RU2348903C1 - Method of determination of navigating parameters by gimballess inertial navigating system - Google Patents

Abstract

FIELD: physics, measuring.

SUBSTANCE: invention concerns data processing in gimballess inertial navigating systems (GINS). Yield parallel evaluation of plurality of matrixes of direction cosines between the bound and navigating frames on the same indications of accelerometres and angular velocity sensor AVS. For each calculated matrix of direction cosines, determine the natural navigating parameters having various frequency character of errors. Thus each of the calculated matrixes has the individual law of guidance, and errors of their evaluation also have a various distribution of modes of frequency depending on modes of a motion of the carrier (the strong maneuver, feeble maneuvering and a cruiser motion without maneuvering) on which the GINS is mounted. Navigating parameters for each calculated matrix of direction cosines submit on inlets of the block of the master filter formativing an optimum combination of navigating solutions depending on the frequency range of their errors, and also from parameters of a motion of the carrier.

EFFECT: increase of accuracy of notation of the output GINS navigating parametres.

12 dwg

Description

Technical field

The invention relates to the field of data processing in strapdown inertial navigation systems (SINS).

State of the art

One of the closest analogues of the present invention is described in the patent of Russian Federation No. 2272995 (2006) "Method for the generation of navigation parameters and vertical location." This analogue was not selected as a prototype due to the almost complete application of this method only to platform, and not to strapdown systems. The only remark regarding strapdown systems in the proposed analogue is the phrase that this idea can be implemented in strapdown technology, however, nothing concrete and real for this application was given. Confirmation of this is also the material of the author of this invention in his other withdrawn application No. 2004129484 for the invention “A method for generating navigation parameters and elevation verticals of a strapdown inertial system”, where under the guise of the SINS operation the operation of two structurally identical stabilized gyro platforms is actually described.

In fact, real applications of the method for generating navigation parameters in platform and strapdown versions have a significant difference. The most important disadvantage of this method is that the damping of the gyro platform is carried out in the absence of ballistic deviations. In other words, in the dynamic movement (for example, in various maneuvers of the aircraft), the proposed correction is not used, otherwise the platform will be disturbed due to acceleration of the object. Hence, for example, for coarse sensitive elements (gyroscope drift of the order of 0.1 deg / s) in strapdown systems, this method is generally not applicable, since during the execution of the maneuver, the SINS computing platform will diverge at large angles with respect to the true navigation one.

The closest analogue of the present invention can be considered the method according to US patent No. 54422817 (1995), which consists in the fact that the signals of accelerometers and gyroscopes are used to calculate orientation angles by calculating the matrix of guiding cosines between the connected and navigation coordinate systems. In this case, the error of the accelerometer acceleration signals is compensated (the American term “sculling”), due to the fact that the associated coordinate system has an angular velocity with respect to the inertial system. After that, the accelerations are recalculated from the associated coordinate system to the navigation system and their integration in order to determine the current speeds and coordinate increments.

The indicated method has a traditional drawback of any inertial navigation systems, namely, that errors in determining the orientation are uniquely determined by the accuracy of the angular velocity sensors (ACS) and accelerometers, while errors in determining the coordinates grow over time in proportion to the speed of the ACS drift. This drawback, especially for relatively coarse sensitive elements (TLS and accelerometers at the level of 0.1 deg / s and 10 ^-3 g, respectively) leads to the fact that after some time (this depends on the accuracy of the sensitive elements), the autonomous operation calculated on board the carrier navigation the system will differ from the true navigation system by large angles, which in fact leads to inoperability of the navigation method. The limitation of the traditional method is that inertial system errors are inseparable from the useful signal (i.e. true navigation parameters). This conclusion is based on the fact that inertial system errors are of a low-frequency nature (the so-called Schuler oscillations), which are inseparable from the actual movements of the carrier on which the system is installed.

It should be noted that the traditional method of calculating navigation parameters does not depend on the parameters of the object’s movement, that is, the accuracy of the system (in a first approximation) does not depend on what parameters the carrier moves with, that is, whether it maneuvers or moves at a constant speed (cruising mode) . Errors of the traditional method depend solely on the accuracy of the sensitive elements and are not corrected in any way by internal connections. Disclosure of invention

The technical problem is to significantly increase the accuracy of the calculation of the output navigation parameters SINS (orientation angles, linear speeds and location coordinates).

