CN113670303B - SINS/DVL integrated navigation flow velocity compensation method based on RBF neural network - Google Patents

SINS/DVL integrated navigation flow velocity compensation method based on RBF neural network Download PDF

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CN113670303B
CN113670303B CN202111055237.6A CN202111055237A CN113670303B CN 113670303 B CN113670303 B CN 113670303B CN 202111055237 A CN202111055237 A CN 202111055237A CN 113670303 B CN113670303 B CN 113670303B
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CN113670303A (en
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刘沛佳
王忠勇
朱政宇
郝万明
简立华
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Zhengzhou University
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    • G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
    • G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
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Abstract

The invention provides an SINS/DVL integrated navigation flow rate compensation method based on an RBF neural network, which is suitable for flow rate compensation of SINS/DVL integrated navigation under the condition of DVL bottom tracking unlocking. The method expands the output speed to the ground and convection speed of the DVL under the ADCP mode, and establishes that the flow velocity measuring unit works in parallel with the SINS/DVL integrated navigation system. On the basis, an RBF neural network embedded SINS/DVL integrated navigation system is designed to compensate the flow rate: when the DVL works normally, the earth-to-earth speed and the convection speed can be output at the same time, the earth-to-earth speed is used for combined navigation on one hand, the RBF neural network is trained by combining the convection speed, and the RBF neural network is utilized to approach a relation function of the earth-to-earth speed and the convection speed; when the DVL bottom is in tracking unlocking, the RBF neural network is used as a speed predictor, the convection speed output by the DVL is used as input, and the combined navigation of the SINS is assisted by predicting the ground speed, so that the flow speed compensation is realized.

