CN115331329A

CN115331329A - Master aircraft with mobile device-based system for collecting and disseminating flight data

Info

Publication number: CN115331329A
Application number: CN202210316211.0A
Authority: CN
Inventors: M·I·斯特林; A·B·杜加斯; J·R·O·施密特
Original assignee: Boeing Co
Current assignee: Boeing Co
Priority date: 2021-05-11
Filing date: 2022-03-29
Publication date: 2022-11-11

Abstract

The application discloses a host aircraft with a mobile device-based system for collecting and disseminating flight data. The host aircraft includes a flight data system that includes a mobile device, a central data server, and a transceiver. The mobile device has a processor, a memory programmed with a method embodied as computer readable instructions, and Radio Frequency (RF) communication circuitry, a GPS receiver, and a sensor suite. The sensor suite collects raw flight data. Execution of the instructions by the processor causes the mobile device to process the raw flight data into synthetic data and filter human-induced motion of the mobile device from the synthetic data using a filtering model and thereby generate time-stamped filtered flight data. The central data server is in wireless communication with the RF communication circuitry and receives time-stamped filtered flight data therefrom. A transceiver communicatively coupled to the central data server propagates the time-stamped filtered flight data to a user remotely located from the host aircraft.

Description

Master aircraft with mobile device-based system for collecting and disseminating flight data

Technical Field

The subject disclosure relates to host aircraft-based systems and accompanying methods for collecting and disseminating flight data to one or more remote user devices.

Background

Historically, a wide range of ground-based networks of radar tracking stations have been used to track, coordinate and regulate air traffic. Recently, the ever-evolving capabilities of digital satellite-based communication systems have enabled the deployment of low frequency (-0.25 Hz) auto-dependent surveillance broadcast (ADS-B) as a reliable and accurate source for the delivery of collected flight data. As understood in the art, ADS-B is a computer system that broadcasts the host aircraft's bearing and altitude via available satellite-based communication links and relays. Using ADS-B, pilots and air traffic controllers are better able to visualize the relative bearings and flight trajectories of aircraft operating within the same airspace.

Extracting flight data in real-time outside of a conventional air traffic control environment can be labor intensive and time consuming. For example, avionics sensor data, typically collected using an onboard Flight Data Recorder (FDR), is accessed post-flight using specially configured computer-based interrogation equipment. The interrogation equipment is used to extract the collected binary data from the hard disk drive or other memory device and interpret the binary data or its pattern as a meaningful flight event. Real-time access may be achieved using an Aircraft Interface Device (AID). However, AID-assisted access to data stored on FDRs is a relatively complex process that is typically reserved for support of official survey purposes, flight Operations Quality Assurance (FOQA) tasks, and other critical tasks. Moreover, the adoption rate of AIDs in the current market is still low, mainly due to the high cost, time and effort required to retrofit existing aircraft.

Disclosure of Invention

In view of the above discussion, there is a need for a simplified, low-cost solution for accessing and communicating certain types of collected flight data in real-time, possibly for purposes such as determining takeoff or "wheel up" and landing/"wheel down" times, reporting air turbulence or flight altitude layer changes, takeoff and landing distances/speeds, cabin pressure anomalies, and the like. Extensions of the present teachings may include collecting and disseminating information related to real-time tracking and assessment of flight-related activities, such as propeller/engine on/off status, crew on-time/location, ongoing de-icing or anti-icing efforts, aircraft weight estimation, hard landing and/or runway condition assessment, and other beneficial information.

In particular, disclosed herein are hardware and software based solutions, whether via distributed hardware, using a software application ("app"), or otherwise, for collecting and transmitting certain types of aircraft flight data using a suitably equipped mobile device (e.g., a tablet, smartphone, or other suitably configured portable electronic device). The solution relies on using the onboard/resident sensor functionality of the mobile device itself, instead of pilot reporting, aircraft sensors, aircraft Communication Addressing and Reporting System (ACARS) messages, or other conventional techniques, to collect raw flight data.

In various embodiments described herein, a mobile device is equipped with a Global Positioning System (GPS) receiver and may be implemented as an Electronic Flight Bag (EFB). As understood in the art, an EFB may be embodied as a tablet computer or mounted device equipped with a sensor suite, typically but not necessarily a device running an iOS operating system. Exemplary constituent sensors of the sensor suite may include one or more of a multi-axis accelerometer, gyroscope, barometer, or compass/magnetometer. The method takes advantage of the sensing capabilities of available sensors in making collected flight data available to data consumers or remote user devices that are accessible to the user in real time. Future sensors not specifically mentioned herein but which may be included in the continuing development of EFB hardware and software capabilities include hygrometers, thermometers, lidar sensors, and the like, any or all of which may have some utility within the scope of the subject disclosure. Accordingly, the particular hardware and software configuration of the sensor suite housed within the mobile device may be within the scope of the present disclosure and vary depending on the intended use or application(s).

As will be understood by those skilled in the art in view of the discussion of the figures below, the present solution takes advantage of the sensors and processing capabilities of the handheld mobile device to extract more real-time information about a given flight than is typically available with ADS-B and other conventional data sources. Furthermore, the method set forth below does this quickly without requiring the host aircraft or its flight crew to manually report the collected flight data. Using a GPS receiver, recorded events can be annotated with precise timestamps and accurate locations, providing an operable or informative level of specificity for collected and reported flight data.

Furthermore, the disclosed solution provides a model-based process for distinguishing between motion of a host aircraft and human-induced motion of a mobile device. It is contemplated that the mobile device may freely interact with the flight crew, for example, when the pilot picks up, tilts, and rotates the mobile device to view displayed information of interest before touching the screen and finally dropping the mobile device again. Since the mobile device is handheld and therefore not secured to the host aircraft via the fixture during expected user interaction, the mobile device may sometimes be inadvertently dropped or bumped during its normal use. Such motion is detected by the sensor suite and reported as an aggressive data spike, which without the present teachings may result in inaccurate flight information calculation and reporting.

Thus, the subject disclosure combines collected data from a sensor suite of a mobile device, knowledge of expected host aircraft motion, and knowledge of typical user motion to distinguish sensor readings describing the motion of the host aircraft from sensor readings applied to the motion of the mobile device by the user's actions. The user motion is then filtered out using the methods described herein, for example using a statistical filtering model programmed into the memory of the mobile device.

