EP1517281A2 - Réseau de sécurité pour prévenir de situations dangereuses dans la circulation aérienne - Google Patents

Réseau de sécurité pour prévenir de situations dangereuses dans la circulation aérienne Download PDF

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EP1517281A2
EP1517281A2 EP03028757A EP03028757A EP1517281A2 EP 1517281 A2 EP1517281 A2 EP 1517281A2 EP 03028757 A EP03028757 A EP 03028757A EP 03028757 A EP03028757 A EP 03028757A EP 1517281 A2 EP1517281 A2 EP 1517281A2
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conflict
prediction
region
aircraft
predicted
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EP1517281A3 (fr
EP1517281B1 (fr
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Micha Dr. Janssen
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Comsoft GmbH
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Comsoft GmbH
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
    • G08G5/04—Anti-collision systems
    • G08G5/045—Navigation or guidance aids, e.g. determination of anti-collision manoeuvers
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
    • G08G5/0073—Surveillance aids
    • G08G5/0078—Surveillance aids for monitoring traffic from the aircraft
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
    • G08G5/0073—Surveillance aids
    • G08G5/0086—Surveillance aids for monitoring terrain

Definitions

  • the present invention relates to a system and a method for alerting of potentially hazardous situations in air traffic on the basis of surveillance data wherein predicted conflicts are identified based on the prediction of future air traffic situations.
  • Safety net are automated tools intended to alert controllers of potentially hazardous situations predicted in the near future. They are based on trajectories predicted from surveillance data.
  • a safety net is transparent to the controller. Whenever the system detects a potential danger in the near future, it displays an alert message. The controller can then identify an appropriate avoiding manoeuvre and communicate the instructions to the pilot(s) concerned. This means that the radar controller should not adopt a passive attitude by blindly relying on the safety net function and waiting until an alert is indicated to him;
  • a conflict is a violation of defined safety criteria involving one or more aircraft.
  • a real conflict is a conflict that actually occurs. Such a conflict is a critical situation which may have dramatic consequences and should always be avoided.
  • a predicted conflict is a conflict that is likely to occur in the near future. Such a conflict is based on a prediction of air traffic situation in the near future.
  • Safety net functions and safety net systems according to the present invention identify both real and predicted conflicts, whereas the latter may be seen as a generalization of the former.
  • the identification of a real conflict is a deterministic process, i.e., the decision of whether or not one or more aircraft are in real conflict is simply determined by the actual track data of the aircraft
  • the identification of a predicted conflict is not a deterministic process.
  • the reason for this lies in the non-deterministic nature of future air traffic situation prediction in which errors are unavoidable. This is because aircraft may perform unexpected manoeuvres or change the speed in the prediction time frame etc. The farther in the future a prediction is made, the greater the prediction error.
  • the warning time of a conflict alert is defined as the time interval between the first communication of an alert and the start of the predicted violation of the safety criteria.
  • the warning time should be sufficiently large to cover the time needed by the radar controller to formulate an appropriate avoiding manoeuvre and communicate it to the pilot(s), plus the time need by the latter to perform a manoeuvre and eliminate the potential danger.
  • unpredictable late manoeuvres might severely restrict the warning time by generating a predicted conflict in very short term.
  • a basic idea of the present invention consists of defining a stochastic model for trajectory prediction in which the uncertainty of the prediction is represented as a function of the prediction time.
  • the stochastic model(s) for predicting trajectories is preferably used to construct a stochastic model for conflict prediction.
  • probabilities of conflict are compared to minimum confidence levels and allow to decide whether a given predicted situation is a predicted conflict or not.
  • the minimum confidence level required to predict a conflict is defined as a function of the urgency of the conflict, expressed by its prediction time. This function is independent of the uncertainty of trajectory prediction.
  • the present invention provides a safety net method or method for alerting of potentially hazardous situations in air traffic on the basis of surveillance data wherein predicted conflicts are identified based on the prediction of future air traffic situations by definition of a stochastic model for trajectory prediction in which the uncertainty of the prediction is preferably based on a variable aircraft 3D speed vector and is represented as a function of the prediction time. Further, preferably at least one stochastic model of predicted trajectories is used for constructing a stochastic model for conflict prediction in this method.
  • the probabilities of conflict are compared to minimum confidence levels, wherein a decision is made whether a predicted situation is a predicted conflict or not based upon this comparison to minimum confidence levels.
  • the method preferably defines a minimum confidence level for predicting a conflict as a function of the urgency of the conflict, expressed by its prediction time. Further, a time interval between a first communication, e.g. by displaying etc., of an alert and the start of the predicted violation of the safety criteria is preferably sufficiently large to cover the time needed by a radar controller to formulate an appropriate avoiding manoeuvre and communicate it to the pilot(s) plus the time needed by the latter to perform the manoeuvre and eliminate the potential danger.
  • each air space region is classified to belong to a region type.
  • each region type has a different set of trajectory prediction and conflict prediction parameters and hence different stochastic models for trajectory and conflict prediction.
  • manoeuvre region types has been introduced in addition to the standard region types. Regions of these types are centred at points in the vicinity of which aircraft are expected to perform a manoeuvre. These points preferably correspond to VORDME (Very High Frequency Omnidirectional Radio Range Distance Measuring Equipment) and NDB (Non Directional Beacon) way points. Such manoeuvre regions are aimed at predicting aircraft manoeuvres.
  • VORDME Very High Frequency Omnidirectional Radio Range Distance Measuring Equipment
  • NDB Non Directional Beacon
  • a different set of trajectory parameters is used and the uncertainty of the trajectory prediction is adapted according to the respective region types and the properties (e.g. position) of the respective aircraft(s).
  • the method steps are repeated in cycles wherein a confirmation window records the conflicts of the last cycles and wherein, at the end of one cycle, the confirmation windows are updated with the conflicts predicted during this cycle. Further, a conflict is confirmed and communicated if the conflict was predicted at least c times in the last cycles and wherein the parameter c depends on the prediction time frame and the region type of the aircraft involved in the conflict.
  • the method is suitable for all main safety nets such as, i.a., Short Term Conflict Alert (STCA), Minimum Safe Altitude Warning (MSAW) and/or Area Proximity Warning (APW) wherein the method may be adapted in order to fulfil special requirements of one or more safety nets.
  • STCA Short Term Conflict Alert
  • MSAW Minimum Safe Altitude Warning
  • APIW Area Proximity Warning
  • the method is preferably suitable for predicting violations of defined separation criteria between at least two aircraft, for predicting minimum safe altitude violations of at least one aircraft, and/or for predicting penetrations of protected airspaces by at least one aircraft as well as for predicting other violations of safety criteria in air traffic.
  • the present invention provides a safety net system or system for alerting of potentially hazardous situations in air traffic comprising a surveillance data detection and processing means wherein predicted conflicts are identified based on the prediction of future air traffic situations by a stochastic control means which defines a stochastic model for trajectory prediction in which the uncertainty of the prediction is represented as a function of the prediction time. Further, preferably at least one stochastic processing and control means for predicted trajectories constructs a stochastic model for conflict prediction. The conflict is further preferably communicated by a conflict communication means.
  • the system according to the present invention for alerting of potentially hazardous situations in air traffic preferably comprises: structures or data structures, such as an input track, which contains all input track information relevant to the safety net; a trajectory, i.e., a 3D trajectory for one aircraft; a predicted track, which is an extrapolation of an input track; and/or a conflict, which is the safety net output containing all relevant conflict information as well as statistical data which are all data useful for a statistical analysis of the system.
  • the system further comprises the data structure region type, which is a set of safety net parameters that depend on the region type.
  • the system further preferably comprises the following main modules: input track filtering, which eliminates input tracks which do not satisfy certain selection criteria; trajectory prediction, which generates predicted trajectories from input tracks; trajectory probing, which discretizes the prediction time frame into a finite number of time points and generates a predicted track on a given trajectory for each such time point; a conflict prediction, which performs conflict prediction on predicted tracks and which is specific to each individual safety net; a conflict confirmation which confirms conflicts over several input track updates and/or online statistical data collection, which collects online statistics and analyses the performance of the safety net systems.
  • the system further comprises the main module region type identification, which identifies the region type of an input track by determining the air space region containing the track.
  • the input of a safety net function is essentially based on radar data and does not require continuous inputs from the radar controller for its proper functioning.
