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 determination method being implemented by a computer and comprising the steps of: - obtaining the measurements: - of two angular orientations of the object relative to a sensor; - a radiometric signature of the object; - determining at least one property of a manoeuvre of the object by applying an estimator to the obtained measurements.

Description

[0001]Method for determining a property of a maneuver of a carrier, associated calculator, system and carrier The present invention relates to a method for determining at least one property of a maneuver of an object. The present invention also relates to a calculator capable of implementing the determination method as well as to a system and a carrier comprising such a calculator. 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. 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, i.e. a passive angle measurement trajectography technique. 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). 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 the Kalman filter assuming a uniform rectilinear motion to diverge. There is a need for a method for determining a property of an object maneuver that allows maintaining a relevant estimate of the object's position, even in the presence of a maneuver of the object.For this purpose, the description describes a method for determining at least one property of a maneuver of an object, the determination method being implemented by a computer and comprising the steps of: - obtaining measurements: - of two angular orientations of the object relative to a sensor, and - of a radiometric signature of the object, and - determining at least one property of a maneuver of the object by applying an estimator to the measurements obtained. According to particular embodiments, the determination method has one or more of the following characteristics, taken in isolation or in all technically possible combinations: - an image of the object is provided comprising pixels, the radiometric signature being a quantity representative of the number of gray 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 determination step is the presence of a maneuver of the object or the type of maneuver performed by the object. - the determination step includes an initialization sub-step making it possible to obtain an initial estimate of the position of the object, the estimator being applied to the measurements obtained and the initial estimate. - the initialization sub-step is implemented by applying an estimator to the angular orientations. - the estimator used during the initialization sub-step 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 capable of determining at least one property of a maneuver of an object, the computer being capable of: - obtaining measurements: - of two angular orientations of the object relative to a sensor, and - of a radiometric signature of the object, and - determining at least one property of a maneuver 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 maneuver of an object, the determination system comprising: - a first sensor capable of measuring two angular orientations of an object relative to the first sensor, - a second sensor capable of measuring a radiometric signature of the object, and - a computer as previously described, the computer being capable of 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 calculator as previously described or a determination system as previously described. In the present description, the expression "suitable for" means indifferently "adapted for", "adapted to" or "configured for".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: - Figure 1 is a schematic representation of a carrier provided with a system for determining a property of a maneuver of an object, - Figure 2 is a flowchart of an example of implementation of a method for determining a property of a maneuver of an object, and - Figure 3 illustrates an example of a maneuver that the determination method according to Figure 2 makes it possible to discriminate, and - Figure 4 illustrates an example of a maneuver that can be discriminated using angular measurements only. A carrier 10 is shown schematically in Figure 1. The carrier 10 shown is, for example, an airplane. Alternatively, the carrier 10 is any type of aircraft such as a helicopter.It is also possible to consider here a carrier which is a land or naval vehicle. The carrier 10 comprises a system 12 for determining at least one property of a maneuver of an object. A determined property may, depending on the case, be the presence of a maneuver and the type of maneuver. For this, the determination system 12 seeks, for example, to obtain in real time the distance between the object and the determination system 12. Without this being limiting, it is assumed in the following that the determination system 12 seeks to characterize the maneuver of another carrier 10, called the observed carrier 14. 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. The determination system 12 can be seen as optronic equipment of the carrier 10.This includes equipment having 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”. The determination system 12 comprises a sensor 16 and a computer 18. The sensor 16 is capable of measuring two angular orientations of the observed carrier 14 relative 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 geographical 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.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. 