Method for determining a property of a manoeuvre of a carrier, and associated computer, system and carrier

Abstract

The present invention relates to a method for determining at least one property of a manoeuvre of an object, the method for determining being implemented by a computer and comprising the steps of:- obtaining measurements:- of two angular orientations of the object with respect to a sensor,- of a radiometric signature of the object,- determining at least one property of a manoeuvre of the object by application of an estimator on the measurements obtained.

Description

Method for determining a property of a manoeuvre of a carrier, and associated computer, system and carrier The present invention relates to a method for determining at least one property of a manoeuvre of an object. The present invention also relates to a computer suitable for implementing the method for determining as well as to a system and a carrier comprising such a computer.In the field of passive estimation of distance to an object, in particular a carrier, optronic equipment, in particular airborne equipment, is used, making it possible to determine the distance without active telemetry requiring a laser or radar emission. Telemetry may in fact be limited by its range or by its absence of discretion, the emission being able to be detected by the object.For this purpose, it is known to resort to an estimator which is a Kalman filter applied to measurements of angular orientations giving two angles under which the object is seen by a passive sensor. Such a technique is often designated as a TPA technique, that is to say a passive trajectography technique by angle measurement.Such a Kalman filter may 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).However, it may be shown that convergence of the Kalman filter is possible only for an assumed movement, which is most often a uniform rectilinear movement. In particular, a manoeuvre of the object causes the estimation of the Kalman filter assuming a uniform rectilinear movement to diverge.There is a need for a method for determining a property of a manoeuvre of an object making it possible to retain a relevant estimation of the position of the object, even in the presence of a manoeuvre thereof.To this end, the description describes a method for determining at least one property of a manoeuvre of an object, the method for determining being implemented by a computer and comprising the steps of:- obtaining measurements:- of two angular orientations of the object with respect to a sensor, and- of a radiometric signature of the object, and- determining at least one property of a manoeuvre of the object by applying an estimator to the measurements obtained.According to particular embodiments, the method for determining has one or more of the following features, taken in isolation or according to all technically possible combinations:- an image of the object comprising pixels is provided, the radiometric signature being a quantity representative of the number of grey levels which are added to the pixels of the image due to the presence of the object.- the at least one property determined during the step of determining is the presence of a manoeuvre of the object or the type of manoeuvre performed by the object.- the step of determining comprises an sub-step of initializing making it possible to obtain an initial estimation of the position of the object, the estimator being applied to the measurements obtained and the initial estimation.- the sub-step of initializing is implemented by applying an estimator to the angular orientations.- the estimator used during the sub-step of initializing is an extended Kalman filter in modified spherical coordinates.- the estimator applied to the measurements obtained is an invariant extended Kalman filter.The description also describes a computer suitable for determining at least one property of a manoeuvre of an object, the computer being suitable for:- obtaining measurements:- of two angular orientations of the object with respect to a sensor, and- of a radiometric signature of the object, and- determining at least one property of a manoeuvre of an object by applying an estimator to the measurements obtained.The description also proposes a system for determining at least one property of a manoeuvre of an object, the system for determining comprising:- a first sensor suitable for measuring two angular orientations of an object with respect to the first sensor,- a second sensor suitable for measuring a radiometric signature of the object, and- a computer as previously described, the computer being suitable for obtaining the angular orientations and the radiometric signature of the object by receiving the measurements from each of the sensors.The description also describes a carrier comprising a computer as previously described or a system for determining as previously described.In the present description, the expression “suitable for” means indifferently “adapted for”, “adapted to” or “configured for”.Features and advantages of the invention will appear upon reading the description which follows, given only by way of non-limiting example, and made with reference to the appended drawings, in which:- figure 1 is a schematic representation of a carrier provided with a system for determining a property of a manoeuvre of an object,- figure 2 is a flowchart of an example of implementation of a method for determining a property of a manoeuvre of an object, and- figure 3 illustrates an example of a manoeuvre that the method for determining according to figure 2 makes it possible to discriminate, and- figure 4 illustrates an example of a manoeuvre that can be discriminated using angular measurements only.A carrier 10 is represented schematically in figure 1.The carrier 10 represented is, for example, an airplane.As a variant, the carrier 10 is any type of aircraft such as a helicopter.It is also possible to envisage considering here a carrier which is a land or naval vehicle.The carrier 10 comprises a system for determining 12 of at least one property of a manoeuvre of an object.A determined property may, depending on the case, be the presence of a manoeuvre and the type of manoeuvre.For this purpose, the system for determining 12 seeks, for example, to obtain in real time the distance between the object and the system for determining 12.Without this being limiting, it is assumed in the following that the system for determining 12 seeks to characterize the manoeuvre of another carrier 10, called observed carrier 14.