Description

SINS/DVL integrated navigation flow velocity compensation method based on RBF neural network
Technical Field
The invention belongs to the technical field of underwater navigation, and relates to an SINS/DVL combined navigation flow velocity compensation method based on an RBF neural network.
Background
Currently, the underwater navigation mostly adopts a combined navigation technology. Among various combined navigation systems, the strapdown inertial navigation system (Strapdown Inertial Navigation System, SINS)/Doppler log (Doppler Velocity Log, DVL) combined navigation system has a simple structure, does not need to draw a priori map or lay an external auxiliary base station, has concealment and complete autonomy, and is widely applied to underwater vehicles. In SINS/DVL integrated navigation, the high-precision velocity parameter of the DVL output is used to suppress SINS error accumulation. However, DVL has limited range, and bottom tracking loss of lock may occur during operation in middle sea areas. In this case, the DVL output is not the ground speed of the craft (speed relative to the water bottom), but the convection speed (speed relative to the water flow), which would introduce positioning errors if it were used directly for navigation. Therefore, the research of the flow velocity compensation method in SINS/DVL integrated navigation has important significance.
Patent document (publication number: CN110274591 a): the deep submersible ADCP assisted SINS navigation method utilizes the ground speed obtained by the satellite navigation positioning system prior to the submersible being submerged, in combination with a doppler flow profiler (Acoustic Doppler Current Profiler, ADCP), deriving flow rates in a plurality of depth units and ground speed of the aircraft, and realizing flow rate compensation in the process of submerging the aircraft. Further, patent literature (application number: CN 202011599375.6): the SINS/DVL ocean current speed estimation method of the deep-diving long-voyage submersible considers the error accumulation problem caused by the speed initial value error in the diving process, and corrects the flow speed of each depth unit and the ground speed of the submersible by using the ground speed measured by ADCP when approaching the seabed. The method proposed in the above patent document is suitable for flow rate compensation during the submergence of an aircraft. Currently, flow rate compensation during travel of an aircraft is generally achieved through flow rate estimation, which generally expands flow rate parameters into state variables of a combined navigation system, which may lead to reduced observability of other critical navigation states, thereby affecting navigation accuracy. Furthermore, most flow rate estimation methods assume that the flow rate is unchanged, and this constraint may fail in complex marine environments, especially in long-endurance navigation.
Disclosure of Invention
Aiming at the above requirements and problems, the invention provides a SINS/DVL combined navigation flow rate compensation method based on a radial basis (Radial Basis Function, RBF) neural network by utilizing the superior nonlinear function approximation capability of the RBF neural network, and provides a new solution for flow rate compensation in SINS/DVL combined navigation during long voyage. The invention adopts the following technical scheme:
an SINS/DVL combined navigation flow rate compensation method based on RBF neural network is described as follows with reference to figure 1:
step 1: expanding the output speed of the DVL to the ground and convection in the ADCP mode, and establishing a flow velocity measuring unit to work in parallel with the SINS/DVL integrated navigation system;
in navigational applications, the DVL emits four beams of acoustic signals to the water bottom, and the three-dimensional ground speed in the vehicle carrier coordinate system (b-system, upper front right axis) is calculated from the doppler shifts of the four beams. DVLs are typically integrated with an ADCP mode in which their output contains both ground and convection velocities, and the flow rate can be obtained by the difference between the two velocities, which is most useful for water flow mapping. The DVL outputs beam measurement in ADCP mode, defines the DVL output ground speed along four beam directions asConvection velocity is +.>The flow velocity vector is:
in the method, in the process of the invention,and->Velocity components relative to the water bottom and water flow along the four beam directions of the DVL, di=1, 2,3,4 being the beam number; e represents an earth coordinate system, which is fixedly connected with the earth; w represents the water flow coordinate system, andthe water flows are fixedly connected and move along with the water flows.
Further, the flow velocity in the navigation coordinate system (n system, axial northeast day) was obtained as follows:
wherein M is a transformation matrix from four beam directions of DVL to b system, M is a constant matrix of 4 x 3 dimension after DVL is fixedly installed,a posture transformation matrix from b series to n series, in the above formula and below, [ ·· · ]] T Representing the transpose of the matrix.
Based on the above formula, the flow velocity parameter can be obtained by DVL beam measurement, when the DVL beam measurement is used for navigation, the SINS and the DVL need to adopt a tightly combined navigation method, and under the SINS/DVL tightly combined frame, a flow velocity measurement unit is established by using the formula (1) and the formula (2) to work in parallel with the combined navigation system, as shown in figure 2. The SINS/DVL integrated navigation system model is as follows:
selecting SINS speed error δV n ＝[δV E δV N δV U ] T Attitude misalignment angle phi = [ phi ] x φ y φ z ] T Zero offset of accelerometerGyroscope drift epsilon b ＝[ε x ε y ε z ] T DVL bias error δb D ＝[δb D1 δb D2 δb D3 δb D4 ] T And scale factor error δs D Constructing a state vector:
the linearized SINS and DVL error equations are as follows:
wherein f b For specific force output by the accelerometer, there areAnd->
Based on equation (3), a combined navigation system equation can be established:
where A is a system matrix and ω is system noise.
The measurement modeling of DVL is:
wherein omega is D For measuring noise of DVL, V D Unified refers toAnd->In this and the following, the vector with superscript A represents the sensor measurement, where there is an error.
In the SINS/DVL tightly integrated navigation method,is output by SINS +.>The structure comprises:
in the method, in the process of the invention,since the error states are small, coupling terms between the error states are omitted in the derivation process. In this and in the following, the vector with superscript-is obtained from the measurement transformation of the sensor, [. Cndot.x ]]Is an antisymmetric matrix of vectors.
The measurement equation of the integrated navigation system is as follows:
where v is the measurement noise.
The measurement matrix comprises:
the flow velocity measurement unit works in parallel with the SINS/DVL integrated navigation system, so that the comprehensive application of the DVL in navigation and flow velocity measurement is realized, navigation and flow velocity parameters can be synchronously acquired on the premise of not increasing a sensor, and a foundation is laid for designing the RBF neural network to perform flow velocity compensation.
Step 2: designing an RBF neural network for flow rate compensation based on the established flow rate measurement unit;
in the design of RBF neural network, selectAs input to the RBF neural network, namely:
selectingAs target outputs of the RBF neural network, namely:
fig. 3 shows a designed RBF neural network, defining its actual output vector as net_out= [ y ] 1 ,y 2 ,y 3 ,y 4 ] T The elements are obtained by the following formula:
wherein m is the number of hidden nodes, phi i And Center i Radial basis function and center vector, w, respectively, of the i-th hidden node ij Weights from the ith hidden node to the jth output node, phi 0 Is a hidden threshold for the RBF neural network.
Construction training sample setTraining the RBF neural network, wherein p is the number of samples, and k is the sample number. In theory, any function can be expressed as a weighted sum of a set of radial basis functions by adjusting Center during RBF neural network training i And w ij And realizing function approximation. Therefore, the designed RBF neural network can realize +.>And->Approximation of the relation function, after training, as a speed predictor, to +.>For input pair +.>And the prediction is carried out, and the designed RBF neural network has a simple structure, so that a good function approximation effect can be obtained only by a small quantity of training samples.
Step 3: and embedding the designed RBF neural network into the SINS/DVL integrated navigation system to compensate the flow rate.
After the RBF neural network is embedded into the SINS/DVL integrated navigation system, the flow rate compensation technical route is shown in figure 4, and two working modes of the RBF neural network can be known from the figure: the training mode and the compensation mode correspond to normal and abnormal conditions of the DVL, respectively. When DVL works normally, it can output at the same timeAnd->The training process of RBF neural network is shown in figure 4 (a), which shows +.>On one hand, assisting SINS to carry out integrated navigation, and on the other hand, assisting SINS to carry out integrated navigation>Training RBF neural network together, and making RBF neural network approach +.>And->Is a function of the relationship of (2); when the DVL bottom tracking is out of lock, output +.>The flow rate compensation mode of RBF neural network is shown in FIG. 4 (b), which will be +.>Inputting RBF neural network, and using the same to predict +.>And assisting the SINS to carry out integrated navigation, so as to realize flow velocity compensation.
The invention has the beneficial effects that:
(1) The invention expands the output speed to the ground and convection speed of the DVL under the ADCP mode, establishes the parallel operation of the flow velocity measuring unit and the integrated navigation system, synchronously acquires navigation and flow velocity parameters, designs the RBF neural network to realize flow velocity compensation on the basis, and can effectively eliminate the influence of the flow velocity on the SINS/DVL integrated navigation precision under the condition of DVL bottom tracking unlocking. The invention provides a brand new solution for flow rate compensation in SINS/DVL integrated navigation during long voyage through the comprehensive application of DVL in navigation and flow rate measurement without adding a new sensor.
(2) The flow velocity compensation method provided by the invention overcomes the existing problems, the flow velocity is not required to be expanded into a state variable of the integrated navigation system, the problem of observability reduction of other key navigation states is avoided, and meanwhile, the periodic change of the flow velocity can be dynamically tracked without being limited by the assumption of unchanged flow velocity.
(3) The RBF neural network designed by the invention has a simple structure, can approach an objective function only by a small amount of samples, can be used as a speed predictor after training is finished, compensates the flow speed when the DVL bottom is in tracking loss of lock, and realizes an effective application of the RBF neural network in navigation.
Drawings
FIG. 1 is a flowchart showing the steps of the present invention;
FIG. 2 is a flow rate measurement unit constructed in accordance with the present invention;
FIG. 3 is a schematic diagram of an RBF neural network according to the present invention;
FIG. 4 is a schematic diagram of a RBF neural network flow rate compensation technique of the present invention;
FIG. 5 is a schematic illustration of a navigation trajectory in an embodiment of the present invention;
FIG. 6 is a flow rate comparison chart of an embodiment of the present invention;
FIG. 7 is a comparison of positioning traces in an embodiment of the invention;
FIG. 8 is a comparison of positioning errors in an embodiment of the present invention.
Detailed Description
The flow rate compensation effect of the present invention is verified in connection with the specific examples below.
The conditions of this embodiment are set as follows: the vehicle navigates along the mower trajectory as shown in fig. 5 for a total time of 7900s, wherein the upper left 270 ° curve trajectory duration is 300s, and the other straight lines and 180 ° curve trajectories durations are 600s and 200s, respectively. Initial position of aircraft [34.53975764 DEG 113.52107006-200 m ]] T Initial attitude [0 DEG 60 DEG] T The velocity (in b series) is kept at V b ＝[0 2 0] T . The flow rate is set as a superposition of constant and periodic oscillations, and can be modeled as:
wherein T is c The flow rate oscillation period was set to 200s. Since the flow rate is generally considered to be acting only on the horizontal plane, the vertical flow rate is ignored and set to zero in the present invention. SINS and DVL parameters are shown in Table 1, the DVL is set to work normally for 0-1200s, the lock loss is tracked from 1201s, and the specific failure time is identified in FIG. 5.
Table 1 sensor parameters
In this embodiment, the DVL bottom tracks out of lock from 1201s, thus within the front 1200sAnd->Can be used for training RBF neural networks. The RBF neural network designed by the invention has simple structure, and inputs and outputsThe output is only 4-dimensional, and a better approximation effect can be obtained only by less training samples, so that in order to reduce training time and calculation amount, only +.>And->I.e. training sample set +.>The flow velocity direction characteristic detection device comprises a first straight line, a first curve and a sample in a second straight line track, and comprises flow velocity direction characteristics, so that the problem of flow velocity direction characteristic missing caused by selecting only the sample in a single straight line track is avoided. The RBF neural network can accurately predict +.>The root mean square error of the four beam direction predictors is 2.4X10 respectively -3 m/s，2.3×10 -3 m/s，4.1×10 -3 m/s and 2.3X10 -3 m/s。
Further, prediction using RBF neural networkThe flow rate is obtained by combining the formula (1) and the formula (2), and the root mean square error of the flow rate in the east direction and the flow rate in the north direction is 1.6X10 respectively as shown in figure 6 -2 m/s and 1.7X10 -2 m/s, therefore, the invention can accurately and dynamically track the periodic change of the flow speed.
Predicted using RBF neural networkThe combined navigation of the SINS is assisted, and compared with the combined navigation result without flow velocity compensation, the figure 7 and the figure 8 are respectively a positioning track and a positioning error comparison graph, and as can be seen from the graphs, the positioning track without flow velocity compensation is greatly deviated under the influence of the flow velocity after DVL bottom tracking losing lock, and the positioning mean square is achievedRoot error is 1413.8m; after the flow rate compensation, the positioning track is accurately corrected, and the positioning root mean square error is 7.9m.
The above-mentioned embodiments are merely preferred embodiments of the present invention, and the protection scope of the present invention is not limited thereto, and any changes or substitutions easily come within the scope of the present invention as those skilled in the art can easily understand and cover the present invention.