  • the system preferably also makes use of aircraft intention information, e.g. cleared flight levels, provided by a flight plan or controller input etc.
  • the safety net i.e. the method and system according to the present invention, preferably consider different air space regions, representing volumes of air space.
  • Each air space region has a region type.
  • Each region type is defined by or is represented by a different set of trajectory prediction and conflict prediction/confirmation parameters.
  • manoeuvre region types are preferably introduced.
  • regions of these types are centered at points in the vicinity of which aircraft are expected to perform a manoeuvre. These points preferably correspond to VORDME (Very High Frequency Omnidirectional Radio Range Distance Measuring Equipment) and NDB (Non Directional Beacon) way points.
  • VORDME Very High Frequency Omnidirectional Radio Range Distance Measuring Equipment
  • NDB Non Directional Beacon
  • region types preferably are divided into the two classes, standard region types and manoeuvre region types.
  • standard region types are "Upper En-Route Control Areas” (upper CTA), “Lower En-Route Control Areas” (lower CTA) or “Outer Terminal Air Spaces”, “Terminal Manoeuvre Areas” (TMA), “Control Zones” (CTA) and/or “Stacks” (holding areas).
  • Standard region types are preferably defined by a unique region type identifier, a set of trajectory prediction parameters and/or a set of conflict prediction/confirmation parameters.
  • Manoeuvre region types are preferably defined by a unique region type identifier, a set of trajectory prediction parameters and/or a reference to a standard region type, called base type.
  • the manoeuvre region type inherits the conflict prediction/confirmation parameters from its base type.
  • Each air space region is preferably defined by the attributes: "unique region identifier”, “unique region type identifier”, “lateral geometry of the region”, “height band of the region”, “region priority”, “activity flag” and/or “exclusion flag”.
  • a region has preferably one of the following lateral geometries.
  • a region can have the lateral geometry of a simple closed polygon, defined as a list of vertices. It is noted that standard geometrical algorithms are preferably used to determine whether a point lies in a simple closed polygon.
  • a region may further have a lateral geometry of a distance radius circle, defined by a centre and a distance radius.
  • a region can have the geometry of a time radius circle, defined by a centre and a time radius. An aircraft is considered to belong to a time radius circle if the condition distance to centre ⁇ aircraft lateral speed * time radius is fulfilled.
  • Manoeuvre regions are preferably represented by time radius circles and the time radius of the circle is preferably close or proportional to the maximum prediction time.
  • the lower or upper heights of a region are preferably specified as altitudes and/or as flight levels.
  • regions can be included in or overlap other regions. Hence, an aircraft can fall within more than one defined region. In this case, the region with the highest priority is selected.
  • the activity flag of a region determines whether the region is active, i.e., if it has to be considered by the safety net.
  • the exclusion flag specifies whether the safety net function is inhibited in the region.
  • the activity and exclusion flags may preferably be changed dynamically.
  • regions may preferably be defined dynamically.
  • the safety net For receiving input, as discussed above, the safety net comprises a safety net interface.
  • Such interface of any safety net function preferably comprises the following data structures, as is shown in Fig. 1.
  • the input track data structure comprises all input track information relevant to the safety net.
  • a conflict data structure is a safety net output containing all relevant conflict information and the parameters are configuration parameters of the safety net system.
  • the input track comprises data such as: track identification; mode 3/A code; call sign, if available from flight plan data; lateral position; mode C; sea level mode C or aircraft altitude; lateral speed vector; lateral manoeuvre indication, with direction and rate of turn; vertical manoeuvre indication, with direction and rate of climb/decent; current time; cleared (assigned) flight level, if available; and/or Reduced Vertical Separation Minimum (RVSM) compliance flag, if available from flight plan data.
  • data such as: track identification; mode 3/A code; call sign, if available from flight plan data; lateral position; mode C; sea level mode C or aircraft altitude; lateral speed vector; lateral manoeuvre indication, with direction and rate of turn; vertical manoeuvre indication, with direction and rate of climb/decent; current time; cleared (assigned) flight level, if available; and/or Reduced Vertical Separation Minimum (RVSM) compliance flag, if available from flight plan data.
  • RVSM Reduced Vertical Separation Minimum
  • sea level mode C or aircraft altitude are preferably suitable for air space regions whose lower or upper height bands are defined as an altitude.
  • aircraft altitude aircraft mode C
  • the sea level mode C can be obtained from a QNH pressure value, assumed to be available from elsewhere in the system.
  • the method and system e.g. suitable for the STCA, MSAW, APW etc. preferably comprises the following data structures: an input track, which contains all input track information relevant to the safety net; a trajectory, i.e., a 3D trajectory for one aircraft; a predicted track which is preferably the extrapolation of an input track; a conflict, which is the safety net output containing all relevant conflict information; the region type, which is a set of safety net parameters that depend on the region type; and/or statistical data, which are all data useful for statistical analysis of the system.
  • a safety net function is preferably composed of one or more of the following main modules: a region type identification, which identifies the region type of an input track by determining the air space region containing the track; an input track filtering, which eliminates input tracks which do not satisfy certain selection criteria; a trajectory prediction, which generates predicted trajectories from input tracks; a trajectory probing, which discretizes a prediction time frame into a finite number of time points and generates a predict track on a given trajectory for each such time point; a conflict prediction, which performs conflict prediction on predicted tracks and which is preferably specific to each individual safety net; a conflict confirmation, which confirms conflicts over several input track up dates; and/or an on-line statistical data collection which collects online statistics and analyses the performance of the safety net system.
  • a region type identification which identifies the region type of an input track by determining the air space region containing the track
  • an input track filtering which eliminates input tracks which do not satisfy certain selection criteria
  • a trajectory prediction which generates predicted traject
  • the configuration parameters of the safety net preferably comprise: general safety net parameters; communication parameters; input track filtering parameters; trajectory probing parameters; conflict prediction parameters; region definition parameters; and/or region type parameters.
  • the region type parameters preferably comprise all safety net parameters whose values depend on the region type. These parameters comprise: trajectory prediction parameters; conflict prediction parameters; and/or conflict confirmation parameters.
  • one or more of the input track filtering and/or conflict prediction parameters depend on the safety net concerned. The remaining parameters are common to all safety nets and are specified later in the description.
  • Fig. 2 shows the generic data flow diagram representing the main modules of preferred safety net functions and the corresponding data links as well as the configuration parameters of the safety net.
  • the input track filtering module receives input track data containing all input track information relevant to the safety net and eliminates input tracks which do not satisfy certain selection criteria based on input track filtering parameters, then passing all relevant track information to the trajectory prediction module.
  • the region type identification module identifies the region type of an input track by determining the air space region containing the track based on the input track data and thus, identifies the region type data, i.e. the set of safety net parameters that depend on the region type.
  • the region type identification module and identification process is further based on region definition parameters and region type parameters, wherein the region type parameters contain all safety net parameters whose values depend on the region type, as discussed above.
  • the trajectory prediction module generates predicted trajectories from input tracks such as the output of the input track filtering module and the region type identification module, i.e. input track data and region type data.
  • the predicted trajectories of the trajectory prediction module i.e. the trajectory data, which is the 3D trajectory for one aircraft, is communicated to the trajectory probing module which discretizes the prediction time frame into a finite number of time points and generates a predicted track on a given trajectory for each such time point, i.a. based on trajectory probing parameters.
  • the predicted track generated by the trajectory probing module is received by the conflict prediction module which further receives region type data and conflict prediction parameters and then performs conflict prediction on the predicted tracks.
  • the conflict prediction module and the conflict prediction are specific to each individual safety net.
  • the output of the conflict prediction module i.e. the conflict data are confirmed by the conflict confirmation module over several input track updates and preferably the region type data.
  • the output of the conflict confirmation module is the conflict data as the safety net output containing all relevant
  • the on-line statistical data collection module collects on-line statistics and analyses the performance of the safety net system. Further parameters received by the safety net are, e.g. general safety net parameters and/or communication parameters.
  • the safety net function reads a preferably continuous stream of input tracks, received from e.g. the radar tracking system, for example by means of a UDP (User Datagram Protocol) communication medium.
  • the safety net may be considered as a cyclic algorithm that performs conflict prediction on a set of input tracks whose time stamps differ by less then TAU, where TAU denotes the cycle time of the safety net.
  • TAU is approximately 4 to 6 seconds.