In certain exemplary embodiments, a flight data system for use on a host aircraft includes a mobile device, a central data server, and a transceiver. Mobile devices (e.g. mobile phone)

Or another suitably configured tablet computer,

Or another suitably equipped smartphone, or another portable electronic device) includes a processor, non-transitory memory programmed with computer/processor-readable and executable instructions, radio Frequency (RF) communication circuitry, a GPS receiver, and a sensor suite. The sensor suite is configured to collect raw flight data, wherein the content of the raw flight data varies with the configuration of the sensor suite, as described above.

Execution of the instructions by the processor causes the mobile device, perhaps in response to using a software application ("app") resident thereon, and via generation and communication of corresponding electronic control signals, to process the raw flight data into a composite data set. This may require processing, analysis and possibly deletion of some of the above-mentioned raw flight data as required. The mobile device then filters the human-induced motion of the mobile device from the synthetic dataset, e.g., using any or all of a statistical filtering model, band pass filter, threshold, etc. The mobile device can generate time-stamped filtered flight data in this manner.

In certain embodiments, a central data server in secure wireless communication with the RF communication circuitry of the mobile device ultimately receives the time-stamped filtered flight data from the mobile device. A transceiver communicatively coupled to the central data server transmits or otherwise propagates the time-stamped filtered flight data to an external device, which in turn is remotely located ("remote user device") from the mobile device or host aircraft.

In a possible hardware configuration, the sensor suite includes a barometer configured to generate cabin pressure readings as part of the raw flight data. In this particular example, the time-stamped filtered flight data includes time-stamped takeoff and landing times of the host aircraft. These times are determined via the mobile device in a manner based on the cabin pressure readings.

Alternatively or concurrently, the sensor suite may include at least one multi-axis accelerometer configured to generate a composite set of acceleration readings as part of the raw flight data. In such embodiments, the time-stamped filtered flight data may include time-stamped turbulence data of the host aircraft based at least in part on the composite acceleration readings.

Execution of the computer readable instructions by the processor causes the mobile device to filter the human-induced motion of the mobile device from the synthetic dataset, such as by calculating a roll standard deviation of the composite accelerometer readings. The statistical filtering model performs such calculations. The statistical filter model may also or alternatively calculate a roll average of the sum of absolute values of the composite accelerometer readings, and then use the roll average to detect the dynamic attitude of the mobile device, where "attitude" includes its velocity (velocity and heading).

In one possible configuration, the sensor suite includes a gyroscope configured to output an angular rate signal indicative of a rotation of the mobile device as part of the raw flight data described above. The mobile device in such embodiments is configured to detect human-induced motion based in part on the angular rate signal from the gyroscope.

Within the scope of the subject disclosure, the remote user device may optionally be embodied as a computer system of an additional aircraft, wherein the transceiver is configured to propagate the time-stamped filtered flight data to the additional aircraft in such representative usage scenarios. The communication may be via air-to-air signal transmission or radio broadcasting, which may then include point-to-point transmission or relay transmission using satellite, aircraft, or ground-based relays. The remote user device may also include an air traffic control tower or tracking station, wherein the transceiver is configured to propagate the time-stamped filtered flight data to the air traffic control tower or tracking station as air-to-ground signal transmissions. Also, air-to-air communications are not excluded and may have beneficial use for airborne/orbital air traffic control platforms.

Also disclosed herein is a method of collecting and disseminating flight data on a host aircraft using a flight data system that includes the mobile device described above. A method according to a non-limiting exemplary embodiment includes collecting GPS data using a GPS receiver located on a host aircraft (e.g., as part of a mobile device). The method also includes collecting raw flight data via a sensor suite of the mobile device, and then processing the raw flight data to generate a composite data set. Further, the method in this embodiment includes filtering human-induced motion of the mobile device from the synthetic dataset via a statistical filtering model of the mobile device, thereby generating time-stamped filtered flight data.

The method in this non-limiting representative embodiment may include wirelessly transmitting the time-stamped filtered flight data to a central data server located on the host aircraft via RF communication circuitry of the mobile device, and receiving the time-stamped filtered flight data via the central data server. Thereafter, the method includes propagating the time-stamped filtered flight data via a transceiver coupled to a central data server, possibly including transmitting an air-to-ground signal to an air traffic control tower and/or broadcasting a message directly to another aircraft or to another aircraft using one or more relay stations. In some cases, the remote user device may be one or more other EFBs/mobile devices, where a software-based app on the mobile device may be used to launch the method and update the other EFB(s).

Also disclosed herein is a non-transitory computer-readable medium having recorded thereon instructions for collecting and disseminating flight data on a host aircraft. Execution of the instructions by the processor of the mobile device causes the mobile device to collect GPS data using a GPS receiver, collect raw flight data using a sensor suite, process the raw flight data into a composite data set, and filter human-induced motion of the mobile device from the composite data set via a statistical filtering model to generate time-stamped filtered flight data. The mobile device then wirelessly transmits the time-stamped filtered flight data to a remote user device via the RF communication circuitry, the remote user device in this embodiment comprising at least one of a central data server located on the host aircraft or another mobile device.

The above summary is not intended to represent each possible embodiment or every aspect of the subject disclosure. Rather, the foregoing summary is intended to illustrate some of the novel aspects and features disclosed herein. The above features and advantages and other features and advantages of the subject disclosure are readily apparent from the following detailed description of the representative embodiments and modes for carrying out the subject disclosure when taken in connection with the accompanying drawings and appended claims.

Drawings

The drawings described herein are for illustration purposes only and are exemplary in nature and are not intended to limit the scope of the present disclosure.

FIG. 1 is a perspective view of a representative host aircraft equipped with a flight data system operable to collect and propagate flight data using suitably equipped mobile devices in accordance with the subject disclosure.

FIG. 2 is a schematic illustration of an exemplary embodiment of a flight data system described herein.

Fig. 3 is a time plot of representative air pressure readings collected on the host aircraft of fig. 1 and 2 using a mobile device according to the subject disclosure.

4-6 are representative plots of various types of flight data that may be collected, filtered, and propagated using the flight data system shown in FIG. 2.

FIG. 7 is a flow chart depicting an exemplary method for collecting and disseminating flight data using the flight data system as depicted in FIG. 2.