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.The system for determining 12 may be seen as an optronic equipment item of the carrier 10.It is in particular an equipment item having an orientable 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 item is more often designated by the term IRST equipment, the abbreviation IRST referring to the English designation of “InfraRed Seach and Track”.The system for determining 12 comprises a sensor 16 and a computer 18.The sensor 16 is suitable for measuring two angular orientations of the observed carrier 14 with respect to the sensor 16.Typically, the sensor 16 gives two angular values which are the azimuth and the elevation.The two orientations are defined in the local geographic frame, that is to say a frame centered on the sensor 16 with a first axis x corresponding to north, a second y corresponding to east and a third axis z corresponding to down.More specifically, the azimuth is the rotation about the third axis z which is positive in the north-to-east direction whereas the elevation is the rotation about a fourth axis y’, the fourth axis y’ being deduced from the second axis y by the rotation in azimuth. The elevation is, furthermore, chosen positive upward.The sensor 16 thus provides at each instant a pair of angular orientations of the observed carrier 14.The sensor 16 also makes it possible to obtain a radiometric signature of the observed carrier 14.According to the example described, the radiometric signature is the number of grey levels which are added to the pixels of the image due to the presence of the observed carrier 14.In such a case, the signature is a representation of the energy of the observed carrier 14 which is deduced from the infrared signature according to the following relationship.The infrared signature can be decomposed in the following manner: Where:: the total power radiated by the observed carrier 14 (the power radiated in all directions of space). The total power is thus written:where is the elementary solid angle, andis the proportion of the total power radiated by the observed carrier 14 in the direction . This function may be obtained empirically, by averaging the corresponding power functions for different types of carrier, or theoretically, so as to reflect the high proportion of power radiated in the rear sector and the low proportion radiated in the front sector.With these notations, the energy of the observed carrier 14 measured in the image can be rewritten as follows: Where:is a constant, andthe other terms are defined subsequently after the introduction of specific frames.To obtain the value of the radiometric signature, an analysis of the grey levels without the observed carrier 14 is performed in order to obtain a reference level.When the reference level is exceeded, it is considered that the difference comes from the observed carrier 14 and is therefore representative of the radiometric signature.The radiometric signature will be defined here as the sum of the exceedances of the reference level at each pixel.According to a particular example, the sensor 16 is an optronic sensor 16.Preferably, the sensor 16 is a passive sensor 16, that is to say that the sensor 16 emits no pulse intended for the environment.In such a case, the sensor 16 provides only a two-dimensional angular measurement.A camera is an example of a passive optronic sensor 16.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 register memories or other types of display devices, transmission devices or storage devices.As specific examples, the computer 18 is produced in the form of a programmable logic component, such as an FPGA (Field Programmable Gate Array), or alternatively an integrated circuit, such as an ASIC (Application Specific Integrated Circuit).The computer 18 is suitable for implementing a method for determining at least one property of a manoeuvre of the observed carrier 14.An example of operation of the computer 18 is now described with reference to figure 2 which illustrates a flowchart of implementation of a method for determining at least one property of a manoeuvre of the observed carrier 14.The method for determining comprises an step of obtaining E20 and a step of determining E22.During the step of obtaining E20, the computer 18 receives a plurality of pairs of measured angular orientations.More precisely, the sensor 16 measures at each instant the two angular orientations.The sensor 16 sends these measurements to the computer 18.The computer 18 thus has, for each measurement instant, a pair of angular orientations.During the step of obtaining E20, the sensor 16 measures a radiometric signature of the observed carrier 14.Here, the radiometric signature is obtained by an analysis of the grey levels in the image.During the step of determining E22, the computer 18 determines at least one property of a manoeuvre of the object.The step of determining E22 comprises an sub-step of initializing SE1 and an sub-step of applying SE2.During the sub-step