RU2564379C1 - Platformless inertial attitude-and-heading reference - Google Patents

Abstract

FIELD: measurement equipment.

SUBSTANCE: device comprises a three-component block of sensors of angular speeds, a three-component block of sensors of linear accelerations, a course corrector, a computing unit, an unit to form a matrix of guiding cosines, an integrating unit, the Kalman filter and an unit of measurement functions generation connected to each other in an appropriate manner. The design of the device provides for an adaptive (pendulum) correction of a platformless inertial navigation system, realised by means of the Kalman filter, in which the amplification ratio varies with an account of current values of overload modules and angular speeds in the unit of measurement functions generation. At the same time they may use the sensors of angular speed and the sensors of linear acceleration of medium and low accuracy, also of a micromechanical type.

EFFECT: expansion of functional capabilities.

3 dwg

Description

The invention relates to measuring equipment and can be used for marine, air and ground objects. The objective of the invention is to improve the accuracy of the strapdown inertial navigation system (SINS) by creating a device for continuous correction of inertial course-line vertical.

, $$\dot{\vartheta }$$

, $$\dot{\gamma }$$

с последующим их интегрированием. Недостатком такого устройства является накапливание во времени погрешности и, как следствие, ограниченное время работы. Для устранения указанного недостатка в систему необходимо вводить дополнительную информацию, характеризующую реальную угловую ориентацию объекта, в частности беспилотного летательного аппарата (БПЛА). Источником такой информации служат датчики линейного ускорения (ДЛУ). Основные погрешности системы маятниковой коррекции возникают в результате действия постоянных или медленно меняющихся ускорений. В настоящий момент данная проблема решается путем отключения маятниковой коррекции на высокоманевренных участках полета или путем комплексирования блока гироскопов (БГ) с другими системами ориентации (магнитометрическая, видеосистема и др.). Проблема коррекции курсовертикали БИНС заключается в том, что при маневрировании ЛА моменты времени, когда статические оценки крена и тангажа обладают достаточной точностью, могут возникать недопустимо редко. В связи с этим предлагается устройство адаптивной коррекции углов крена и тангажа, в котором коррекция выполняется непрерывно.The classic algorithm for calculating orientation angles is to recalculate the readings of the angular velocity sensors (DLS) / projections of the absolute angular velocity - ω _x , ω _y , ω _z / into angular velocities $$\dot{\psi }$$

, $$\dot{\vartheta }$$

, $$\dot{\gamma }$$

with their subsequent integration. The disadvantage of this device is the accumulation in time of the error and, as a result, the limited time. To eliminate this drawback, it is necessary to enter additional information into the system that characterizes the real angular orientation of the object, in particular an unmanned aerial vehicle (UAV). The source of such information is linear acceleration sensors (DLU). The main errors of the pendulum correction system arise as a result of the action of constant or slowly varying accelerations. At present, this problem is being solved by turning off the pendulum correction in highly maneuverable flight areas or by combining a block of gyroscopes (BG) with other orientation systems (magnetometric, video system, etc.). The problem of correcting the SINS vertical line is that when maneuvering an aircraft, time instants when the static roll and pitch estimates have sufficient accuracy can occur unacceptably rarely. In this regard, a device for adaptive correction of roll angles and pitch, in which the correction is performed continuously.

Known strapdown inertial kursertical, described in patent RU 2249791 C2, IPC G01C 21/16, publ. 04/10/2005, accepted by us as a prototype.

This strap-down inertial course-line with a correction loop contains a three-channel block of angular velocity sensors (DLS), a three-channel block of linear acceleration sensors (DLU), a block of integrators, a shaper of derivatives of orientation angles, a correction block, a block for calculating the observed vertical, a block for calculating errors of the vertical, filter, course show unit.

The angular velocities measured by the three-channel block of the TLS and converted into derivatives of the orientation angles contain errors due to systematic and random measurement errors. It is assumed that when integrating angular velocities, the error does not accumulate due to the subtraction of the constant components of the error. The roll and pitch are corrected by a correction unit using DLU signals. The course is adjusted by the correction block using the course show block. Heading errors are compensated in the correction block by passing through a high-pass filter.

The disadvantage of this device is that when maneuvering the aircraft, the moments of time when the roll and pitch estimates are accurate enough can occur unacceptably rarely due to the presence of slowly changing and rapidly changing linear and rotational accelerations in the accelerometer signals. This can lead to significant errors in roll and pitch readings.

The aim of the invention is the provision of continuous correction SINS in roll angles and pitch with the required accuracy, including in dynamic flight modes.

This goal is achieved due to the fact that in the strapdown inertial directional line containing a three-component block of angular velocity sensors, a three-component block of linear acceleration sensors, a course corrector and an integrating block, the outputs of which are connected respectively to the first, second, third and fourth inputs of the computing unit, are additionally introduced a block for generating a matrix of guide cosines, a Kalman filter and a block for generating measurement functions, the first inputs of a block for forming guides x cosines, a Kalman filter and a block for generating measurement functions are connected to the output of a three-component block of angular velocity sensors, the second inputs of a Kalman filter and a block for forming measurement functions are connected to the output of a three-component block of linear acceleration sensors, the output of the block forming a matrix of guide cosines is connected to an integrating block, the filter output Kalman is connected to the second input of the block forming the matrix of guide cosines and to the third input of the block forming the measurement functions, the output to orogo connected to the third input of the Kalman filter.