The technical result is achieved in that the method for determining the navigation parameters of the SINS is based on using the signals of the accelerometer unit and gyroscopic TLS by calculating the matrix of guiding cosines between the connected and navigation coordinate systems, compensating for the errors of the accelerometers by taking into account the rotation of the connected system, and recalculating the readings of the accelerometers from the connected to the navigation system coordinates and their integration to calculate current speeds and increments of coordinates, and differs in that DYT parallel calculation aggregate matrix of direction cosines between bound and navigational coordinate systems for the same indications accelerometers and CRS. For each computed matrix of guide cosines, their own navigation parameters are determined that have different frequency errors. Moreover, each of the calculated matrices has an individual control law (correction, damping), and their calculation errors also have a different frequency spectrum depending on the modes of movement of the medium on which the SINS is installed. The navigation parameters for each calculated matrix of guide cosines are fed to the inputs of the master filter unit, which forms the optimal combination of navigation solutions depending on the frequency range of their errors, as well as on the parameters of the carrier’s movement.

In contrast to the known method of calculating navigation parameters, not one matrix of directing cosines between the connected and navigation coordinate system is calculated at the same time, but several. In this case, the calculation of these matrices is carried out in parallel according to the same readings of accelerometers and TLS and each of the calculated matrices has its own error correction (damping) law, so that their errors have a different frequency spectrum, depending both on the parameters of the correction laws and on the motion parameters the carrier on which the SINS is installed. For each of the calculated matrices, their own navigation parameters are determined. The navigation parameters then go to the so-called master filter, which forms the optimal combination of navigation solutions due to the different frequency spectrum of errors of each platform and the laws of changes in orientation angles and accelerations. In other words, in one on-board computer they implement not one system for calculating navigation parameters, but a set of systems, but at the same time the information source for them is the same, i.e. based on measuring only one block of sensitive elements (accelerometers and TLS). The master filter deals with the totality of the solutions of each of the systems and forms a single solution that is optimal (best in the rms sense) for a given flight mode and the entire frequency range of errors. Each of the computing platforms is perturbed in one way or another, but the use of a master filter allows you to choose from particular solutions general, having undisturbed parts of the solutions in its composition.

Indeed, the use of various types of radial corrections leads to the perturbation of the computing platform in maneuvers and the unperturbed part in cruise flight. Since “fast maneuvers” have a significantly short time compared to low-maneuverable movement, low frequencies (when the object does not maneuver) are unperturbed, while high frequencies of orientation parameters are perturbed due to the presence of significant accelerations of the carrier. When using integral correction according to the output indications of the adaptive Kalman filter, the system in maneuvers is unperturbed, while for weakly maneuverable movement it has significant low-frequency errors. The master filter separates the perturbed and non-perturbed parts of the solutions of each of the computing platforms and forms a general solution that is optimal (in the rms sense) for the entire frequency range of the navigation parameters.

Unlike the aforementioned method according to RF patent No. 2272995, they do not use the difference of the same name information, but their individual solutions for each of all computing platforms, and their combinations in the master filter are combined both on the basis of frequency characteristics of errors and on the basis of motion parameters of the SINS carrier . List of drawings

Figure 1. A block diagram of a traditional method for calculating the SINS navigation parameters in a generalized form.

Figure 2. The block diagram of the proposed method for calculating the navigation parameters SINS in a generalized form.

Figure 3, 4, 5. Examples of block diagrams of the implementation of three computing platforms in a generalized form.

6. An example of a block diagram of the implementation of a master filter in a generalized form.

7, 8, 9, 10. Comparison of the frequency spectra of the pitch angle over three computing platforms and from the output of the master filter implemented in the compact CompaNav-2 SINS using micromechanical gyroscopes and accelerometers and the reference spectrum obtained from high-precision laser SINS "I-42."

11, 12. Comparison of the readings of the pitch and roll parameters obtained by the proposed and traditional methods, with similar parameters of the reference system of laser SINS "I-42".

