Method for determining navigation data for a vehicle based on correlation of sonar or radar data and hybridization with an inertial unit, and system capable of implementing such a method

Abstract

The present invention relates to a method for determining navigation data for a vehicle having an inertial unit (6), an antenna (7) and a processing module associated with the antenna (7). The method comprises: - a temporal registration step (22), during which the time offset (DT) between the inertial unit navigation data (DOJNS) and the antenna navigation data (D1_SAS) is corrected; - a step (24) of calculating hybridized antenna navigation data (D2_SAS), based on the antenna (7) and the registered inertial unit navigation data (D2JNS); and - a bias correction step (26), during which an error on the registered inertial unit navigation data (D2JNS) that is inherent to the inertial unit, referred to as bias, is corrected on the basis of the hybridized antenna navigation data (D2_SAS).

Description

[0001] TITLE: Method for determining vehicle navigation data by correlating sonar or radar data and hybridizing it with an inertial measurement unit, and a system capable of implementing such a method

[0002] The present invention relates to a method for determining the navigation data of a vehicle, of the type equipped with an inertial navigation system providing navigation data and sonar or radar equipment providing navigation data. It also relates to a system capable of implementing such a method and a method for imaging underwater targets comprising such a method.

[0003] The invention is primarily developed for underwater applications, specifically sonar sensors, but the challenges faced by airborne or space-based radar platforms are exactly the same, with identical equations. In fact, the main algorithms are the same in both communities.

[0004] In the fields of underwater prospecting, bathymetry, and mine warfare, sonar can be used to obtain an image of a target encountered by a vehicle operating beneath the sea surface during its navigation. In a more advanced mode, to construct a three-dimensional image, the sonar acquires multiple views of the target from different angles, which are then superimposed to reconstruct the target. Another advanced mode involves reprojecting the sonar images onto a geographic coordinate system and summing the received signals pixel by pixel, either coherently or incoherently. Currently, this registration is generally performed through post-processing image operations: firstly, this does not necessarily extract the full possible accuracy from the recorded data; secondly, the registered navigation data is not available in real time within the vehicle.

[0005] By knowing the vehicle's position at the times corresponding to the different shots, the formation of the 3D image can be automated. Devices exist that can estimate the navigation trajectory, that is, the vehicle's position at any given time: inertial measurement units and sonars are examples.

[0006] Several hybridization processes are also known, in which inertial navigation data is corrected using sonar data. A well-known example is called hybrid P2C2 (hybrid ping-to-ping crosscorrelation). This algorithm can be seen as an improvement on the P2C2 (ping-to-ping crosscorrelation) algorithm, which uses sonar data alone. The Displacement of Phase Center Algorithm (DPCA) method, described in the seminal article by RW Sheriff, "Synthetic aperture beamforming with automatic phase compensation for high-frequency sonars," Proceedings of the 1992 Symposium on Autonomous Underwater Vehicle Technology, IEEE, pp. 236-245, 1992, is a well-known and practically used specific application of P2C2.

[0007] The hybrid P2C2 is described in document WO99 / 18452 and in D. Billon and F. Fohanno, "Two improved ping-to-ping cross-correlation methods for synthetic aperture sonar: theory and sea results", OCEANS '02 MTS / IEEE, Biloxi, Ml, USA, 2002, pp. 2284-2293 vol.4.

[0008] One difficulty among others in the application of hybrid P2C2 algorithms stems from the fact that sonar data and inertial measurement unit data are not always perfectly synchronized in time.

[0009] In general, P2C2-type algorithms are insufficient to achieve a sufficiently accurate measurement of the relative antenna displacement. Various hybridization techniques are known to those skilled in the art to improve the accuracy of this measurement. For an overview of possible strategies, see the article by R.E. Hansen, T.O. Sæbo, K. Gade, and S. Chapman, "Signal Processing for AU Based Interferometric Synthetic Aperture Sonar," in Proceedings from Oceans 2003 MTS / IEEE, San Diego, CA, USA, September 2003. Two categories of hybridization are distinguished: tight hybridization and loose hybridization.

[0010] The first category, called tight hybridization, is a unique optimal filter (Kalman, extended Kalman, particle filtering, or other equivalent methods) whose state vector is the error in the vehicle's position and attitude, as well as for the various parameters involved in the measurement system (biases, misalignments, etc.). This filter takes as input the inertial increments (velocities and rotations) measured by an inertial measurement unit (IMU), pressure sensor measurements for pitch, and also (typically) velocity measurements taken by a Doppler log, and finally the data estimated by P2C2 (the specific P2C2 variant is not relevant here). This filter uses the equations for the mechanization of an IMU and estimates, as accurately as possible, the position and attitude errors, as well as all the errors of the overall measurement system, at each instant.

