Abstract:
A smart airport automation system gathers and reinterprets a wide variety of aircraft and airport related data and information around unattended or non-towered airports. Data is gathered from many different types of sources, and in otherwise incompatible data formats. The smart airport automation system then decodes, assembles, fuses, and broadcasts structured information, in real-time, to aircraft pilots. The fused information is also useful to remotely located air traffic controllers who monitor non-towered airport operations. The system includes a data fusion and distribution computer that imports aircraft position and velocity, weather, and airport specific data. The data inputs are used to compute safe takeoff and landing sequences, and other airport advisory information for participating aircraft.

Description:
CROSS REFERENCE TO RELATED APPLICATION 
     This application is a continuation of U.S. patent application Ser. No. 10/431,163, filed May 6, 2003, now U.S. Pat. No. 6,950,037, the entirety of which is incorporated herein by reference. 
    
    
     FIELD OF THE INVENTION 
     The present invention relates to air traffic and flight operations control systems, and more particularly to automated systems that collect, organize, retransmit, and broadcast airport and aircraft advisory information collected from sensors and other data sources. 
     BACKGROUND OF THE INVENTION 
     Large, busy airports often include a control tower and staffed with air traffic controllers. Some airports are so busy the air traffic control is maintained 24-hours a day, and seven days a week. But some control towers are closed at night. Other airports are so small, or used so infrequently, that there never was a control tower installed so there never are any air traffic controllers on-hand. 
     At a minimum, pilots flying in or out of airports need to know about other traffic in the area, runways to use, taxi instructions, weather, crosswind advisories, etc. When there is no control tower or staff, pilots must depend on their own sight and hearing, and then self-separate using the Common Traffic Airport Frequency (CTAF) radio channel. 
     Gary Simon, et al., describe an automated air-traffic advisory system and method in U.S. Pat. No. 6,380,869 B1, issued Apr. 30, 2002. Such system automatically provides weather and traffic advisories to pilots in an area. An airspace model constantly updates records for a computer processor that issues advisory messages based on hazard criteria, guidelines, airport procedures, etc. The computer processor is connected to a voice synthesizer that allows the pilot information to be verbally transmitted over the CTAF-channel. 
     Kim O&#39;Neil for Advanced Aviation Technology, Ltd., wrote that there are significant opportunities to improve communication, navigation and surveillance services at Scatsta Aerodrome in the Shetland Islands and in helicopter operations in the North Sea, including approaches to offshore installations. See, http://www.aatl.net/publications/northsea.htm. These improvements can allegedly lead to radical improvements in safety, efficiency and reductions in costs. A key element in achieving these improvements, according to O&#39;Neil, is the full adoption of satellite navigation and data link services and in particular ADS-B. Various forms of VHF and other frequency data links make these improvements possible, and they provide major cost/benefits over existing costs and services. O&#39;Neil says it is time to upgrade existing procedural services to a level more in line with modern aircraft operations. Current procedures, methods and operating practices are expensive, inefficient and adversely affect the commercial operation of air transportation services. Satellite navigation can significantly improve operating procedures, reduce decision heights at airports and improve routes and holding patterns. These all lead to corresponding gains in safety, efficiency and cost reduction. ADS-B messages also provide a communication infrastructure on which many other services can be built at low cost. 
     Additional services suggested by the prior art include: Airline Operational Communications for aircraft operations efficiency, maintenance and engine performance for improving flight safety, Flight Watch, automated ATIS and related meteorological services, differential GPS corrections and integrity data for improved navigation and flight safety, asset management, emergency and disaster management and coordination, remote monitoring and many other functions. The publication of RTCA MASPS and MOPS, ICAO SARPs, EUROCAE MOPS and American and European Standards for data link and ADS-B, indicates that these technologies can be introduced and certified for many beneficial and cost/effective operational services. 
     SUMMARY OF THE INVENTION 
     Briefly, a smart airport automation system embodiment of the present invention gathers and reinterprets a wide variety of aircraft and airport related data and information around unattended or non-towered airports. Data is gathered from many different types of sources, and in otherwise incompatible data formats. The smart airport automation system then decodes, assembles, fuses, and broadcasts structured information, in real-time, to aircraft pilots. The fused information is also useful to remotely located air traffic controllers who monitor non-towered airport operations. The system includes a data fusion and distribution computer that imports aircraft position and velocity, weather, and airport specific data. The data inputs are used to compute safe takeoff and landing sequences, and other airport advisory information for participating aircraft. The smart airport automation system determines whether the runway is occupied by another aircraft, and any potential conflicts, including, for example, in-flight loss of separation between aircraft. The gathered data inputs are organized into useful information and packaged for both graphical display and computer-synthesized voice messages. The data is then broadcast over a data link and the synthesized voice messages are broadcast through a local audio transmitter to aircraft. The smart airport automation system&#39;s data is intended for use within at least a 5-nautical mile radius of the airport. The pilots in the area receive voice annunciated audio broadcast signals and data link messages that carry text and pictures for an onboard display screen. 
     An advantage of the present invention is that a smart airport automation system is provided that enhances pilot situation awareness in airport terminal areas. 
     Another advantage of the present invention is that a smart airport automation system is provided that helps raise pilot awareness of aircraft in the air or on the runway and may thereby reduce runway incursions and mid-air conflicts. 