Detailed Description

The subject disclosure may be embodied in many different forms. Representative examples are shown in the various figures and described in detail below, with the understanding that the described embodiments are illustrative of the principles disclosed and are not intended to limit the broad aspects of the disclosure. For that reason, elements and limitations described below but not explicitly set forth in the claims should not be incorporated into the claims by implication, inference or otherwise, either individually or collectively. Furthermore, the drawings discussed herein may not be drawn to scale and are provided purely for illustrative purposes. Accordingly, the specific and relative dimensions shown in the figures should not be construed as limiting.

Further, unless specifically stated: the singular includes the plural and vice versa; the words "and" or "are both conjunctive and disjunctive words; the words "any" and "all" mean "any and all"; and the words "include," have, "and variations thereof, including words of" including but not limited to "are intended to mean" including but not limited to. Moreover, the words "example" or "exemplary" are used herein to mean serving as an example, instance, or illustration. Any aspect or design described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other aspects or designs. Rather, the use of the exemplary word is intended to present concepts in a concrete fashion. Moreover, approximating words, such as "about," "nearly," "substantially," "generally," "approximately," and the like, may each be used herein in a sense, for example, "at, near, or nearly at … …," or "within 0-5% of … …," or "within acceptable manufacturing tolerances," or any logical combination thereof. Finally, directional adjectives and adverbs, such as forward, aft, inboard, outboard, starboard, port, vertical, horizontal, upward, downward, forward, aft, left, right, etc., may be with respect to the direction of forward travel of the aircraft or other vehicle.

Referring to the drawings, wherein like reference numbers refer to the same or similar components throughout the several views, host aircraft 10 is depicted relative to runway surface 11 in an attitude that host aircraft 10 assumes may take during takeoff and landing operations. The host aircraft 10 in the non-limiting exemplary configuration of fig. 1 is a fixed wing commercial aircraft or passenger aircraft, and thus includes a fuselage 12 connected to a set of wings 14, a tail or tail assembly 15, and one or more propellers 16 (e.g., jet turbine engines, open or closed propeller systems, etc.). In this representative flight phase, the landing gear assembly 17 of the host aircraft 10 is in an extended orientation with the attached set of wheels 18 exposed below the fuselage 12. For perspective, a representative Air Traffic Control (ATC) tower diagram 20 is shown in fig. 1, wherein radio communication is occurring between the aircrew (not shown) of the host aircraft 10 and the ATC tower 20.

It is within the scope of the subject disclosure that a flight data system 50, as described in detail below, resides on or within host aircraft 10. Thus, host aircraft 10 is the "host" of flight data system 50 in the sensors to provide the necessary support structure and possibly communication infrastructure. Operation of the flight data system 50 occurs during takeoff and landing maneuvers, as shown in FIG. 1, as well as the ascent, cruise and descent phases of the flight. The use of the flight data system 50 advantageously utilizes the collected sensor data on the host aircraft 10 in addition to, or sometimes in lieu of, relying on radio-based/manual pilot reporting, aircraft Communication Addressing and Reporting System (ACARS) information, ADS-B interaction, or other legacy or integrated communication systems and data of the type generally described above.

Referring to FIG. 2, a flight data system 50 is schematically illustrated in accordance with a possible embodiment. The flight data system 50 in the illustrated configuration includes the mobile device 30, the central data server 34, and the transceiver 36, the function and structure of each of which are described in detail below. One key technical aspect of flight data system 50 is the utilization of the miniaturized hardware and associated software capabilities of mobile device 30 (e.g., a commercial off-the-shelf portable electronic device, typically a tablet computer or smartphone running an iOS or other compatible operating system). The mobile device 30 contemplated herein includes an easily accessible touch screen 32 that serves as an intuitive graphical user interface, enabling a user to pick up the mobile device 30, interact with the mobile device 30, and drop the mobile device 30 as desired throughout the flight of the host aircraft 10 and during pre-or post-flight activities.

The hardware capabilities of the mobile device 30 are specifically designed for artificial intelligence/machine learning computations that rely on large matrix operations, which are more suitable than conventional avionics hardware when running event detection algorithms of the type described herein (e.g., the event detection logic 50L shown in FIG. 7). The present solution relies on design modularity and easy communication of the mobile device 30 with other networked devices via the internet or other networked connections, enabling the mobile device 30 to function as an internet of things (IoT) sensor with relatively low development effort.

To this end, the mobile device 30 as used herein includes a Global Positioning System (GPS) receiver 45 and a sensor suite 40, wherein the GPS receiver 45 is in communication with an orbital constellation of GPS satellites 25, as understood in the art. The mobile device 30 also includes a Central Processing Unit (CPU) or processor 44, a sufficient amount of memory 42 (i.e., one or more computer-readable media, such as Solid State Drive (SSD) based memory, programmed with computer-readable instructions embodying event detection logic 50L), and Radio Frequency (RF) communication circuitry 41. The sensor suite 40 is configured herein to collect raw Flight Data (FD) _RAW ). 4-6, execution of the computer readable instructions by processor 44 causes mobile device 30 to use statistical filter 50F to generate raw Flight Data (FD) from sensor suite 40 _RAW ) Filters out human-induced motion of the mobile device 30 in the processed and possibly reduced set ("composite data set"), thereby generating time-stamped filtered Flight Data (FD) _FILT )。

The central data server 34 is in wireless communication with the mobile device 30 via the RF communication circuitry 41 and is configured to receive time-stamped filtered Flight Data (FD) from the mobile device 30 during a flight procedure of the host aircraft 10 _FILT ). On host aircraft 10, other mobile devices (not shown) equipped as EFBs may communicate with mobile device 30. A transceiver 36 (including one or more transmit and receive antennas) is coupled to the central data server 34, wherein the transceiver 36 is configured to couple the filtered Flight Data (FD) _FILT ) To remote user equipment 20U (i.e., a physical data consumer located remotely from host aircraft 10).

Filtered Flight Data (FD) after receipt by remote user device 20U _FILT ) May display the filtered Flight Data (FD) on a suitable display screen by way of example and not limitation _FILT ) For reference, or filtered Flight Data (FD) _FILT ) Fed to another system, such as to update the expected arrival time or perform other suitable response or control actions. Within the scope of the present disclosure, the remote user device 20U may include any or all of the following: ATC tower 20 of fig. 1 or various similar ATC towers 20A located along the flight path of host aircraft 10, one or more ground or airborne tracking stations 20B, and/or additional aircraft 20C that are typically located near host aircraft 10 or that may follow a distance behind host aircraft 10 when traveling in a similar flight path.

