Method for estimating the distance to an object, associated computer, system and carrier

Abstract

The present invention relates to a method for estimating the distance to an object, the estimation method being implemented by a computer (18) and comprising the steps of:- obtaining measurements of angular velocity parameters of the object and measurements of angular orientations of the object relative to a sensor (16), in order to obtain a plurality of sets of measurements, and- estimating the distance to the object by applying an estimator to each set of measurements, the estimator assuming a motion of the object.

Description

TITLE: METHOD FOR ESTIMATING THE DISTANCE TO AN OBJECT, ASSOCIATED COMPUTER, SYSTEM AND CARRIER The present invention relates to a method for estimating the distance to an object. The present invention also relates to a computer suitable for implementing the estimation method, as well as to a system and a carrier comprising such a computer.In the field of passive estimation of distance between a carrier and an object, optronic equipment, in particular airborne equipment, is used to determine the distance without active telemetry requiring laser or radar emission. Laser telemetry may in fact be limited by its range or by its lack of discretion, since the laser emission can be detected by the targeted object.For this purpose, it is known to use an estimator which is a Kalman filter applied to angular orientation measurements giving two angles at which the object is seen by a passive sensor. Such a technique is often referred to as a TPA technique, i.e. a passive trajectory-measurement technique by angle measurement.Such a Kalman filter may be expressed in a Cartesian reference frame (relative or absolute) or in a spherical reference frame or in a hybrid form alternating between the two types of reference frames according to the phases of the Kalman filter (typically a prediction phase in Cartesian coordinates and a correction phase in spherical coordinates).In the general case, this problem of passive distance estimation from angular measurement is known to be “unobservable” in the sense that it is not solvable. To make this problem solvable, it is appropriate:to make an assumption about the trajectory of the observed object (uniform rectilinear motion, uniform circular motion, etc.)for the carrier to perform a maneuver that will make the sought distance “observable.”However, in order for the estimation to converge and be accurate, the object should follow the assumed motion, which is most often uniform rectilinear motion, for a sufficient duration enabling good convergence to be obtained. This duration may be difficult to obtain in practice because of the movements of the object.There is a need for a method for estimating the distance to an object, in particular a carrier, which is as fast as possible in order to relax this assumption regarding the duration of validity of the trajectory of the targeted object.To this end, the description describes a method for estimating the distance to an object, in particular a carrier, the estimation method being implemented by a computer and comprising the steps of:- obtaining measurements of angular velocity parameters of the object and measurements of angular orientations of the object relative to a sensor, in order to obtain a plurality of sets of measurements, and- estimating the distance to the object by applying an estimator to each set of measurements, the estimator assuming a motion of the object.According to particular embodiments, the estimation method has one or more of the following features, taken in isolation or in any technically possible combination:- each angular velocity parameter is the angular velocity measured by the sensor.- the sensor carrying out the angular orientation measurements is part of a platform whose line of sight is stabilized by an angular velocity setpoint, each angular velocity parameter being the angular velocity setpoint.- the sensor comprises a sensing block and a processing block, the processing block comprising a plurality of sub-blocks, the measurements of angular velocity parameters of the object and of angular orientations being obtained by obtaining the output of a respective sub-block of the processing block of the sensor.- during the obtaining step, measurements of the acceleration of the object are also obtained, each set of measurements comprising at least one acceleration measurement.- the sensor carrying out the angular orientation measurements comprises an inertial measurement unit.- the sensor carrying out the angular orientation measurements comprises gyrometers.- the estimator is a Kalman filter.The description also describes a computer suitable for estimating the distance to an object, the computer being suitable for:- obtaining measurements of angular velocity parameters of the object and measurements of angular orientations of the object relative to a sensor, in order to obtain a plurality of sets of measurements, and- estimating the distance to the object by applying an estimator to each set of measurements, the estimator assuming a motion of the object.The description also proposes a system for estimating the distance to an object, in particular a carrier, the estimation system comprising:- a first sensor suitable for measuring an angular velocity parameter of the object,- a second sensor suitable for measuring the angular orientations of the object relative to the second sensor, and- a computer as described above, the computer being suitable for obtaining each angular velocity parameter and the angular orientations by receiving the measurements from each of the sensors.According to a particular embodiment, the first sensor and the second sensor are one and the same.The description also describes a carrier comprising a computer as described above or an estimation system as described above.In the present description, the expression “suitable for” means equally “adapted for,” “adapted to” or “configured to.”Features and advantages of the invention will become apparent on reading the following description, given solely by way of non-limiting example, and made with reference to the appended drawings, in which:- [Fig 1] figure 1 is a schematic representation of a carrier provided with a system for estimating the distance to an object,- [Fig 2] figure 2 is a block-diagram representation of the components of an example estimation system according to figure 1, and- [Fig 3] figure 3 is a flowchart of an example implementation of a method for estimating the distance to an object.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 or a missile.It is also possible to envisage considering here a carrier that is a land or naval vehicle.The carrier 10 comprises a system 12 for estimating the distance to an object.The estimation system 12 thus seeks to obtain, in real time, the distance between the targeted object, which may be another carrier referred to as the observed carrier 14, and the estimation system 12.