition of gaze tracking technology that can measure where the pilot is looking28. An advanced spatial orientation perceptual model has three distinct use cases. The first case is to use the model in a post-processing mode for mishap analysis (Figure 3). During the investigation of an F-14 mishap for the U.S. Navy, 5

An idiotropic vector is a hypothetical vector characterizing the tendency to perceive the vertical as oriented along the body axis

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McGrath et al.28 developed a 3D visualization tool that has been used extensively for SD mishap analysis. More recently, McGrath has improved on the original 3D visualization tool by using an advanced animation tool and an interactive format that engages the audience and presents the complex results of the spatial orientation model.

The second use case is to use the model in a real-time mode for SD prediction in-flight29 (Figure 4). Multisensory displays have been shown to be intuitive and effective if congruent information is displayed. The Tactile Situational Awareness System (TSAS) is an advanced situational awareness display that uses the sensory channel of touch to provide situational awareness information to pilots. The TSAS system accepts data from various aircraft sensors and presents this information via tactile cueing through an array of vibrotactile stimulators or “tactors” worn by the pilot or aircrew. By arranging the tactors in an intuitive nature around the body (“body referenced”), flight parameters such as attitude, altitude, and velocity, as well as navigational and threat warnings can be provided via the sense of feel to the pilot. According to multiple resource theory29, parsing information across the input modalities can alleviate sensory bottlenecks. Therefore, by simply reducing the perceived operational workload, the pilot is less likely to get into a situation where SD presents a risk. Various implementations of the TSAS technology have shown that tactile displays can play an important role in improving situation awareness in modern military aircraft30,31.

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TAC TOR LOC ATOR SY ST E M

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TAC TOR EL EC TR ONIC S

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The third use case is to incorporate the perceptual model in ground-based simulation to ensure better fidelity for simulators in reproducing realistic scenarios of abnormal situations. SD training is a complex topic and incorporating the perceptual model will allow simulator manufacturers to place aircrew in a situation where there is a high probability of becoming disoriented, and then train them in ground-based simulators to always know where they are in space, while simultaneously operating the aircraft. In essence, the concept is to rehearse high-risk profiles to amplify the mental model and free up short-term memory during real flight.

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III. Ground-Based Simulation – SD Training The ground-based SD training simulation structure for a motion system is shown in Figure 6. The operator simulator control inputs drive a mathematical model of the aircraft dynamics, generating the aircraft states. The aircraft states, plus pilot states, environmental conditions, and aircraft data, are then used to determine if SD is present (Figure 5). The aircraft states are passed through to the motion cueing algorithm to produce the desired motion cues and platform states. If SD is not present, the aircraft states that are passed through are derived from the actual aircraft state. If SD is present, then the aircraft states that are used produce the desired motion cues and platform states are based on the perceived aircraft state. The desired platform states are then transformed from degree-of-freedom space to actuator space, generating the commands to the six actuators, resulting in the actual simulator motion. The desired motion cues are constrained within the physical limits of the motion system using “washout” control techniques34. Washout involves returning the platform state to a neutral position following the initial, or “onset” portion of a motion cue, thus “washing out” the resulting cue at levels below the pilot’s perceptual threshold. Typically, the initial motion is targeted as the actual aircraft state, but by targeting the perceived aircraft state as generated by our perceptual spatial orientation model, the ground-based simulator can be used to demonstrate SD scenarios. The above approach extends the nonlinear motion cueing algorithm developed by Telban et al.32 to account for SD situations. By targeting the perceived aircraft state as the initial target state during SD scenarios, ground-based simulator training will place aircrew in high workload situations where there is a high probability of becoming disoriented (‘goaround’), and then train pilots to always know where they are in space, while simultaneously operating the aircraft.