Further with respect to the sensor suite 40, representative sensors housed within the mobile device 30 include a barometer 47, the barometer 47 configured to generate a set of barometric pressure readings (as opposed to atmospheric pressure readings) indicative of cabin pressure within the host aircraft 10 as raw Flight Data (FD) _RAW ). As explained below, the GPS capabilities of mobile device 30 enable mobile device 30 to use raw Flight Data (FD) _RAW ) (including such barometric pressure readings) generating and outputting time-stamped filtered Flight Data (FD) _FILT )。

The sensor suite 40 may also include one or more multi-axis accelerometers 49, the multi-axis accelerometers 49 configured to generate a set of composite accelerometer readings (i.e., nominal XYZ acceleration components of a representative XYZ cartesian reference frame) as raw Flight Data (FD) _RAW ). In such embodiments, the filtered Flight Data (FD) _FILT ) Such composite accelerometer readings may be included, with different statistical filtering techniques described below for generating time-stamped filtered Flight Data (FD) _FILT ) Time from raw Flight Data (FD) _RAW ) Filtering out human induced motion of the mobile device 30.

In addition, the sensor suite 40 may include a gyroscope 48, the gyroscope 48 configured to generate a set of gyroscope rotation readings in the form of angular rate signals as raw Flight Data (FD) _RAW ) A part of (a). The mobile device 30 is configured to detect the above-mentioned human-induced motion of the mobile device 30 based on such readings. Additional sensors 43 may be used as part of the sensor suite 40, possibly including one or more of a compass/magnetometer, thermometer, gravitometer, hygrometer, lidar sensor, etc., and thus the sensing capabilities of the mobile device 30 may vary with the intended application.

As understood in the art, are housed in modern iOS-based tablet computers (such as

And variations thereof), particularly those configured for use as Electronic Flight Bag (EFB) as described above, are typically updated at an average 3-year cycle, which is very short relative to a typical aircraft-generating design cycle of about 25 years on average. With respect to internal computing hardware, the evolving system-on-a-chip (SoC) functionality for implementing the processing, modeling, machine learning, and other functions of mobile device 30 is an order of magnitude more powerful and more flexible than those used to implement aviation systems on older aircraft, such as host aircraft 10 shown in fig. 1. Furthermore, the enormous amount of data generated by a single flight of host aircraft 10 using such legacy systems may be on the order of several gigabytes or terabytes, which greatly complicates the timely extraction, propagation, and beneficial use of limited portions of such data.

With respect to computer and computer-related terminology used above, embodiments of memory 42, processor 44, and central data server 34 may include various combinations of application specific integrated circuit(s) (ASIC), field programmable gate array(s) (FPGA), electronic circuit(s), central processing unit(s), e.g., microprocessor(s) and associated non-transitory memory component(s) in the form of memory and storage (read-only, programmable read-only, random access, hard drive, etc.). The non-transitory memory may store machine-readable instructions in the form of one or more software or firmware programs or routines, combinational logic circuit(s), input/output circuit(s) and devices, signal conditioning, buffer circuitry, and other components that may be accessed and executed by one or more processors to provide the described functionality.

Communications that occur between the constituent processing nodes of the flight data system 50 of FIG. 2 include exchanging data signals, including, for example, exchanging electrical signals via a conductive medium, exchanging electromagnetic signals via the air, exchanging optical signals via an optical waveguide, and so forth. The data signals may include discrete signals representing inputs from the sensors, analog signals and/or digitized analog signals, actuator commands, and communications between the controller. The term "signal" refers to a physically discernable indicator of transferring information, and can be any suitable waveform (e.g., electrical, optical, magnetic, mechanical, or electromagnetic) capable of propagating through a medium, such as DC, AC, sine, triangle, square, vibration, or the like. A parameter is defined as a measurable quantity representing a physical property of a device or other element that can be discerned using one or more sensors and/or physical models. The parameter may have a discrete value, such as "1" or "0", or may be an infinitely variable value.

Referring briefly to FIG. 3, a representative trace 52 is shown representing a cabin pressure reading 53 in atmospheric pressure (Atm), where the cabin pressure reading 53 is generated in real time and time stamped by the operation of the barometer 47 and GPS receiver 45 described above in FIG. 2. As understood in the art, commercial aircraft (such as the host aircraft 10 of FIG. 1) use environmental control systems to slightly overpressure the aircraft cabin at takeoff, which is approximately at t in FIG. 3 ₁ Starting and continuing to about t ₂ . Likewise, the nacelle of the host aircraft 10 is slightly under-voltage upon landing. Thus, the use of the barometer 47 located within the mobile device 30 helps determine the takeoff and landing times, also referred to as "wheel off" and "wheel on" or "off and on" times. Such information is used in aviationUsed throughout the operation of the airline and forms an important milestone (milestone) on any flight.

Once the time-stamped barometer readings 53 are processed by the mobile device 30 into the synthetic data set described above, the barometer 47 in this alternative embodiment feeds the time-stamped barometer readings 53 into a filtering algorithm, i.e. the statistical filtering model 50F of FIG. 2 as explained below. Filtering in this context suitably takes into account human motion of the mobile device 30. Filtered Flight Data (FD) shown in FIG. 2 _FILT ) May thus include the cabin pressure reading 53 of fig. 3. When communicating with an offline system, such as ATC tower(s) 20A, tracking station(s) 20B, and/or additional aircraft 20C of fig. 2, the exact location of host aircraft 10 on or relative to runway surface 11 may be extracted as part of the event identification functionality of flight data system 50.

Those skilled in the art will appreciate the possible range of sensor applications that are within the scope of the present disclosure. For example, detection of clear sky turbulence (CAT) and other turbulence, aircraft out, off, on, in (OOOI) times, flight phase identification (climb, cruise, descent, etc.), flight altitude layer changes, landing analysis (such as speed at touchdown, exact location of touchdown, landing distance, etc.), takeoff analysis (such as takeoff distance, speed, wheel off position and other data, engine on/off, etc.).

Other possibilities of use include thrust reversers, runway surface conditions, approach patterns, crew on-time and location tracking, detailed de-icing/anti-icing analysis, aircraft weight estimation, GPS spoofing and jamming events, and hard bounce/landing detection and reporting. Still other conceivable data may relate to washroom usage (e.g., from detected number of flushes times the amount of water consumed per flush), wait (holding), cabin pressure anomalies, crowded taxiways and hot spots, altitude loss in rejected take-offs and climbs, all possibilities collected by the sensor suite 40, depending on its configuration.