The invention is illustrated by drawings, which show:

in FIG. 1 is a structural diagram of the inventive device;

in FIG. 2, 3 graphically presents the results of processing the flight data of a helicopter with the claimed heading (pitch estimates - Fig. 2 and roll estimates - Fig. 3).

The inertial inertial course-line vertical (Fig. 1) contains a three-component block 1 of angular velocity sensors, a three-component block 2 of linear acceleration sensors, a 3-course corrector, a computational block 4, a block 5 for generating a matrix of guide cosines, an integrating block 6, Kalman filter 7 and a block 8 for generating functions measurements interconnected accordingly.

The proposed block diagram of the device provides an adaptive (pendulum) correction of the SINS vertical line, implemented by the Kalman filter 7, in which the gain is changed taking into account the current values of the overload modules and angular velocity in block 8.

The roll and pitch changes are described by the Poisson equations in the integrating block 6. The orientation angles are updated in the block of cosines 5. In block 8, the signals of the DLU accelerometers of block 2 are converted, depending on the current flight parameters, which are used for adaptive estimation of the state vector using a filter 7 Kalman. Due to this, the dependence of the accuracy of the pendulum correction on the type of aircraft motion is weakened to a level that allows the use of TLS and DLU sensors of medium and low accuracy, including the micromechanical type. The SINS course is adjusted according to the signals of magnetometric sensors 3.

The essence of the device is described below.

From the measurements of the sensor blocks ДУС 1 and ДРУ 2, the current angles of roll γ, pitch ϑ and yaw ψ are determined from the Poisson equations

where the matrix of guide cosines A, defining the transition from the navigation coordinate system (SK) PNUE to the connected SK OXYZ, and the skew-symmetric matrix Ω have the following form:

The matrix Poisson equation (1) is solved in discrete form, taking into account the initial conditions of the matrix of guide cosines A, that is, the initial values of the roll γ and pitch, ϑ and the yaw angle ψ, are set

The calculation of the roll and pitch by the rotation matrix is performed in the computing unit 4 using the relations:

The state vector of adaptive Kalman filter 7 is presented below:

where ϑ - pitch, γ - roll, V - modulus of the velocity vector at the time i,

$$$$

i is the number of the discrete time moment of measurement of the sensors. For i = 0, $$x_0\in N\left\{{\overline{x}}_0,{\overline{P}}_0\right\}.$$

$$$$

The observation vector, denoted below Z, contains measurements of accelerometers coming from block 2 of linear acceleration sensors, and flight parameters of the aircraft

Here v _i is the vector of measurement errors with a given constant covariance matrix R. The functions f _x , f _y , f _z determine the relationship between the measurements of overloads and flight parameters. The exact relationships for these functions are:

Here V _x , V _y , V _z are the projections of the earth's velocity vector on the connected axis of the aircraft.

It is not possible to fully take into account relations (7) while limiting the composition of the sensors to only DLN and TLS, therefore, the simplifying assumption is made that the angles of attack and slip are small, as well as the assumption that the modulus of the ground speed in the sampling interval Δt is constant.

и уравнения (7) упрощаютсяIn this case, the following relations hold: V _x = V, $$\dot{V}=V_y=V_z=0$$

and equations (7) are simplified

In view of (7), the Jacobi matrix of the observation vector (8) has the form

, $${\text{σ}}_{\text{nyi}}^{\text{2}}$$

, $${\text{σ}}_{\text{nzi}}^{\text{2}}$$

. Данные дисперсии задаются линейной функцией видаRelations (8) are approximate. The degree of approximation depends on the deviation of the overload module from unity. The more the overload modulus differs from unity, the less accurate these equations are and the greater the variance $${\text{σ}}_{\text{nxi}}^{\text{2}}$$

, $${\text{σ}}_{\text{nyi}}^{\text{2}}$$

, $${\text{σ}}_{\text{nzi}}^{\text{2}}$$

. The variance data is defined by a linear function of the form

Where $$n^{\ast }=\left|\sqrt{n_{xi}^2+n_{yi}^2+n_{zi}^2}-\mathrm{one}\right|.$$

Here k ₀ , k ₁ are the coefficients.