  • the algorithm starts by checking if the system is still in the current cycle, i.e. it tests for the condition
  • the type of air space region containing T is identified.
  • the input track filter checks whether T is a valid track. If not, the algorithm drops T and waits for the next track. Otherwise, the trajectory predictor generates a trajectory TR from T.
  • the trajectory probing module then produces a set of predicted tracks ST on the predicted trajectory TR.
  • the conflict prediction module subsequently generates conflicts involving the predicted tracks in ST and inserts these conflicts in the set SC of conflicts of the current cycle. It also stores the set ST in its internal data structures. Finally, statistical data is collected.
  • SC is set to the empty set and t can be set to any valid time value.
  • the cycle time TAU is preferably determined by minimizing the time before confirming and hence communicating a conflict, under the constraint that all pairs of tracks are considered within one cycle. Therefore, TAU is equal to the time between two successive updates of a given track.
  • Fig. 3 represents the generic safety net state diagram of safety net functions as discussed above.
  • the input track filter eliminates all input tracks which do not satisfy certain selection criteria, as already indicated above.
  • an input track is eliminated if any of the conditions "it is in no defined region” and/or "it is in an exclusion region” apply.
  • height information is preferably obtained by extrapolation of the last mode C readout.
  • the extrapolated height is considered invalid and the input track is eliminated if its last mode C readout was more than a certain time MAX_T_LAST_ MODEC ago.
  • trajectory prediction is an approximation process which inevitably leads to positional errors.
  • a stochastic model for trajectory prediction has been defined, in which the uncertainty of the prediction is represented as a function of the prediction time.
  • Trajectory prediction comprises or can be decomposed into prediction in the lateral plane and prediction on the vertical axis. Preferably, these two predictions are performed separately.
  • the mean trajectory in the lateral plane is preferably determined by means of a standard turn angle which preferably depends on the region type.
  • the mean trajectory is preferably obtained by linear extrapolation of the current lateral speed vector.
  • the mean trajectory on the vertical axis is preferably determined by linear extrapolation of the current vertical speed.
  • an intended or assigned flight level is available, e.g. from controller input, flight plan data or some other source, and the aircraft is climbing or descending, the stochastic model is modified to take into account the clipping of the aircraft at the assigned flight level.
  • the uncertainty of the prediction is modelled as a function of the prediction time and the aircraft speed, and preferably further depends on the region type concerned as well as on the aircraft attitude, i.e. whether the aircraft is manoeuvring or not.
  • a trajectory is preferably composed of (1) a lateral trajectory in the space (t, x, y) and (2) a vertical trajectory in the space (t, z). Further, lateral and vertical trajectories are stochastic functions defined by (1) a mean, i.e. most likely, trajectory and (2) an error variance.
  • the error variance models the uncertainty of the prediction.
  • a preferred error variance will be described in detail below.
  • e e (v, t) denotes the lateral (resp. vertical) positional error vector at prediction time t for a lateral (resp. vertical) speed v.
  • the square root ⁇ of the error variance is called the standard error.
  • where ⁇ is a constant and r is a normal random variable of mean 0, i.e., Er 0.
  • the parameters a and b depend on the region type and/or the aircraft attitude, i.e., whether the aircraft is manoeuvring or not. Further below, a method for estimating these parameters from the analysis of the real track data will be discussed.
  • the mean lateral trajectory is preferably obtained by linear extrapolation of the current lateral speed vector.
  • the mean lateral trajectory is preferably obtained by generating a circular trajectory with a current lateral speed and turn rate of the aircraft until the predicted end of the manoeuvre, followed by a tangent straight line.
  • a crucial or difficult task is the estimation of how long the aircraft will perform its manoeuvre.
  • a standard angle of turn, TURNANGLE given as a region type parameter, is introduced.
  • the distribution function of the lateral positional error vector e (e x , e y ) is a normal distribution with mean 0 which is independent of the direction of the vector e.
  • Such assumption has the advantage of providing a stochastic model for the distance between the lateral positions of two aircraft which does not depend on the direction of the relative lateral position of the aircraft. This assumption also justifies the definition of the error variance ⁇ 2 , as given above.
  • (1) i.e.
  • Fig. 4 shows a mean lateral trajectory generated for a manoeuvring aircraft in the (x, y) plane. It is composed of a circular arc followed by a tangent straight line. The figure also represents the error variance ⁇ 2 as a function of the prediction time t.
  • the mean vertical trajectory is preferably determined by linear extrapolation of the current vertical speed.
  • the mean vertical trajectory and the error variance ⁇ 2 need to be modified to take into account the clipping of the aircraft at the assigned flight level.
  • the uncertainty interval is thus "clipped" at the assigned level. It is noted that the 3 ⁇ value corresponds to a 99.74 % probability that the real position of the aircraft lies inside the interval [z min , Z max ].
  • ⁇ * (z max - z min ) / 6.
  • the prediction In case of a temporary loss of mode C information, the prediction must preferably be started at the time of the last mode C readout instead of the current time.
  • Fig. 5 shows the mean vertical trajectory generated for a climbing aircraft with an assigned flight level in the (t, z) plane, which comprises three line segments.
  • the newly introduced standard error ⁇ * is considered as well as the tolerance interval [z a - FL_TOL, z a + FL_TOL].
  • trajectory probing consists of discretizing the prediction time frame into a finite number of equally spaced time points, called probes. For each probe, the probing process generates a static traffic picture ("a snapshot") represented by a set of predicted tracks.
  • a snapshot a static traffic picture
  • This probing approach has, i.a., the important advantages that it provides a clear separation between movement and conflict prediction, that it substantially reduces the complexity of the conflict prediction module which can work with static traffic pictures and does not have to deal with time-dependent 4D-trajectories, thus avoiding complex equations of movement, that it provides a simple stochastic model at each probing point and that it is very simple conceptually.
  • a predicted track is defined by: a track identification; a probe; a position vector; and/or a speed vector (v x , v y , v z ).
  • the position vector is a stochastic variable, preferably defined by: a mean position vector (x, y, z) and/or an error variance vector ( ⁇ xy 2 , ⁇ z 2 ).
  • a probe p is in a short term prediction time frame if 0 ⁇ p ⁇ SHORT_TERM; a medium term prediction time frame if SHORT_TERM ⁇ p ⁇ MEDIUM_TERM; and a long term prediction time frame if MEDIUM_TERM ⁇ p ⁇ LONG_TERM.
  • SHORT_TERM 30 seconds
  • MEDIUM_TERM 60 seconds
  • LONG_TERM 120 seconds.
  • the probing process generates a number, NBPROBES, of preferably equally spaced probes in the interval [0, LONG_TERM]. For a given trajectory, the probing process determines, for each probe p, the following predicted track:
  • the vector (x, y, z) is the position on the mean lateral trajectory at p and the height z is the position on the mean vertical trajectory at p.
  • the norm of the vector (v x , v y ) is equal to the norm of the current lateral speed vector and its direction is parallel to the mean lateral trajectory at p.
  • the rate vz is equal to the slope of the mean vertical trajectory at p.
  • ⁇ xy 2 is the error variance of the lateral trajectory at ((v x 2 + v y 2 ) 1/2 , p) and ⁇ z 2 is the error variance of the vertical trajectory at (v z , p).
  • Fig. 6 shows the lateral position in the (x, y) plane as well as speed (v x , v y ) and error variance ( ⁇ xy 2 ) of predicted tracks for probes 0 to 7.
  • Fig. 7 shows the vertical position (z, t), speed (v z ) and error variance ( ⁇ z 2 ) of predicted tracks for probes 0 to 7.
  • a number of probes NB_PROBES should be large enough to neglect the discretization error.
  • making the number of probes too small deteriorates the system performance. Therefore, a good trade-off has to be found between discretization error and system performance.
  • the at least one stochastic model of predicted track or stochastic models of predicted tracks are used to construct a stochastic model for conflict prediction.
  • a probability of conflict is compared to a minimum confidence level and allows to decide whether a given predicted situation is a predicted conflict or not.
  • p confl is the probability of a conflict and p min is a threshold probability value representing the minimum confidence level required to predict a conflict.
  • p min is a threshold probability value representing the minimum confidence level required to predict a conflict.
  • a conflict is predicted if and only if the relation p confl ⁇ p min holds.
  • the minimum probability p min is defined as a function of the urgency of the conflict, expressed by its prediction time t.