For various raw Flight Data (FD) collected herein _RAW ) Once these data have been storedReduced to a synthetic data set specific to the flight event, the mobile device 30 is configured to distinguish human motion of the mobile device 30 from expected aircraft motion. While the sensor suite 40 may be used to detect many important flight events during a given flight, the mobile device 30 contemplated herein is used as a handheld device, i.e., without external mounts or fixtures. In this way, mobile device 30 may be grasped by a human user, moved within, and interacted with host aircraft 10. However, when the mobile device 30 relies on collected sensor readings, it is expected that human interaction may falsify or confound the event detection logic 50L. Thus, the mobile device 30 is configured to combine the data from the sensor suite 40 with knowledge of the projected movement of the host aircraft 10 and the user of the mobile device 30 to properly and accurately distinguish between the two.

For example, a gyroscope 48 and a multi-axis accelerometer 49 may be found within the sensor suite 40 of fig. 2, which may sometimes output extreme data spikes when the mobile device 30 is lifted, tilted, or dropped. The gyroscope 48 and multi-axis accelerometer 49 will be particularly prone to producing more extreme readings than are typically dictated by normal aircraft operation.

Referring to fig. 4, for example, an acceleration trace 54 represents the Acceleration (ACCEL) vector component sum over time. The sensor suite 40 may include a multi-axis accelerometer 49 configured to generate a composite set of accelerometer readings of trace 54 as raw Flight Data (FD) _RAW ). The flagged anomaly is indicated by point 55, where a valid flight event (FLT EVENTS) is indicated by point 56, i.e., the discrete ON/OFF state of the crew (attentant) function or system.

Fig. 5 shows another trace 60, this time gravity (G-Force) with respect to time (t), nominally shown in minutes (min). Another method uses the absolute value of each of the three components of the acceleration vector measured by the multi-axis accelerometer 49, which results in a measurement in which the value of the roll average may be used to detect rotation of the mobile device 30 without regard to the readings of the gyroscope 48. If the rotation is abrupt, it will be visible in a sharp change in the roll average, indicating human interference. In movingFrom t, the apparatus 30 ₀ To t ₁ After being stationary, a representative user interaction occurs at t ₁ And t ₃ For example when the passenger picks up the mobile device 30, processes it and drops it again.

Actual flight turbulence at t ₃ And t ₄ Is indicated at t ₄ And t ₅ And normal steady-state flight is entered. One approach is to identify such spikes mathematically by taking the derivative of the accelerometer readings over time and then locating a near infinite slope. Thereafter, the mobile device 30 may filter out such spikes. Time-stamped filtered Flight Data (FD) _FILT ) And thus may include time-stamped turbulence data of host aircraft 10 based on the composite acceleration readings. The threshold standard deviation may be used to filter out human interactions. Thus, execution of the computer readable instructions by processor 44 of fig. 1 may cause mobile device 30 to derive raw flight data (FLT) from such rolling standard deviation of accelerometer readings by calculating such a rolling standard deviation _RAW ) Filtering out human induced motion of the mobile device 30.

Fig. 6 depicts a trace 70 representing the absolute rotation rate/angular rate of the gyroscope 48 shown schematically in fig. 2, in radians per second (rad/s) and indicative of the rotation of the mobile device 30. Flight events are represented by points 72, where corresponding portions of trace 70 indicate such events and thus may be valid data. However, at about t ₁ And t ₂ The spikes in the trace 70 that occur may be indicative of direct human interference with the mobile device 30. For example, a pilot or crew member may have picked up the mobile device 30 and interacted with the mobile device 30 via the touch screen 32 of FIG. 1. The gyroscope 48 picks up extreme accelerations in any of the three axes. In response, mobile device 30 may clear the sensor readings of the data as non-descriptive human interference. Acceleration changes caused by normal aircraft motion may be at t ₀ And t ₁ Smaller gyroscope spikes in between and t ₁ And t ₂ Seen between the large peaks present.

Referring now to FIG. 7, the above-described event detection logic 50L may be described in terms of a logic flow, aspects of which may be executed by the processor 44 of FIG. 1 during flight operations of the representative host aircraft 10. Beginning in block B52, the pilot, crew member, or other user initiates a mobile device 30, such as a suitably equipped EFB (such as a tablet or smartphone running an iOS or other compatible operating system).

Embodiments contemplated herein may be initiated by accessing a software application ("app") that may be programmed into memory 42 of mobile device 30 as computer readable instructions, and perhaps displayed as an icon or application ("app") tile on touch screen 32, and accessible via touch interaction, as is well known in the art. Launching the application in this manner may include prompting the user to select from the available sensors of the sensor suite 40, such as by presenting a list of options and then allowing the user to choose from the displayed list. Alternatively, the mobile device 30 may perform a default setting by initiating a sensing operation from all of the constituent sensors of the sensor suite 40. Once the mobile device 30 has been started in this manner, the event detection logic 50L proceeds to block B54.

At block B54, the flight crew of the host aircraft 10 shown in fig. 1 begins flight operations. Depending on the particular flight phase in which the mobile device 30 is activated in block B52, this may include opening the propeller 16, loading passengers and/or cargo into the fuselage 12, boarding gate departure, taxiing, takeoff, ascent, cruise, descent, landing, until the propeller 16 is subsequently closed and may not be limited to just these. The event detection logic 50L then proceeds to block B56.

Block B56 entails using the sensor suite 40 of the mobile device 30 to collect the raw Flight Data (FD) shown in FIG. 2 _RAW ) The mobile device 30 may optionally be configured as an EFB, as described above. When generating raw Flight Data (FD) _RAW ) The event detection logic 50L proceeds to block B57.

At block B57, mobile device 30 may first process raw Flight Data (FD) _RAW ) To produce a composite data set, and then filter out user-generated motion or user-induced motion of the mobile device 30. In terms of data synthesis, mobile device 30 may process, analyze, and possibly delete some of the collected raw Flight Data (FD) from block B56 as needed _RAW ). For example, when determining a flight event related to turbulence, the composite data set may ignore or de-emphasize the perception data related to, for example, cabin pressure.

