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

Abstract

Method for characterizing the movement of an object, associated calculator, system and carrier The present invention relates to a method for characterizing the movement of an object comprising the steps of: - obtaining measurements of two angular orientations of the object relative to a sensor, - applying an estimator to each pair of measured orientations, the estimator assuming a movement for the object, to obtain estimated state vectors - determining a respective 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 movement of the object is the movement assumed by the estimator, and - detecting the nature of the movement of the object, the detected movement being the assumed movement when each estimated state vector belongs to the set determined for the considered estimated state vector,the detected movement being different from the supposed movement otherwise. Figure for the abstract: figure 3,

Description

Title of the invention: Method for characterizing the movement of an object, associated calculator, system and carrier

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

[0002] In the field of passive estimation of distance to an object, in particular a carrier, optronic equipment is used, in particular airborne, making it possible to determine the distance without active telemetry requiring laser or radar emission. Telemetry can in fact be limited by its range or by its lack of discretion, the emission being able to be detected by the object.

[0003] For this, it is known to use an estimator which is a Kalman filter applied to angular orientation measurements giving two angles under which the object is seen by a passive sensor. Such a technique is often referred to as a TPA technique, that is to say a passive trajectography technique by angle measurement.

[0004] Such a Kalman filter can be expressed in a Cartesian frame of reference (relative or absolute) or in a spherical frame of reference or in a hybrid form by alternating between the two types of frames of reference 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 the convergence of the Kalman filter is only possible for an assumed motion, which is most often a uniform rectilinear motion. In particular, a maneuver of the object causes the estimate of a Kalman filter assuming a uniform rectilinear motion to diverge.

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

[0007] For this purpose, the description describes a method for characterizing the movement 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 relative to a sensor, to obtain a plurality of pairs of measured orientations,

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

[0010] - determination of a respective set of possible values ​​for the coordinates of each state vector by applying at least one constraint on 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

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

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

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

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

[0016] - the estimator is a Kalman filter.

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

[0018] - a constraint is a constraint that the distance between the state vector actual 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 that the motion of the object is the motion 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 calculator capable of characterizing a movement of an object, in particular an observed carrier, the calculator being capable of:

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

[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 comprising 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 on 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

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

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

[0027] 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 estimated state vector considered.

[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 capable of measuring two angular orientations of an object relative to the sensor, and

[0030] - a calculator as previously described, the calculator being capable of obtaining each pair of angular orientations by receiving the sensor measurements.

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

[0032] In the present description, the expression “suitable for” means indifferently “adapted for”, “adapted to” or “configured for”.

[0033] Characteristics and advantages of the invention will appear on reading the description which follows, given solely by way of non-limiting example, and made with reference to the appended drawings, in which:

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

[0035] - [Fig.2] [Fig.2] is a flowchart of an example implementation of a process of 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 of the process of [Fig.2].

[0037] A carrier 10 is shown schematically in [Fig.l].

[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 comprises a system 12 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, the characterization system 12 seeks, for example, to obtain in real time the distance between the object and the characterization system 12.

[0044] Without this 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 here represented 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.

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

[0047] This includes equipment with a steerable line of sight and a target tracking function such as a designation pod, an optronic ball or an infrared search and track device. This latter equipment is more often referred to as IRST equipment, the abbreviation IRST referring to the English term for “InfraRed Seach and Track”.

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

[0049] The sensor 16 is capable of measuring 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 reference frame, that is to say a reference frame centered on the sensor 16 with a first axis x corresponding to the north, a second y corresponding to the east and a third axis z corresponding to the bottom.

[0052] More specifically, the azimuth is the rotation around the third axis z which is positive in the north to east direction while the elevation is the rotation around a fourth axis y', the fourth axis y' is deduced from the second axis y by the rotation in azimuth. 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, the sensor 16 is an optronic sensor.

[0055] Preferably, the sensor 16 is a passive sensor, i.e. the sensor 16 does not emit any impulses to 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 computer 18 is an electronic circuit designed to manipulate and / or transform data represented by electronic or physical quantities in registers of the computer and / or memories into other similar data corresponding to physical data in the memories of registers or other types of display devices, transmission devices or storage devices.

[0059] As specific examples, the computer 18 is implemented in the form of a programmable logic component, such as an FPGA (Field Programmable Gate Array). Array Gate), or an integrated circuit, such as an ASIC (Application Specified Integrated Circuit).

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

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

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

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

[0064] More precisely, the sensor 16 measures the two angular orientations at each instant.