[0011] The second category, known as loose hybridization, is a two-stage filter. The first stage, typically implemented in an inertial measurement unit (IMU) with hybridization capabilities, such as the Phins IMU from Exail, is, as in the case of tight hybridization, an optimal filter using all the information available to the IMU; however, unlike tight hybridization, the measurements from the P2C2 are not used. This first filter provides the user with an estimate of the vehicle's position, attitude, and velocity (translational and rotational), as well as an estimate of the variance-covariance matrices for these parameters. Internal errors (bias, misalignments, etc.) are not provided to the user.A second optimal filter, downstream of the first, takes into account these position and attitude estimates and their time derivatives, as well as the data from the P2C2, in order to arrive at a hybrid result.

[0012] The tight hybridization approach is more precise than loose hybridization but requires access to raw inertial increments, which is not always possible for various technical and regulatory reasons. The second method is somewhat cruder but simpler to implement.

[0013] The aim of the invention is therefore to propose a method for determining the navigation data of a vehicle which allows for increased navigation accuracy, and consequently for improved quality of automatic reconstruction of 3D images of targets, through a particular loose hybridization strategy, all without involving a more efficient inertial navigation system whose cost and size would not be compatible with the intended applications.

[0014] To this end, the invention relates to a method for determining the navigation data of a vehicle equipped with an inertial measurement unit (IMU) providing IMU navigation data, an antenna providing raw antenna data, and a processing module associated with the antenna and configured to determine antenna navigation data from the raw antenna data. The method includes correcting the IMU navigation data from the antenna navigation data. The method comprises:

[0015] - a time alignment step, during which the time offset of the inertial navigation data relative to the antenna navigation data is corrected, providing time-aligned inertial navigation data;

[0016] - a step of calculating hybrid antenna navigation data, from the antenna and the calibrated inertial navigation system data; and

[0017] - a bias correction step, during which an error in the inertial navigation data, inherent to the inertial navigation system (IMS), called bias, is corrected based on hybrid antenna navigation data. This provides recalibrated and debiased INS navigation data, forming the vehicle's navigation data. Thanks to the invention, the potential time delay between the data from the two measuring devices—the INS and the sonar or radar antenna—which is inherent to the hardware and can vary over time, is corrected. This temporal recalibration allows for a reliable correction of the cumulative positioning bias inherent in the INS by using measurements from the hybrid antenna itself. Following these corrections, navigation accuracy is improved, notably enabling greater accuracy in the automatic reconstruction of 3D target images.

[0018] According to other advantageous aspects of the invention, the method for determining the navigation data of a vehicle comprises one or more of the following features, taken individually or in all technically possible combinations:

[0019] - the process is such that:

[0020] • the corrected bias of the recalibrated inertial navigation data is a velocity bias;

[0021] • Recalibrated and debiased inertial navigation data includes recalibrated and debiased inertial velocity data; and

[0022] • the process includes a trajectory reconstruction step during which a vehicle trajectory is calculated by time integration of recalibrated and unbiased inertial measurement unit velocity data, providing the vehicle trajectory, forming the vehicle navigation data;

[0023] - the antenna is a synthetic antenna sonar antenna and the processing module determines the antenna navigation data using an antenna displacement estimation algorithm by correlation;

[0024] - the process is such that:

[0025] • The recalibrated inertial navigation data includes recalibrated attitude data; and

[0026] • Hybrid antenna navigation data is obtained by a hybrid antenna displacement estimation algorithm involving recalibrated attitude data;

[0027] - The time alignment step includes:

[0028] • an expression of the inertial navigation data in an antenna navigation data space, providing modified inertial navigation data; • an estimation of the time lag by correlation between the modified inertial navigation data and the antenna navigation data; and

[0029] • a subtraction of the time offset in the inertial navigation data, providing the recalibrated inertial navigation data;

[0030] - the time alignment step includes filtering of inertial navigation data, modified inertial navigation data and / or antenna navigation data;

[0031] - The bias removal step includes:

[0032] • an expression of hybridized antenna navigation data in the inertial navigation data space, providing modified hybridized antenna navigation data;

[0033] • an estimation of the bias from the modified hybrid antenna navigation data and the recalibrated inertial navigation data; and • a removal of the bias in the recalibrated inertial navigation data, providing the recalibrated and debiased inertial navigation data;

[0034] - the bias removal step includes filtering of hybrid antenna navigation data or modified hybrid antenna navigation data;

[0035] - the vehicle is an underwater vehicle, and the antenna is a sonar antenna.