An exemplary edge calculation scenario can be readily envisioned in which the computing power resident on the mobile device 30 is used to utilize raw Flight Data (FD) _RAW ) Into a synthetic data set that, in this non-limiting embodiment, is indicative of, for example, air turbulence. Thus, data from the multi-axis accelerometer 49 and/or gyroscope 48 may be selected in this particular case. In other words, the mobile device 30 will not convert the raw Flight Data (FD) _RAW ) Directly to the end-user for downstream detection of relevant flight events, such as turbulence. Instead, the mobile device 30 will first collect the raw Flight Data (FD) _RAW ) The composite data set is reduced, combined, or otherwise synthesized as data in a preparatory step to isolate data relating to the particular flight event(s) that are detected and ultimately reported.

As part of block B57, the mobile device 30 then filters user-generated motion or user-induced motion of the mobile device 30 from the synthetic dataset described above, whether in the EFB embodiment described above or in another suitable tablet or other portable configuration, using the statistical filter model 50F of fig. 2. In an illustrative use case, when block B56 includes using barometer 47 of FIG. 2 to generate a set of barometric pressure readings, i.e., as raw Flight Data (FD) _RAW ) Block B57 may include generating filtered Flight Data (FD) _FILT ) As is the time-stamped takeoff and landing time of the host aircraft 10 determined via the mobile device 30 based on such barometric pressure readings. Similarly, the composite acceleration readings may be processed to generate filtered Flight Data (FD) including time-stamped turbulence data for the host aircraft 10 based on the composite acceleration readings in this case _FILT )。

In various embodiments, the computer readable instructions are executed by processor 44 to cause mobile device 30 to filter out the human-induced motion of mobile device 30 from the raw flight data. As described above, block B57 may entail calculating a rolling standard deviation and applying a cutoff threshold, calculating a rolling average sum of absolute values, and applying a threshold or cutoff to the rolling average sum, or using other model-based statistical analysis to separate motion that may be of the host aircraft 10 from motion that may be generated by a user through interaction with the mobile device 30.

As part of block B57, mobile device 30 may convert the filtered Flight Data (FD) _FLT ) To a central data server 34 or another remote user device, such as another mobile device 30 equipped with an attached EFB. Such communication between the mobile device 30 and the central data server 34, or in some use cases, another mobile device, may occur in real-time over a wireless link, such as Wi-Fi, bluetooth-like, bluetooth Low Energy (BLE), 802.15.4, infrared channels, satellite bands, etc. Cellular network standards for communicating between the mobile device 30, the central data server 34, and other possible systems may include standards that qualify as 1G, 2G, 3G, 4G, 5G. Network standards may qualify as one or more generations of mobile telecommunications standards by meeting specifications or standards, such as those maintained by the international telecommunications union. In some embodiments, different types of data may be transmitted via different links and standards. In other embodiments, the same type of data may be transmitted via different links and standards.

At block B58 of FIG. 7, filtered Flight Data (FD) from block B57 _FLT ) May be transmitted from host aircraft 10 to an end customer remotely located therefrom. Block B57 may include using the transceiver 36 of FIG. 2 (which is itself coupled to the central data server 34) to propagate the filtered Flight Data (FD) via, for example, radio broadcast or over a WiMAX-based or other suitable wireless communication network or IP connection supporting the desired data transfer rate _FLT ) For use by remote user devices 20U located remotely from mobile device 30 or host aircraft 10. In possible embodiments, ground station(s) 20B and/or communication satelliteMay be used as an internet gateway.

In view of the foregoing disclosure, one skilled in the art can readily envision a variety of use cases. For example, the consumer of data from remote user device 20U may be a crew member of additional aircraft 20C of fig. 2, where transceiver 36 or other suitable communication equipment on host aircraft 10 is configured to time-stamp filtered Flight Data (FD) _FLT ) To additional aircraft 20C, for example, as a radio broadcast or another air-to-air signal transmission.

As used herein, radio broadcasting and air-to-air signal transmission require communication over the appropriate air spectrum frequency or a predetermined or dedicated frequency band thereof. Such communication may be direct/point-to-point, or may be assisted by bouncing or relaying signal transmissions from satellites, aircraft, and/or ground-based relay stations, as understood in the art. For example, in the intended context of "radio broadcasting," host aircraft 10 may transmit a radio frequency signal, e.g., at an L-band (1-2 GHz) or other suitable frequency, where the signal propagates to remote user equipment 20U having compatible receiving equipment, e.g., a trailing aircraft. In some cases, this may occur by bouncing the signal back from an orbiting communication satellite, aircraft, or ground station. Device 20U may likewise be one or more ATC towers 20A, in which case transceiver 36 is configured to propagate time-stamped filtered Flight Data (FD) to ATC tower 20A, again either directly or with relay assistance _FLT ) As an air-to-ground signal transmission.

While the collection and filtering of various sensor data may occur in real-time, it is not necessary within the scope of the present disclosure to always propagate the filtered Flight Data (FD) in real-time _FLT ). That is, for time-insensitive data, the end user may be remote only in terms of collection time, with the end user accessing filtered Flight Data (FD) directly or indirectly from the mobile device 30 and/or the central data server 34 _FLT ). The event detection logic 50L is to filter the Flight Data (FD) _FLT ) The end user 20U who transmits such data then proceeds to block B60.

Block B60 includes determining whether the flight operation is complete via the mobile device 30 and/or the central data server 34. In a simplified implementation, the user may simply exit or close the app. Alternatively, the mobile device 30 may use detection of a duration of very low sensory input as an indication to complete flight operations. The event detection logic 50L then proceeds to block B62.

At block B62, the central data server 34 and/or the mobile device 30 may filter the Flight Data (FD) _FLT ) And possibly raw data (FD) _RAW ) Off-loaded to long term storage, such as an external database, for deeper data analysis or for history retention.

In yet another embodiment, aspects of the subject disclosure may be applied to software app-based updates of other EFBs, as noted above. For example, portions of the subject disclosure may be encoded in a computer readable medium (e.g., memory 42 of mobile device 30) as computer readable instructions executable by processor 44 to update other EFBs, which in this case act as remote user devices 20U. In one possible use case, a user of the mobile device 30 may touch a displayed tile or icon to cause the mobile device 30 to process raw flight data into a composite data set according to the method described above and filter human-induced motion of the mobile device 30 from the composite data set. This occurs through operation of the filtering model described above, and has the effect of generating time-stamped filtered flight data.

