CN113847915B - Navigation method of strapdown inertial navigation/Doppler integrated navigation system - Google Patents
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Abstract
The invention belongs to the technical field of integrated navigation, and particularly relates to a navigation method of a strapdown inertial navigation/Doppler integrated navigation system. If the Doppler fails and the Doppler failure time is greater than the set value, inputting a model input of the Doppler failure time and the previous m times and Doppler output speeds of the Doppler failure time and the previous m times into a trained Doppler prediction model, and predicting to obtain the Doppler output speed at the next time, wherein m is more than 1; and performing integrated navigation by using the Doppler output speed obtained by prediction; wherein the model input is a directional cosine matrix output by a strapdown inertial navigation/Doppler combined navigation systemSum speed ofIs a product of (a) and (b). The invention utilizes the integrated navigation system under the condition of effective DopplerThe Doppler output speed is trained by the Doppler output speed and the data of the Doppler prediction model, so that the Doppler output speed can be predicted by the Doppler prediction model after Doppler failure, the navigation data is uninterrupted, and the precision of the integrated navigation system is maintained in a short period.
Description
Technical Field
The invention belongs to the technical field of integrated navigation, and particularly relates to a navigation method of a strapdown inertial navigation/Doppler integrated navigation system.
Background
Currently, the means available for underwater navigation remain relatively limited. The navigation system of an underwater vehicle must have a long-range, long-endurance, high-precision navigation capability. Strapdown inertial navigation/Doppler (Strapdown Inertial Navigation System/Doppler Velocity Log, SINS/DVL) integrated navigation is one of the main ways of realizing underwater autonomous navigation at present.
Doppler is an instrument which utilizes an ultrasonic transducer mounted on a carrier to emit ultrasonic waves to the sea floor and measures the speed of the carrier according to the Doppler effect principle, which is shown in figure 1. In practical application, due to factors such as complex underwater topography, fish shoal interference, doppler velocimeter overrange and the like, the condition of failure of the Doppler velocimeter can occur. How to achieve maintenance of navigation accuracy under doppler failure conditions is a problem to be studied further.
In order to solve the problem, a kinematic model of the underwater vehicle is generally established, and under the condition of Doppler failure, a virtual velocity observation value is provided by using the kinematic model, and integrated navigation is performed. This approach usually assumes that the carrier is in a constant or uniform acceleration state, and that there is some difference from the actual motion state of the carrier, so the prediction accuracy is not high.
Disclosure of Invention
The invention provides a navigation method of a strapdown inertial navigation/Doppler integrated navigation system, which is used for solving the problem of low integrated navigation prediction precision in the prior art.
In order to solve the technical problems, the technical scheme and the corresponding beneficial effects of the technical scheme are as follows:
the invention provides a navigation method of a strapdown inertial navigation/Doppler combined navigation system, which comprises the following steps:
1) Judging whether Doppler fails or not when the strapdown inertial navigation/Doppler integrated navigation system works:
2) If the Doppler fails and the Doppler failure time is greater than the set value, inputting a model input of the Doppler failure time and the previous m times and Doppler output speeds of the Doppler failure time and the previous m times into a trained Doppler prediction model, and predicting to obtain the Doppler output speed at the next time, wherein m is more than 1; and performing integrated navigation by using the Doppler output speed obtained by prediction;
wherein the model input is a directional cosine matrix output by a strapdown inertial navigation/Doppler combined navigation systemAnd speed->Is a product of (2); and the Doppler predictive model is obtained by training an input value sequence and a Doppler output speed sequence when the Doppler predictive model is in a combined navigation state.
The beneficial effects of the technical scheme are as follows: according to the invention, under the condition that Doppler is effective, the Doppler prediction model is trained by utilizing the data and the Doppler output speed of the integrated navigation system, so that after Doppler failure, the Doppler output speed can be predicted by utilizing the Doppler prediction model, the uninterrupted navigation data is ensured, and the accuracy of the integrated navigation system is maintained in a short period. In addition, the input of the Doppler prediction model is the speed under the carrier system obtained by utilizing SINS/DVL combined navigation, the output is the speed under the Doppler carrier system, the speed is the speed sequence, and the speed obtained by SINS/DVL combined navigation can reflect the variation trend of the Doppler speed to a certain extent, so that the prediction accuracy is improved.