of initializing SE1, the computer 18 applies a first estimator applied to the pairs of angular orientations in order to obtain an initial estimation of the position of the observed carrier 14.According to the example described, the first estimator is an extended Kalman filter in modified spherical coordinates. Other estimators could nevertheless be used here such as a Kalman filter in Cartesian coordinates.The extended Kalman filter in modified spherical coordinates is more often called an MSC-EKF filter. The abbreviation MSC-EKF refers to the corresponding English designation of “Modified Spherical Coordinates Extended Kalman Filter”.The MSC-EKF filter provides the position and the velocity of the observed carrier 14 in modified spherical coordinates in the carried local geographic frame, as well as the associated covariance matrix.The carried local geographic frame designates a frame whose origin is the position of the sensor 16, the first axis () is directed toward geographic North, the second axis () toward East and the last axis () downward.In the following, this frame will be designated by the name GLP frame.More precisely, the state vector estimated by the MSC-EKF filter is written in the form: Where:: azimuth angle of the observed carrier 14 in the GLP frame linked to the carrier 10,: elevation angle of the observed carrier 14 in the GLP frame linked to the carrier 10,: distance to the observed carrier 14,: angular velocity of the observed carrier 14 in azimuth in the GLP frame linked to the carrier 10,: angular velocity of the observed carrier 14 in elevation in the GLP frame linked to the carrier 10,: radial velocity of the observed carrier 14 in the GLP frame linked to the carrier 10, anddesignates the i-th coordinate of the state vector.During the sub-step of applying SE2, the computer 18 applies a second estimator to the initial estimation and all of the measurements obtained.The second estimator is thus applied to the initial estimation, the pairs of angular orientations and the radiometric signatures.The second estimator is an invariant extended Kalman filter.An invariant extended Kalman filter is more often designated by the name IEKF filter, the abbreviation IEKF referring to the corresponding English designation of “Invariant Extended Kalman Filter”.Before detailing the operations O1, O2 and O3 implemented by the IEKF filter, several frames are introduced, namely the Serret-Frenet frame and the ECEF frame.The Serret-Frenet frame is denoted and is defined by the following three vectors:a tangent vector , this vector being defined as tangent to the trajectory (oriented in the direction of movement of the observed carrier 14),a normal vector , this vector being defined as normal to the trajectory (in the osculating plane, oriented toward the center of the osculating circle), anda binormal vector , this vector being defined so that the trihedron is direct.The ECEF frame designates a reference frame whose origin is the center of the Earth and whose axes are linked to the Earth. The acronym ECEF refers to the corresponding English designation of “Earth-Centered Earth-Fixed”.The sub-step of applying SE2 comprises three operations O1, O2 and O3 implemented successively, an initialization operation O1, a prediction operation O2 and an estimation operation O3.During the initialization operation O1, the computer 18 obtains the initial state of the observed carrier 14 and the covariance matrix of the error associated with the initial state.The state of the observed carrier 14 is defined as follows: Where:, being the special Euclidean group which contains all the direct isometries of (that is to say translations, rotations but not symmetries) and which makes it possible to represent the position and the orientation of the observed carrier 14,,is the rotation matrix (of size 3x3) giving the orientation of the Serret-Frenet frame (and therefore of the observed carrier 14) with respect to the ECEF frame,is a vector (of size 3x1) giving the position of the observed carrier 14 in the ECEF frame,is a zero matrix (of size 1x3),γ is the curvature (in the plane of the osculating circle),τ is the torsion (measures how the observed carrier 14 leaves the osculating plane), andu is the velocity of the observed carrier 14.The computer 18 calculates the initial state of the IEKF filter from the state vector according to the equations of the following system: Where:designates the vector giving the origin of the GLP frame in the ECEF frame (corresponds to the position of the carrier 10 assumed known),designates the rotation matrix giving the orientation of the GLP with respect to the ECEF frame (depends only on the position of the carrier 10 assumed known), and represent the track and the slope of the observed carrier 14. They are expressed from and , andis a conversion function making it possible to obtain a rotation matrix from Euler angles in intrinsic Tait-Bryan convention.is the velocity vector of the observed carrier 14 in the ECEF frame which is written mathematically according to the following relationship: The computer 18 also obtains the coefficients of the covariance matrix of the error associated with the initial state .The covariance matrix of the error associated with a state is classically defined as an element of such that: Where:corresponds to the covariance of the quantity A.To express the error associated with a state , it is useful to define more precisely the notion of error in the state space which is partitioned into two sub-spaces, namely the special Euclidean group and .On the sub-space , which represents the velocity (in norm) of the observed carrier 14 as well as the curvature and the torsion characteristic of the trajectory followed by the observed carrier 14, the state error is defined