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 gray 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 as follows:^^^^^^(^^, ^^) = ^^^^^^0 ^^(^^, ^^)Where: ^ ^^^^^^0: the total power radiated by the observed carrier 14 (the power radiated in all directions in space). The total power is written as follows:. is the elementary solid angle, and ^ is the proportion of the total power radiated by the observed carrier 14 in the direction (^^, ^^). This function can 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: Or : is a constant, and^ the other terms are defined later after the introduction of specific references. To obtain the value of the radiometric signature, an analysis of the gray levels is carried out without the observed carrier 14 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 in 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 does not emit any pulses to 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 memories of registers or other types of display devices, transmission devices or storage devices. 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). The computer 18 is capable of implementing a method for determining at least one property of a maneuver of the observed carrier 14.An example of operation of the computer 18 is now described with reference to FIG. 2 which illustrates a flowchart for implementing a method for determining at least one property of a maneuver of the observed carrier 14. The determination method comprises an obtaining step E20 and a determining step E22. During the obtaining step E20, the computer 18 receives a plurality of pairs of measured angular orientations. More precisely, the sensor 16 measures the two angular orientations at each instant. 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 obtaining step E20, the sensor 16 measures a radiometric signature of the observed carrier 14. Here, the radiometric signature is obtained by an analysis of the gray levels in the image.In the determination step E22, the computer 18 determines at least one property of a maneuver of the object. The determination step E22 comprises an initialization sub-step SE1 and an application sub-step SE2. In the initialization sub-step SE1, the computer 18 applies a first estimator applied to the pairs of angular orientations to obtain an initial estimate 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 referred to as an MSC-EKF filter. The abbreviation MSC-EKF refers to the corresponding English name for “Modified Spherical Coordinates Extended Kalman Filter”.The MSC-EKF filter provides the position and velocity of the observed carrier 14 in modified spherical coordinates in the carried local geographic reference frame, as well as the associated covariance matrix. The carried local geographic reference frame designates a reference frame whose origin is the position of the sensor 16, the first axis (^⃗^ ) is directed towards the geographic North, the second axis (^⃗^ ) towards the East and the last axis (^⃗^ ) downwards. In the following, this reference frame will be referred to as the GLP reference 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, and ^ ^^ ^ ^ ^ ^^^^^ denotes the i-th coordinate of the state vector ^^ ^^^^^^. During the application sub-step SE2, the computer 18 applies a second estimator to the initial estimate and all the measurements obtained. The second estimator is thus applied to the initial estimate, 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 referred to as an IEKF filter, the abbreviation IEKF referring to the corresponding English name for “Invariant Extended Kalman Filter”. Before detailing the operations O1, O2 and O3 implemented by the IEKF filter, several reference frames are introduced, namely the Serret-Frenet reference frame and the ECEF reference 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 towards the center of the osculating circle), and ^ a 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 term for "Earth-Centered Earth-Fixed". The application sub-step 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 ^^0 associated with the initial state ^^ ^^ . The state ^^ of the observed carrier 14 is defined as follows: Where: ^^^ ∈ ^^^^(^^), ^^^^(3) being the special Euclidean group which contains all direct isometries of ℝ 3 (i.e. translations, rotations but not symmetries) and which makes it possible to represent the position and orientation of the observed carrier 14, ^ ^^ ∈ ℝ3, ^ ^^^^^^^^^^→^^^^is the rotation matrix (of size 3x3) giving the orientation of the Serret-Frenet frame (and therefore of the observed carrier 14) relative to the ECEF frame, ^ ^^ ^^^^^^^^ is a vector (of size 3x1) giving the position of the observed carrier 14 in the ECEF frame, ^ ^^ ^^,^^is a null 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), and ^ u is the velocity of the observed carrier 14. The calculator 18 calculates the initial state ^^0 of the IEKF filter from the state vector ^^ ^^^^^^ according to the equations of the following system: ^ ^^ ^^^^^^^^ denotes the vector giving the origin of the GLP frame in the ECEF frame (corresponds to the position of the carrier 10 assumed to be known), ^ ^^^^^^^^^^→^^^^^^^^denotes the rotation matrix giving the orientation of the GLP relative to the ECEF frame (depends only on the position of the carrier 10 assumed to be known), ℎ̅ and ^̅^ represent the route and the slope of the observed carrier 14. They are expressed from ^^ ^^^^^^ and ^̇^ ^^^^^^^^ , and ^ euler_vers_rot_mat is a conversion function to obtain a rotation matrix from Euler angles in intrinsic Tait-Bryan convention. ^ ^^0 ^^^^^^^^is the velocity vector of the observed carrier 14 in ECEF reference which is written mathematically according to the following relation: The calculator 18 also obtains the coefficients of the covariance matrix ^^0 of the error ^^0 associated with the initial state ^^0. The covariance matrix ^^ of the error ^^ associated with a state ^^ is classically defined as an element of ℝ9x9 such that:^^ = cov(^^)Where: ^ cov ( ^^ ) 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 subspaces, namely the special Euclidean group ^^^^(3) and ℝ 3 . On the subspace ℝ 3 , which represents the speed (in norm) of the observed