[0036] The invention also relates to a system for determining the navigation data of a vehicle, comprising a means for receiving navigation data from an inertial measurement unit and a processing module associated with an antenna and capable of implementing a method for determining the navigation data of a vehicle as defined above.

[0037] It also relates to a method of three-dimensional underwater target imaging using a multi-view sonar comprising the implementation of a method according to the above and a method of registering the views from the navigation data of the underwater vehicle from the first method.

[0038] The invention will become clearer upon reading the following description, given solely by way of non-limiting example, and made with reference to the drawings in which: Figure 1 is a perspective view of a vehicle comprising a system capable of implementing the method according to the invention to determine its navigation data, Figure 2 is a diagram of an antenna belonging to the vehicle of Figure 1 at two different times, introducing motion parameters;

[0039] Figure 3 is a diagram of the antenna in Figure 2 at two different times, introducing a concept of phase center;

[0040] Figure 4 is a diagram representing the antenna from Figure 3 and a target;

[0041] Figure 5 is a diagram representing two sets of phase centers of the antenna in Figure 3;

[0042] Figure 6 is a flowchart representing the different steps of the process according to the invention.

[0043] We consider a vehicle 1. Vehicle 1 is advantageously a submersible vehicle 1, as shown in Figure 1. In an alternative not shown, vehicle 1 is an airborne or space-borne platform.

[0044] The underwater vehicle 1 shown in Figure 1, when in navigation, is partially or totally submerged and navigates at a distance from a seabed 3.

[0045] The underwater vehicle 1 is used in the context of underwater exploration, wreck searches, or mine warfare, which encompasses all operations related to underwater mines. In particular, the underwater vehicle 1 is capable of detecting a target 5 encountered during its navigation, notably through the use of target imaging techniques.

[0046] The underwater vehicle 1 has an inertial navigation system 6, located inside the vehicle, and at least one antenna 7 positioned outside the underwater vehicle 1. It preferably has two antennas 7, located respectively on the port and starboard sides of the underwater vehicle 1. The underwater vehicle 1 also includes a processing module 9 associated with the antenna 7 and a computer 8 to which the inertial navigation system 6 and the processing module 9 are connected for processing the received signals.

[0047] The term "inertial unit" here refers to both a standalone inertial unit and an inertial unit already hybridized in a known way with external devices, for example with a Doppler log, a pressure sensor and a computer embedded in the inertial unit performing a fusion process of inertial and Doppler information.

[0048] The inertial navigation system provides known navigation data from the D0_INS inertial navigation system, including:

[0049] - The position of the underwater vehicle 1 in latitude, longitude and immersion;

[0050] - The speed of the underwater vehicle 1 in the same frame of reference as its position; and - The angles of heading i, of roll i <p et de tangage 6, représentés sur la figure 1, formant les attitudes du véhicule sous-marin 1.

[0051] As inherent to the operation of an inertial navigation system, the navigation data of the D0_INS inertial navigation system suffer from a cumulative error over time, referred to as bias in the rest of the description.

[0052] In the example in Figure 1, the antennas 7 are sonar antennas, preferably synthetic sonar antennas. In an alternative not shown, the antennas 7 are, for example, radar antennas.

[0053] The antennas 7 extend along the length of the underwater vehicle 1, substantially parallel to a principal axis X of the underwater vehicle 1 corresponding to a rectilinear advance of the underwater vehicle 1. Each antenna 7 consists of acoustic transmitters and receivers, equally distributed along an antenna axis f generally collinear with the principal axis X of advance of the underwater vehicle 1.

[0054] As is known, the transmission and reception of acoustic signals provides raw antenna data D0_SAS. Each antenna 7 is associated with a processing module 9, configured to determine antenna navigation data D1_SAS from the raw antenna data D0_SAS. In particular, as explained later in the description, the raw antenna data D0_SAS is used by the processing module 9 to estimate the displacement of the antenna 7, and therefore of the underwater vehicle 1, as well as the bearing Gt corresponding to the angle between the principal axis X and an axis between the underwater vehicle 1 and an external reference point P. Alternatively, the raw antenna data D0_SAS is received and processed by the computer 8.

[0055] Advantageously, antenna 7 has more than one bathymetric function, allowing a bathymetric capacity to be determined using, for example, an interferometric antenna.

[0056] Advantageously, antenna 7 has more than one so-called multi-aspect capability, allowing it to acquire several images along offset viewing axes.

[0057] Antenna 7 is also shown at different times in Figures 2 to 5, introducing various parameters useful for the description below.