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 estimation system 12 may be viewed as optronic equipment of the carrier 10.It is in particular equipment 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 is more often designated by the term IRST equipment, the abbreviation IRST referring to the English name “InfraRed Search and Track.”The estimation system 12 comprises a sensor 16 and a computer 18.The sensor 16 is for example a passive optronic sensor, i.e. able to measure quantities without the sensor 16 having to emit a signal.The sensor 16 is able to obtain measurements of angular parameters of a target remote from the sensor 16, relative to the sensor 16.According to the described example, the sensor 16 tracks the observed carrier 14 and is suitable for measuring two angular orientations of the observed carrier 14 in a fixed reference frame.Typically, the sensor 16 gives two angular values which are the azimuth and the elevation.The two orientations are defined in the local geographic reference frame, i.e. a reference frame centered on the sensor 16 with a first axis x corresponding to north, a second axis 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, while the elevation is the rotation about a fourth axis y′, the fourth axis y′ being derived from the second axis y by the azimuth rotation. 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 is also suitable for providing the angular velocity of the orientation toward the observed carrier 14.According to a particular example, the sensor 16 includes an inertial measurement unit.As visible in the example of figure 2, the inertial measurement unit comprises a sensing block 20 and a processing block 22.The sensing block 20 comprises gyrometers 24 and accelerometers 26.The gyrometers 24 provide the angular velocity of the observed carrier 14, while the accelerometers 26 provide the acceleration of the carrier 10.The processing block 22 is suitable for receiving the measurements from the gyrometers 24 and the measurements from the accelerometers 26 and for processing the received measurements in order to obtain the position, the velocity of the carrier 10 and the attitude (angular orientations) and the angular velocity of the observed carrier 14.The processing block 22 comprises several sub-blocks: a first correction sub-block 28, a second correction sub-block 30, a first integration sub-block 32, a second integration sub-block 34 and a third integration sub-block 36.The first correction sub-block 28 receives the measurements from the gyrometers 24 and is suitable for applying a correction to the received measurements.According to the proposed example, the first correction sub-block 28 corrects the effect of the Earth's rotation on the received angular velocity measurements.The correction applied by the first sub-block also comprises compensation for the rotation speed of the rotating trihedron relative to the Earth.The first correction sub-block 28 thus outputs the angular velocity of the observed carrier 14 by correcting the raw measurements of the gyrometer 24.The first integration sub-block 32 receives as input the angular velocity thus obtained and temporally integrates it to obtain angular measurements (attitude).The second correction sub-block 30 receives the measurements from the accelerometers 26 and is suitable for applying a correction to the received measurements.According to the proposed example, the second correction sub-block 30 corrects the effect of the Earth's rotation on the received acceleration measurements.The correction applied by the second correction sub-block 30 also comprises gravity compensation.The second correction sub-block 30 thus outputs the acceleration of the carrier 10 by correcting the raw measurements from the accelerometers 26.The second integration sub-block 34 receives as input the acceleration thus obtained and temporally integrates it to obtain the velocity of the carrier 10, and the correction sub-block 30 furthermore receives the velocity from the second integration sub-block 34.The third integration sub-block 36 receives as input the velocity calculated by the second integration sub-block 34 and temporally integrates it to obtain the position of the observed carrier 14.In the proposed example, the corrections to be applied are obtained using other calculated values.More precisely, each correction sub-block receives the position from the third integration sub-block 36 and the angular measurements from the first integration sub-block 32.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 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 (from the English Field Programmable Gate Array), or alternatively an integrated circuit, such as an ASIC (from the English Application Specific Integrated Circuit), or alternatively in the form of a processor programmable using a computer language.The computer 18 is suitable for implementing an estimator on measurements from the sensor 16 in order to estimate the distance to the observed carrier 14.As visible in figure 2, the computer 18 implements a Kalman filter which comprises a state prediction unit 38, a first extraction unit 40, a second extraction unit 42, a correction unit 48, a first subtractor 44, a second subtractor 46, a correction unit 48, and an adder 49.The state prediction unit 38 receives the velocity and the position of the observed carrier 14 and predicts the state.The first extraction unit 40 obtains an angular velocity prediction from the predicted state.The first subtractor 44 is connected to the output of the first correction sub-block 28 of the sensor 16 and thus receives the measured angular velocity of the observed carrier 14.The first subtractor 44 is also connected to the output of the first extraction unit 40 and thus receives an angular velocity predictionThe first subtractor 44 is thus suitable for performing the subtraction between the measured angular velocity and the predicted angular velocity.The result of such a subtraction is an angular velocity correction which is sent to the correction unit 48.The second extraction unit 42 obtains an angular orientation prediction from the predicted state.The second subtractor 46 is connected to the output of the first integration sub-block 32 of the sensor 16 and thus receives the measured angular orientation of the observed carrier 14.The second subtractor 46 is also connected to the output of the second extraction unit 42 and thus receives an angular orientation predictionThe second subtractor 46 is thus suitable for performing the subtraction between the measured angular orientation and the predicted angular orientation.The result of such a subtraction is an angular orientation correction which is sent to the correction unit 48.The correction unit 48 is suitable for receiving the angular velocity correction obtained by the first subtractor 44 and the angular orientation correction from the second subtractor 46 and for converting these corrections into a correction of the predicted state.The adder 49 receives the correction of the predicted state from the correction unit 48 to which it is connected.The adder 49 is also connected to the prediction block and thus adds the