carrier 14 as well as the curvature and torsion characteristic of the trajectory followed by the observed carrier 14, the state error ^^^^ is defined here as an element of ℝ3 such that: ^^ − ^^^^^^ = ^̂^ − ^^ = [^̂^ − ^^] ^̂^ − ^^ Where: ^ ^^, ^̂^ and ^̂^ are the estimated values ​​of the curvature, torsion and velocity of the observed carrier 14, and ^ ^^, ^^ and ^^: the true values ​​of the curvature, torsion and velocity of the observed carrier 14. On the subspace of the special Euclidean group ^^^^(3), which represents the position and orientation of the observed carrier 14 in the ECEF frame, the state error ^^ ^^ is defined as an element of ^^^^(3) such that: Where: ^ ^̂^ ^^^^^^^^ and ^̂^^^^^^^^^→^^^^denote respectively the estimated values ​​of the position and orientation of the observed carrier 14, and ^ ^^ ^^^^^^^^and ^^^^^^^^^^→^^^^denote respectively the true values ​​of the position and orientation of the observed carrier 14. In the previous equation, the error on the orientation of the observed carrier 14^^^^^^^^^^^→^^^^̂^^^^^^^^^→^^^^ is a rotation matrix (i.e. belonging to the group ^^^^(3)). When the rotation error is small, this rotation matrix can be approximated by linearization on the Lie algebra of the group ^^^^(3) as follows: Where: ^ (^^ ^^ ) ×is an antisymmetric matrix (i.e. belonging to the Lie algebra of^ ^^^(3)). As a result, the error associated with state ^^ is finally defined as an element of ℝ 9 and can be expressed mathematically as follows: To obtain the covariance matrix ^^0 of the error ^^0 associated with the initial state ^^0, the calculator 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: ^ ^^ ^^ , ^^ ^^ and ^^ ^^denote the coordinates of the vector giving the position of the observed carrier 14 in the Serret-Frenet frame. 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 allows, as described below, to calculate the covariance of the error on the position of the observed carrier 14 in the Serret-Frenet frame, and ^ ^^, h̅ and p̅ are the spherical coordinates of the velocity vector of the observed carrier 14 in the Serret-Frenet frame. The function for calculating the vector ^^^^^^ from the vector ^^^^^^^^ is noted ^^^^^^→^^^^^^ and verifies by definition the following mathematical relation: ^^^^^^ = ^^^^^^→^^^^^^(^^^^^^^^) The covariance matrix ^^^^^^ is then calculated according to the following formula: Where: ^ ^^ ^ ^^ ^ ^^→^^^^^^ denotes the Jacobian matrix associated with the function ^^^^^^→^^^^^^, ^ ^^ ^^^^^^denotes the covariance matrix associated with the vector ^^ ^^^^^^ , and ^ ^^ ^^ denotes the transpose of the matrix M. The calculator 18 then obtains the covariance matrix ^^0 of the error ^^0 associated with the initial state ^^0 from the coefficients of the covariance matrix ^^ ^^^^ as follows: During the O2 prediction operation, the computer 18 predicts the state ^^ ^^+1|^^ and the associated covariance Pk+1|k at time ^^^^+1 of the next measurement, from the estimated state^^^^|^^ and the associated covariance Pk|k at time ^^^^ of the last measurement.In the previous notation, denotes the predicted value for the quantity A at time ^^ ^^ knowing the value for quantity A at the moment ^^ ^^ . The calculator 18 deduces the predicted state x k+1|k right now ^^ ^^+1 from the last estimated state ^^ ^^|^^ on the date ^^ ^^ using the following system: Where: ^^^^^^^ = [^^ 0 ^^]^^ denotes the instantaneous rotation velocity vector of the observed carrier 14 in the Serret-Frenet frame, (^^^^^^)× represents the matrix associated with the vector product (noted "×") with the vector ^^^^^^ , such that ∀^^ ∈ ℝ3 (^^^^^^) ^^^^× ⋅ ^^ = ^^ × ^^,^ Δ^^ = ^^^^+1 − ^^^^, and^ ^^^^^^ = [^^ 0 0]^^ denotes the velocity vector of the observed carrier 14 in the Serret-Frenet frame, As a remark, it can be noted that this system comes from a time integration over the time interval Δ^^ 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 speed u are constant. Under this assumption, the evolution model is governed by the following equations: Where: ^ ^^ = the random vector (of size 9x1) modeling the model noise (assumed to be Gaussian with zero mean and covariance matrix ^^). With the assumptions used and without deviation from the evolution model, the speed, curvature and torsion are constant, so that the observed carrier 14 describes a circular helix. During the prediction operation O2, the calculator 18 also calculates the associated covariance P k+1|k right now ^^ ^^+1 from the last associated covariance ^^ ^^|^^ using the following relationship: Where: ^^^ = exp(^^ Δ^^), ^^ being a matrix such that: As a note, this formulation comes 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 Δ^^ makes it possible to obtain^^^^+1|^^ according to the following equation: The previous formulation is thus obtained by noting that At the end of the prediction operation O2, the computer 18 thus has the state x k+1|k as well as the associated covariance P k+1|k right now ^^ ^^+1 . During the estimation operation O3, the calculator 18 estimates the state x k+1|k+1 and the associated covariance P k+1|k+1 with the measure ^^ ^^+^^ made just now ^^ ^^+1 by the sensor 16. In this sense, the estimation operation O3 can be interpreted as an update operation. During this estimation operation O3, the computer 18 estimates the estimated state x k+1|k+1 as well as the associated covariance P k+1|k+1 using the following system: Where: ^ ^^ = ^^ ⋅ ^^^^+1 denotes the correction vector applied to the predicted state x k+1|k , and ^^^(^^^^) is 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, ℎ ^^^^ 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 ℎ^^^^ verifying:ℎ^^^^ = atan2(^^^^ , ^^^^)^^ ^^ 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, ^^A ^^^^ is the derivative of a quantity A with respect to the position ^^ of the observed carrier 14, ℎ ^^^^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 