[0058] The method for determining navigation data for the underwater vehicle 1, implemented by the computer 8 under the control of one or more suitable computer programs, uses a hybrid P2C2 algorithm. This algorithm can be seen as an improvement on a P2C2 algorithm using antenna data alone, which should be briefly described before describing the hybrid P2C2.

[0059] The following description refers to Figure 3. The sonar antenna 7 consists of a transmitter, denoted X. n the position at time t = nT Rof the emission of the nth impulse (with T R the pulse repetition period), and G receptors typically aligned and equally distributed at a position R 3 (1 < g < G). We consider a target located at a range r np = r0+ par of X n , with r0 a certain initial, constant and known range on the order of a few meters and Ar a certain distance step very small compared to the maximum sonar range cT R )2 (Ar is on the order of a few centimeters and CÏ R I2 on the order of a few hundred meters). During the round-trip flight time of the sonar pulse towards target 5, antenna 7 moves, undergoing a continuous translational and rotational motion, so that the receiver Gs of antenna 7 are no longer in the same position R 3 (1 < g < G), than at the date of issue, but at a position R 3 p , 1 < g < G (which depends on the distance r np + r 3 ptransmitter-target-g-th receiver and antenna movement parameters 7: the further away the target 5, the longer the flight time c(r np + r 3 p (The longer the time it takes for antenna 7 to move), the more time the signal has to move. Implicitly, the signal arrives at the g-th sensor of the receiving antenna 7 at a time t = nT R + c(r0+ pAr + r 3 p ^ = t np In the equations that follow, the index pair (n, p) implicitly corresponds to this date t np .

[0060] We note C 3 p a phase center of the g-th transmitter-receiver pair (

[0061]

[0062] X n , R^ p ). This phase center corresponds to a fictitious position of antenna 7 if it had not moved between transmission and reception. It can be shown that C 3 p is the midpoint of segment [X n R 3 p Implicitly, C3 p therefore depends on t np therefore of the distance to target 5 and of the movement of vehicle 1. However, to a first approximation, we can consider that at r np fixed, the {C 3 p} are aligned on a straight line parallel to the axis of antenna 7 at the average signal reception date, that is, with the points {R^ p}.

[0063] The image plane is now defined as follows, with reference to Figure 4. First, antenna 7 must be equipped with its position and attitude at time t. np , of an antenna marker

[0064]

[0065] (O np , p^, such that

[0066]

[0067] is along the longitudinal axis of antenna 7, e

[0068]

[0069] tp^ in a principal pointing direction essentially orthogonal to a surface of the antenna 7. We denote p^ the unit direction vector going from the origin O n from antenna 7 to target 5 at t np We then note

[0070]

[0071] the unit vector equal to the normalized version of the projection of p^ onto the axis (O np The plane defined by these two vectors

[0072]

[0073] p^ and passing through O, is the image plane corresponding to target 5. The angle of grazing y np of the image plane is such that r

[0074]

[0075] ^ = (0, cos y np ,sm y np Ÿ (knowledge of this grazing angle requires a measurement, classically carried out by interferometry).

[0076] Now considering two consecutive sonar pulses emitted at (n - 1)T R and nT RWe therefore have two sets of phase centers, shown in Figure 5, for the same target 5 located at a distance r n from the transmitter to the nth pulse. These two sets are denoted {

[0077]

[0078] C^_ l p} et { c np}' with 1 9 G - The P2C2 algorithm aims to determine, by correlation of received signals, the sub-antenna portions, that is to say (

[0079]

[0080] G™" p , G^_“ x p ) and (G™ n , G™ p x ) such as the signals received at the phase centers {

[0081]

[0082] c£_ l p , 1 < G™” p < g < G"f l x p < G}, {C° p , 1 < G n < g < G™ p x<G} correlate at best, which at the same time allows us to determine the relative position of these sub-antennas (at the phase center level), and the position of antenna 7 at the reception dates. In Figure 5, the index of the sub-antenna center at the reception of the n - 1 pulse is denoted gn-i, p , and its counterpart for r min, r max r min, ^max

[0083] the impulse n is denoted g^ p We have ~ 77-1 2 77-1 and g„ ~ —.

[0084]

[0085] The P2C2 algorithm provides an estimate L^,^,f^ of motion parameters L n ,p n > T by maximizing the correlation of the signals received between the two sub-antennas <rS 1 cmin rmaz r\ Çr9 -i rmin n cvn.ax ■ tC n _i p , i S — 9 — ' J nl,p — — ' J np — 9 — ^np — with.

