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

Abstract

The present invention relates to a method for characterising the movement 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 a movement of the object, with a view to obtaining 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 movement assumed when each estimated state vector belongs to the set determined for the estimated state vector in question, the detected movement being different from the assumed movement otherwise.

Description

[0001] Method for characterizing the movement of an object, associated calculator, system and carrier

[0002] 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.

[0003] In the field of passive estimation of distance to an object, in particular a carrier, optronic equipment is used, in particular airborne, 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.

[0004] 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.

[0005] 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).

[0006] 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.

[0007] There is a need for a method of characterizing the motion of an object that can detect a change in the motion of the object relative to the motion assumed by the estimator.

[0008] 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:

[0009] - obtaining measurements of two angular orientations of the object relative to a sensor, to obtain a plurality of pairs of measured orientations,

[0010] - applying an estimator to 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, - 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, one constraint being a constraint that 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, 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.

[0012] According to particular embodiments, the characterization method has one or more of the following characteristics, taken in isolation or in all technically possible combinations:

[0013] - the assumed movement is a uniform rectilinear movement.

[0014] - the estimator is a Kalman filter.

[0015] - the state vector is expressed in modified spherical coordinates.

[0016] - 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.

[0017] - 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.

[0018] - a constraint is applied to at least one of the estimated position and the estimated velocity of the object.

[0019] The description also describes a calculator suitable for characterizing a movement of an object, in particular an observed carrier, the calculator being suitable for:

[0020] - obtaining measurements of two angular orientations of an object relative to a sensor, to obtain a plurality of pairs of measured orientations,

[0021] - 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 comprising a plurality of coordinates,

[0022] - determining 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

[0023] - 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.

[0024] The description also provides a system for characterizing the movement of an object, in particular a carrier, the characterization system comprising:

[0025] - a sensor capable of measuring two angular orientations of an object relative to the sensor, and

[0026] - a calculator as previously described, the calculator being capable of obtaining each pair of angular orientations by receiving the measurements from the sensor.

[0027] The description also describes a carrier comprising a calculator as previously described or a characterization system as previously described.

[0028] In this description, the expression "suitable for" means indifferently "adapted for", "adapted to" or "configured for".

[0029] 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:

[0030] - figure 1 is a schematic representation of a carrier equipped with a system for characterizing the movement of an object,

[0031] - [Figure 2 is a flowchart of an example of implementation of a method for characterizing the movement of an object, and

[0032] - Figure 3 is a schematic representation of the results obtained after implementing a step of the method of Figure 2.

[0033] A carrier 10 is shown schematically in Figure 1.

[0034] The carrier 10 shown is, for example, an airplane.

[0035] Alternatively, the carrier 10 is any type of aircraft such as a helicopter.

[0036] It is also possible to consider here a carrier 10 which is a land or naval vehicle.

[0037] The carrier 10 comprises a system 12 for characterizing the movement of an object. The characterization system 12 seeks to characterize the movement of an object as faithfully as possible.

[0038] To do this, the characterization system 12 seeks, for example, to obtain in real time the distance between the object and the characterization system 12.

[0039] 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.

[0040] 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.

[0041] The characterization system 12 can be seen as optronic equipment of the carrier 10.

[0042] These include 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. The latter equipment is more often referred to as IRST equipment, the abbreviation IRST referring to the English term "InfraRed Seach and Track".

[0043] The characterization system 12 comprises a sensor 16 and a computer 18.

[0044] The sensor 16 is capable of measuring two angular orientations of the observed carrier 14 relative to the sensor 16.

[0045] Typically, the sensor 16 gives two angular values ​​which are ip the azimuth and 6 the elevation.

[0046] Both orientations are defined in the local geographic reference frame, i.e. a reference frame 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.

[0047] More specifically, azimuth is the rotation around the third z-axis which is positive in the north-east direction while elevation is the rotation around a fourth y'-axis, the fourth y'-axis being deduced from the second y-axis by the azimuth rotation. Elevation is, moreover, chosen to be positive upwards.

[0048] The sensor 16 thus provides at each instant a pair of angular orientations of the observed carrier 14.

[0049] According to a particular example, the sensor 16 is an optronic sensor.

[0050] Preferably, the sensor 16 is a passive sensor, that is to say that the sensor 16 does not emit any pulses to the environment.

[0051] In such a case, the sensor 16 provides only a two-dimensional angular measurement. A camera is an example of a passive optronic sensor.

[0052] 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 the memories of registers or other types of display devices, transmission devices or storage devices.

[0053] As specific examples, the computer 18 is implemented in the form of a programmable logic component, such as an FPGA (Field Programmable Gate Array), or an integrated circuit, such as an ASIC (Application Specific Integrated Circuit).