here as an element of such that: Where:, and are the estimated values of the curvature, of the torsion and of the velocity of the observed carrier 14, and, and : the true values of the curvature, of the torsion and of the velocity of the observed carrier 14.On the sub-space of the special Euclidean group , which represents the position and the orientation of the observed carrier 14 in the ECEF frame, the state error is defined as an element of such that: Where:and respectively designate the estimated values of the position and of the orientation of the observed carrier 14, andand respectively designate the true values of the position and of the orientation of the observed carrier 14.In the preceding equation, the error on the orientation of the observed carrier 14 is a rotation matrix (i.e. belonging to the group ).When the rotation error is small, this rotation matrix can be approximated by linearization on the Lie algebra of the group as follows: Where:is an antisymmetric matrix (i.e. belonging to the Lie algebra of ).It results therefrom that the error associated with the state is finally defined as an element of and can be expressed mathematically as follows: To obtain the covariance matrix of the error associated with the initial state , the computer 18 first calculates the coefficients of the covariance matrix .This covariance matrix is calculated in a new space within which the state vector is expressed mathematically: where:, and designate the coordinates of the vector giving the position of the observed carrier 14 in the Serret-Frenet frame. Let us note that, by definition of the Serret-Frenet frame, these three coordinates are zero. This space is not of interest for the representation of the state of the observed carrier 14 but makes it possible, as is described subsequently, to calculate the covariance of the error on the position of the observed carrier 14 in the Serret-Frenet frame, and, and are the spherical coordinates of the velocity vector of the observed carrier 14 in the Serret-Frenet frame.The function making it possible to calculate the vector from the vector is denoted and verifies by definition the following mathematical relationship: The covariance matrix is then calculated according to the following formula: Where:designates the Jacobian matrix associated with the function ,designates the covariance matrix associated with the vector , anddesignates the transpose of the matrix M.The computer 18 then obtains the covariance matrix of the error associated with the initial state from the coefficients of the covariance matrix as follows:  During the prediction operation O2, the computer 18 predicts the state and the associated covariance at the instant of the next measurement, from the estimated state and the associated covariance at the instant of the last measurement.In the preceding notation, designates the predicted value for the quantity A at the instant knowing the value for the quantity A at the instant .The computer 18 deduces the predicted state at the instant from the last estimated state at the date by using the following system: Where:designates the instantaneous rotational velocity vector of the observed carrier 14 in the Serret-Frenet frame,represents the matrix associated with the cross product (denoted “”) with the vector , such that ,, anddesignates the velocity vector of the observed carrier 14 in the Serret-Frenet frame,By way of remark, it may be noted that this system comes from a time integration over the time interval of the system corresponding to the evolution model describing the kinematics of the observed carrier 14.This model assumes that the curvature γ, the torsion and the velocity u are constant. Under this hypothesis, the evolution model is governed by the following equations: Where:is the random vector (of size 9x1) modeling the model noise (assumed Gaussian with zero mean and covariance matrix ).With the hypotheses used and without deviation from the evolution model, the velocity, the curvature and the torsion are constant, so that the observed carrier 14 describes a circular helix.During the prediction operation O2, the computer 18 also calculates the associated covariance at the instant from the last associated covariance by using the following relationship:Where:, being a matrix such that: As a remark, this formulation comes from the fact that it can be shown that the evolution of the error associated with the state is governed by the following equation: The integration of this equation over the time interval makes it possible to obtain according to the following equation:The preceding formulation is thus obtained by noting thatAt the end of the prediction operation O2, the computer 18 thus has the state as well as the associated covariance at the instant .During the estimation operation O3, the computer 18 estimates the state and the associated covariancewith the measurementcarried out at the instant by the sensor 16.In this sense, the estimation operation O3 can be interpreted as an updating operation.During this estimation operation O3, the computer 18 estimates the estimated state as well as the associated covariance by using the following system:Where:denotes the correction vector applied to the predicted state , andis a function defined by: where is the identity matrix of order 3 and denotes the matrix associated with the vector product with the matrix .is the identity matrix of order 9,is a matrix such that:Where:is the first coordinate of the vector giving the position of the observed carrier 14 in the GLP frame,is an observation function of the azimuth angle of the observed carrier 14 in the GLP frame, a function which depends non-linearly on the position of the observed carrier 14, this function satisfying: is the first coordinate of