[0086]

[0087] • ^p defined as above, and more formally as the vector orienting the bisector axis of the angle ^- Rn-i, P R ni,p> R n,p R n,p). located in the image plane and pointing towards the front of antenna 7, that is, roughly in the direction of translation of the antenna: ££ p is therefore a kind of average

[0088]

[0089] dec n-liP And;

[0090] • r^p the vector orthogonal to £ p , in the image plane, and oriented towards the transmission direction of antenna 7 (to starboard); r^ p is therefore a kind of average of r n-l p And

[0091]

[0092] ;

[0093] II Ÿ A — n -iA n -tK n _i p I bSnp _

[0094] • L m rtp i = [J n r),' a difference in abscissa along the main axis ■> rip between

[0095]

[0096] cos — 2 —

[0097] two sensors maximizing the cross-correlation between two successive pings;

[0098] II Ÿ A —nl A xn"+^ K R fl n "-- li 1 P - p K n,p I i ​​T'n 5 "p

[0099] • z np

[0100]

[0101] = - - - - - — + un time gap, cr„ p corresponding to a difference in ordinate along the r-axis p between two sensors maximizing the cross-correlation between two successive pings

[0102]

[0103] and where is an additional near-field term whose detail is not useful for understanding the invention;

[0104] •?„ pthe rotation allowing the transition from the orientation of the physical antenna 7 at the reception of ping n - 1, to the orientation of the physical antenna 7 at the reception of ping n in the image plane defined as which also corresponds to the rotational movement to go from the axis of {C^_1}, to that of {c^ p}.

[0105] L motion parameters n ,p n , t n are represented in Figure 2. Formally, this P2C2 process can be seen as the resolution of:(L n , / ? n , T n ) = arg max JJ s n-1 ^,z)s n ( - L, t - T - ( +. ) d^dz (E1)

[0106] with s n The signal received for pulse n and the signal received for pulse n - 1. Between the reception times of two pulses n and n + 1 directed at a target 5 located at a reference range r np , vehicle 1 undergoes a rotation increment denoted £l np= Ç^^,np> ^p,np> ^,np ) (components in radians) in the antenna frame (O^ P > ^n P > Pn p > ^n P The rotation operator, denoted H, transforms the coordinates of an arbitrary vector x expressed in the coordinate system.

[0107]

[0108] (O„_1, in the coordinates in the frame (O n ,, n, n), is H(x) = exp(fl np ')x with, to the first order, H (%) « % + £l np xx, or in matrix form: ( 1 <*>p,np \

[0109] 1 ^p,np IX

[0110]

[0111] ^p,np <*>p,np 1 /

[0112] The angle p n is therefore formally defined as follows:

[0113]

[0114] Pn = (H (fn-1) ^nl)- ^np ~ (Pn X fn-l)-7np

[0115] By asking n-lp = (1,0,0) it comes with an excellent approximation:

[0116] P

[0117]

[0118] np ^Ç,np- CO^ Yrip ^p,np- ^^Ynp ffîPnp' ^np') (E2) The angle pP p is counted positively in the trigonometric (resp. antitrigonometric) direction for a sighting to port (resp. starboard). This equation (E2) links / ? J to two gyrometric parameters which are ü)^ n and a) pn Similarly, it can be shown that there is a link between L np ,z np , through non-linear equations, theoretically linking L np ,z np to the inertial rotation increments denoted £l np (components in radians) but also speed

[0119]

[0120] = IT

[0121]

[0122] dv ^ np , ôv p np , ôv^ np ^ in the antenna O frame npt ^ npt p npt np ').

[0123] Lnp fzPpnp'^np' ^^np) (E3)

[0124] ^np fr^np' ^-np' ^Kip) (E4)

[0125] The exact expression of (E3) and (E4) is complex but can be determined by elementary geometric considerations known to those skilled in the art. Their exact form is unnecessary for understanding the invention. These equations are assumed to be numerically invertible by various algorithms known to those skilled in the art.

[0126] Furthermore, it is clear that these equations (E2, but also E3, E4) depend on the index pair n,p, meaning that the estimation process is performed densely over time t np = nT R + c(r0+ par + r^ p ), with p varying to sweep all possible target ranges. The data arrival rate L np ,p np ,z np is therefore much greater than the repetition period of the T pulses R (several tens of hertz).

[0127] In the case of a multi-aspect sonar antenna and with the notations introduced previously, the processing module 9 associated with the antenna 7 allows the estimation of two triplets

[0128]

[0129] on two image reference points offset from the main image reference point and generate sonar images in the associated reference points.