The central data server 34, in wireless communication with the RF communication circuitry 41, then receives the time-stamped filtered flight data from the mobile device 30, where the transceiver 36 ultimately transmits or otherwise propagates the filtered flight data set to the remote user device 20U. In such a possible use case, the remote user device 20U may be embodied as another EFB, for example, one EFB on a host aircraft or possibly on another aircraft. Thus, using the software app executed by the mobile device 30 in this manner may facilitate updating other EFBs as needed. Similarly, the computer readable medium of the central data server 34 may be programmed with instructions, execution of which by a processor of the central data server 34 causes the central data server 34 to receive time-stamped filtered flight data from the mobile devices 30, as described above, and subsequently update one or more mobile devices 30 equipped as EFBs.

Using the flight data system 50 and accompanying methods described above, the mobile device 30 may be used as an EFB with its possible capabilities to extract more information about the flight related capabilities such as ADS-B, and may provide data faster and more easily than, for example, FDRs or AIDs. In addition to utilizing the sensor suite 40 of the mobile device 30 in the disclosed manner, the present teachings are capable of automatically identifying human interference with the mobile device 30 when extracting purely flight-related motion information.

As opposed to rack-mounted or fixture-mounted sensing systems, i.e., the mobile device 30 is designed for portability, hands-on, and interaction, and thus the present solution contemplates the mobile device 30 deriving filtered Flight Data (FD) _FLT ) Extreme motion in time. Once a large number of flight events are detected and transmitted to the remote user device in real time, the data may be used for situational awareness tools and the like (e.g., aircraft health management or any international operations tool) beneficial purposes. These and other benefits will be readily appreciated by those skilled in the art in view of the foregoing disclosure.

The following clauses provide an exemplary configuration of the flight data system 50 and associated method of using the exemplary configuration, wherein the method is interchangeable with the event detection logic 50L described above.

Clause 1: a flight data system for use on a host aircraft, comprising:

a mobile device having a processor, memory programmed with computer readable instructions, radio Frequency (RF) communication circuitry, global Positioning System (GPS) receiver, and a sensor suite configured to collect raw flight data, wherein execution of the computer readable instructions by the processor causes the mobile device to process the raw flight data into a composite data set and filter human-induced motion of the mobile device from the composite data set using a statistical filtering model, thereby generating time-stamped filtered flight data; a central data server in wireless communication with the RF communication circuitry and configured to receive the time-stamped filtered flight data therefrom; and a transceiver communicatively coupled to the central data server, wherein the transceiver is configured to propagate the time-stamped filtered flight data to a remote user device.

Clause 2: the flight data system of clause 1, wherein the mobile device comprises a tablet computer or a smartphone.

Clause 3: the flight data system of

clause

1 or 2, wherein the sensor suite includes a barometer configured to generate cabin pressure readings as part of the raw flight data, and wherein the time-stamped filtered flight data includes time-stamped takeoff and landing times of the host aircraft determined via the mobile device based on the cabin pressure readings.

Clause 4: the flight data system of any of clauses 1-3, wherein the sensor suite includes a multi-axis accelerometer configured to generate a set of composite accelerometer readings as part of the raw flight data, and wherein the time-stamped filtered flight data set includes time-stamped turbulence data of the host aircraft based at least in part on the composite accelerometer readings.

Clause 5: the flight data system of clause 4, wherein execution of the computer readable instructions by the processor causes the mobile device to filter the human-induced motion of the mobile device from the composite data set by calculating a roll standard deviation of the composite accelerometer readings.

Clause 6: the flight data system of any of clauses 1-5 or 7-10, wherein execution of the computer readable instructions by the processor causes the mobile device to filter the human-induced motion of the mobile device from the composite data set by calculating a rolling average of a sum of absolute values of the composite accelerometer readings, and then detecting a pose of the mobile device using the rolling average.

Clause 7: the flight data system of any of clauses 1-6 or 8-10, wherein the sensor suite includes a gyroscope configured to generate an angular rate signal indicative of rotation of the mobile device as part of the raw flight data, and wherein the mobile device is configured to detect the human-induced motion based on the angular rate signal.

Clause 8: the flight data system of any of clauses 1-7 or 8-10, wherein the remote user device is part of an additional aircraft, and wherein the transceiver is configured to propagate the time-stamped filtered flight data to the additional aircraft as an air-to-air signal transmission.

Clause 9: the flight data system of clause 8 or 10, wherein the air-to-air signal transmission is a radio broadcast.

Clause 10: the flight data system of any of clauses 1-9, wherein the remote user equipment comprises an air traffic control tower, and wherein the transceiver is configured to propagate the time-stamped filtered flight data to the air traffic control tower as air-to-ground signaling.

Clause 11: a method of collecting and disseminating flight data on a host aircraft using a flight data system, the method comprising: collecting Global Positioning System (GPS) data using a GPS receiver on the host aircraft; collecting raw flight data via a sensor suite of a mobile device within the flight data system; processing the raw flight data into a synthetic data set; filtering, via a statistical filtering model of the mobile device, human-induced motion of the mobile device from the synthetic dataset, thereby generating time-stamped filtered flight data; wirelessly transmitting the time-stamped filtered flight data to a central data server located on the host aircraft via RF communication circuitry of the mobile device; receiving, via the central data server, the time-stamped filtered flight data; and propagating the time-stamped filtered flight data via a transceiver coupled to the central data server, including transmitting air-to-ground signals to an air traffic control tower and/or broadcasting messages to another aircraft.

Clause 12: the method of clause 11, wherein the sensor suite includes a barometer and collecting the raw flight data via the sensor suite includes collecting a cabin pressure reading via the barometer, the method further comprising: generating a time-stamped takeoff and landing time of the host aircraft determined based on the cabin pressure readings from the barometer as part of the filtered flight data.

Clause 13: the method of clauses 11 or 12, wherein the sensor suite includes a multi-axis accelerometer, and collecting the raw flight data via the sensor suite includes collecting composite accelerometer readings from the multi-axis accelerometer, the method further comprising: generating time-stamped turbulence data of the host aircraft based on the composite accelerometer readings as part of the filtered flight data.

Clause 14: the method of any of clauses 11-13, wherein filtering out the human-induced motion of the mobile device comprises calculating a roll standard deviation of the accelerometer readings and/or a roll average of a sum of absolute values of the composite accelerometer readings.