the vector giving the position of the observed carrier 14 in the GLP frame,is the second coordinate of the vector giving the position of the observed carrier 14 in the GLP frame,is the derivative of a quantity A with respect to the position of the observed carrier 14,is an observation function of the elevation angle of the observed carrier 14 in the GLP frame, a function which depends non-linearly on the position of the observed carrier 14, this function satisfying: is the third coordinate of the vector giving the position of the observed carrier 14 in the GLP frame,is an observation function of the energy of the observed carrier 14 in the image, a function which depends non-linearly on the position and on the orientation of the observed carrier 14, this function satisfying: is the vectorization operator of a matrix by concatenation of its columns,is the matrix such that:, andis the matrix such that: is the innovation which is defined by:is the Kalman gain, this gain being obtained by the following formula:Where:denotes the inverse matrix of the matrix A,is the covariance matrix of the noise vectorwhich is a random vector modelling the measurement noise (assumed here to be Gaussian with zero mean).As a remark, it can be noted that these expressions derive from the observation model as is now described.For the sensor 16, the measurement vector associated with the observed carrier 14 is defined as follows: Where:is the azimuth angle of the observed carrier 14 in the GLP frame linked to the carrier 10,is the elevation angle of the observed carrier 14 in the GLP linked to the carrier 10, andis the energy of the observed carrier 14.The observation model, which makes it possible to relate the state to the measurement vector , is written mathematically according to the following system: Where:is the first component of the noise vector ,is the second component of the noise vector ,is a constant, specific to the sensor 16, making it possible to convert an incident photon flux into a grey level in the image,is the size of the surface of the entrance pupil of the sensor 16,is the power radiated by the observed carrier 14 per unit of solid angle in the direction ,corresponds to the angular direction under which the sensor 16 is seen from the observed carrier 14, andis the third component of the noise vector .The angles and correspond to the yaw and pitch angles associated with the rotation matrix giving the orientation of the LDV frame of the sensor 16 with respect to the Serret-Frenet frame of the observed carrier 14.The yaw and pitch angles correspond respectively to the first two rotations Z and Y’ in intrinsic Tait-Bryan convention (convention commonly used in aeronautics).The LDV frame corresponds to the line-of-sight frame. The LDV frame is defined as follows: its origin corresponds to the optical centre of the sensor 16 (coincident as a first approximation with the centre of mass of the carrier 10), the first axis is oriented in the viewing direction of the sensor 16, the second axis is parallel to the lines of the image (oriented from left to right) and the last axis is parallel to the columns of the image (oriented from top to bottom).In practice, is calculated by using the following formula: Where:is the rotation matrix giving the orientation of the LDV frame with respect to the GLP frame (matrix calculated by composition of the attitudes of the LDV frame in the carrier 10 frame and of the carrier 10 attitudes in the GLP frame).By using the observation functions , and , the preceding expressions can be reformulated according to the following system: The innovation corresponding to the measurement thus becomes:Where:is the true position of the observed carrier 14 in the ECEF frame at the instant , anddenotes the true orientation of the observed carrier 14 in the ECEF frame at the instant .Furthermore, the true position and orientation of the observed carrier 14 can be expressed as a function of the predicted position and orientation and of the state error according to the following relationships:Where:is the state error on the position of the observed carrier 14, error expressed in the Lie algebra of , andis the state error on the attitude of the observed carrier 14, error expressed in the Lie algebra ofThe combination of the preceding equations makes it possible to obtain after first-order linearization the following relationship: It is deduced therefrom that the covariance of the innovation is written:It follows that the Kalman gain is written:With finally the relationship , it follows:The method which has just been described therefore makes it possible to exploit the radiometric information of the observed carrier 14 measured in an image in order to remove the ambiguities which can exist for certain manoeuvres.This is notably the case for manoeuvres represented in Figure 3.In the case of manoeuvre 1, the observed carrier 14 evolves from the beam sector to the rear sector, which results in a significant increase in the energy measured in the image at the level of the observed carrier 14, due to the unmasking of its nozzle whereas in the case of manoeuvre No. 2, the observed carrier 14 evolves from the beam sector to the front sector, which results in a more or less marked decrease in the energy measured in the image at the level of the observed carrier 14.These detected manoeuvres are added to the manoeuvres which can already be detected from the angular observations.Figure 4 presents two examples of manoeuvres which can be discriminated from angular observations alone.The method thus makes it possible to better characterize the manoeuvres performed by the observed carrier 14.The joint exploitation of the angular and radiometric measurements thus makes it possible to maintain a good estimation accuracy