correction to the state predicted by the prediction block.The adder 49 thus obtains a corrected predicted state which gives the estimation of the position and of the velocity of the observed carrier 14.The estimation of the position of the observed carrier 14 corresponds to an estimation of the distance since the position of the estimation system 12 is known.The computer 18 is thus suitable for implementing a method for estimating the distance to the observed carrier 14.An example of operation of the computer 18 is now described with reference to figure 3, which illustrates a flowchart of an example implementation of the estimation method.The estimation method comprises an obtaining step E50 and a determination step.The estimation method aims to estimate the distance to the observed carrier 14, i.e. to give the distance between the estimation system 12 and the observed carrier 14.According to the described example, the estimation method comprises an obtaining step E50 and an estimation step E52.During the obtaining step E50, measurements of the angular velocity of the observed carrier 14 are obtained by the computer 18 as well as measurements of angular orientations.As visible in figure 2, the computer 18 obtains the measurement of the angular velocity from the output of the first correction sub-block 28 and the measurement of angular orientations from the output of the first integration sub-block 32.The computer 18 thus obtains a set of measurements for each instant.During the estimation step E52, the computer 18 applies a Kalman filter to each set of measurements in order to obtain an estimation of the distance (or of the position) of the observed carrier 14.According to the described example, the estimator is a Kalman filter which has both angular measurements (attitudes) and angular velocity measurements.The Kalman filter makes it possible to predict a state vector written in modified spherical coordinates: Where:denote the six coordinates of the vector ,is the distance,the elevation,the azimuth,the radial velocity,the angular velocity in elevation, andthe angular velocity in azimuth.Such a technique for applying a Kalman filter in modified spherical coordinates is often designated by the acronym MSC-KF, which refers to the corresponding English name “Modified Spherical Coordinate Kalman Filter.”In these notations, the set of measurements available to the computer 18 is a set of four measurements: angular measurements which are denoted and and angular velocity measurements which are denoted and .The prediction equations of the Kalman filter (corresponding to the assumption of uniform rectilinear motion of the observed carrier 14 expressed in a spherical reference frame) are written as follows: Where:, and denote the acceleration of the carrier 10, using here the notation used in “Angle Only Tracking Filter in Modified Spherical Coordinates” by D. Stallard, Journal of Guidance Control Dynamics, Vol14, issue 3, May 1991.The observation equations for calculating the innovation (corresponding to the evolution of the quantities) are written: Where:denotes the innovation of the component xi of the state vector X,and denote functions representing any change of reference frame necessary to express the angular velocity measurements in the reference frame in which the state vector is expressed.Because of the availability of the angular velocity measurements, the computer 18 has 4 observation equations instead of the 2 observation equations provided by the angular positions.This makes it possible to avoid the convergence time of the estimate of the angular velocities from the angular positions.In simulations, the applicant thus obtained a 20% gain in the convergence duration of an estimator using angular velocities and angular measurements compared with the same estimator using only angular measurements. This gain was measured for a distance accuracy of 10%.Such an acceleration of the convergence of the estimator reduces the duration during which the observed carrier 14 must comply with the motion model used for the estimation, which makes it possible to extend the field of operational use of the passive distance estimation method.A reduction in static biases is also expected because static biases have no impact on velocity measurements.It may be noted that this improvement in convergence can be obtained for existing systems by adapting the processing block 22 and the computer 18 in a very simple manner. Indeed, the method here uses an intermediate output of the processing block 22.Other embodiments benefiting from the preceding advantages are also conceivable.Instead of using the angular velocity itself as the angular velocity parameter, in the presence of a platform whose line of sight is stabilized by an angular velocity setpoint, it is possible during the obtaining step E50 to obtain the angular velocity setpoint.The platform is thus a gyrostabilized platform, the stabilization setpoints of which are used.Indeed, the stabilization setpoints reflect, at a current instant, the angular velocities making it possible to keep the line of sight on the observed carrier 14, whereas the measurements may be affected by defects of the gyrometers 24.Alternatively or additionally, during the obtaining step E50, measurements of the acceleration of the object are also obtained, each set of measurements comprising at least one acceleration measurement.Indeed, these acceleration measurements are available at the output of the second correction sub-block 30.In such a case, the state vector predicted by the estimator becomes a state vector with 9 components instead of 6 components.Furthermore, other estimators may be used in the present method.In particular, the Kalman filter is based on an assumption of uniform rectilinear motion, but an estimator assuming uniform circular motion could be considered in this method.From a hardware point of view, sensors other than an inertial measurement unit are conceivable.The sensor 16 may, for example, be devoid of an accelerometer 26, so that the sensor 16 then comprises gyrometers 24 and a processing block 22 reduced to the first correction sub-block 28 and to the first integration sub-block 32.It is also possible to have a first sensor measuring the angular orientations and a second sensor measuring the angular velocities.The sensor 16 is here more generally an orientable optronic device having means for measuring the angular attitude and the angular velocity that are integral with the optronic line of sight.Preferably, the sensor 16 is a passive sensor, i.e. the sensor 16 emits no pulse toward the environment.Thus, in a general case, the system 12 for estimating the distance to the observed carrier 14 comprises a first sensor suitable for measuring an angular velocity parameter of the object and a second sensor suitable for measuring the angular orientations of the object relative to the second sensor, the computer 18 receiving the measurements from each of the sensors. The first sensor and the second sensor are, according to the embodiments, two different sensors or one and the same sensor. 