  • the function p min (t) is independent of the uncertainty of trajectory prediction. Following the discussion above, the function p min (t) should be increasing with the prediction time t, as can be seen in Fig. 8.
  • the ability to specify, for each prediction time, the minimum confidence level required to predict a conflict enables an optimal trade-off between in-time conflict prediction and nuisance alert rate.
  • the lower bound p min L (t) achieves the highest warning times but features also the highest nuisance alert rate.
  • the upper bound p min U (t) achieves the lowest warning times but attains the lowest nuisance alert rate.
  • the choice of the function p min (t) is preferably assisted by estimating both its warning time loss with respect to the function p min L (t) and its nuisance alert rate increase with respect to the function p min U (t). Further in the specification, a statistical method will be presented which determines these properties for each function p min (t) and enables a convenient optimization of this function.
  • p min (t) is preferably defined as a piecewise constant function with three distinct values for short, medium and long term prediction time frames, as can be seen in Fig. 9:
  • the stochastic model is compared with a deterministic model, which does not use any uncertainty information for trajectory prediction.
  • a deterministic model can be obtained by setting the error variance of trajectory prediction to zero in the stochastic model as discussed above.
  • the deterministic model is unable to estimate to what extent a predicted conflict may be trusted, because it does not use any uncertainty information for trajectory prediction.
  • the deterministic model does not take into account the predicted penetration depth of the safety cylinder centered at each aircraft. Therefore, contrary to the stochastic model, it does not distinguish between marginal and clear predicted conflicts, depending on the uncertainty of the predicted position of an aircraft inside another aircraft's safety cylinder.
  • systems based on such a deterministic model typically address this problem by introducing complex heuristics, which consider the region type, trajectory type, aircraft speed, penetration depth of the safety cylinder, etc.
  • the validation and optimization of these heuristics are difficult tasks in general. As a consequence, an optimal trade-off between in-time conflict prediction and nuisance alert rate is not as easy to achieve as in the stochastic model.
  • a conflict confirmation mechanism is introduced in order to take into account erroneous track data or transient track values.
  • a confirmation window records the conflicts of the last WINSIZE cycles.
  • the confirmation windows are updated with the conflicts predicted during this cycle. If a conflict was predicted at least c times in the last WINSIZE cycles, the conflict is confirmed and communicated, preferably displayed, to the user/controller.
  • the parameter c depends on at least the prediction time frame, as described above, and/or the region type of the aircraft involved in the conflict.
  • WINSIZE 5
  • MIN_NBCONFL_SHORT 1
  • MIN_NBCONFL_MEDIUM 2
  • MIN_NBCONFL_LONG 3.
  • a preferred method for determining optimal turn angles is given.
  • a standard angle of turn TURNANGLE is used to predict lateral manoeuvre trajectories.
  • a preferred method for determining an optimal standard turn angle for each region type is presented. The preferred method is based on the minimization of an objective error function and on observed manoeuvres from real track data.
  • two manoeuvre trajectories are considered for an aircraft with radial acceleration a r .
  • the optimal angle of turn ⁇ * is preferably approximated for each region type from real track data.
  • the expectations are preferably approximated by the following formulae: where ⁇ i and a r,i , respectively, are the total turn angle and radial acceleration for manoeuvre i (0 ⁇ i ⁇ n) observed from real track data.
  • Distinct values of the parameters a and b are preferably calculated in the lateral plane and on the vertical axis for each defined region type and aircraft attitude, i.e., whether the aircraft is manoeuvring or not.
  • the calculation of error variance parameters is as follows. For each track update in the time frame [0, LONG_TERM] starting at update i, the positional error ⁇ e ⁇ 2 is computed by comparison with the predicted trajectory generated for update i. y; is now defined as the corresponding mean value of ⁇ e ⁇ 2 2 /dt 2 and x i is defined as the square speed v 2 of track update i.
  • a linear regression between x and y with the set S of pairs (x i ,y i ) generated for track updates of aircraft within a given region type and within a given attitude is constructed for each region type and aircraft attitude (manoeuvring or not).
  • n is defined as the number of elements in S.
  • Tables 1 to 9 present preferred specification of the safety net parameters as well as approximate preferred values of the parameters which are preferably common to all safety nets.
  • Table 1 shows general safety net parameters;
  • Table 2 shows the specification of communication parameters;
  • Table 3 shows the specification of input track filtering parameters;
  • Table 4 shows the specification of trajectory probing parameters;
  • Table 5 shows the specification of region definition parameters;
  • Table 6-9 show region type parameters wherein Table 6 shows the specification of general region type parameters;
  • Table 7 shows the specification of trajectory prediction parameters;
  • Table 8 shows the specification of conflict prediction parameters;
  • Table 9 shows the specification of conflict confirmation parameters.
  • Example TYPEID Region Type Identification 0 CLASS Class of Region Type (standard, manoeuvre) Standard BASE_TYPEID Base type identification for manoeuvre region type 0 Specification of Trajectory Prediction Parameters Depending on Region Type Parameter Description
  • warning time and nuisance alert rate will be analysed.
  • the stochastic model preferably is parameterized by the minimum confidence level function p min (t). This function defines a certain trade-off between in-time conflict prediction and nuisance alert rate.
  • the proposed method preferably serves as an assisting tool for the optimization of the function p min (t) and hence of the latter trade-off.
  • the lower bound p min L(t) achieves the highest warning times but features also the lowest desirability values.
  • the upper bound p min U (t) achieves the lowest warning times but attains the highest desirability values.
  • the choice of the function p min (t) is preferably assisted by estimating both its warning time loss with respect to the function p min L (t) and its desirability loss with respect to the function p min U (t). Now the warning time and desirability losses will be formally defined.
  • a set S of n conflicts is considered which have been generated both by the function p min (t) and a lower bound p min L (t).
  • the warning time loss of p min (t) with respect to p min L (t) in set S is preferably defined as the square root of the mean square of the relative warning time differences:
  • a set S of n conflicts is considered which have been generated by the function p min (t) but not by an upper bound p min U (t).
  • d i be the alert desirability of conflict i in S.
  • the desirability loss of p min (t) with respect to p min U (t) in set S is preferably defined as the square root of the mean square of the desirability values:
  • a tolerance d tol on the desirability may be imposed and the relation e d ⁇ -d tol may be used as an acceptance test of the function p min (t).
  • TMA Terminal Manoeuvre Area
  • U Upper Airspace
  • Warning Time and Desirability Losses for Different Functions p min (t) p min (t) Warning Time Loss e w in % Desirability Loss e d in sec No P_MIN_SHORT, P_MIN_MEDIUM, P_MIN_LONG TMA UA TMA UA 1 0.1,0.1,0.1 0 0 64 76 2 0.9,0.5,0.1 31 20 80 86 3 0.1,0.2,0.3 35 21 35 70 4 0.1,0.5,0.5 48 35 23 54 5 0.1,0.5,0.9 49 45 23 25 6 0.9, 0.9, 0.9 89 65 0 0 0
  • Example 2 shows a decreasing function p min (t). It features a higher warning time loss and slightly higher desirability loss than example 1. Furthermore, example 2 has nearly the same warning time loss but a much higher desirability loss (especially for TMA) than example 3. Therefore, examples 1 and 3 are preferred to example 2. In general, decreasing functions p min (t) are preferably avoided, as already argued above. It can also be observed that the warning time and desirability losses are similar for TMA and UA in example 5, while they differ significantly in example 3. This can be explained by a higher uncertainty of trajectory prediction in TMA than in UA. Finally, it can be observed that examples 4 and 5 yield similar warning time and desirability losses for TMA but quite different losses for UA. Again, this is due to a higher uncertainty of trajectory prediction in TMA than in UA.
  • STCA Short Term Conflict Alert
  • a Short Term Conflict Alert (STCA) system is a safety net aimed at detecting traffic situations that might lead to a violation of defined separation criteria between at least two aircraft in a near future and warning the radar controller of this potential danger.
  • STCA Short Term Conflict Alert
  • a conflict in the context of the STCA safety net, will preferably correspond to a simultaneous infringement of defined lateral and vertical separation minima between two aircraft.
  • a conflict is further characterized by (1) its nature, (2) its severity and/or (3) its uncertainty.
  • conflict is expressed in terms of properties of the conflict.
  • properties of the conflict include different types of separation infringements and geometrical properties such as the so called crossing and divergence properties of a pair of aircraft.
