Method for characterizing the motion of an object, computer, system and associated carrier

Abstract

Method for characterizing the motion of an object, computer, system and associated carrier. The present invention relates to a method for characterizing the motion of an object comprising the steps of: - obtaining measurements of two angular orientations of the object with respect to a sensor, - applying an estimator to each pair of measured orientations, the estimator assuming motion for the object, to obtain estimated state vectors, - determining a set of possible values ​​for the coordinates of each state vector by applying a constraint to the estimated state vectors, a constraint being a constraint according to which the motion of the object is the motion assumed by the estimator, and - detecting the nature of the motion of the object, the detected motion being the motion assumed when each estimated state vector belongs to the set determined for the considered estimated state vector.The detected movement being different from the movement that would otherwise be assumed. Figure for the abbreviation: figure 3.

Description

Title of the invention: Method for characterizing the motion of an object, computer, system and associated carrier

[0001] The present invention relates to a method for characterizing the motion of an object. The present invention also relates to a computer for implementing the characterization method, as well as to a system and a carrier comprising such a computer.

[0002] In the field of passive distance estimation to an object, particularly a carrier, optronic equipment, especially airborne equipment, is used to determine the distance without active rangefinding, which requires laser or radar emission. Rangefinding can be limited by its range or its lack of discretion, as the emission can be detected by the object.

[0003] To achieve this, it is known to use an estimator which is a Kalman filter applied to angular orientation measurements, giving two angles at which the object is seen by a passive sensor. Such a technique is often referred to as a TPA technique, that is, a passive trajectory tracking technique using angle measurement.

[0004] Such a Kalman filter can be expressed in a Cartesian frame (relative or absolute) or in a spherical frame or in a hybrid form by alternating between the two types of frames according to the phases of the Kalman filter (typically a prediction phase in Cartesian and a correction phase in spherical).

[0005] However, it can be shown that convergence of the Kalman filter is only possible for an assumed motion, which is most often uniform rectilinear motion. In particular, a maneuver of the object causes the estimate of a Kalman filter assuming uniform rectilinear motion to diverge.

[0006] There is a need for a method of characterizing the motion of an object which makes it possible to detect a change in the motion of the object with respect to the motion assumed by the estimator.

[0007] To this end, the description describes a method for characterizing the motion of an object, in particular an observed carrier, the method being implemented by a computer and comprising the steps of:

[0008] - obtaining measurements of two angular orientations of the object with respect to a sensor, to obtain a plurality of measured orientation pairs,

[0009] - application of an estimator on each pair of measured orientations, the estimator assuming motion for the object, to obtain a plurality of estimated state vectors, each state vector having a plurality of coordinates,

[0010] - determination of a set of respective possible values ​​for the coordinates of each state vector by applying at least one constraint to the estimated state vectors, a constraint being one according to which the object's motion is the motion assumed by the estimator, to obtain a determined set of possible values ​​for each estimated state vector, and

[0011] - detection of the nature of the object's movement,

[0012] the detected motion being the assumed motion when each estimated state vector belongs to the set determined for the considered estimated state vector, or

[0013] the detected movement being different from the assumed movement when at least one estimated state vector does not belong to the set determined for the considered estimated state vector.

[0014] According to particular embodiments, the characterization process has one or more of the following characteristics, taken individually or in all technically possible combinations:

[0015] - the assumed motion is a uniform rectilinear motion.

[0016] - the estimator is a Kalman filter.

[0017] - the state vector is expressed in modified spherical coordinates.

[0018] - a constraint is a constraint according to which the distance between the state vector real and the estimated state vector is less than or equal to the product of a constant with the standard deviation of the uncertainty of the estimate of the estimated state vector.

[0019] - the constraint according to which the movement of the object is the movement assumed by The estimator is a differential constraint. "Differential constraint" is a term used in interval analysis and refers to a constraint involving the calculation of a derivative.

[0020] - a constraint is applied to at least one of the estimated position and the velocity estimated value of the object.

[0021] The description also describes a computer suitable for characterizing the movement of an object, in particular an observed carrier, the computer being suitable for:

[0022] - obtain measurements of two angular orientations of an object with respect to a sensor, to obtain a plurality of measured orientation pairs,

[0023] - application of an estimator on each pair of measured orientations, the estimator assuming motion for the object, to obtain a plurality of estimated state vectors, each state vector having a plurality of coordinates,

[0024] - determine a respective set of possible values ​​for the coordinates of each state vector by applying at least one constraint to the estimated state vectors, a constraint being one according to which the object's motion is the motion assumed by the estimator, to obtain a determined set of possible values ​​for each estimated state vector, and

[0025] - detect the nature of the object's movement,

[0026] the detected motion being the assumed motion when each estimated state vector belongs to the set determined for the considered estimated state vector, or

[0027] the detected motion being different from the assumed motion when at least one estimated state vector does not belong to the set determined for the considered estimated state vector.