The current state vector (5) is calculated according to the Poisson equations (1) taking into account (2), (3). In this case, the equations of the object are accepted in the form:

Here x _iq is the vector in which the roll and pitch components are calculated according to relations (3), and the velocity component is taken equal to its a priori value at the time of the current measurements; w _i is the perturbation vector with the covariance matrix Q _i :

, $$w_{\gamma i}$$

учитываются дисперсиями $${\sigma }_{\vartheta }^2,$$

$${\sigma }_{\gamma }^2$$

и задаются с учетом точности гироскопов. Случайный процесс $$w_{Vi}$$

учитывается дисперсией $${\sigma }_v$$

.Random processes $$w_{\vartheta i}$$

, $$w_{\gamma i}$$

accounted for by variances $${\sigma }_{\vartheta }^2,$$

$${\sigma }_{\gamma }^2$$

and are set taking into account the accuracy of gyroscopes. Random process $$w_{Vi}$$

accounted for by variance $${\sigma }_v$$

.

The Kalman filter is constructed to estimate the state vector (5) with a discrete object model (3) and a discrete observation model (9) taking into account (10).

начального априорного распределения вектора состояния.To run the filter algorithm, statistics x _{0 are used} , $${\overline{P}}_0$$

initial a priori distribution of the state vector.

, P_i апостериорного нормального распределения $$N\left\{{\widehat{x}}_{i,}{P}_i\right\}$$

и статистики $${\overline{x}}_{i+1},$$

$${\overline{P}}_{i+1}$$

априорного распределения вектора состояния $$N\left\{{\overline{x}}_{i+1},{\overline{P}}_{i+1}\right\}$$

для следующего (i+1)-го момента времени.At the next i-th sample of sensor measurements, Kalman filter 7 determines statistics $${\widehat{x}}_i$$

, P _{i of the} posterior normal distribution $$N\left\{{\widehat{x}}_{i,}{P}_i\right\}$$

and statistics $${\overline{x}}_{i+\mathrm{one}},$$

$${\overline{P}}_{i+\mathrm{one}}$$

a priori distribution of the state vector $$N\left\{{\overline{x}}_{i+\mathrm{one}},{\overline{P}}_{i+\mathrm{one}}\right\}$$

for the next (i + 1) th moment in time.

выполняется по известным соотношениямCalculation of statistics of a posterior distribution $$N\left\{{\widehat{x}}_{\mathrm{one}},P_i\right\}$$

performed by known relations

- оценка вектора наблюдений.Here K _i is the matrix gain of the filter; $${\overline{Z}}_i$$

- assessment of the observation vector.

The operation of the inventive device was tested on a Robinson helicopter and evaluated by processing the flight data of the helicopter, for which:

1. The aircraft orientation was determined by the algorithm for integrating the measurements of the SNA receiver with sensors of the TLS and DLU on a moving observation interval.

2. The roll and pitch were determined according to the algorithm using a strapdown inertial vertical course with pendulum correction.

In the tasks of processing the flight data of the helicopter (Figs. 2 and 3), it was necessary to observe the proximity of the roll and pitch estimates to the estimates obtained in a different way, namely, using the orientation algorithm when combining information from the TLS and DLN with the measurements of the projections of the ground velocity coming from the receiver SNA. Also, the correspondence of the obtained estimates to the indications of the control device was checked.

For cases when the position of the aircraft is close to steady, there is an ideal case of adaptive pendulum correction. In this case, the roll and pitch estimates determined by the Kalman filter are replaced by the estimates calculated directly from the readings of the DLU in the computer.

по наблюдениям (9) с учетом одношагового алгоритма ориентации (3). Получаемые при этом оценки вектора (5) на каждом шаге пересчитываются в матрицу направляющих косинусов (3).Thus, using the proposed device solves the problem of estimating the vector (5) $$x_i^T=\left[\vartheta \gamma V\right]$$

according to observations (9), taking into account the one-step orientation algorithm (3). The resulting estimates of vector (5) at each step are recalculated into the matrix of guide cosines (3).

Calculations show that the device works when changing pitch and roll angles within absolute values up to 70-80 degrees.

The technical result of the use of the invention is to increase accuracy and ensure continuity of the correction of pitch and roll angles under conditions of maneuvering in flight. The invention allows the use of sensors DUS and DLU medium and low accuracy, including micromechanical type.

The inventive device is feasible and can be used on all types of aircraft. Micromechanical gyroscopic sensors can be used as angular velocity sensors, while the block for generating the matrix of guide cosines, the Kalman filter, and the block for generating measurement functions can be implemented on standard elements of computer technology.

Claims (1)

A strap-down inertial course-line containing a three-component block of angular velocity sensors, a three-component block of linear acceleration sensors, a course corrector and an integrating block, the outputs of which are connected to the first, second, third and fourth inputs of the computing unit, characterized in that the matrix forming unit is additionally introduced into it guide cosines, Kalman filter and a block for generating measurement functions, the first inputs of the block forming a matrix of guide cosines, f Kalman filter and a unit for generating measurement functions are connected to the output of a three-component block of angular velocity sensors, the second inputs of a filter for Kalman and a block for forming measurement functions are connected to the output of a three-component block of linear acceleration sensors, the output of the block forming the guide cosines matrix is connected to an integrating block, the Kalman filter output is connected to the second input of the block forming the matrix of guide cosines and to the third input of the block forming the measurement functions, the output of which is connected en to the third input of the Kalman filter.

Images