  • the severity of a conflict depends on its nature and is preferably determined by combining the various properties of the conflict. In order to express the severity of a conflict, distinct conflict categories will be defined.
  • the uncertainty of a conflict is based on the uncertainty of trajectory prediction, in which errors are unavoidable, as already discussed above. This uncertainty is represented by a stochastic model for minimum separation infringement.
  • the conflict characterisation provides the radar controller with detailed information about a conflict situation, which enables him to analyse the situation more precisely and rapidly, before making a decision and possibly formulating an avoiding manoeuvre in order to eliminate the conflict.
  • conflict characterisation further helps making more precise and detailed off-line analysis of conflict situations.
  • the latter analysis may in turn be used, e.g., for tuning the STCA system.
  • the generic conflict prediction module comprises two main modules, i.e., a conflict category prediction module and a coarse pair filtering module.
  • the conflict category prediction module predicts the conflict category of predicted pairs, i.e., pairs of predicted tracks, and generates conflicts.
  • the coarse pair filtering module generates predicted pairs by restricting the set of predicted tracks that need to be compared to a given predicted track in the conflict category prediction algorithm.
  • conflict nature this is represented by a set of properties of the conflict.
  • the latter preferably include properties of the geometrical situation of the conflict, such as the crossing and divergence properties of a pair of aircraft and/or different types of separation infringements, expressing the severity of separation loss.
  • the crossing and divergence tests are applied to a pair of aircraft.
  • the crossing and divergence properties represent some safety conditions about the local geometry of a conflict situation. These properties are based on current or predicted track positions and speed vectors and/or on the assumption that the speed vectors remain constant.
  • the divergence property is considered to represent the evolution of the lateral and vertical separations of the aircraft in the near future whereas the crossing property is considered to describe the geometry of both linearly extrapolated lateral trajectories, and allows us to distinguish between nearly parallel, converging or diverging trajectories. In this context it should be noted that a diverging pair is not the same as a diverging trajectory.
  • the cross-over point is defined as the point in the (x, y) plane that both aircraft will pass through, usually at different times, assuming their current or predicted track headings are continued. The cross-over point will already have been reached for aircraft on diverging headings and does not exist for aircraft on parallel headings.
  • the cross-over time is defined as the time at which the first aircraft in the pair is predicted to reach the cross-over point. The cross-over time is set to have occurred and the pair is considered as crossed if any of the following conditions are satisfied:
  • the pair of aircraft is considered to be diverging if any of the following conditions are satisfied:
  • the second of the above divergence conditions is introduced in order to account for the rapidly changing lateral closing speed in the vicinity of the time of minimum lateral distance.
  • the risk of collision when separation minima are infringed depends on the geometry of the conflict, expressed e.g. by the crossing and divergence properties defined above.
  • the safety margins can be eroded significantly in a very short time. Therefore, the risk of collision in this case is much higher than e.g. for a pair of crossed and diverging aircraft.
  • DL_MAJOR ⁇ DL_MINOR is always valid.
  • the severity of a conflict preferably depends on its nature and is determined by combining the various properties of the conflict. In order to express the severity of a conflict, at least four distinct conflict categories will be defined by combining the two types of separation infringements with the crossing and divergence properties.
  • conflict categories are defined in Table 11 below: Conflict Categories of STCA Category Condition 1 major separation infringement and not (crossed and diverging) 2 (major separation infringement and (crossed and diverging)) or (minor separation infringement and not (crossed and diverging)) 3 Minor separation infringement and (crossed and diverging) 4 not (major or minor separation infringement)
  • conflict categories may be extended by identifying and combining additional properties of a conflict.
  • the uncertainty of a conflict is based on the uncertainty of trajectory prediction and is preferably represented by a stochastic model for minimum separation infringement. In the latter model, probabilities of major and minor separation infringements are estimated.
  • a combined region type needs to be determined. This is preferably achieved, e.g., by means of a decision matrix.
  • Some region types are preferably defined as combined. Such region types contain only pairs, not single aircraft.
  • a Reduced Vertical Separation Minimum (RVSM) region is an upper airspace region in which the minimum vertical separation has been reduced from 2000 ft to 1000 ft, provided that both aircraft in the pair are RVSM-compliant.
  • the 1000 ft minimum is raised to 2000 ft, e.g. for safety reasons.
  • each STCA region is defined by a flag identifying it as RVSM or not.
  • an associated non-RVSM region R 2 with region type RT 2 is defined. Regions R 1 and R 2 have the same geometry and R 1 has higher priority than R 2 . If an aircraft is RVSM-compliant and R 1 is active, it will fall in R 1 , otherwise in R 2 .
  • Region types RT 1 and RT 2 differ only by the value of the minimum separation DV_MIN and their combined region type is RT 2 .
  • the mode 3 / A code selection specifies which codes have to be protected by the STCA function. At least three modes of operation, identified by the parameter MODE, are preferred:
  • a conflict preferably contains at least one or all of the following information, restricted to the prediction time frame: unique conflict identification (contiguous conflicts for a given pair of aircraft have the same conflict identification); duration of conflict identification; STCA system identification (sic, sac); current time; time to conflict; lateral and vertical starting positions of a conflict; current lateral and vertical separations; predicted minimum lateral and vertical separations; a conflict nature; predicted conflict category; probabilities of major and minor separation infringements; and/or information of both aircraft involved in the conflict.
  • the nature of the conflict preferably comprises predicted crossing and divergence flags at starting time of conflict and/or predicted major and minor separation infringement flags.
  • the information about each aircraft preferably comprises: track identification; mode 3/A code; call sign, if available from input track; and/or track server identification (sic, sac).
  • the STCA conflict prediction module preferably comprises the data structures: predicted track, i.e., extrapolation of an input track; predicted pair, i.e., a pair of predicted tracks; conflict, i.e., output of STCA containing all relevant conflict information; and/or region type including the set of conflict prediction parameters that depend on the region type.
  • the STCA conflict prediction module preferably comprises the conflict category prediction module that predicts the conflict category of predicted pairs and generates conflicts and/or a coarse pair filtering module that generates predicted pairs by restricting the set of predicted tracks that need to be compared to a given predicted track in the conflict category prediction algorithm.
  • the conflict prediction module comprises two main modules.
  • the conflict prediction module preferably includes coarse pair filtering in conflict category prediction parameters. All preferred approximate parameters specific to STCA are specified below.
  • Fig. 10 shows the data flow diagram of the conflict prediction module and the corresponding data links of STCA, as discussed above.
  • the coarse pair filter generates predicted pairs by restricting the set of predicted tracks that need to be compared to a given predicted track in the conflict category prediction algorithm.
  • the coarse pair filter preferably comprises the following sub-filters: the coarse proximity filter; the mode 3/A code selection filter; and/or the split tracks filter.
  • the coarse pair filter comprises three sub-filters.
  • the coarse proximity filter generates a set of predicted pairs from predicted tracks.
  • the mode 3/A code selection and split tracks filters then eliminate pairs in this set which do not satisfy certain criteria.
  • a predicted pair is considered to be a pair of predicted tracks generated for the same probe.
  • the coarse proximity filter identifies the set of predicted tracks that are in a neighbourhood of a given predicted track.
  • the basic idea underlying the coarse proximity filter comprises partitioning the (x, y) plane into square cells. Preferably, for each probe, a separate grid of cells is defined. The predicted tracks generated during one STCA cycle are inserted into their corresponding grid. Preferably, two predicted tracks generated for probe p are called neighbours if both following conditions are satisfied: the tracks are in the same or an adjacent cell of grid p and their vertical separation is less than DZ_MAX.
  • the coarse proximity filter preferably generates predicted pairs of neighbours. At the end of a cycle, the grids are emptied. This ensures that only pairs of predicted tracks of the same cycle are generated.
  • Fig. 11 exemplarily shows a grid for a given probe, as well as two neighbours in adjacent cells in the (x, y) plane.
  • the filter Whenever a new predicted track for probe p is received, the filter generates predicted pairs involving the track and its neighbours. The track is subsequently inserted in the corresponding cell of grid p. This sequential insertion mechanism of tracks into cells ensures that each predicted pair is generated only once (sequential filtering).
  • the size EDGE of the cells and the maximum vertical distance DZ_MAX are chosen large enough to include all predicted pairs of potential concern. However, choosing these parameters too large implies that an excessive number of pairs are processed by the conflict category prediction algorithm and the system performance is degraded. It has to be noted that the optimal values of EDGE and DZ_MAX may vary with the probe. Later in the description a preferred method for determining optimal values of these parameters for each probe will be presented.