[0028] The description also proposes a system for characterizing the movement of an object, in particular a carrier, the characterization system comprising:

[0029] - a sensor suitable for measuring two angular orientations of an object with respect to the sensor, and

[0030] - a computer as previously described, the computer being suitable for obtaining each pair of angular orientations by receiving measurements from the sensor.

[0031] The description also describes a carrier comprising a computer as previously described or a characterization system as previously described.

[0032] In this description, the expression "specific to" means interchangeably "suitable for", "adapted to" or "configured for".

[0033] Some features and advantages of the invention will become apparent from the following description, given solely by way of non-limiting example, and made with reference to the accompanying drawings, in which:

[0034] - [Fig. 1] [Fig. 1] is a schematic representation of a carrier equipped with a system for characterizing the movement of an object,

[0035] - [Fig.2] [Fig.2] is a flowchart of an example of the implementation of a method for characterizing the movement of an object, and

[0036] - [Fig.3] [Fig.3] is a schematic representation of the results obtained after implementation of a step in the process of [Fig.2].

[0037] A carrier 10 is schematically represented in [Fig.1].

[0038] The carrier 10 shown is, for example, an airplane.

[0039] Alternatively, the carrier 10 is any type of aircraft such as a helicopter.

[0040] It is also possible to consider here a carrier 10 which is a land or naval vehicle.

[0041] The carrier 10 includes a system for characterizing the movement of an object.

[0042] The characterization system 12 seeks to characterize the movement of an object as faithfully as possible.

[0043] For this purpose, the characterization system 12 seeks, for example, to obtain in real time the distance between the object and the characterization system 12.

[0044] Without being limiting, it is assumed in the following that the characterization system 12 seeks to characterize the movement of another carrier 10, called the observed carrier 14.

[0045] The observed carrier 14 is represented here by a square to symbolize the fact that the observed carrier 14 is, in this context, generally very far from the carrier 10, typically several tens of kilometers away.

[0046] The characterization system 12 can be seen as an optronic equipment of the carrier 10.

[0047] This includes equipment with a steerable line of sight and a target tracking function, such as a targeting pod, an optronic ball, or an infrared search and track device. The latter is more commonly referred to as IRST equipment, the abbreviation IRST standing for "Infrared Search and Track".

[0048] The characterization system 12 includes a sensor 16 and a computer 18.

[0049] The sensor 16 is adapted to measure two angular orientations of the observed carrier 14 compared to sensor 16.

[0050] Typically, the sensor 16 gives two angular values ​​which are V' the azimuth and 0 the elevation.

[0051] The two orientations are defined in the local geographic coordinate system, that is to say a coordinate system centered on the sensor 16 with a first x-axis corresponding to north, a second y-axis corresponding to east and a third z-axis corresponding to bottom.

[0052] More specifically, the azimuth is the rotation about the third axis z, which is positive in the north-east direction, while the elevation is the rotation about a fourth axis y'. The fourth axis y' is derived from the second axis y by the azimuth rotation. The elevation is, moreover, chosen to be positive upwards.

[0053] The sensor 16 thus provides at each instant a pair of angular orientations of the observed carrier 14.

[0054] According to a particular example, sensor 16 is an optronic sensor.

[0055] Preferably, the sensor 16 is a passive sensor, that is to say that the sensor 16 emits no pulse towards the environment.

[0056] In such a case, the sensor 16 provides only a two-dimensional angular measurement.

[0057] A camera is an example of a passive optronic sensor.

[0058] The calculator 18 is an electronic circuit designed to manipulate and / or transform data represented by electronic or physical quantities in registers of the calculator and / or memories into other similar data corresponding to physical data in register memories or other types of display devices, transmission devices or storage devices.

[0059] As specific examples, the calculator 18 is implemented as a programmable logic component, such as an FPGA (Field Programmable Gamut). Gate Array), or even an integrated circuit, such as an ASIC (from the English Application Specifies Integrated Circuit).