[0054] The computer 18 is capable of implementing a method for characterizing the movement of the observed carrier 14.

[0055] 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.

[0056] The characterization method comprises an obtaining step E20, an application step E22, a determination step E24 and a detection step E26.

[0057] During the measurement step, the calculator 18 receives a plurality of pairs of measured angular orientations.

[0058] More precisely, the sensor 16 measures the two angular orientations at each instant.

[0059] Sensor 16 sends these measurements to computer 18.

[0060] The calculator 18 thus has, for each measurement instant, a pair of angular orientations.

[0061] 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.

[0062] The estimator assumes motion for the object to obtain estimated state vectors with satisfactory accuracy.

[0063] In the example described, the estimator is a Kalman filter.

[0064] As explained earlier, such an estimator assumes a motion for the object that is a uniform rectilinear motion.

[0065] According to the embodiment of Figure 2, the coordinates of the state vector are expressed in modified spherical coordinates.

[0066] Mathematically, the state vector X is expressed as follows: = [ r ' r * cos ] • r is the distance,

[0067] • ip is the azimuth,

[0068] • 9 is the elevation,

[0069] • r is the radial velocity,

[0070] • ijj is the azimuth speed, and

[0071] • ê is the elevation speed.

[0072] 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".

[0073] By implementing such an estimation technique, the calculator 18 thus obtains for each pair of measured orientations an estimated state vector.

[0074] 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.

[0075] The set to be constrained thus corresponds here to a set of six state variables [ r 6 / r ijj cos 6 0] for each instant t.

[0076] For each of these six state variables, the set is composed of an interval corresponding to the instants located in the interval [t, t + dt].

[0077] This set mathematically corresponds to a “tube”, the free unknown variable being the trajectory of the observed carrier 14 inside this tube.

[0078] In other words, the estimated trajectory must be determined at each instant in the set at the same instant, the goal being to obtain the real trajectory by reducing the size of each set.

[0079] The reduction of the size of the intervals is done by applying the constraint(s) on the state vector estimates.

[0080] By interval arithmetic, the more estimated state vectors the computer 18 has, the more the tubes are constrained and therefore the number of possible trajectories decreases.

[0081] 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.

[0082] In the particular case of the process corresponding to figure 2, the calculator 18 applies two distinct constraints.

[0083] The first constraint is a static constraint.

[0084] According to the static constraint, at each instant t, the following condition is verified: [X](t) = X(t) + 3 • [- &(t), &(t)] Where:

[0085] • [X](t) is the slice of the tube [%](•) at time t,

[0086] • X(t) denotes the estimated state vector, and

[0087] • ff(t) denotes the standard deviation of uncertainty in the estimation of the estimated state vector X(t), this value being provided by the estimator for each estimated state vector.

[0088] A value of 3 provides good confidence in the estimated state vector since, in a Gaussian model, this corresponds to a confidence of more than 90%.

[0089] However, depending on the desired trust, another value could be considered.

[0090] 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.

[0091] Stated differently, static constraint implies the following mathematical relationship:

[0092] [X](t) - X(t) < K (t)

[0093] Or :

[0094] • K denotes a constant.

[0095] The second constraint is a constraint that the observed carrier motion 14 is the motion assumed by the estimator.

[0096] Thus, the second constraint is a differential constraint called L constraint MRU aiming to verify that the movement is a uniform rectilinear movement.

[0097] Mathematically, this second constraint is written:

[0098] Or : denotes the vector each component of which is the time derivative of the corresponding component of the vector [A], and

[0099] • ftransition MRU denotes a function connecting the state vector and its derivative given a uniform rectilinear motion.

[0100] To calculate such a constraint, the calculator 18 calculates the derivative of the tube.

[0101] This is done from the equations of the uniform rectilinear motion model expressed in modified spherical coordinates:

[0102] Or :

[0103] • x1, ... , x6 designate the six coordinates of the vector X, and

[0104] • has s denotes the acceleration of sensor 16 in a frame centered on the line of sight of sensor 16 in Tail-Bryan convention with intrinsic angles.

[0105] Calculator 18 then applies the contractor Ca_. dt

[0106] This contractor is obtained in several stages.

[0107] The first step corresponds to the integration towards the future of the tube to produce [ ](-), the tube being written [V](-) = î tra position MRU (MO) ■

[0108] For each slice [V] (t k ), he comes:

[0109] The second step corresponds to the integration towards the past of the tube to produce the new contracted slice [X](. ), the tube being written [V](. ) = f t transition MRU (MO)-

[0110] For each slice [V] (t k ), he comes:

[0111] Figure 3 illustrates the contraction effects on all the tubes by applying the two constraints knowing the state vector estimated at time tk.