  • the mode 3/A code selection filter eliminates all pairs in which one or both tracks do not have a selected code, depending on the mode of operation MODE.
  • split tracks filter multi-radar tracking can produce duplicate system tracks for the same airframe, known as split tracks.
  • split tracks arise from the garbling effect in the detected mode 3/A codes.
  • Different radars may detect mode 3/A codes that differ by a few bits although they actually correspond to the same aircraft. Thus, it is an important feature to detect split tracks and eliminate pairs of such tracks in order to avoid the declaration of nuisance alerts.
  • a pair of input tracks is considered to satisfy the split conditions if all the following conditions are satisfied: vertical separation ⁇ VSD; lateral separation ⁇ LSD; and mode 3/A code bit differences ⁇ SCD.
  • a confirmation mechanism is added to the split conditions in order to take into account spurious track effects.
  • a confirmation window records the result of the split conditions test of the last WINSIZE cycles. Two input tracks are considered as split and all corresponding predicted pairs are eliminated by the filter if and only if the pair of input tracks met the split conditions at least once in the last WINSIZE cycles.
  • the pairs of input tracks meeting the split conditions are collected in a split pairs set.
  • the confirmation windows are updated with the split pairs set and the latter is emptied.
  • the grids of the coarse proximity filter are emptied, and/or the confirmation windows of the split filter are updated with the split pairs set and the latter is emptied (coarse filter clean up).
  • conflict category prediction will be described in more detail.
  • the basic idea of the conflict category prediction algorithm consists of predicting the conflict category of a predicted pair. All pairs whose predicted category is larger than a specified minimum category are eliminated. Finally, conflicts are built from the remaining pairs.
  • the conflict category of a pair is predicted by predicting a major or minor separation infringement for the pair and/or applying the crossing and divergence tests to the mean positions and speed vectors of the predicted tracks in the pair (as has been discussed in detail above).
  • a stochastic model for minimum separation infringement has been developed from the mean positions and error variance vectors of the predicted tracks in a pair in order to predict separation infringements.
  • probabilities of major and minor separation infringements are compared to a given threshold.
  • the stochastic model is decomposed to lateral and vertical models.
  • a pair of predicted tracks T 1 and T 2 is considered to be generated for probe p. It is now an object to estimate the probability of a lateral separation infringement between T 1 and T 2 .
  • the first task consists of determining the two-dimensional stochastic model of the combined lateral positional error of the pair (T 1 , T 2 ). The probability of lateral separation infringement is then approximated by a one-dimensional stochastic model.
  • ⁇ 1 2 E ⁇ e 1 ⁇ 2 2
  • ⁇ 2 2 E ⁇ e 2 ⁇ 2 2 denote their respective lateral error variances.
  • Fig. 12 shows lateral predicted tracks with error vectors and variances in the (x, y) plane.
  • d denotes the distance between the mean lateral positions of T 1 and T 2 , e the combined positional error vector, ⁇ 2 the combined lateral error variance and d min the minimum distance below which a lateral separation infringement occurs.
  • d denotes the distance between the mean lateral positions of T 1 and T 2 , e the combined positional error vector, ⁇ 2 the combined lateral error variance and d min the minimum distance below which a lateral separation infringement occurs.
  • the probability of lateral separation infringement is the probability that the vector d + e lies inside the circle centred at 0 and of radius d min .
  • the first task consists of determining the stochastic model of the combined vertical positional error of the pair (T 1 , T 2 ). The probability of vertical separation and infringement is then calculated.
  • e 1 and e 2 are the vertical positional errors of T 1 and T 2 .
  • ⁇ 1 2 E ⁇ e 1 ⁇ 2 2
  • ⁇ 2 2 E ⁇ e 2 ⁇ 2 2 denote their respective vertical error variances.
  • the distribution function of e is a normal distribution with mean 0.
  • d denotes the distance between the mean vertical positions of T 1 and T 2 , ⁇ 2 the combined vertical error variance and d min the minimum distance below which a vertical separation infringement occurs.
  • the probability of vertical separation infringement is the probability that the normal random variable u of mean d and variance ⁇ 2 lies in the interval [-d min , d min ] as illustrated in Fig. 14.
  • d 1 and d v are considered to be the distances between the lateral and the vertical positions of the predicted tracks in the pair.
  • p(a, b) is the probability that d 1 ⁇ a and d v ⁇ b.
  • p min is a threshold probability value.
  • a separation infringement is preferably predicted as follows: If p(DL_MAJOR, DV_MIN) ⁇ p min , a major separation infringement is predicted; If p(DL_MAJOR, DV_MIN) ⁇ p min and p(DL_MINOR, DV_MIN) ⁇ p min , a minor separation infringement is predicted;
  • the threshold p min depends on the prediction time frame as well as the combined region type of the predicted pair, as discussed above. Further in the specification, a method will be presented which restricts the interval of possible values of p min . This method is based on the concept of so-called conflict patterns.
  • a conflict is preferably computed from the set S of predicted pairs of two given aircraft whose predicted conflict category is less than or equal to a region type parameter CAT_MAX.
  • the preferred conflict attributes are determined as follows:
  • the size EDGE of the cells and the maximum vertical distance DZ_MAX are preferably chosen large enough to include all predicted pairs of potential concern. However, choosing these parameters too large implies that an excessive number of pairs are processed by the conflict category prediction algorithm and degrades system performance. Furthermore, the optimal values of EDGE and DZ_MAX may vary with the probe.
  • Optimal values meeting this condition are based on some worst case values of: the minimum distance d min , above which there is no separation infringement, the threshold probability p min for separation infringement prediction; and/or the maximum combined error variance ⁇ max 2 of the trajectory prediction.
  • the optimal separation is derived as a function of the parameters d min , p min and ⁇ max ⁇ Then, for the lateral and vertical cases, the worst case values of these parameters are calculated. Finally, an example is presented showing the evolution of the parameters EDGE and DZ_MAX with the probe.
  • u is considered to be a normal random variable of mean d ⁇ 0 and variance ⁇ 2 .
  • u* denotes the standard normal random variable of mean 0 and variance 1.
  • u ⁇ u* + d.
  • the standard error ⁇ is preferably assumed to satisfy the relation 0 ⁇ ⁇ ⁇ ⁇ max .
  • the parameters d min , p min and ⁇ max preferably depend on the region type. Since the coarse proximity filter parameters EDGE and DZ_MAX are independent of the region type, worst case values for d min , p min and ⁇ max in the set SR of all region types have to be found.
  • p min minimum P_MIN_SHORT in SR.
  • p min minimum P_MIN_MEDIUM in SR.
  • p min minimum P_MIN_LONG in SR.
  • the worst case values of parameters d min and ⁇ max are preferably determined as follows for each probe.
  • MAX_SPEEDLAT denote an upper bound on the lateral speed.
  • ⁇ LS max (LS_INTERCEPT, LS_SLOPE ⁇ MAX_SPEEDLAT 2 + LS_INTERCEPT);
  • ⁇ LM max (LM_INTERCEPT, LM_SLOPE ⁇ MAX_SPEEDLAT 2 + LM_INTERCEPT);
  • ⁇ L max (0, ⁇ LS , ⁇ LM ).
  • ⁇ max maximum (probe ⁇ (2 ⁇ L ) 1/2 ) in SR.
  • the worst case values of parameters d min and ⁇ max are preferably determined as follows for each probe.
  • MAX_SPEEDVERT denote an upper bound on the vertical speed.
  • ⁇ max maximum (probe ⁇ (2 ⁇ V ) 1/2 ) in SR.
  • EDGE and DZ_MAX start with a preferred value d min for probe 0, grow until probe 30 seconds, decrease and stabilize at d min for probe 70 seconds.
  • tables 12 to 17 present preferred specification as well as preferred values of preferred parameters specific to STCA.
  • Table 12 shows the specification of coarse proximity filter parameters
  • table 13 shows the specification of mode 3/A code selection filter parameters
  • table 14 shows the specification of split tracks filter parameters
  • table 15 shows the specification of combined region type parameters
  • tables 16 and 17 show region type parameters wherein table 16 shows the specification of region definition parameters and table 17 shows the specification of conflict prediction parameters depending on region type of STCA.