Clause 15: the method of any of clauses 11-14, wherein the sensor suite includes a gyroscope configured to output an angular rate signal indicative of rotation of the mobile device, the method further comprising: detecting, via the mobile device, the human-induced motion based on the angular rate signal.

Clause 16: a computer-readable medium having recorded thereon instructions for collecting and disseminating flight data on a host aircraft, wherein execution of the instructions by a processor of a mobile device causes the mobile device to: collecting Global Positioning System (GPS) data using a GPS receiver; collecting raw flight data using a sensor suite; processing the raw flight data into a composite data set; filtering human-induced motion of the mobile device from the synthetic dataset via a statistical filtering model, thereby generating timestamped filtered flight data; and wirelessly transmitting the time-stamped filtered flight data via the RF communication circuit to at least one of a user device or a central data server located on the host aircraft.

Clause 17: the computer readable medium of clause 16, wherein the sensor suite includes a barometer and the raw flight data includes cabin pressure readings collected by the barometer, and wherein execution of the instructions causes the mobile device to: generating a time-stamped takeoff and landing time of the host aircraft determined based on the cabin pressure readings from the barometer as part of the time-stamped filtered flight data.

Clause 18: the computer readable medium of clause 16 or 17, wherein the sensor suite includes a multi-axis accelerometer, the raw flight data including a composite accelerometer reading from the multi-axis accelerometer, and wherein execution of the instructions causes the mobile device to: generating time-stamped turbulence data of the host aircraft based on the composite accelerometer readings as part of the filtered flight data.

Clause 19: the computer-readable medium of any of clauses 16-18 or 20, execution of the instructions causes the mobile device to filter out the human-induced motion of the mobile device by calculating a roll standard deviation of the accelerometer readings or a roll average of a sum of absolute values of the composite accelerometer readings.

Clause 20: the computer readable medium of any of clauses 16-19, wherein the sensor suite includes a gyroscope configured to output an angular rate signal indicative of rotation of the mobile device, and wherein execution of the instructions causes the mobile device to: detecting, via the mobile device, the human-induced motion based on the angular rate signal.

While some of the best modes and other embodiments have been described in detail, various alternative designs and embodiments exist for practicing the present teachings as defined in the appended claims. Those skilled in the art will recognize that modifications may be made to the disclosed embodiments without departing from the scope of the subject disclosure. Moreover, the present concepts expressly include combinations and subcombinations of the described elements and features. The detailed description and drawings are supportive and descriptive of the present teachings, with the scope of the present teachings being defined solely by the claims.

Claims

1. A flight data system (50) for use on a host aircraft (10), comprising:

a mobile device (30) having a processor (44), a memory (42) programmed with computer readable instructions, radio Frequency (RF) communication circuitry (41), a Global Positioning System (GPS) receiver (45), and a sensor suite (40) configured to collect raw flight data, wherein execution of the computer readable instructions by the processor (44) causes the mobile device (30) to process the raw flight data into a composite data set and filter human-induced motion of the mobile device (30) from the composite data set using a statistical filtering model (50F) to generate time-stamped filtered flight data;

a central data server (34) in wireless communication with the RF communication circuitry (41) and configured to receive the time-stamped filtered flight data therefrom; and

a transceiver (36) communicatively coupled to the central data server (34), wherein the transceiver (36) is configured to propagate the time-stamped filtered flight data to a remote user device (20U).

2. The flight data system (50) of claim 1, wherein the mobile device (30) comprises a tablet computer or a smartphone.

3. The flight data system (50) of claim 1 or 2, wherein the sensor suite (40) includes a barometer (47), the barometer (47) configured to generate a cabin pressure reading (53) as part of the raw flight data, and wherein the time-stamped filtered flight data includes a time-stamped takeoff and landing time of the host aircraft (10) determined via the mobile device (30) based on the cabin pressure reading (53).

4. The flight data system (50) of claim 1 or 2, wherein the sensor suite (40) includes a multi-axis accelerometer (49), the multi-axis accelerometer (49) configured to generate a set of composite accelerometer readings as part of the raw flight data, and wherein the time-stamped filtered flight data set includes time-stamped turbulence data of the host aircraft (10) based at least in part on the composite accelerometer readings.

5. The flight data system (50) of claim 4, wherein execution of the computer readable instructions by the processor (44) causes the mobile device (30) to filter the human-induced motion of the mobile device (30) from the synthetic dataset by calculating a roll standard deviation of the composite set of accelerometer readings.

6. The flight data system (50) of claim 4, wherein execution of the computer readable instructions by the processor (44) causes the mobile device (30) to filter the human-induced motion of the mobile device (30) from the synthetic dataset by calculating a roll average of a sum of absolute values of the set of composite accelerometer readings, and then detecting a pose of the mobile device (30) using the roll average.

7. The flight data system (50) of claim 1 or 2, wherein the sensor suite (40) includes a gyroscope (48), the gyroscope (48) being configured to generate an angular rate signal indicative of a rotation of the mobile device (30) as part of the raw flight data, and wherein the mobile device (30) is configured to detect the human-induced motion based on the angular rate signal.

8. The flight data system (50) of claim 1 or 2, wherein the remote user device (20U) is accessible to or accessible by a crew member of an additional aircraft (20C), and wherein the transceiver (36) is configured to propagate the time-stamped filtered flight data to the additional aircraft (20C) as an air-to-air signal transmission.

9. The flight data system (50) of claim 8, wherein the air-to-air signal transmission is a radio broadcast.

10. A method of collecting and disseminating flight data on a host aircraft (10) using a flight data system (50), the method comprising:

collecting Global Positioning System (GPS) data using a GPS receiver (45) on the host aircraft (10);

collecting raw flight data via a sensor suite (40) of a mobile device (30) within the flight data system (50);

processing the raw flight data into a synthetic data set;

filtering human-induced motion of the mobile device (30) from the synthetic dataset via a statistical filtering model (50F) of the mobile device (30), thereby generating time-stamped filtered flight data;

wirelessly transmitting the time-stamped filtered flight data to a central data server (34) located on the host aircraft (10) via RF communication circuitry (41) of the mobile device (30);

receiving the time-stamped filtered flight data via the central data server (34); and

propagating the time-stamped filtered flight data via a transceiver (36) coupled to the central data server (34), including at least one of transmitting an air-to-ground signal to an air traffic control tower or broadcasting a message to another aircraft.