Further, in step 2), the doppler prediction model is a neural network model.
Further, in order to accurately predict the doppler output speed, the neural network model is a nonlinear autoregressive neural network model.
Further, an error speed equation and an attitude error equation of the inertial navigation system in the strapdown inertial navigation/Doppler integrated navigation system are as follows:
wherein δv is the speed error,is the derivative of the speed error δv; phi is the posture error, < >>Is the derivative of the attitude error phi; f (f) n Is a representation of the specific force in a navigational coordinate system; />The method comprises the steps of representing the rotation angular velocity of the earth in a navigation coordinate system; />Is the representation of the rotational angular velocity of the n-system relative to the e-system in the navigation coordinate system; />Is accelerometer zero bias, epsilon is gyro zero bias, and +.>
Further, the state variables selected by the strapdown inertial navigation/Doppler integrated navigation system are as follows:
wherein δv N 、δv E 、δv D Speed errors in the north direction, the east direction and the ground direction respectively; phi (phi) N 、φ E 、φ D The attitude angle errors in the north direction, the east direction and the ground direction are respectively;the accelerometer zero offset is respectively in the x direction, the y direction and the z direction under the carrier coordinate system; epsilon x 、ε y 、ε z And the zero offset of the gyroscope in the three directions of x, y and z under the carrier coordinate system is respectively shown.
Further, the observation equation of the strapdown inertial navigation/Doppler integrated navigation system is as follows:
wherein z represents an observed quantity;velocity in n series of inertial navigation system and Doppler output, respectively, and a conversion matrix from the Doppler carrier coordinate system d to the carrier coordinate system b; />A directional cosine matrix of the inertial navigation system; v d Velocity in carrier coordinate system d for Doppler; η is zero mean gaussian white noise; h is an observation matrix, andI 3×3 is a 3 x 3 identity matrix +.>Is->Is a diagonal symmetric matrix of (a).
Further, in step 2), m=3 in order to ensure the prediction accuracy while ensuring the operation efficiency.
Further, in order to ensure the output precision of the integrated navigation system, if the judgment result in the step 1) is that the doppler fails and the doppler failure moment is smaller than or equal to the set value, the strapdown inertial navigation/doppler integrated navigation system is switched into a pure inertial navigation state, and enters the integrated navigation state after the doppler failure is repaired.
Further, the nonlinear autoregressive neural network model comprises an input layer, a hidden layer and an output layer, wherein the activation function of the hidden layer is a ReLU function, and the activation function of the output layer is a linear function.
Drawings
Figure 1 is a schematic diagram of the doppler principle of operation of the present invention;
FIG. 2 is a block diagram of SINS/DVL integrated navigation system of the invention;
FIG. 3 is a diagram of the structure of the NARX neural network model of the present invention;
fig. 4 is a flow chart of the method of the present invention.
Detailed Description
The basic idea of the invention is as follows: when the Doppler is effective, a Doppler prediction model is trained by utilizing a strapdown inertial navigation/Doppler integrated navigation system (hereinafter referred to as SINS/DVL integrated navigation system or integrated navigation system) and Doppler output data, a trained Doppler prediction model is accessed when the Doppler is invalid, the Doppler output data is predicted by utilizing the trained Doppler prediction model, and the predicted data and the inertial navigation system are used for integrated navigation, so that the navigation data is uninterrupted, and the accuracy of the integrated navigation is maintained. The following describes a navigation method of a strapdown inertial navigation/Doppler integrated navigation system in detail with reference to the drawings and embodiments.