The implementation of the invention

In Fig. 1 ... 6, the numbers indicate the blocks in general form: blocks 1, 2, 3 of the computing platforms, the master filter 4, block 5 of the sensitive elements (accelerometers and DOSs); block 56 compensation of errors of accelerometers by taking into account the rotation of the associated coordinate system ("sculling"); block 6 recalculation of accelerations associated with the navigation coordinate system; unit 7 for calculating speeds and coordinates; block 8 calculating the matrix of guide cosines (between the coupled and the inertial system); block 9 calculating the gain K _{1 of the} first computing platform; block 10 calculating the gain K ₂ , K _{3 of the} second computing platform; an integration unit 11 of an integral branch of the second computing platform 2; Kalman adaptive filter 12 of the third computing platform; Kalman filters FC1 (13, 14) and FC2 (15, 16) in the implementation scheme of the master filter 4 of three computing platforms.

The traditional and proposed methods for calculating the navigation parameters of the SINS are summarized in figures 1 and 2.

In the scheme of the computing platform of the traditional method, the signals of the accelerometers from block 5 through block 56 are fed to block 6, from which the signals are sent to block 7. At block 8, signals of the angular velocities of coordinate systems are received: connected from block 5 and navigation from block 7. The outputs of block 8 are orientation angles.

In the traditional method, after the error compensation (block 56) of the accelerometers (from block 5) caused by the rotation of the associated coordinate system, the accelerations are recalculated from the coupled to the navigation system (block 6). In this case, the matrix of guide cosines between the connected and navigation coordinate systems (block 8) is calculated (usually through quaternion transforms) according to the angular velocities of the connected and navigation coordinate systems. In this case, the angular velocities of the coupled system directly come from block 5 of the sensor sensitive elements, while the angular velocities of the navigation system are calculated by integrating the accelerations of the navigation system (block 7). The second integration of these accelerations provides the determination of coordinate increments (also block 7).

This method has a traditional drawback due to the dependence of the accuracy of the system on the errors of the sensitive elements. At the same time, system errors are on the stability boundary, which is unacceptable, especially when using “coarse” sensitive elements. In the proposed method, several computational schemes (platform blocks 1, 2, 3) are simultaneously implemented for determining navigation parameters (based on the signals of accelerometers and TLSs of block 5). Moreover, each of the computing circuits (or “computing platforms”) has its own control law, which on the one hand ensures the stability of the solution (without using block 56), but is perturbed depending on the mode of movement of the medium. To reduce the perturbation effect, it is necessary to choose the parameters of the control law depending on the mode of carrier movement (rate of change of its course, roll angles and accelerations). In addition, each of the control laws provides its own frequency range for changing errors of navigation parameters. The master filter (block 4) provides a combination of navigation solutions received from each of the computing platforms (1, 2, 3), based on the principle of frequency separation of individual solutions, so that the general solution provides the best system readings in the entire frequency range.

Depending on the sets of specific blocks for the implementation of the proposed method, a significant increase in the accuracy of all navigation parameters (orientation angles, speeds, coordinates) is possible.

An example of the method

The example considers the implementation of the method, which allows to increase the accuracy of determining orientation angles (pitch, roll, course).

As a system that implements the proposed method, an inertial system can be used, each of the three computing platforms (1, 2, 3) of which has control laws shown in a generalized form in Figs. 3, 4, 5. There is no block 56 between blocks 5 and 6, but there are blocks for the implementation of the laws of control (correction) of signals from block 6 to block 8.

Figure 3 presents the traditional radial correction of the first computing platform according to the accelerometer signals: K ₁ · a _x , K ₁ · a _y with a variable (through block 9) coefficient K ₁ (depending on the parameters of the carrier’s movement), which are fed to the computational model ( the so-called “image”) of the gyro platform moment sensor (block 8 for calculating the matrix of guide cosines). At the same time, when choosing rigid feedback on accelerations (large coefficient K ₁ ), the first platform will have accurate readings during cruise flight and will have large errors in maneuvers (platform perturbations in accelerations). In the frequency range, this platform should be used exclusively for low frequencies of change of orientation angles.

When choosing a radial-integral correction for accelerations (second platform, Figure 4, blocks 10, 11) type:

U ₁ = K ₃ · δV _y ;

U ₂ = K ₃ · δV _x ,

Where

;

;

,

,

where U ₁ , U ₂ - control signals of the computing platform;

and _x , and _y are the acceleration signals of the object in the horizontal plane;

and with relatively small values of the coefficients K _{2 of the} radial and K ₃ integral (block 11) branches, the second platform will provide the best readings for the average frequency with low carrier maneuvering.