[0112] The upper part of figure 3 corresponds to the reduction of the tube tk (that of the instant tk) linked to the application of the first condition.

[0113] As expected, this leads to a reduction of the tube represented by a square whose size is given by the value of K • (t).

[0114] The lower part of Figure 3 corresponds to a reduction of a set of tubes before the tk tube linked to the application of the second condition after the application of the first condition.

[0115] In this case, the trajectory must pass through the reduced tk tube as seen in the upper part of figure 3. To respect the condition of uniform rectilinear movement

[0116] This implies that the tubes prior to time tk will gradually reduce their size in a funnel-like shape.

[0117] As seen in the lower part of Figure 3, applying both conditions to a single estimate allows a large reduction in the size of the possible set. Applying them to each state vector will lead to an even more drastic reduction.

[0118] In the example described, the determination step E24 is implemented progressively, so that each new estimate of a state vector leads to a reduction in the size of the possible set.

[0119] During the detection step E26, the computer 18 detects the nature of the movement of the observed carrier 14 using the determined sets.

[0120] More precisely, two cases will arise.

[0121] According to a first case, each estimated state vector belongs to the set determined for the estimated state vector considered.

[0122] In such a case, this implies that the assumption of uniform rectilinear motion is verified.

[0123] The calculator 18 thus detects a movement which is the supposed movement.

[0124] From a mathematical point of view, this nominal case corresponds to the fact that each tube contracts due to the constraints applied to include an increasingly restricted interval of possible trajectories among which we find the real trajectory of the observed carrier 14 (which is in the example assumed to be a uniform rectilinear movement).

[0125] According to a second case, at least one estimated state vector does not belong to the set determined for the estimated state vector considered.

[0126] This implies that the assumption of uniform rectilinear motion is not verified.

[0127] The computer 18 thus detects a movement which is different from the supposed movement.

[0128] The second case occurs in particular when the set determined after application of the constraints becomes the empty set.

[0129] In fact, the presence of an empty set implies that there is a rupture of the tube and that the dynamics of the estimator output is inconsistent with the hypothesis of uniform rectilinear motion.

[0130] Such detection of the nature of the movement is interpreted as a detection of a maneuver performed by the observed carrier 14.

[0131] Indeed, a movement different from a uniform rectilinear movement corresponds to the fact that the observed carrier 14 performs a maneuver.

[0132] The method described thus advantageously applies an interval analysis, and more particularly in the context of tube analysis, to the problem of passive trajectography.

[0133] 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. In particular, the choice of the parameter K corresponds to a choice of width of the estimator outputs and as such to a confidence in the estimation of the Kalman filter. The larger the parameter K, 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.

[0134] This simplicity 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).

[0135] The method also has the advantage of being relatively insensitive to noise structure since no noise shape assumption is used.

[0136] Other embodiments of this method are possible.

[0137] According to another embodiment, another estimator is used.

[0138] As an example, an estimator assuming uniform circular motion can be considered.

[0139] Alternatively, it could be considered 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.

[0140] Thus, as a simple example, it could be considered to use only the constraint that the motion of the object is the motion assumed by the estimator.

[0141] It is also possible to use more constraints.

[0142] In particular, it could be considered to use constraints relating to the estimated position and / or the estimated velocity of the observed carrier 14.

[0143] This addition is relatively easy since the process parameters can be physically interpreted directly.

[0144] The method could also be used to switch to a second estimator.

[0145] 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.

[0146] It can be noted here that the method can be implemented as soon as a computer with access to the orientation measurements is available, the way in which these orientations were acquired being completely indifferent. In particular, it is possible to carry out post-processing well after the acquisition of the measurements, in particular for mission analysis.

[0147] Furthermore, the method does not use information on the direction of the object, this being the general problem for the context of passive estimation of object trajectory. In fact, the method is based on the combination of the 6 states of the system (i.e. the three-dimensional position and the three-dimensional speed) and the verification that all of these quantities over time are compatible or not with a uniform rectilinear movement.

Claims

CLAIMS 1. 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 motion 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 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 - 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 estimated state vector considered, 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 estimated state vector considered.

2. Characterization method according to claim 1, in which the assumed movement is a uniform rectilinear movement.

3. Characterization method according to claim 1 or 2, in which the estimator is a Kalman filter.

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

5. Characterization method according to any one of claims 1 to 4, in which a constraint is a constraint according to which the distance between the real 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 according to which the movement of the object is the movement 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 speed of the object.

8. Calculator (18) suitable for characterizing a movement of an object, in particular an observed carrier (14), the calculator (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 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 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 - 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 estimated state vector considered, 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 estimated state vector considered.

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

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