Method embodiment:
before implementing the navigation method of the strapdown inertial navigation/Doppler integrated navigation system, the SINS/DVL integrated navigation system is introduced.
Specific structure diagram of SINS/DVL integrated navigation system is shown in figure 2, i.e. the velocity information output by DVL is used for assisting inertial navigation to carry out integrated navigation. The velocity output by the DVL is converted into a navigation coordinate system through the gesture of the inertial navigation system, the difference between the velocity output by the DVL and velocity information output by the inertial navigation is used as a measurement value, the state of the integrated navigation system is estimated through Kalman filtering, and the inertial navigation system is corrected.
The integrated navigation system firstly establishes an integrated navigation model of the system, namely a state equation and an observation equation, and the integrated navigation model established by the invention is as follows:
1) And (5) a state equation.
The North-East-Down (NED) geographic coordinate system is selected as a navigation system and is denoted as n. The error velocity equation and attitude error equation of the inertial navigation system can be expressed as:
wherein δv is the speed error,is the derivative of the speed error δv; phi is the posture error, < >>Is the derivative of the attitude error phi; f (f) n Is a representation of the specific force in a navigational coordinate system; />The direction cosine matrix is output by the combined navigation system; />The method comprises the steps of representing the rotation angular velocity of the earth in a navigation coordinate system; />Is the representation of the rotational angular velocity of the n-system relative to the e-system in the navigation coordinate system; accelerometer zero bias->Modeling zero offset epsilon with a gyroscope as a constant value comprises the following steps:
the SINS/DVL integrated navigation system selects the following state variables:
wherein δv N 、δv E 、δv D Speed errors in the north direction, the east direction and the ground direction respectively; phi (phi) N 、φ E 、φ D The attitude angle errors in the north direction, the east direction and the ground direction are respectively;the accelerometer zero offset is respectively in the x direction, the y direction and the z direction under the carrier coordinate system; epsilon x 、ε y 、ε z And the zero offset of the gyroscope in the three directions of x, y and z under the carrier coordinate system is respectively shown.
The state equations of the integrated navigation system can be listed according to equations (1), (2) as:
wherein F is a state transition matrix; w is gaussian white noise.
2) And (5) observing an equation.
Taking the speed difference between the inertial navigation system and the Doppler velocimeter as the combined navigation observation quantity, namely:
wherein z represents an observed quantity;the velocity of the inertial navigation system and the velocity of the Doppler output under the n system are respectively; and:
wherein,a conversion matrix from the Doppler carrier coordinate system d to the carrier coordinate system b; />A directional cosine matrix of the inertial navigation system; v d The velocity in carrier coordinate system d is the doppler.
The observation equation of the SINS/DVL integrated navigation system is established as follows:
wherein, eta is zero-mean Gaussian white noise; h is the observation matrix, and:
wherein I is 3×3 A 3×3 identity matrix;is->Is a diagonal symmetric matrix of (a).
3) The neural network model assists the SINS/DVL integrated navigation algorithm.
The Doppler prediction model in the embodiment adopts a neural network model, and the mapping relation between the input and the output of the neural network model is as follows:
wherein M (·) is a nonlinear mapping function; y (k+1) is the output of the neural network model; m is the order of the input delay; y (k), y (k-1) and y (k-m) are respectively the output of the neural network model at the moment k, the moment k-1 and the moment k-m; x (k), x (k-1), x (k-m) are model inputs at time k, time k-1, time k-m, respectively, and define the model inputs x (k) as:
wherein,and->And the direction cosine matrix and the speed are respectively output by the k-moment combined navigation system. And (3) making:
y(k)＝v d (k) (11)
wherein v is d (k) The Doppler output velocity at time k.
The output y (k+1) of the neural network model is the doppler output velocity at time k+1. That is, after the neural network model training is completed, the data of the k-m time to k time integrated navigation system is utilizedDoppler output velocity v d The doppler output velocity at time k +1 can be predicted.