     A further advantage of the present invention is that a smart airport automation system provides efficiently fused information from disparate sources and then distributes this information in various formats to various users in order to increase safety and efficiency in the area around a non-towered airport. 
     Another advantage of the present invention is that it provides airport situation awareness to the surrounding air traffic management system for their monitoring of airports with or without radar surveillance. 
     These and other objects and advantages of the present invention will no doubt become obvious to those of ordinary skill in the art after having read the following detailed description of the preferred embodiments, which are illustrated in the various drawing figures. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a functional block diagram of a smart airport automation system embodiment of the present invention; 
         FIG. 2  is a functional block diagram of advisory generator embodiment of the present invention that can be used in the system illustrated in  FIG. 1 ; 
         FIG. 3  is a flowchart of a process embodiment of the present invention for predicting an aircraft flight path; 
         FIG. 4  is a flowchart of a process embodiment of the present invention for determining if data from an aircraft has become unavailable and therefore the smart airport must extrapolate the trajectory of that aircraft; 
         FIG. 5  is a flowchart of a process embodiment of the present invention for predicting unconstrained aircraft trajectories; 
         FIG. 6A  is a set of mathematical equations useful in the capture pathway leg processing; 
         FIG. 6B  is a diagram depicting the geometry of the capture pathway process 
         FIG. 7  is a flowchart of a process embodiment of the present invention for capturing a pathway leg; and 
         FIG. 8  lists some equations useful in a FlyTurn process subroutine called in the process illustrated in  FIG. 7 . 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
       FIG. 1  illustrates a smart airport automation system embodiment of the present invention, and is referred to herein by the general reference numeral  100 . The system  100  gathers a wide variety of data and information from many different types of sources and in many different formats. It then interprets, fuses and structures information for use in real-time by pilots, e.g., especially approaching or leaving non-towered airports. Such information is also useful to air traffic controllers overseeing non-towered (or unattended towered) airport operations. 
     A data fusion and distribution computer  102  is provided with aircraft-position-and-velocity data inputs  104 , weather data inputs  106 , and airport data inputs  108 . These are processed into structured information, e.g., airport advisories, takeoff and landing sequences for participating aircraft, separation monitoring, and conflict detection. Such processing outputs information organized and packaged for graphical display and computer-synthesized voice message broadcasts. The data fusion and distribution computer  102  computes and generates airport information, aircraft intending to land, aircraft intending to depart, landing sequence order, potential loss of separation, occupied runways, advisories, etc. 
     Data for display in the airplane cockpit for the pilots in the immediate area is constructed by a data display generator  110 . Voice announcements for the pilots in the immediate area are composed by a synthesized voice message generator  112 . These messages are broadcast thru a local audio transceiver  114  over a radio link  116  to the several onboard transceivers  118  in the immediate area. Such messages are intended for use by aircraft operating in the terminal maneuvering area including at least those within a five-nautical mile radius of the airport. It can also be sent through networks to air traffic control, airport safety and security and other interested parties, such as, for example, remote system maintenance personnel. Transceivers  118  output to a cockpit data display  120  and cockpit sound system  122 . 
     Such information generated by the data fusion and distribution computer  102  is provided to a data network connection  124 , e.g., via the Internet. Such would allow traffic controllers and other overseers to monitor remote unattended airports and intervene when necessary. The data network connection  124  may also be used to control special airport lighting systems, e.g., runway lights, taxi messages, warning lights, etc. 
     The aircraft position and velocity data inputs  104  can be synthesized from airport surveillance radar, onboard GPS-based surveillance broadcast systems, and multilateration transponder-based systems, etc. For example, some conventional aircraft include automated dependent surveillance broadcast (ADS-B) systems that broadcast GPS position, velocity, and intent information about the particular aircraft to other aircraft and ground stations. ADS-B reports provide identity, position, altitude, velocity, heading, and other information about an aircraft. A complete collection of such reports from a particular area can provide a very good current picture of airport traffic conditions. Other information sources include automated surface observation system (ASOS), automated weather observation system (AWOS), traffic information service broadcast (TIS-B), and flight information services broadcast (FIS-B) transmissions. Transponder-equipped aircraft signals can provide ground station with enough data to compute the precise locations of the aircraft by multilateration. 
     The airport data  108  preferably includes airport name and identifier, runway configuration data, preferred runway landing directions, typical airport approach and departure patterns and associated pathways, noise-sensitive areas, and other airport-unique information. Information collection and fusion involves local weather, preferred runway, aircraft-in-pattern, runway occupied/not. The information collected can also be used to activate specialized lighting (e.g., to support runway incursion alerts and ground conflicts). 
     The messages, displays, and text preferably received by the pilots in the approaching and leaving aircraft include (a) weather and other airport information, (b) sequencing information on how the particular aircraft should sequence to and from the runway relative to other aircraft, (c) traffic information related to potential loss of separation warnings, and (d) safety alerts including runway incursion information. Tables I-IV are examples of audio advisories spoken by cockpit sound system  122 . 
     