on the distance of the observed carrier 14 during and after the manoeuvre of the observed carrier 14, in a greater number of scenarios.Other embodiments making it possible to obtain the same advantages are also conceivable.Any technique other than an MSC-EKF filter making it possible to carry out the sub-step of initializing SE1 can be considered here.It is also possible to use an approach different from the addition of energy in the measurement vector presented previously.In the case of an ambiguity between two types of manoeuvres (one leading the observed carrier 14 to go in one direction and the second to go in the opposite direction), the predictions for a distance which increases and a distance which decreases can be calculated simultaneously. One of the two hypotheses may be abandoned when the infrared signature is incompatible in a more or less blatant manner with the estimated distance.From a hardware point of view, it is possible to use one sensor for the angular observations and a different sensor to obtain the radiometric signature.Preferably, each of these sensors is a passive sensor, that is to say that the sensor emits no pulse toward the environment.Thus, in a general case, the system for determining 10 comprises a first sensor adapted to measure two angular orientations of an object with respect to the first sensor, a second sensor adapted to measure a radiometric signature of the object, and a computer 18 adapted to obtain the angular orientations and the radiometric signature of the object by reception of the measurements from each of the sensors.The method uses a single IRST sensor, which notably makes it possible to dispense with a need for a weather sensor.The method also does not involve the use of a database giving the emissivity and the supposed speed of the different types of objects which it is possible to encounter. The method is thus usable for any type of objects.It is also interesting to note that the method does not assume taking into account the influence of the atmosphere since it is based on the variation of the emissivity of the target over time. This makes it possible in particular to avoid resorting to an inversion or to a modelling of the atmosphere in the implementation of the method.Advantageously, the method for determining described uses two estimators.The first estimator makes it possible to estimate the trajectory of the object (only if the latter is in uniform rectilinear motion).The second estimator used alone does not make it possible to estimate the trajectory of the target if it is not well initialized. THowever, it makes it possible to estimate trajectories more complex than trajectories of uniform rectilinear motion if it is well initialized, only a few trajectories remaining ambiguous. The second estimator is initialized passively thanks to the first estimator.The ambiguities on the remaining trajectories can be removed if it uses radiometry measurements in addition to the angular measurements.The method thus seeks to maintain the trajectory of the object and this, for any one of these manoeuvres.  

Claims

Method for determining at least one property of a manoeuvre of an object, the method for determining 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),- of a radiometric signature of the object,- determining at least one property of a manoeuvre of the object by application of an estimator on the measurements obtained.Method for determining according to claim 1, wherein an image of the object comprising pixels is provided, the radiometric signature being a quantity representative of the number of grey levels which are added to the pixels of the image due to the presence of the object.Method for determining according to claim 1 or 2, wherein the at least one property determined during the step of determining is the presence of a manoeuvre of the object or the type of manoeuvre performed by the object.Method for determining according to any one of claims 1 to 3, wherein, the step of determining comprises an sub-step of initializing making it possible to obtain an initial estimation of the position of the object, the estimator being applied on the measurements obtained and the initial estimation.Method for determining according to claim 4, wherein the sub-step of initializing is implemented by application of an estimator on the angular orientations.Method for determining according to any one of claims 1 to 5, wherein the estimator used during the sub-step of initializing is an extended Kalman filter in modified spherical coordinates.Method for determining according to any one of claims 1 to 6, wherein the estimator applied on the measurements obtained is an invariant extended Kalman filter.Computer (18) adapted to determine at least one property of a manoeuvre of an object, the computer (18) being adapted to:- obtain measurements:- of two angular orientations of the object with respect to a sensor (16), and- of a radiometric signature of the object, and- determine at least one property of a manoeuvre of an object by application of an estimator on the measurements obtained.System for determining (12) at least one property of a manoeuvre of an object, the system for determining (12) comprising:- a first sensor adapted to measure two angular orientations of an object with respect to the first sensor,- a second sensor adapted to measure a radiometric signature of the object, and- a computer (18) according to claim 8, the computer (18) being adapted to obtain the angular orientations and the radiometric signature of the object by reception of the measurements from each of the sensors.Carrier (10) comprising a computer (18) according to claim 8 or a system for determining (12) according to claim 9.

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