In the third platform (Figure 5), when using accelerometer correction through an adaptive Kalman filter (block 12) with a gain of K, a “weak” control (K of the order of 1 · 10 ^-6 ) is used to damp errors by the output of the Kalman filter. At the same time, the third platform provides high frequencies with strong maneuvers of the aircraft. As measurements for the adaptive Kalman filter, the readings of accelerometers in the navigation coordinate system are used:

z _x = -gF _x + a _x ;

z _y = gФ _y + a _y ,

where Ф _x , Ф _у - orientation errors of the computing platform,

and _x , a _y are the accelerations of the navigation coordinate system.

Adaptive Kalman filter is implemented as follows:

;

;

;

;

P _{K / K-1} = FR _K-1 Ф ^Т + GQG ^T ;

>порога, R_K=S·R_ном. if

> threshold, R _K = S · R _nom.

K _K = P _{K / K-1} H ^T [HP _{K / K-1} H ^T + R _K ] ^-1 ;

P _K = (IK _K H) · P _{K / K-1} ,

where f is the transition matrix of the model,

G is the input matrix

- оценка вектора состояния (в данном случае - оценка ошибок ориентации Ф_x, Ф_у),

- assessment of the state vector (in this case, the estimation of orientation errors Ф _x , Ф _у ),

P is the covariance matrix of estimation errors,

H - matrix of measurements,

K _K is the filter gain matrix,

Q - covariance matrix of input noise,

R _K - covariance matrix of measuring noise,

S is the gain of the noise matrix.

The difference between this adaptive Kalman filter and the traditional one is the adaptive adjustment of the measuring noise matrix R _K depending on the square of the value of the updated process, depending on the real estimation errors. Thus, the task of the Kalman filter is to filter out accelerations and evaluate the orientation errors of the computing platform, which ensures a low degree of SINS perturbation in strong maneuvers.

It should be noted that the coefficients K ₁ , K ₂ , K ₃ are not constant values, but depend on the motion parameters of the carrier, i.e. from modes: strong maneuver; weak maneuvering; cruising without maneuvering. In other words, they implement the mathematical functions of the coefficients K _i = f (γ, Н _ψ , а), where γ is the roll, H _ψ is the derivative of the course, and acceleration of the support in the horizon plane. In this case, the equation of orientation errors has the form:

- для первой платформы;

- for the first platform;

- для второй платформы;

- for the second platform;

- для третьей платформы.

- for the third platform.

Here Ф _{N, E} are the SINS orientation errors;

- дрейфы гироскопов;

- drifts of gyroscopes;

a - acceleration of the carrier in the horizon;

- ошибки оценивания, допускаемые адаптивным фильтром Калмана.

- estimation errors made by the adaptive Kalman filter.

From the above equations it follows that when choosing different values of the coefficients K, the influence of the gyro drift will be different (the higher the gain in feedback, the less the influence of the gyro drift on orientation errors).

The master filter 4 carries out frequency separation of the readings of all three platforms 1, 2, 3 and generates output readings, which are the optimal solution for the entire frequency range and for various movements of the aircraft. Frequency separation of signals is based on optimal filtering (Kalman filter) of the readings of all platforms, which is more specifically discussed below.

An example of a block diagram of the implementation of the master filter 4 in a generalized form is presented in Fig.6.

Schematically, the initial signals of the three platforms are supplied, respectively: 1st — to the block 13 FC1, 2nd — to the block 14 FC1, 3rd — to the block 15 FC2, as well as the indicated initial signals of the 2nd and 3rd platforms on two separate subtracting devices for the corresponding subtraction of the output signals of block 14 FC1 and block 15 FC2 from them. The output of the first specified subtractor is then summed with the output signal of block 13 FC1 and served on block 16 FC2, the output of which is summed with the output of the second specified subtractor.

,

низкочастотной составляющей углов ориентации. При этом первая платформа обеспечивает точную оценку

, тогда как низкочастотная составляющая второй платформы имеет уходы (за счет нежесткой обратной связи). После чего в показаниях второй платформы компенсируют неточную низкочастотную составляющую

и заменяют в сигнале ϑ₂ точной составляющей

. Таким образом, сигнал

содержит низкочастотную составляющую с первой платформы, а средние и высокие частоты определяются второй платформой.Navigation parameters, such as orientation angles, ϑ ₁ , ϑ ₂ from the first and second platforms are fed to the Kalman filter (FC1, blocks 13, 14). This Kalman filter as a model of a useful signal selects its low-frequency component. Moreover, all Kalman filter parameters (object model and noise matrix) are the same for the first and second platforms. As a result, there are estimates at the output of this filter

,

low-frequency component of the orientation angles. At the same time, the first platform provides an accurate assessment

, while the low-frequency component of the second platform has cares (due to non-rigid feedback). Then, in the readings of the second platform, the inaccurate low-frequency component is compensated

and replace the exact component in the signal ϑ ₂

. So the signal

contains a low-frequency component from the first platform, and medium and high frequencies are determined by the second platform.