       
         
               
               
             
           
               
                   
                 TABLE I 
               
               
                   
                   
               
             
             
               
                   
                 Airport Advisory: “Moffett Field, wind 320 at 10, 
               
               
                   
                 active runway 32R, there are two aircraft within 5 
               
               
                   
                 miles of the airport” 
               
               
                   
                   
               
             
          
         
       
     
     
       
         
               
               
             
           
               
                   
                 TABLE II 
               
               
                   
                   
               
             
             
               
                   
                 Sequence Advisory: “Aircraft 724 is #1. Aircraft 004 
               
               
                   
                 is #2 follow traffic on right downwind.” 
               
               
                   
                   
               
             
          
         
       
     
     
       
         
               
             
           
               
                 TABLE III 
               
               
                   
               
             
             
               
                 Runway Advisory: “Runway is occupied by aircraft 724” 
               
               
                   
               
             
          
         
       
     
     
       
         
               
               
             
           
               
                   
                 TABLE IV 
               
               
                   
                   
               
             
             
               
                   
                 Traffic Advisory: “Warning! Warning! Aircraft 724 
               
               
                   
                 has traffic 1:00, 3 miles, 1,100 ft heading 
               
               
                   
                 southeast. Aircraft 004 has traffic 11:00, 3 miles, 
               
               
                   
                 800 ft heading south.” 
               
               
                   
                   
               
             
          
         
       