Claims

Method for estimating the distance to an object, in particular a carrier (14), the estimation method being implemented by a computer (18) and comprising the steps of:- obtaining measurements of angular velocity parameters of the object and measurements of angular orientations of the object relative to a sensor (16), in order to obtain a plurality of sets of measurements, and- estimating the distance to the object by applying an estimator to each set of measurements, the estimator assuming a motion of the object.Estimation method according to claim 1, wherein each angular velocity parameter is the angular velocity measured by the sensor (16).Estimation method according to claim 1, wherein the sensor (16) carrying out the angular orientation measurements is part of a platform whose line of sight is stabilized by an angular velocity setpoint, each angular velocity parameter being the angular velocity setpoint.Estimation method according to any one of claims 1 to 3, wherein the sensor (16) comprises a sensing block (20) and a processing block (22), the processing block (22) comprising a plurality of sub-blocks, the measurements of angular velocity parameters of the object and of angular orientations being obtained by obtaining the output of a respective sub-block of the processing block (22) of the sensor (16).Estimation method according to any one of claims 1 to 4, wherein, during the obtaining step, measurements of the acceleration of the object are also obtained, each set of measurements comprising at least one acceleration measurement.Estimation method according to any one of claims 1 to 5, wherein the sensor (16) carrying out the angular orientation measurements comprises an inertial measurement unit.Estimation method according to any one of claims 1 to 5, wherein the sensor (16) carrying out the angular orientation measurements comprises gyrometers (24).Estimation method according to any one of claims 1 to 7, wherein the estimator is a Kalman filter.Computer (18) suitable for estimating the distance to an object, the computer (18) being suitable for:- obtaining measurements of angular velocity parameters of the object and measurements of angular orientations of the object relative to a sensor (16), in order to obtain a plurality of sets of measurements, and- estimating the distance to the object by applying an estimator to each set of measurements, the estimator assuming a motion of the object.System (12) for estimating the distance to an object, in particular a carrier (14), the estimation system (12) comprising:- a first sensor suitable for measuring an angular velocity parameter of the object,- a second sensor suitable for measuring the angular orientations of the object relative to the second sensor, and- a computer (18) according to claim 9, the computer (18) being suitable for obtaining each angular velocity parameter and the angular orientations by receiving the measurements from each of the sensors.Estimation system according to claim 10, wherein the first sensor and the second sensor are one and the same.Carrier (10) comprising a computer (18) according to claim 9 or an estimation system (12) according to claim 10 or 11. 

Images