  • DZ_MAX is determined automatically 50 Fl MAX_SPEEDLAT Maximum lateral speed (for automatic determination of EDGE) 0.18 NM/sec MAX_SPEEDVERT Maximum vertical speed (for automatic determination of DZ_MAX) 1 Fl/sec Specification of Mode 3/A Code Selection Filter Parameters of STCA Parameter Description
  • Example MODE Mode of operation At least one track in pair has selected code CODE Mode 3/A code 4324 SELECT CODE selected ?
  • conflict patterns consists of imposing the behaviour of the STCA system in defined situations.
  • a conflict pattern defines a set of scenarios for which the output of STCA is specified. Two classes of conflict patterns can be identified, depending on whether or not a conflict alert is desired for the pattern. A conflict pattern for which an alert is desired will be called positive conflict pattern, whereas a conflict pattern for which an alert is not desired will be called negative conflict pattern.
  • a preferred idea is to impose restrictions on the interval of possible values of the minimum confidence level function p min (t). Positive conflict patterns will impose restrictions on the upper bound of p min , whereas negative conflict patterns will restrict the lower bound of p min . Note that it will not always be possible to find a function p min (t) which is adequate for all conflict patterns in a given set. Indeed, the upper bound of p min imposed by a positive conflict pattern may be smaller than the lower bound imposed by a negative conflict pattern, resulting in a contradiction.
  • conflict pattern if the behaviour of the STCA system in given scenarios shall be imposed, then the latter must be predictable by STCA. For example, a scenario containing a late manoeuvre is not predictable by STCA. In this case, the predicted aircraft separation will not correspond to the actual minimum aircraft separation of the scenario. However, imposing conditions based on aircraft separations requires that the latter be predictable. Therefore, conflict patterns will exclusively define sets of scenarios for which the aircraft 3-D speed vectors remain constant.
  • a conflict pattern is preferably defined by the following information:
  • the maximum prediction time is only relevant for positive conflict patterns. It specifies the prediction time above which STCA is not required to yield a conflict alert.
  • the information of whether an aircraft is levelled or climbing/descending determines which error variance model is used in the vertical trajectory prediction of STCA.
  • the attributes d l and d v correspond to the lateral and vertical aircraft separations at any time in the prediction time frame.
  • the minimum lateral aircraft speed v l, min is used only for negative conflict patterns.
  • the minimum vertical speed for a climbing/descending aircraft is implicitly defined as the maximum vertical speed for a levelled aircraft v v, level, max .
  • the CD flag is used to determine the minimum lateral separation above which there is no lateral separation infringement. If the flag is set to TRUE, this minimum is given by the parameter DL_MAJOR. Otherwise, it is given by the parameter DL_MINOR.
  • p min (t) be defined as a piecewise constant function with three distinct values for short, medium and long term prediction time frames, i.e.,
  • p min U (t) be the upper bound of p min (t) restricted by a positive conflict pattern.
  • P_MIN_SHORT ⁇ min ⁇ p min U (t) 0 ⁇ t ⁇ SHORT_TERM ⁇
  • P_MIN_MEDIUM ⁇ min ⁇ p min U (t) SHORT_TERM ⁇ t ⁇ MEDIUM_TERM ⁇
  • p min L (t) be the lower bound of p min (t) restricted by a negative conflict pattern.
  • Table 18 shows the restricted intervals for the function p min (t) imposed by a set of positive and negative conflict patterns in upper airspace. The following values for the parameters defining the safety cylinder centered at each aircraft are assumed:
  • Table 19 shows the restricted intervals for the function p min (t) imposed by a set of positive and negative conflict patterns in TMA. The following values for the parameters defining the safety cylinder centered at each aircraft are assumed:
  • MSAW minimum safe altitude warning
  • a minimum safe altitude warning (MSAW) system is a safety net aimed at predicting minimum safe altitude violations in a near future and warning the radar controller of this potential danger. This example is based on the generic specification, design and algorithms which are applicable to all safety nets and are discussed above in detail and thus describes solely the aspects which are specific to the MSAW safety net.
  • conflict In the context of the MSAW safety net, the notion of conflict as defined above corresponds to a minimum safe altitude violation.
  • the uncertainty of a conflict is based on the uncertainty of trajectory prediction, in which errors are unavoidable, as discussed above. This uncertainty will be represented by a stochastic model for minimum safe altitude violation.
  • the generic conflict prediction module as defined above predicts a minimum safe altitude violation of a predicted track and generates conflicts. In the latter preferred model, a probability of minimum safe altitude violation is estimated.
  • MSAW regions are defined by a minimum safe altitude and/or a maximum altitude of potential concern, above which aircraft are considered to be in no danger of coming into close proximity with the minimum safe altitude within the prediction time frame.
  • the maximum altitude of potential concern is preferably specified by a single maximum altitude MAPC, which is preferably valid for the whole region.
  • the minimum safe altitude is preferably specified in any of the following ways (see Fig. 17):
  • manoeuvre regions do not contain the additional attributes defined here above.
  • the mode 3/A code selection specifies which codes have to be protected by the MSAW function.
  • the information preferably required in an input track is specified above.
  • a conflict preferably comprises the following information, restricted to the prediction time frame: unique conflict identification, wherein contiguous conflicts for a given aircraft have the same conflict identification; duration of conflict identification; MSAW system identification (sic, sac); current time; time to conflict; lateral and vertical starting positions of conflict; current lateral and vertical distances to starting positions of conflict; predicted minimum altitude; probability of minimum safe altitude violation; and/or information about the aircraft involved in the conflict.
  • the information about the aircraft preferably comprises: track identificationon; mode 3/A code; call sign, if available from input track; and/or track server identificationon (sic, sac).
  • An input track T is eliminated if it does not have a selected mode 3/A code or, if, for each non-manoeuvre region, any of the following relations hold: Lateral distance of T to region > MAX_DIST_LAT; Vertical distance of T to region > MAX_DIST_VERT; and/or Altitude of T > MAPC; where MAPC denotes the maximum altitude of potential concern of the region.
  • the distance to region it is noted that the distance of a point x to a set S is defined as the minimum distance of x to each point of S.
  • m denotes the lateral position of T.
  • the lateral distance d of T to a region R is preferably calculated as follows:
  • a bounding box of a set S in R n is the cross product of n intervals which encloses the set S.
  • conflict prediction the basic idea of the conflict prediction algorithm consists of predicting a minimum safe altitude violation of a predicted track. All predicted tracks that are not predicted to violate the minimum safe altitude are eliminated. Finally, conflicts are built from the remaining predicted tracks.
  • the highest priority, non-manoeuvre region containing the mean position vector of the predicted track is determined; a stochastic model for minimum safe altitude violation for this region is developed from the mean position and error variance vectors of the predicted track. In this model, a probability of minimum safe altitude violation is compared to a minimum confidence level.
  • the stochastic model is preferably decomposed into lateral and vertical models.
  • m denotes the mean lateral position of T
  • d f the distance of m to the lateral frontier of R
  • e the lateral positional error vector of T
  • ⁇ 2 its lateral error variance
  • u* is the standard normal random variable of mean 0 and variance 1. This probability is preferably obtained by standard normal distribution table look-up.
  • m denotes the mean vertical position of T, e the vertical positional error of T and ⁇ 2 its vertical error variance, as is shown in Fig. 21.
  • p 1 being the probability of lateral region penetration of T into R
  • p v being the probability that the altitude of T is less than the minimum safe altitude of R at the mean lateral position of T
  • these probabilities are preferably estimated as described above.
  • p min is considered to be a threshold probability value.
  • a minimum safe altitude violation is predicted if and only if the relation p tot ⁇ p min holds.
  • the threshold p min depends on the prediction time frame as well as the region type of T as already discussed in detail above.
  • the lateral geometry of a region is a polygon with a large number of edges, it may be useful to predict a minimum safe altitude violation for the minimum bounding box of the region. If no minimum safe altitude violation is predicted for this bounding box, there is no need to predict a minimum safe altitude violation for the region itself.
  • a conflict is computed from the set S of predicted tracks of given aircraft for which a minimum safe altitude violation was predicted.