     
       FIG. 2  illustrates a smart airport automation system advisory generator embodiment of the present invention, and is referred to herein by the general reference numeral  200 . The advisory generator  200  comprises an airport advisory subsystem  202 , a conflict advisory subsystem  204 , and a sequence advisory subsystem  206 . A process  208  uses weather and airport configuration data to determine the active runway in use. A process  210  inputs airport and airport advisory configuration data along with aircraft position data from process  234  to determine an airport advisory message. A process  212  broadcasts an airport advisory via an audio broadcast  214  and a data broadcast  216 . A process  218  determines whether local weather conditions are “visual” or “instrument” meteorological conditions and, in combination with aircraft position data from process  234 , feeds this to a process  220  which determines potential aircraft conflicts (e.g., predicted reductions in safe separation distance) based on weather-based safe separation criteria. It inputs conflict determination configuration data, and generates a conflict list  222 . A process  224  sends out a conflict detection advisory message via an verbal broadcast  226  and a data broadcast  228 . 
     Any ADS-B information sent by aircraft so equipped is contributed to a process  232  for determining the most recent absolute track data of local air traffic. A process  234  determines the most recent runway relative track data from aircraft and airport configuration data inputs as well as a local weather data source. A process  236  predicts aircraft route intentions and forwards these to a process  238  that predicts unconstrained aircraft trajectories. Airport configuration and sequence configuration data are used by process  238 . The results are forwarded to a process  240  for determining runway usage sequences. A process  242  broadcasts runway sequence advisory messages via an synthesized voice broadcast  244  and a data broadcast  246 . Subsystem  248  provides intelligent queuing of the audio broadcast advisories. 
       FIG. 3  represents a process  300  for predicting the aircraft route intent using intent inferencing through the use of a unique system of predefined “pathways”. The term “pathway” reflects a series of individual airspace volumes and associated band of direction (i.e., ground track angles) in the terminal area that represent potential “legs” in an aircraft&#39;s potential intent. For example, you can define pathways that represent a “downwind”, “base”, and “final” path segments for a given runway configuration. Process  300  starts with a step  301 . A step  302  initializes the process with a first aircraft in a list. A step  303  chooses the next i th  aircraft in the list to process. Step  304  checks altitude and range from the airport. If both are less than a preset maximum, step  305  initiates a loop to determine what pathway the aircraft is on. A step  306  chooses an i th  pathway. A step  307  sets the number of pathway legs for the j th  pathway. A step  308  chooses a k th  pathway leg for the j th  pathway. A step  309  checks to see if the current aircraft&#39;s ground track angle is within two designated ground track angles (i.e., capture angles) for the pathway leg. If within the designated pathway&#39;s capture angles, a step  310  checks to see if the aircraft location is within the pathway leg coverage volume. If the answer is “no” to either process  309  or  310 , and the last pathway leg for a given pathway is not chosen (when compared in step  315 ), then step  314  moves the analysis on to the next pathway leg. If it is the last pathway leg, steps  316  and  313  move the search on to the next pathway, but if this is the last pathway, step  317  is used to set the aircraft pathway and pathway leg to “UNKNOWN” (analogous to step  312 ). If the aircraft position is within the pathway leg volume, then, a step  318  sets the current aircraft pathway to “j” and leg to “k”. A test  319  sees if the outermost loop is finished. If no, the process proceeds to a step  311  where the aircraft list is incremented and the process repeats for the next aircraft on the list. If yes, a step  320  returns with the aircraft ID, the aircraft pathway and leg selections for all of the aircraft on the list. 
       FIG. 4  represents a process  400  for determining that data for a particular aircraft has become unavailable (e.g., due to surveillance dropouts) and therefore the trajectory must be extrapolated. It determines when aircraft are sending outdated ADS-B messages and predicts their trajectories based on its last known status. It starts with a step  401 . A step  402  initializes the process with a first aircraft in a list. A step  403  chooses the next aircraft to process in a program loop. A step  404  calculates the delta-time based on the difference between the current time and the time associated with the last aircraft state message time. A test  405  sees if the delta-time exceeds a predetermined sequence update time. If so, a step  406  predicts the future trajectory based on extrapolation of the last aircraft state message. A step  407  sets current state, current pathway and pathway leg to the predicted ones. A test  408  sees if the loop has finished. A step  409  increments the loop index. Process  400  estimates an aircraft&#39;s state information for no more than a configurable coast time interval, such as 15 seconds at which point it deletes that aircraft from the processing string. 
       FIG. 5  represents a process  500  for predicting unconstrained aircraft trajectories. The process  500  determines whether an aircraft needs to turn to the next pathway leg or fly straight to the next pathway leg. If the plane is not on an arrival or departure leg, and is on an UNKNOWN leg, the simulation assumes the plane will fly straight for a given maximum time to some final approach pathway. The process  500  returns the trajectory data for each aircraft including a time history of the trajectory e.g., for each time step there is a new x ac , y ac , z ac , V xac , V yac , V zac . If the aircraft&#39;s ground track angle is already aligned with the current aircraft pathway leg, the simulation assumes it will capture the next pathway leg. If the aircraft is on the last leg, e.g., the runway, and its ground track angle is aligned with the runway ground track angle, it flies straight until it reaches the end of the runway (X ac =X runwaywaypoint ). Process  500  starts with a step  501 . A step  502  initializes the process with a first aircraft in a list. A step  503  chooses the next aircraft to process. A test  504  sees if the aircraft&#39;s pathway is considered UNKNOWN. If so, a step  505  assumes a constant trajectory until a predetermined number of seconds has elapsed, i.e., Tfinal. A test  506  sees if the loop is finished. If so a step  507  returns the predicted trajectory data. If not, a step  508  increments the loop