[0130] The "Displacement of Phase Center Algorithm" (DPCA) method is a special case of P2C2 and proceeds by approximating equation (E1) by assuming that an estimated L^ p of L np is known because it is estimated elsewhere as for a correlation log; that the spatio-temporal correlation is achieved by several temporal cross-correlations of the different sensors composing the antenna, and that the pair

[0131]

[0132] is obtained by linear regression.

[0133] The hybrid P2C2 estimates directly

[0134]

[0135] using gyrometric data. The rotation increment is measured in the inertial measurement unit (IMU) frame 6, then transformed into the antenna frame.

[0136]

[0137] (O„, so that the coordinates of

[0138]

[0139] there are

[0140]

[0141] >n ,(o pn , cn (in radians). We then estimate directly

[0142]

[0143] through equation (E2). The rest of the algorithm then consists of estimating V n And

[0144]

[0145] by a simplification of (E1) which can be written as:

[0146] = max - (j + ^^dÇdt (E5)

[0147]

[0148] One difficulty among others in applying (E2) and (E5) arises from the fact that the antenna data and the inertial measurement unit data are not always perfectly synchronized in time. Therefore, the estimated

[0149]

[0150] The measurement used in the hybrid P2C2 can be erroneous because it can be delayed relative to the actual movement. This delay on p n then also induces an additional error on due to the coupling induced by (E5).

[0151] The method for determining navigation data of the underwater vehicle 1 according to the invention, which makes it possible to overcome the aforementioned limitations, is detailed below with reference to Figure 6.

[0152] Initially, the antenna data D0_SAS is converted into antenna navigation data D1_SAS for each point 5 in the scene. This conversion 20 is advantageously performed using an algorithm 201 for estimating antenna displacement between two pings, based on a classic (non-hybrid) inter-ping correlation of the P2C2 (ping-to-ping cross correlation) type described by the aforementioned equation (E1). It is advantageously complemented by an algorithm 202 providing the bathymetric capacity in order to determine the grazing angle y n at each point 5 of the scene and / or the multi-aspect capability. The D1_SAS sonar navigation data for target 5 is a series that includes, for each temporal recurrence of algorithm 201:

[0153] > _ 201 - *

[0154] - a parameter L np difference in abscissa along the antenna axis

[0155]

[0156] between two sensors maximizing the cross-correlation between two successive pings;

[0157] - a parameter T^, 201 propagation delay which temporally represents the displacement of a sensor along the principal axis in the image plane

[0158]

[0159] between two successive pings; and

[0160] > _ 201

[0161] - a parameter p np rotation of the relative bearing of the target in the image plane between two successive pings, induced by the rotation of the antenna.

[0162] These parameters determine, for each recurrence of algorithm 201, the estimates of the actual displacement L np , t np , p npof the phase center in the image plane from antenna measurements alone. This step does not involve the D0_INS inertial navigation data in order to guarantee the independence between the D1_SAS antenna navigation data and the DOJNS inertial navigation data.

[0163] In a second step, a time alignment step 22 subtracts a time offset DT from the inertial navigation data DOJNS. This offset may exist between the navigation data of the inertial navigation system D1JNS and the navigation data of the antenna D1_SAS. This time offset DT is of hardware origin and can vary over time.

[0164] The time alignment step 22 advantageously includes a step 221 of expressing the inertial navigation data in an antenna navigation data space by calculating equations (E3) and (E4), a step 222 of estimating the time offset and a step 223 of subtracting the time offset.

[0165] In step 221, the DOJNS inertial navigation data is transformed (rotation increments into radians (^ >np , p np, ^np et 8v speed increments np 0.6v p np ,6v^ np (in meters per second) expressed in the coordinate system

[0166]

[0167] (O np , ~p^>, X^p) of antenna 7, so as to calculate, for a particular point of the scene located at a distance r np = r0 + pr of the transmitter at the date of emission of pulse n, of the parameters L

[0168]

[0169] ^p 221 ', f^ p ~ 221 etc p 221 which are estimates of L n , z n , p n from inertial measurements alone, using equations (E2, E3 and E4). We then obtain modified inertial navigation data D1_INS.

[0170] Advantageously, the D1_SAS L^ antenna navigation data 201 ', f^ 201 and P^ 201 and the modified inertial navigation data D1_INS L^ 221 ', f^ 221 and > _ 221

[0171] p n are then filtered by a bandpass filter with a cutoff frequency lower than 1 / (2T R ), WHERE R is the period of repetition of the pulses.