ℎ^^^^ verifying:^^ ^^ is the third position of the observed carrier 14 in the GLP frame, ℎ ^^ 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 the orientation ^^ of the observed carrier 14, this function ℎ^^ verifying: vec(. ) is the vectorization operator of a matrix by concatenation of its columns, ^^ is the matrix such that: , and ^^ is the matrix such that: ^ ^^ ^^+1 is the innovation which is defined by: ^ ^^ is the Kalman gain, this gain being obtained by the following formula: Where: o ^^ −1denotes the inverse matrix of matrix A, where^^ is the covariance matrix of the noise vector ^^ =[^^^^^ ^^^^^^ ^^^^]^^ which is a random vector modeling 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 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, and ^ ^^ is the energy of the observed carrier 14. The observation model, which makes it possible to link 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, allowing to convert an incident photon flux into gray 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, and ^ ^^ ^^is 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 relative 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 center of the sensor 16 (merged in first approximation with the center of mass of the carrier 10), the first axis is oriented in the direction of sight 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 using the following formula:^^^^^^→^^^^^^ = [^^^^^^^^^→^^^^]^^^^^^^^^^^→^^^^^^^^^^^^^^^^→^^^^^Where: ^ ^^^^^^^^^→^^^^^^is the rotation matrix giving the orientation of the LDV frame relative to the GLP frame (matrix calculated by composing the attitudes of the LDV frame in the carrier frame 10 and the carrier 10 attitudes in the GLP frame). Using the observation functions ℎ. ^^^^ , ℎ ^^^^ and ℎ ^^ , the previous expressions can be reformulated according to the following system: ^^^^ = ℎ^^^^(^^) + ^^^^^^{ ^^^^ = ℎ^^^^(^^) + ^^^^^^^^ = ℎ^^(^^,^^) + ^^^^Innovation ^^ ^^+1 corresponding to the measure ^^ ^^+1 becomes: Where: ^ ^^ ^ ^ ^ ^ + ^^^ 1 ^^^ is the true position of the observed carrier 14 in the ECEF frame at time ^^ ^^+1 , and ^ ^^ ^ ^ ^ ^ + ^^^ 1 ^^^→^^^^denotes the true orientation of the observed carrier 14 in the ECEF frame at time ^^ ^^+1 . Furthermore, the true position and orientation of the observed carrier 14 can be expressed as a function of the predicted position and orientation and 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 ^^^^ ( 3 ) , and ^ is the state error on the attitude of the observed carrier 14, error expressed in the Lie algebra of ^^^^(3) The combination of the preceding equations makes it possible to obtain after first-order linearization the following relation:^^^^+1 = ^^ ^^ + ^^We deduce that the As a result, the Kalman gain is written as: With finally the relationship vient :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 may exist for certain maneuvers. This is particularly the case for maneuvers represented in Figure 3. In the case of maneuver 1, the observed carrier 14 moves from the transverse 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 maneuver No. 2, the observed carrier 14 moves from the transverse 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 maneuvers are added to the maneuvers which can already be detected from the angular observations.Figure 4 presents two examples of maneuvers that can be discriminated from angular observations alone. The method thus makes it possible to better characterize the maneuvers performed by the observed carrier 14. The joint exploitation of the angular and radiometric measurements thus makes it possible to maintain good estimation accuracy on the distance of the observed carrier 14 during and after the maneuver 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 initialization sub-step SE1 can be considered here. It is also possible to use a different approach to adding energy to the measurement vector presented previously.In the case of an ambiguity between two types of maneuvers (one leading the observed carrier 14 to go in one direction and the second to go in the opposite direction), the predictions for an increasing distance and a decreasing distance can be calculated simultaneously. One of the two hypotheses can be abandoned when the infrared signature is more or less flagrantly incompatible with the estimated distance. From a hardware point of view, it is possible to use one sensor for angular observations and a different sensor to obtain the radiometric signature. Preferably, each of these sensors is a passive sensor, i.e. the sensor does not emit any pulses to the environment.Thus, in a general case, the determination system 10 comprises a first sensor capable of measuring two angular orientations of an object relative to the first sensor, a second sensor capable of measuring a radiometric signature of the object, and a computer 18 capable of obtaining the angular orientations and the radiometric signature of the object by receiving the measurements from each of the sensors. The method uses a single IRST sensor, which in particular makes it possible to dispense with the need for a weather sensor. The method also does not involve the use of a database giving the emissivity and the assumed speed of the different types of objects that it is possible to encounter. The method can thus be used for any type of object. 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 inversion or modeling of the atmosphere in the implementation of the method. Advantageously, the determination method described uses two estimators. The first estimator makes it possible to estimate the trajectory of the object (only if it 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. However, 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 angular measurements. The method thus seeks to maintain the trajectory of the object for any of these maneuvers.