counter. If test  504  returns a no, a step  509  calculates the distance from the aircraft to the waypoint along the leg track. A test  510  sees if the ground track angle and distance variation from the nominal pathway leg ground track angle and centerline exceed predetermined minimums. If they do, a step  511  calls FlyTurn to project the future aircraft trajectory and align the aircraft with the pathway leg ground track. A step  512  sets the pathway leg and waypoints. A step  513  selects the next pathway leg. A test  514  checks the alignment of the aircraft ground track angle relative to the next pathway leg. If test  514  returns a yes, a test  515  tests an inner loop index to determine whether the current pathway leg is the next-to-last. If test  514  returns a no, the process proceeds to step  522 , which is discussed below. If step  515  returns a no, a test  516  tests loop index j to determine whether the current pathway is the final one. If finished with the loop, a step  517  assumes straight flight to the next waypoint. If test  515  returns a no, a test  518  sees if the ground track angle deviation is greater than zero. If not, a test  519  looks for a minimum runway offset. If yes, a step  520  calculates the overshoot correction required to align the aircraft with the final pathway leg. A step  521  increments the j-loop counter. A step  522  calls a capture-pathway-leg process to simulate a turn onto pathway leg j. The distance to the waypoint along the track can be computed with, d=√{square root over ((x ac −x w )+(y ac −y w ))}{square root over ((x ac −x w )+(y ac −y w ))}, dist2waypt=d*cos(φ), and φ is the angle difference between the aircraft ground track angle and pathway leg ground track angle, and (x w , y w ) is the waypoint location. 
       FIG. 6A  lists some capture pathway leg equations that are useful in the capture pathway leg process. In order to capture a pathway leg, a plane may need to fly a certain distance before initiating the turn. To calculate that distance, the process calculates the turn as if it was initiated right away to determine the geographic location of the point at the end of the turn. The straight distance to fly is then calculated as the distance between the end point of the turn to the intersection with the leg to be captured. The distance is calculated by using vector addition. First the unit vector for the straight leg is calculated simply using current ground track angle of the aircraft. A unit vector for the leg direction is calculated using leg ground track angle. A vector from the reference frame center to the leg waypoint is the sum of the vector from the center to the end point of the turn, the unit vector on straight leg multiplied by the straight distance a, and the unit vector on the leg multiplied by the distance to fly on the leg, a and b are the two constants to solve for.  FIG. 6B  helps to clarify the geometry involved in the capture pathway process. 
       FIG. 7  represents a process  700  for capturing a pathway leg. The process  700  starts with a step  701 . A step  702  calls a FlyTurn subroutine to calculate the turn geometry. A step  703  checks to see that the aircraft is not flying parallel to the leg. A step  704  determines the distance to fly before turning. A test  705  tests for track “a” greater or equal to zero. If yes, a step  706  determines the distance “b”. A test  707  sees if “b” is not negative. If not negative, then a step  708  simulates a straight segment and updates the aircraft state. A step  709  calls FlyTurn to capture a radial. A step  710  returns the aircraft state and time. If test  705  was “no”, then a step  711  uses the turn geometry calculated with FlyTurn and updates the state. A test  712  sees if legtrack=0. If so, a step  713  calculates the overshoot correction to align the aircraft with the runway. 
       FIG. 8  lists some equations useful in a FlyTurn process subroutine. The FlyTurn process simulates the aircraft in a turn. It assumes a predefined constant turn rate. The simulation simulates incremental turns for each time step, and calculates the new state of the aircraft at each time step. The total number of iterations needed to simulate the whole turn may not be an exact integer number of time steps. Calculations must account for the turn made during the last fraction of a timestep. 
     An airport automation system embodiment of the present invention includes a set of data inputs for extracting aircraft and airport-related information local to an airport for a plurality of sources and in a plurality of different data formats. A processor is used for computing from the set of data inputs an airport advisory information, takeoff and landing sequencing for participating aircraft, runway occupied status, separation monitoring, and conflict detection, and for providing unified nearby aircraft positions and velocities, weather, and airport structured information. A broadcasting system sends graphical display and audio messages to the cockpits of local aircraft from the processor. Such system can synthesize aircraft position and velocity data from at least one of airport surveillance radar, airborne surveillance broadcast transceivers, onboard local aircraft, multilateration, and other transponder-based systems. The data inputs typically include airport-unique information is gathered for broadcast, and includes at least one of airport name, airport identifier, active runway, airport visual flight rule patterns, and airport instrument-approach pathways. A connection, e.g., to the internet, can be used for activating specialized airport runway lighting that is dependent on any information being broadcast. 
     A smart airport automation system advisory generator has a process that inputs weather and airport configuration data to determine that active runway in use, and a process that inputs airport configuration data to determine an airport advisory message, and that broadcasts an airport advisory via an audio broadcast and a data broadcast. A conflict advisory subsystem determines aircraft position and velocity state information, and determines potential aircraft conflicts. It sends conflict detection advisory message broadcasts. A sequence advisory subsystem uses aircraft surveillance information in determining a most recent absolute track data of local air traffic, and predicts aircraft route intentions, unconstrained aircraft trajectories, and aircraft runway usage sequences, for broadcasting runway sequence advisory messages. 
     Although the present invention has been described in terms of the presently preferred embodiments, it is to be understood that the disclosure is not to be interpreted as limiting. Various alterations and modifications will no doubt become apparent to those skilled in the art after having read the above disclosure. Accordingly, it is intended that the appended claims be interpreted as covering all alterations and modifications as fall within the true spirit and scope of the invention.