[0172] During step 222 of the time lag estimation, the time lag DT is estimated by correlation between the parameters

[0173]

[0174] (Lnp , T np , p np ) D1_SAS antenna navigation data and parameters (

[0175]

[0176] L^, 221 ', T^, 221 , P^ p 221 ) modified D1JNS inertial navigation data. This correlation step consists of

[0177]

[0178] find 8p such that L n p-Sp , T n: P-5p , n p-3p best corresponds to

[0179]

[0180] (L np - p 221 , / ^p 221 ). This term Sp corresponds to the time gap DT = ^^ to be estimated, which maximizes the inter-correlation between the parameters

[0181]

[0182] (L n , z n , p n ) and (L n , z n , — 221„

[0183] Pn )■

[0184] Finally, step 223, which subtracts the time-shift data, acts on the D0JNS inertial navigation data by temporally shifting it by the previously estimated time-shift value DT, providing data of _ 223 77Q > _ 223

[0185] D2JNS L inertial navigation system recalibrated np ', Tp p , p np We denote D21_INS the recalibrated inertial navigation unit velocity data and D22_INS the recalibrated inertial navigation unit attitude data included in the recalibrated inertial navigation unit data D2JNS.

[0186] In a third step, the antenna data D0_SAS are converted into hybrid antenna navigation data D2_SAS. This conversion 24 is performed using a so-called hybrid P2C2 antenna displacement estimation algorithm 241, similar to antenna displacement estimation algorithm 201, in which the recalibrated inertial measurement unit attitude data D22JNS and the target's crossing angle estimated by interferometry are also incorporated; this hybrid P2C2 algorithm is explained 223 by the aforementioned equation (E5), using the term for equations (E3, E4, E5). np The D2_SAS hybrid antenna navigation data, corresponding to the data (L„ ',fp, n) are more accurate than the D1_SAS antenna navigation data with respect to the rotation of antenna 7 between two successive pings. They are also advantageously more accurate than the direct application of equations (E2) and (E3) because the time offset DT has been corrected.

[0187] As before, the 24 conversion is advantageously complemented by an algorithm 242 providing bathymetric and / or multi-aspect capability.

[0188] In a fourth step, a bias correction step 26 corrects the aforementioned bias present in the recalibrated inertial navigation data D2_l NS, using the hybrid antenna navigation data D2_SAS and the recalibrated inertial navigation data D2JNS. This bias correction step 26 preferably consists of at least one step 261 for expressing the hybrid antenna navigation data in the inertial navigation data space, and a step 262 for estimating and removing the bias.

[0189] In the following description and without limitation, we consider that the bias is a speed bias, and that the bias correction is therefore done on speed data.

[0190] During step 261 of expressing hybrid antenna navigation data in the inertial navigation data space, hybrid antenna velocity data is calculated from the hybrid antenna navigation data D2_SAS, before being expressed in terms of the recalibrated inertial navigation data D21JNS, by a change of reference frame known to those skilled in the art. Formally, the velocity increments are recovered

[0191]

[0192] from an inversion of equations E3, E4, E5 evaluated for the D2_SAS hybrid antenna navigation data corresponding to _ 241 _ _ 241

[0193] (L„ ',f^, n )■ The algorithm produces modified hybrid antenna navigation data D3_SAS, here consisting of modified hybrid antenna velocity data Al^ip 61 , which are advantageously filtered at the end of step 261.

[0194] Modified hybrid antenna navigation data D3_SAS, corresponding to AI^ 2 P 61 , are then used in step 262 of bias estimation and removal. The velocity bias is estimated using an averaging operation between the modified hybrid antenna velocity data D3_SAS corresponding to Al^ 2 p 61 and the D21_INS recalibrated inertial navigation unit velocity data. The bias thus estimated is then removed from the D21_INS recalibrated inertial navigation unit velocity data, providing D3_INS recalibrated unbiased navigation unit data, here consisting of unbiased recalibrated inertial navigation unit velocity data.

[0195] Advantageously, in a final stage, a trajectory reconstruction step 28 estimates a D4_INS trajectory of the underwater vehicle 1 by time integration of the recalibrated unbiased D3_INS inertial measurement unit velocity data.

[0196] Whether the corrected bias is a velocity bias or not, step 26 provides D3JNS recalibrated inertial navigation data which can either form the navigation data of the underwater vehicle 1 in themselves, or be subjected to a transformation during step 28, which is not necessarily a time integration as presented in the case of the velocity bias, allowing the recovery of the desired underwater vehicle navigation data, generally the trajectory of the underwater vehicle 1. The precise determination of the navigation data of the underwater vehicle 1, in accordance with the process which has just been described, allows for example to carry out a precise automatic recalibration of these different views, acquired by the antenna 7 during navigation at different points of view with respect to the target 5, in order to produce a 3D image of the target 5 by tomography.