[0065] The sensor 16 sends these measurements to the 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 with 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 speed, and • 0 is the elevation speed.

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

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

[0077] During the determination step E24, the calculator 18 determines a respective set of possible 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 is composed of an interval corresponding to the instants located in the interval [ / . t + dt]-

[0080] This set mathematically corresponds 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 aim being to obtain the real trajectory by reducing the size of each set.

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

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

[0084] In such 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 method corresponding to [Fig.2], the computer 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 the tube [X] ( ■ ) at the instant z, * X(?) denotes the estimated state vector, and • dit) denotes the standard deviation of uncertainty in the estimation of the estimated state vector X0, this value being provided by the estimator for each estimated state vector.

[0090] A value of 3 allows good confidence in the estimated state vector to be obtained 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 imposes that the distance between the real state vector (assumed to be inside the slice [X] (t)) and the estimated state vector is 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, the static constraint 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 movement of the observed carrier 14 is the movement assumed by the estimator.

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

[0098] Mathematically, this second constraint is written:

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

[0100] Where: • 4^) denotes the vector of which each 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 •'transitionMRIj ° knowing a uniform rectilinear movement.

[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 • designates the acceleration of the sensor 16 in a frame centered on the line of 16 sensor aiming in Tail-Bryan convention with intrinsic angles.

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

[0106] This contractor is obtained in several stages.

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

[0108] For each slice [ V] ( tk ), it comes:

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

[0110] The second step corresponds to the integration towards the past of the tube to produce the new contracted slice [X] (.), 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 all the tubes by applying 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 schematized 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 tk tube as visible in the upper part of [Fig.3]. To respect the condition of uniform rectilinear movement

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

[0119] As visible in the lower part of [Fig.3], the application of the two conditions on a single estimate allows a large reduction in the size of the possible set.

[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 progressively, so that each new estimation of a state vector leads to a reduction 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 using the determined sets.

[0123] More precisely, two cases will arise.

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

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

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

[0127] From a mathematical point of view, this nominal case corresponds to the fact that each tube contracts due to the applied constraints to include 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 movement).

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

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

[0130] The computer 18 thus detects a movement which 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 rupture of the tube and that the dynamics of the output of the estimator is inconsistent with the hypothesis of a uniform rectilinear movement.

[0133] Such detection of the nature of the movement is interpreted as a detection of a maneuver performed by the observed carrier 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 method described thus advantageously applies an interval analysis, and more particularly in the context of tube analysis, to the problem of passive trajectography.

[0136] The method is simple to adjust 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 width of the outputs of the estimator and as such to a confidence in the estimation of the Kalman filter. The larger the parameter, the less confidence is given to the Kalman filter and therefore the less sensitive the estimator is to detect a maneuver of the observed carrier.

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

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

[0140] Other embodiments of this method are conceivable.

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

[0142] As an example, an estimator assuming uniform circular motion can be considered.

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

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

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

[0146] In particular, it could be envisaged to use constraints relating to the estimated position and / or the estimated speed of the observed carrier 14.

[0147] This addition is relatively easy since the process parameters can be physically interpreted directly.

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

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

Claims

Claims

1. A method for characterizing the movement 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 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 a movement for the object, to obtain a plurality of estimated state vectors, each state vector comprising 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 movement of the object is the movement assumed by the estimator, to obtain a determined set of possible values ​​for each estimated state vector,and - detection of the nature of the movement of the object, the detected movement being the assumed movement when each estimated state vector belongs to the set determined for the considered estimated state vector, or 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.,

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 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 estimate of the estimated state vector.

6. A characterization method according to any one of claims 1 to 5, wherein the constraint according to which 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) capable of characterizing a movement of an object, in particular an observed carrier (14), the computer (18) being capable of: - 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 a movement for the object, to obtain a plurality of estimated state vectors, each state vector comprising 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 movement of the object is the movement assumed by the estimator, to obtain a determined set of possible values ​​for each estimated state vector, and - detecting the nature of the movement of the object,the detected movement being the assumed movement when each estimated state vector belongs to the set determined for the considered estimated state vector, or 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.,

9. System (12) for characterizing the movement of an object, in particular a carrier (14), the characterization system (12) comprising: - a sensor (16) capable of measuring two angular orientations of an object relative to the sensor (16), and - a calculator (18) according to claim 8, the calculator (18) being capable of obtaining each pair of angular orientations by receiving the measurements from the sensor (16).

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

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