[0060] The computer 18 is suitable for implementing a method of characterizing the movement of the observed carrier 14.

[0061] An example of the operation of the computer 18 is now described with reference to [Fig.2] which illustrates a flowchart of the implementation of a method for characterizing the movement of the observed carrier 14.

[0062] The characterization process comprises a obtaining step E20, an application step E22, a determination step E24 and a detection step E26.

[0063] During the measurement step, the calculator 18 receives a plurality of measured angular orientation pairs.

[0064] More specifically, the sensor 16 measures the two angular orientations at every instant.

[0065] Sensor 16 sends these measurements to computer 18.

[0066] The calculator 18 thus has, for each measurement instant, a pair of angular orientations.

[0067] During the application step E22, the calculator 18 applies an estimator to each pair of measured orientations to obtain a plurality of estimated state vectors.

[0068] The estimator assumes motion for the object to obtain estimated state vectors exhibiting satisfactory accuracy.

[0069] In the example described, the estimator is a Kalman filter.

[0070] As explained previously, such an estimator assumes a motion for the object which is a uniform rectilinear motion.

[0071] According to the embodiment of [Fig.2], the coordinates of the state vector are expressed in modified spherical coordinates.

[0072] Mathematically, the state vector X is expressed as follows:

[0073] XP / r tp 8 r / r ÿcosO

[0074] where: • r is the distance, • 7 is the azimuth, • 6 is the elevation, • is the radial velocity, • 7' is the azimuth velocity, and • 0 is the rate of climb.

[0075] Such a technique for applying a Kalman filter in modified spherical coordinates is often referred to by the acronym MSC-KF, which refers to the The corresponding English name is "Modified Spherical Coordinate Kalman Filter".

[0076] By implementing such an estimation technique, the computer 18 thus obtains for each pair of measured orientations an estimated state vector.

[0077] During the determination step E24, the calculator 18 determines a set of possible respective values ​​for the coordinates of each state vector by applying at least one constraint on the estimated state vectors.

[0078] The set to be constrained thus corresponds here to a set of six state variables [ [ / r ip 0 yr ^COS0 d] for each instantz.

[0079] For each of these six state variables, the set consists of an interval corresponding to the instants located in the interval [ / . t + dt]-

[0080] This set corresponds mathematically to a "tube", the free unknown variable being the trajectory of the observed carrier 14 inside this tube.

[0081] In other words, the estimated trajectory must be at each instant in the set determined at the same instant, the goal being to obtain the actual trajectory by decreasing the size of each set.

[0082] The reduction of the size of the intervals is done by applying the constraint(s) on the estimates of state vectors.

[0083] By interval arithmetic, the more estimated state vectors the computer 18 has, the more the tubes are constrained and thus the number of possible trajectories decreases.

[0084] In such an arithmetic, it can be noted that the unknowns are described not as random variables as would be the case in a probabilistic framework, but by intervals.

[0085] In the particular case of the process corresponding to [Fig.2], the calculator 18 applies two distinct constraints.

[0086] The first constraint is a static constraint.

[0087] According to the static constraint, at each instant, the following condition is verified:

[0088] [X] (f) = î(f) + 3 • [ - o(*)]

[0089] Where: • [X] (?) is the slice of tube [X] ( ■ ) at instantz, * X(?) denotes the estimated state vector, and • said) denotes the standard deviation of uncertainty of estimation of the estimated state vector X0, this value being provided by the estimator for each estimated state vector.

[0090] A value of 3 gives good confidence in the estimated state vector since, in a Gaussian model, this corresponds to a confidence of more than 90%.

[0091] However, depending on the desired confidence, another value could be considered.

[0092] Also, more generally, the static constraint requires that the distance between the real state vector (assumed to be inside the slice [X] (t) ) and the estimated state vector be less than or equal to the product of a constant with the standard deviation of the uncertainty associated with the estimation of the estimated state vector.

[0093] In other words, static stress implies the following mathematical relationship:

[0094] [X](f)-î0 <K-a( / )

[0095] Where: • K denotes a constant.

[0096] The second constraint is a constraint according to which the observed movement of the carrier 14 is the movement assumed by the estimator.

[0097] Thus, the second constraint is a differential constraint called LMRV constraint, the purpose of which is to verify that the movement is a uniform rectilinear movement.

[0098] Mathematically, this second constraint can be written as:

[0099] / ryu.n dt J transitionMRll ' 1 ' '

[0100] Where: • 4^) denotes the vector whose component is the time derivative dt of the corresponding component of the vector [A], and • fv denotes a function relating the state vector and its derivative •' transttionMRIj ° given uniform rectilinear motion.