Claims

DEMANDS 1. A method for determining the navigation data of a vehicle (1) having an inertial measurement unit (6) providing inertial measurement unit navigation data (D0_INS), an antenna (7) providing raw antenna data (D0_SAS), and a processing module (9) associated with the antenna (7) and configured to determine antenna navigation data (D1_SAS) from the raw antenna data (D0_SAS), the method comprising a correction of the inertial measurement unit navigation data (D0_INS) from the antenna navigation data (D1_SAS), characterized in that the method comprises: - a time alignment step (22), during which the time offset (DT) of the inertial navigation data (DOJNS) relative to the antenna navigation data (D1_SAS) is corrected, providing time-aligned inertial navigation data (D2JNS); - a calculation step (24) of hybrid antenna navigation data (D2_SAS), from the antenna (7) and the recalibrated inertial navigation data (D2JNS); and - a bias correction step (26), during which an error on the recalibrated inertial navigation data (D2JNS) inherent in the inertial navigation system, called bias, is corrected on the basis of the hybrid antenna navigation data (D2_SAS), providing recalibrated and debiased inertial navigation data (D3JNS), forming the vehicle navigation data (1).

2. A method for determining the navigation data of a vehicle (1) according to claim 1, wherein: - the corrected bias of the recalibrated inertial navigation data (D2JNS) is a velocity bias; - the recalibrated and debiased inertial navigation data (D3 NS) include recalibrated and debiased inertial velocity data; and - the method includes a trajectory reconstruction step (28) during which a vehicle trajectory (1) is calculated by time integration of the recalibrated and debiased inertial velocity data (D3_l NS), providing the vehicle trajectory (D4JNS), forming the vehicle navigation data (1).

3. Method for determining the navigation data of a vehicle (1) according to claim 1 or 2, wherein the antenna (7) is a synthetic antenna sonar antenna and the processing module (9) determines the antenna navigation data (D1_SAS) by means of an antenna displacement estimation algorithm by correlation (201).

4. A method for determining the navigation data of a vehicle (1) according to claim 3, wherein: - Recalibrated inertial navigation data (D2JNS) includes recalibrated attitude data (D22JNS); and - Hybrid antenna navigation data (D2_SAS) are obtained by a hybrid antenna displacement estimation algorithm (241) involving recalibrated attitude data (D22JNS).

5. A method for determining the navigation data of a vehicle (1) according to any one of the preceding claims, wherein the time-recalibration step (22) comprises: - an expression (221) of the inertial navigation data (D0_INS) in an antenna navigation data space (D1_SAS), providing modified inertial navigation data (D1_INS); - an estimate (222) of the time lag (DT) by correlation between the modified inertial navigation data (D1_INS) and the antenna navigation data (D1_SAS); and - a subtraction of the time offset (DT) in the inertial navigation data (DOJNS), providing the recalibrated inertial navigation data (D2JNS).

6. Method for determining the navigation data of a vehicle (1) according to claim 5, wherein the time recalibration step (22) includes filtering the inertial navigation data (DOJNS), the modified inertial navigation data (D1 NS) and / or the antenna navigation data (D1_SAS).

7. Method for determining the navigation data of a vehicle (1) according to any one of the preceding claims, wherein the bias removal step (26) comprises:- an expression (261) of the hybrid antenna navigation data (D2_SAS) in the inertial navigation data space (D2JNS), providing modified hybrid antenna navigation data (D3_SAS); - an estimation of the bias based on modified hybrid antenna navigation data (D3_SAS) and recalibrated inertial navigation data (D2JNS); and - a removal of bias in the recalibrated inertial navigation data (D2JNS), providing recalibrated and unbiased inertial navigation data (D3JNS).

8. Method for determining the navigation data of a vehicle (1) according to claim 7, wherein the bias removal step (26) includes filtering the hybrid antenna navigation data (D2_SAS) or the modified hybrid antenna navigation data (D3_SAS).

9. A method according to any one of the preceding claims, wherein the vehicle (1) is an underwater vehicle, and the antenna (7) is a sonar antenna.

10. System (8) for determining the navigation data of a vehicle (1), comprising a means for receiving navigation data from an inertial measurement unit (6) and a processing module (9) associated with an antenna (7) and capable of implementing a method for determining the navigation data of a vehicle (1) according to one of the preceding claims.

11. Method for imaging underwater target in three dimensions using a multi-view sonar comprising the implementation of a method according to claim 9 and a method for registering the views from the navigation data of the underwater vehicle (1) from the first method.

Images