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Discussion

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Spatial orientation awareness is a complex and multi-faceted problem. The visual system, vestibular system, somatosensory system, memory of preceding motion, expectation based on planned action, and perceptual motor interactions are all intimately involved. Guedry et al.33 noted that for models to yield valid predictions of the dynamics of spatial orientation, perception must also include expectation. Expectation of an orientation based on learned responses to previous planned actions and stimuli have been studied in a small number of in-flight experiments but the results are inconclusive. McCarthy and Stott34 showed that 3/3 naïve non-pilots, 6/8 pilots, and 0/2 test pilots experienced the well-known inversion illusion in flight (a variant of the somatogravic illusion involving the perception one is upside down). Money et al.35 also showed individual and experiential differences experiencing the inversion illusion. Gilson et al.36 recorded differences in the G-excess illusion (which shares some common causes as the somatogravic illusion), and Graybiel et al.37 showed small individual differences in the oculogravic illusion. SD has also been linked to experience and cognitive function, although the exact relationships between these elements are unknown. In a review of U.S. Navy mishaps, McGrath and Rupert38 show that the typical SD mishap pilot is not necessarily lacking in experience. Importantly, Otakeno et al.39 showed that pilots did not demonstrate greater visual dominance over vestibular cues than non-pilots (i.e. their training has not affected their perception). Also Cohen, et al.40, in a landmark paper on spatial orientation during Naval catapult operations, did not see differences in subjects due to flight experience when encountering a target illusion during catapult launch. As described above, a critical improvement to the mishap analysis tool and thus the ability to model and predict SD is the addition of instrumentation that can measure where the pilot is looking. It is now technically feasible to record the gaze of pilots unobtrusively using panel-mounted cameras and there are a large number of hardware and software solutions (many addressing the driver drowsiness detection problem). It is therefore possible to not only measure which flight instrument is being monitored, but also whether the pilot is directing gaze away from the panel and for how long. The current OMS spatial orientation model is adequate to address many types of mishaps including somatogravic illusion mishaps. However, the OMS model and other contemporary models do not address all of the relevant operational factors encountered in flight operations and/or all of the factors present in spatial orientation awareness. A complete model would be able to simulate such factors as night vision goggle operations, dynamic G-excess illusions, and factors such as distraction, expectation, and experience. (These and other specific model improvements are listed in the Conclusion.) G-excess is of particular interest as many traditional “somatogravic” mishaps during go-around also involve an angle of bank that may contribute to an increased vector magnitude and G-excess effect (Figure 7).

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IV. Conclusion

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The perceptual model presented in this paper should be exploited to improve ground-based simulation training. First, to provide improved instruction during go-around training, and second, to understand the effects of distraction associated with high workload and multi-tasking. Motion-based simulator operators/trainers should incorporate a model that improves realism by giving the user the perception of somatogravic pitch-up instead of the true aircraft pitch, because the former is more relevant to the mental model of pilots during real flight. Future improvements or applications of the current mathematical model of orientation perception include:  The model of orientation perception should improve the extent to which it accounts for such variables as head and gaze orientation, the presence of night vision goggles, the magnitude of acceleration vectors, and the dynamics of changing vectors.  The model should account for the G-excess effect, which is a probable SD contributor. Experiments on “static” and “dynamic” G-excess perception should be repeated, since the original experiments were based on a small number of subjects who exhibited large individual variability. These laboratory findings should then be incorporated back into the model.  The model should expand beyond vestibular and visual inputs and account for auditory and tactile orientation cues also. Experiments should be carried out to evaluate the effects of new orientation cueing displays that incorporate veridical 3D audio and tactile cueing. The model should then be modified based on the empirical findings concerning orientation in the presence or absence of such cues.  The model should incorporate an estimate of the likely confidence or merit of any given model simulation, based upon the quality of the data inputted, the amount and quality of past data being used to model the current situation, and the presence of mathematical singularities.

Disclaimer The views and findings contained in this report are those of the authors, not those of any government or private agency. Citation of trade names does not constitute endorsement by any government or private agency. The mention of any person’s name does not imply that the person endorses the contents of this report.

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