[0101] To calculate such a constraint, the calculator 18 calculates the derivative of the tube.

[0102] This is done from the equations of the uniform rectilinear motion model expressed in modified spherical coordinates:

[0103] x~ ' Vr ' 4' er / / r ■Xj' x2 x4 dX ' dt ~ ' -X4X! 1 x6 xi + xl-xl + x2 • 1 0 ' 0 0 -O} ïpcosQ *5 x5(x6tanx3) -2x4 ay . e , , - 2x6x4 - x|tanx3 ( £ ......

[0104] Where: . Xj, ..., x6 denote the six coordinates of the vector X. and • denotes the acceleration of sensor 16 in a frame of reference centered on the line of 16 sensor sighting in Tail-Bryan convention with intrinsic angles.

[0105] The calculator 18 then applies the C± contractor

[0106] This contractor can be obtained in several stages.

[0107] The first step corresponds to the future integration of the tube to produce [F] ( - ), the tube being written [ V] ( ■ ) - ftransitumMRV{ [X] ( ■)) .

[0108] For each slice [V] (tk), we get:

[0109] [F] (tk^ = [X] (4) n ([F] + dt[V] (tk))

[0110] The second step corresponds to the integration into the past of the tube to produce the new slice [X] (.) contracted, the tube being written [YES] For each slice [F] (tk), it comes:

[0112] [X] (4J = [F] «J n ([X] (tk) +dt[V] (tk))

[0113] Fig. 3 illustrates the contraction effects on the set of tubes by the application of the two constraints knowing the state vector estimated at time tk.

[0114] The upper part of [Fig.3] corresponds to the reduction of the tube tk (that of the instant tk) linked to the application of the first condition.

[0115] As expected, this leads to a reduction of the tube, schematically represented by a square whose size is given by the value of K ■ a(A

[0116] The lower part of [Fig.3] corresponds to a reduction of a set of tubes before the tube tk linked to the application of the second condition after the application of the first condition.

[0117] In this case, the trajectory must pass through the reduced tube tk as seen in the upper part of [Fig. 3]. To satisfy the condition of uniform rectilinear motion

[0118] This implies that the tubes prior to time tk will gradually reduce their size in a shape resembling a funnel shape.

[0119] As can be seen in the lower part of [Fig.3], applying the two conditions to a single estimate allows for a large reduction in the possible set size.

[0120] Applying this to each state vector will lead to an even more drastic reduction.

[0121] In the example described, the determination step E24 is implemented incrementally, so that each new estimation of a state vector leads to a decrease in the size of the possible set.

[0122] During the detection step E26, the computer 18 detects the nature of the movement of the observed carrier 14 thanks to the determined sets.

[0123] More specifically, two cases will arise.

[0124] According to a first case, each estimated state vector belongs to the set determined for the considered estimated state vector.

[0125] In such a case, this implies that the assumption of uniform rectilinear motion is verified.

[0126] The calculator 18 thus detects a movement which is the assumed movement.

[0127] From a mathematical point of view, this nominal case corresponds to the fact that each tube contracts due to the constraints applied to understand an increasingly restricted range of possible trajectories among which we find the actual trajectory of the observed carrier 14 (which in the example is assumed to be a uniform rectilinear motion).

[0128] According to a second case, at least one estimated state vector does not belong to the set determined for the considered estimated state vector.

[0129] This implies that the assumption of uniform rectilinear motion is not verified.

[0130] The calculator 18 thus detects a movement that is different from the movement assumed.

[0131] The second case occurs in particular when the set determined after application of the constraints becomes the empty set.

[0132] In fact, the presence of an empty set implies that there is a break in the tube and that the dynamics of the estimator output are inconsistent with the assumption of uniform rectilinear motion.

[0133] Such detection of the nature of the movement is interpreted as a detection of a maneuver carried out by the observed wearer 14.

[0134] Indeed, a movement different from a uniform rectilinear movement corresponds to the fact that the observed carrier 14 performs a maneuver.

[0135] The described process thus advantageously applies interval analysis, and more particularly in the context of tube analysis, to the problem of passive trajectory analysis.

[0136] The process is simple to set up because, on the one hand, it involves few parameters and, on the other hand, the parameters have a physical meaning.