Specifically, the neural network model may be a nonlinear autoregressive (Nonlinear autoregressive exogenous, NARX) neural network model, and the specific structure of the neural network model is shown in fig. 3, and the neural network model includes an input layer, a hidden layer and an output layer. W is the connection weight required to be adaptively adjusted in the neural network model; b is offset; f1 is an activation function of the hidden layer, and a nonlinear function ReLU function is selected; f2 is an activation function of the output layer, and a linear function is selected; the number of input variables of the input layer is 6 (here, 6 is the number that x and y are added because x and y are both 3-dimensional velocity sequences), and x (k) and y (k) are respectively corresponding to each other; the maximum delay order of the input layer is 3; the number of hidden layer neurons is 10; the number of neurons of the output layer is 3, and the Doppler output speed at the moment k+1 is correspondingly output; the neural network model is optimized by adopting an Adam optimization algorithm.
After the above description is given, the whole integrated navigation process (i.e. a navigation method of the strapdown inertial navigation/doppler integrated navigation system of the present invention) is described below with reference to fig. 4, where the order m of the input delay takes a value of 3.
Step one, powering on the SINS/DVL integrated navigation system to perform initial alignment.
And step two, after the initial alignment is completed, the SINS/DVL integrated navigation system enters an integrated navigation state, and an integrated navigation model is shown in formulas (4) and (7).
Step three, according to the direction cosine matrix output by SINS/DVL integrated navigation systemAnd speed->And equation (10) calculates and stores the model input x and outputs the Doppler output velocity v d Recorded as y and stored.
And step four, training an NARX neural network model by using the x sequence and the y sequence stored in the step three, wherein the NARX neural network model is shown in figure 3 so as to obtain a trained NARX neural network model (namely a trained Doppler prediction model).
Step five, judging whether Doppler fails or not:
if the Doppler fails and k is more than 1000, inputting x (k), x (k-1), x (k-2), x (k-3) and y (k), y (k-1), y (k-2) and y (k-3) into a trained Doppler prediction model to obtain y (k+1), namely Doppler output speed at the next moment;
if the Doppler failure is less than or equal to 1000, the SINS/DVL integrated navigation system is switched into a pure inertial navigation state, and the step III is switched after the Doppler failure is repaired;
if Doppler does not fail, the method goes to step three.
It should be noted that k in this step may refer to the doppler failure time, and the comparison judgment of k and 1000 is further added in the judgment condition, because, in the case where k is less than or equal to 1000, it is explained that the data sent to the NARX neural network model for training is less, then the prediction accuracy of the NARX neural network model may be lower, in order to ensure the navigation accuracy, in the case where k is less than or equal to 1000, the purely inertial navigation state is shifted, and only in the case where k is greater than 1000, the prediction result of the NARX neural network model is used.
And step six, updating the y sequence according to the Doppler output speed predicted in the step five, and performing SINS/DVL integrated navigation according to the Doppler output speed predicted.
Step seven, updating the x sequence according to the combined navigation result obtained in the step six and the formula (10); and turning to the fifth step for repeated judgment.
And step eight, ending the navigation of the SINS/DVL integrated navigation system, and stopping the operation of the SINS/DVL integrated navigation system.
The method has the following effects: 1) The invention can maintain or short-term maintain the precision of the inertial navigation/Doppler integrated navigation system under the condition of Doppler failure; 2) The data of the integrated navigation system and the Doppler data are effectively utilized to train the neural network, and the prediction accuracy of the Doppler speed sequence is improved.
In this embodiment, the doppler prediction model is a nonlinear autoregressive neural network model. As other embodiments, other models in the prior art, such as convolutional neural network models, may also be used.