Claims

CLAIMS 1. Method for determining at least one property of a maneuver of an object, the determination 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), - of a radiometric signature of the object, - determining at least one property of a maneuver of the object by applying an estimator to the measurements obtained.

2. Determination method according to claim 1, in which an image of the object comprising pixels is provided, the radiometric signature being a quantity representative of the number of gray levels which are added to the pixels of the image due to the presence of the object.

3. Determination method according to claim 1 or 2, in which the at least one property determined during the determination step is the presence of a maneuver of the object or the type of maneuver performed by the object. 4.Determination method according to any one of claims 1 to 3, in which the determination step comprises an initialization sub-step making it possible to obtain an initial estimate of the position of the object, the estimator being applied to the measurements obtained and the initial estimate.

5. Determination method according to claim 4, in which the initialization sub-step is implemented by applying an estimator to the angular orientations.

6. Determination method according to any one of claims 1 to 5, in which the estimator used during the initialization sub-step is an extended Kalman filter in modified spherical coordinates.

7. Determination method according to any one of claims 1 to 6, in which the estimator applied to the measurements obtained is an invariant extended Kalman filter.

8. A computer (18) capable of determining at least one property of a maneuver of an object, the computer (18) being capable of: - obtaining measurements: - of two angular orientations of the object relative to a sensor (16), and - of a radiometric signature of the object, and - determining at least one property of a maneuver of an object by applying an estimator to the measurements obtained.

9. A system (12) for determining at least one property of a maneuver of an object, the determination system (12) comprising: - a first sensor capable of measuring two angular orientations of an object relative to the first sensor, - a second sensor capable of measuring a radiometric signature of the object, and - a computer (18) according to claim 8, the computer (18) being capable of obtaining the angular orientations and the radiometric signature of the object by receiving the measurements from each of the sensors. 10.Carrier (10) comprising a calculator (18) according to claim 8 or a determination system (12) according to claim 9.

Images