[0137] In particular, the choice of the parameter K corresponds to a choice of output width of the estimator and, as such, to a confidence level in the Kalman filter estimation. The larger the parameter, the less confidence is placed in the Kalman filter and therefore the less sensitive the estimator is for detecting a maneuver by the observed carrier.

[0138] This simple character comes from the fact that the analysis of tubes is implemented here in the space of states and not in the space of observations (azimuth and elevation).

[0139] The process also has the advantage of being relatively insensitive to noise structure since no assumption of noise shape is used.

[0140] Other embodiments of this process are conceivable.

[0141] According to another embodiment, another estimator is used.

[0142] By way of example, an estimator assuming uniform circular motion may be considered.

[0143] Alternatively, it could be envisaged to use other constraints during the determination step E24 provided that the constraint that the motion of the object is the motion assumed by the estimator is used.

[0144] Thus, according to a simple example, it could be envisaged to use only the constraint that the motion of the object is the motion assumed by the estimator.

[0145] It is also possible to use more constraints.

[0146] In particular, consideration could be given to using constraints relating to the estimated position and / or estimated speed of the observed carrier 14.

[0147] This addition is relatively easy since the process parameters can be interpreted physically in a direct manner.

[0148] The method could also be used to perform a switchover to a second estimator.

[0149] When the computer 18 determines that the assumed motion is no longer verified, the computer 18 changes estimator by considering a second estimator adapted for a context where the motion is not the assumed motion.

Claims

Demands

1. A method for characterizing the motion of an object, in particular an observed carrier (14), the method being implemented by a computer (18) and comprising the steps of: - obtaining measurements of two angular orientations of the object with respect to a sensor (16), to obtain a plurality of pairs of measured orientations, - applying an estimator to each pair of measured orientations, the estimator assuming motion for the object, to obtain a plurality of estimated state vectors, each state vector having a plurality of coordinates, - determining a set of possible values ​​for the coordinates of each state vector by applying at least one constraint to the estimated state vectors, a constraint being a constraint according to which the motion of the object is the motion assumed by the estimator, to obtain a determined set of possible values ​​for each estimated state vector,and - detection of the nature of the object's motion, the detected motion being the assumed motion when each estimated state vector belongs to the set determined for the considered estimated state vector, or the detected motion being different from the assumed motion when at least one estimated state vector does not belong to the set determined for the considered estimated state vector.

2. A characterization method according to claim 1, wherein the assumed motion is a uniform rectilinear motion.

3. A characterization method according to claim 1 or 2, wherein the estimator is a Kalman filter.

4. A characterization method according to any one of claims 1 to 3, wherein the state vector is expressed in modified spherical coordinates.

5. A characterization method according to any one of claims 1 to 4, wherein a constraint is a constraint such that the distance between the actual state vector and the estimated state vector is less than or equal to the product of a constant with the standard deviation of the uncertainty of the estimation of the estimated state vector.

6. A characterization method according to any one of claims 1 to 5, wherein the constraint that the motion of the object is the motion assumed by the estimator is a differential constraint.

7. A characterization method according to any one of claims 1 to 6, wherein a constraint is applied to at least one of the estimated position and the estimated velocity of the object.

8. A computer (18) suitable for characterizing the motion of an object, in particular an observed carrier (14), the computer (18) being suitable for: - obtaining measurements of two angular orientations of an object relative to a sensor (16), to obtain a plurality of pairs of measured orientations, - applying an estimator to each pair of measured orientations, the estimator assuming motion for the object, to obtain a plurality of estimated state vectors, each state vector having a plurality of coordinates, - determining a respective set of possible values ​​for the coordinates of each state vector by applying at least one constraint to the estimated state vectors, a constraint being a constraint according to which the motion of the object is the motion assumed by the estimator, to obtain a determined set of possible values ​​for each estimated state vector, and - detecting the nature of the motion of the object.the detected motion being the assumed motion when each estimated state vector belongs to the set determined for the considered estimated state vector, or the detected motion being different from the assumed motion when at least one estimated state vector does not belong to the set determined for the considered estimated state vector.

9. A system for characterizing the movement of an object, in particular a carrier (14), the system for characterizing (12) comprising: - a sensor (16) adapted to measure two angular orientations of an object with respect to the sensor (16), and - a computer (18) according to claim 8, the computer (18) being adapted to obtain each pair of angular orientations by receiving the measurements from the sensor (16).

10. Carrier (10) comprising a computer (18) according to claim 8 or a characterization system (12) according to claim 9.

Images