Claims (9)
1. The navigation method of the strapdown inertial navigation/Doppler integrated navigation system is characterized by comprising the following steps of:
1) Judging whether Doppler fails or not when the strapdown inertial navigation/Doppler integrated navigation system works:
2) If the Doppler fails and the Doppler failure time is greater than the set value, inputting a model input of the Doppler failure time and the previous m times and Doppler output speeds of the Doppler failure time and the previous m times into a trained Doppler prediction model, and predicting to obtain the Doppler output speed at the next time, wherein m is more than 1; and performing integrated navigation by using the Doppler output speed obtained by prediction;
3) Updating the Doppler output speed sequence according to the Doppler output speed at the next moment obtained by prediction;
4) Updating the sequence input by the model according to the speed of the integrated navigation, wherein the updated sequence input by the model is used for predicting the Doppler output speed at the next moment;
wherein the model input is a directional cosine matrix output by a strapdown inertial navigation/Doppler combined navigation systemAnd speed of output of strapdown inertial navigation/Doppler combined navigation system +.>Is a product of (2); and the Doppler predictive model utilizes the input value sequence and the multiple general purpose when in the combined navigation stateTraining the Doppler output speed sequence, wherein the Doppler output speed sequence is obtained when Doppler does not lose efficacy.
2. The navigation method of a strapdown inertial navigation/doppler integrated navigation system according to claim 1, wherein in step 2), the doppler prediction model is a neural network model.
3. The navigation method of a strapdown inertial navigation/doppler integrated navigation system of claim 2, wherein the neural network model is a nonlinear autoregressive neural network model.
4. The navigation method of the strapdown inertial navigation/doppler integrated navigation system according to claim 1, wherein an error velocity equation and an attitude error equation of the inertial navigation system in the strapdown inertial navigation/doppler integrated navigation system are:
wherein δv is the speed error,is the derivative of the speed error δv; phi is the posture error, < >>Is the derivative of the attitude error phi; f (f) n Is a representation of the specific force in a navigational coordinate system; />The method comprises the steps of representing the rotation angular velocity of the earth in a navigation coordinate system; />Rotational angular velocity of n series relative to e seriesA representation in a navigational coordinate system; />Is accelerometer zero bias, epsilon is gyro zero bias, and +.>
5. The method for navigating a strapdown inertial navigation/doppler integrated navigation system of claim 4, wherein the state variables selected by the strapdown inertial navigation/doppler integrated navigation system are:
wherein δv N 、δv E 、δv D Speed errors in the north direction, the east direction and the ground direction respectively; phi (phi) N 、φ E 、φ D The attitude angle errors in the north direction, the east direction and the ground direction are respectively;the accelerometer zero offset is respectively in the x direction, the y direction and the z direction under the carrier coordinate system; epsilon x 、ε y 、ε z And the zero offset of the gyroscope in the three directions of x, y and z under the carrier coordinate system is respectively shown.
6. The navigation method of the strapdown inertial navigation/doppler integrated navigation system according to claim 1, wherein the observation equation of the strapdown inertial navigation/doppler integrated navigation system is:
wherein z represents an observed quantity;velocity in n series of inertial navigation system and Doppler output, respectively, and +.> A conversion matrix from the Doppler carrier coordinate system d to the carrier coordinate system b; />A directional cosine matrix of the inertial navigation system; v d Velocity in carrier coordinate system d for Doppler; η is zero mean gaussian white noise; h is the observation matrix, and->I 3×3 Is a 3 x 3 identity matrix +.>Is->Is a diagonal symmetric matrix of (a).
7. The navigation method of a strapdown inertial navigation/doppler integrated navigation system according to claim 1, wherein in step 2), m=3.
8. The navigation method of the strapdown inertial navigation/doppler integrated navigation system according to claim 1, wherein if the determination result in the step 1) is that the doppler fails and the doppler failure time is less than or equal to a set value, the strapdown inertial navigation/doppler integrated navigation system is shifted to a pure inertial navigation state, and enters the integrated navigation state after the doppler failure is repaired.
9. A navigation method of a strapdown inertial navigation/doppler integrated navigation system according to claim 3, wherein the nonlinear autoregressive neural network model comprises an input layer, a hidden layer and an output layer, the activation function of the hidden layer is a ReLU function, and the activation function of the output layer is a linear function.
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