  • the conflict attributes are preferably determined as follows:
  • MSA_TYPE Type of minimum safe altitude (single minimum altitude, offset to terrain, obstacle region, sloping path) Single minimum altitude MSA Single minimum altitude 10 Fl OFFSET Offset to terrain 3 Fl ALPHA Vertical angle of sloping path 3 deg ALPHA_TOL Tolerance on angle ALPHA 1.4 deg RWY_LAT Latitude of runway threshold (for sloping path) 46:00:00 N RWY_LON Longitude of runway threshold (for sloping path) 15:00:00 E RWY_ALT Altitude of runway threshold (for sloping path) 300 meters
  • the APW function/system is a safety net aimed at predicting penetrations of protected air space regions in a near future and warning the radar controller of this potential danger.
  • the uncertainty of a conflict is based on the uncertainty of trajectory prediction and will be represented by a stochastic model for penetration depth of a protected region. In the latter model, a probability of protected region penetration is estimated.
  • APW regions define aircraft access restrictions. For example,
  • each APW region is defined by
  • manoeuvre regions do not contain the additional attributes defined here above and are considered as non-protected regions.
  • a conflict preferably comprises the following information, restricted to the prediction time frame: unique conflict identification, wherein contiguous conflicts for a given aircraft have the same conflict identification; duration of conflict identification; APW system identification (sic, sac); current time; time to conflict; lateral and vertical starting positions of conflict; current lateral and vertical distances to protected region; predicted minimum lateral and vertical distances to protected region; probability of protected region penetration; information about the aircraft involved in the conflict; and/or protected region identification.
  • the information about the aircraft preferably comprises: track identification; mode 3/A code; call sign, if available from input track; and/or track server identification (sic, sac).
  • the configuration parameters of the APW system which are common to all safety nets are specified above, e.g. in Tables 1 to 9. Preferred further or specific parameters to APW are specified below in Tables 20 and 21.
  • An input track T is eliminated if, for each protected region within which T is not allowed to fly, any of the following relations hold: Lateral distance of T to region > MAX_DIST_LAT; Vertical distance of T to region > MAX_DIST_VERT. m denotes the lateral position of T. Please note that the distance of a point x to a set S is preferably defined as the minimum distance of x to each point of S.
  • the lateral distance d of T to a region R can be calculated as follows:
  • d is preferably substituted for the lateral distance of T to the minimum bounding box of R, as shown in Figure 18.
  • a bounding box of a set S in R n is the cross product of n intervals which encloses the set S.
  • conflict prediction the basic idea of the conflict prediction algorithm consists of predicting the penetration of a protected region by a predicted track. All predicted tracks that are not predicted to penetrate any protected region within which the aircraft is not allowed to fly are eliminated. Finally, conflicts are built from the remaining predicted tracks.
  • the highest priority, non-manoeuvre region containing the mean position vector of the predicted track is preferably determined. If the latter region is a protected region within which the aircraft is not allowed to fly, a stochastic model for penetration of this region is developed from the mean position and error variance vectors of the predicted track. In this model, a probability of region penetration is compared to a minimum confidence level.
  • the stochastic model is preferably decomposed in lateral and vertical models. Otherwise, the predicted track is eliminated.
  • the APW reference can be made to the lateral stochastic model as discussed above with respect to e.g. the MSAW.
  • m denotes the mean vertical position of T, [z min , z max ] the height band of R, e the vertical positional error of T and ⁇ 2 its vertical error variance, as can be seen in Fig. 22.
  • a region penetration is preferably predicted if and only if the relation p tot ⁇ p min holds.
  • the threshold p min preferably depends on the prediction time frame as well as the region type of T, as discussed above in detail.
  • the lateral geometry of a protected region is a polygon with a large number of edges, it may be useful to predict a penetration of the minimum bounding box of the region. If the predicted track is not predicted to penetrate this bounding box, there is no need to predict a penetration of the region itself.
  • a conflict is computed from the set S of predicted tracks of a given aircraft which are predicted to penetrate a given protected region within which the aircraft is not allowed to fly.
  • the conflict attributes are preferably determined as follows:
  • the present invention provides a reliable method and system for alerting of potentially hazardous situations in air traffic which fulfil the objects as defined above.
  • the system and method assist the controller in his work and help him to better analyse complex traffic situations and take correct decisions in time. Further, the rate of nuisance alerts is reduced.
  • the present invention may also be used with regard to other forms of traffic, such as rail traffic, e.g. trains, road traffic, e.g. cars, or sea traffic, e.g. ships or submarines, etc.
  • rail traffic e.g. trains
  • road traffic e.g. cars
  • sea traffic e.g. ships or submarines

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  • Engineering & Computer Science (AREA)
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  • General Physics & Mathematics (AREA)
  • Traffic Control Systems (AREA)
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US7667647B2 (en) 1999-03-05 2010-02-23 Era Systems Corporation Extension of aircraft tracking and positive identification from movement areas into non-movement areas
US7739167B2 (en) 1999-03-05 2010-06-15 Era Systems Corporation Automated management of airport revenues
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US7889133B2 (en) 1999-03-05 2011-02-15 Itt Manufacturing Enterprises, Inc. Multilateration enhancements for noise and operations management
US7908077B2 (en) 2003-06-10 2011-03-15 Itt Manufacturing Enterprises, Inc. Land use compatibility planning software
US7965227B2 (en) 2006-05-08 2011-06-21 Era Systems, Inc. Aircraft tracking using low cost tagging as a discriminator
US8072382B2 (en) 1999-03-05 2011-12-06 Sra International, Inc. Method and apparatus for ADS-B validation, active and passive multilateration, and elliptical surveillance
US8203486B1 (en) 1999-03-05 2012-06-19 Omnipol A.S. Transmitter independent techniques to extend the performance of passive coherent location
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CN113566653A (zh) * 2021-07-28 2021-10-29 中国电子科技集团公司第二十八研究所 一种避免弹道冲突的发射时间自动规划方法
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Cited By (18)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8072382B2 (en) 1999-03-05 2011-12-06 Sra International, Inc. Method and apparatus for ADS-B validation, active and passive multilateration, and elliptical surveillance
US7739167B2 (en) 1999-03-05 2010-06-15 Era Systems Corporation Automated management of airport revenues
US8203486B1 (en) 1999-03-05 2012-06-19 Omnipol A.S. Transmitter independent techniques to extend the performance of passive coherent location
US8446321B2 (en) 1999-03-05 2013-05-21 Omnipol A.S. Deployable intelligence and tracking system for homeland security and search and rescue
US7782256B2 (en) 1999-03-05 2010-08-24 Era Systems Corporation Enhanced passive coherent location techniques to track and identify UAVs, UCAVs, MAVs, and other objects
US7889133B2 (en) 1999-03-05 2011-02-15 Itt Manufacturing Enterprises, Inc. Multilateration enhancements for noise and operations management
US7777675B2 (en) 1999-03-05 2010-08-17 Era Systems Corporation Deployable passive broadband aircraft tracking
US7667647B2 (en) 1999-03-05 2010-02-23 Era Systems Corporation Extension of aircraft tracking and positive identification from movement areas into non-movement areas
US7908077B2 (en) 2003-06-10 2011-03-15 Itt Manufacturing Enterprises, Inc. Land use compatibility planning software
EP1780512A1 (fr) * 2005-10-25 2007-05-02 Saab Ab Système de surveillance d`altitude pour aéronef
US7965227B2 (en) 2006-05-08 2011-06-21 Era Systems, Inc. Aircraft tracking using low cost tagging as a discriminator
RU2534924C2 (ru) * 2013-03-29 2014-12-10 Негосударственное образовательное учреждение высшего профессионального образования Московский институт предпринимательства и права Устройство для моделирования процесса принятия решения в условиях неопределенности
US10347142B2 (en) 2014-11-05 2019-07-09 Honeywell International Inc. Air traffic system using procedural trajectory prediction
US9898934B2 (en) 2016-07-25 2018-02-20 Honeywell International Inc. Prediction of vehicle maneuvers
US11532238B2 (en) * 2019-06-27 2022-12-20 Gulfstream Aerospace Corporation Custom aircraft trajectory with a terrain awareness and warning system
CN113566653A (zh) * 2021-07-28 2021-10-29 中国电子科技集团公司第二十八研究所 一种避免弹道冲突的发射时间自动规划方法
CN115064009B (zh) * 2022-05-10 2023-11-07 南京航空航天大学 一种终端区无人机与有人机冲突风险等级划分方法
CN115064009A (zh) * 2022-05-10 2022-09-16 南京航空航天大学 一种终端区无人机与有人机冲突风险等级划分方法

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