с измерениями от третьей платформы. При этом параметры фильтра Калмана (ФК2, блоки 15, 16) выбирают таким образом, чтобы его оценки содержали средние и низкие частоты сигнала.Similar transformations form for a signal combination

with measurements from the third platform. At the same time, the parameters of the Kalman filter (FC2, blocks 15, 16) are chosen so that its estimates contain the medium and low frequencies of the signal.

After a combination with estimates of FC2, a signal ϑ ^{f is} formed from the third platform, which has optimal parameters for the entire frequency range (low, medium and high frequencies).

The proposed method and its implementing system are used in the Russian commercially available small-sized SINS CompaNav-2 (TeKnol LLC) on micromechanical accelerometers and gyroscopes.

A comparison was made of the readings of the simultaneously operating SINS CompaNav-2 and the reference laser SINS I-42 on a carrier - the Yak-18 aircraft. Comparison shows that the proposed solution in comparison with the traditional one provides a critical increase in accuracy. The test data of the system is officially confirmed by the Test Act of the LII named after M.M.Gromova (Zhukovsky, Moscow region).

For visual confirmation, Figures 7, 8, 9, 10 show the frequency spectra of measurements of various computing platforms and from the output of the master filter in comparison with the spectrum of the reference signal obtained in real tests of KompaNav-2 on the Yak-18 aircraft. From a comparison of the spectra given, it is obvious that the first platform provides accurate low frequencies, the second - medium, while the third provides the most adequate high frequencies of orientation angles. The total frequency spectrum of the pitch angle from the output of the master filter and the frequency spectrum of the output reference signal are almost the same (see Figure 10).

A comparison of the indications of temporary realizations in real tests of the parameters of the pitch and roll orientation angles of such a system as compared to the traditional design and the signals of the reference system are shown in Figs. 11, 12. The proposed method improves the accuracy of calculating the orientation angles up to 5-6 times (in the rms sense) by Compared with traditional, which is determined by the difference between the signals of KompaNav-2 and the standard and the traditional method and standard.

As for improving the accuracy of calculating the parameters of speeds and coordinates, it is possible to use similar computing platforms with similar control laws (correction, damping) and a similar master filter to further improve the accuracy of the SINS in terms of calculating speeds and coordinates. Also, the correction of orientation errors in the matrix of guide cosines automatically leads to an increase in the accuracy of their calculation, that is, the use of a matrix of guide cosines corrected according to the readings of the master filter reduces the errors in determining the accelerations of the navigation system, and hence the speeds and coordinates.

Claims (1)

A method for determining the navigation parameters of a strapdown inertial navigation system based on the use of signals from the unit of accelerometers and gyroscopic angular velocity sensors by calculating the matrix of guiding cosines between the connected and navigation coordinate systems, compensating for the errors of the accelerometers by taking into account the rotation of the connected system, recalculating the readings of the accelerometers from the connected to the navigation system coordinates and their integration to calculate current velocities and increments of coordinates dynamite, characterized in that they carry out different modes of movement of the carrier on which the strapdown inertial navigation system is installed, these modes are strong maneuver, weak maneuvering and cruising movement without maneuvering, while the parameters of the carrier movement are measured, these parameters are roll, course derivative and acceleration carrier in the horizon plane, then these parameters are used to calculate the gain of systems that implement individual control laws in parallel of the used matrices of directional cosines between the connected and navigation coordinate systems according to the same readings of accelerometers and angular velocity sensors, for each calculated matrix of directional cosines, their own navigation parameters having different frequency errors are determined, and calculation errors of each of the matrices also have different frequency spectrum depending on the modes of media movement, the navigation parameters for each calculated matrix of guide cosines are fed to the inputs of the master ltra forming the optimal combination navigation solution depending on the frequency range of the error, and the parameters of movement of the carrier.

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