Claims (1)

1. The SINS/DVL integrated navigation flow rate compensation method based on the RBF neural network is characterized by comprising the following steps of:
step 1: expanding the output speed of the DVL to the ground and convection in the ADCP mode, and establishing a flow velocity measuring unit to work in parallel with the SINS/DVL integrated navigation system;
ground speed output in ADCP mode using DVLAnd convection speedObtaining flow rate measurement:
in the method, in the process of the invention,and->Velocity components relative to the water bottom and water flow along the four beam directions of the DVL, di=1, 2,3,4 being the beam number; e represents an earth coordinate system, which is fixedly connected with the earth; w represents a water flow coordinate system, and is fixedly connected with water flow and moves along with the water flow; b is an aircraft carrier coordinate system;
further, the flow velocity in the navigation coordinate system is obtained as follows:
wherein n is a navigation coordinate system, M is a transformation matrix from four beam directions of DVL to b system, M is a constant matrix of 4 x 3 dimension after DVL is fixedly installed,for the b-to n-series pose transformation matrix, above and below, [ … ]] T Representing a transpose of the matrix;
based on the above, the flow velocity parameters can be obtained by DVL beam measurement, when the DVL beam measurement is used for navigation, the SINS and the DVL need to adopt a tightly combined navigation method, under the SINS/DVL tightly combined frame, a flow velocity measurement unit is established by using the formulas (1) and (2) to work in parallel with the combined navigation system, the navigation and the flow velocity parameters are synchronously obtained, the comprehensive application of the DVL in navigation and flow velocity measurement is realized, and a foundation is laid for designing an RBF neural network to carry out flow velocity compensation;
step 2: designing an RBF neural network for flow rate compensation based on the established flow rate measurement unit;
RBF neural network designed for flow rate compensation, selectionAs input to the RBF neural network, namely:
selectingAs target outputs of the RBF neural network, namely:
defining the actual output vector of the RBF neural network as Net_out= [ y ] 1 ,y 2 ,y 3 ,y 4 ] T The elements are obtained by the following formula:
wherein m is the number of hidden nodes, phi i And Center i Radial basis function and center vector, w, respectively, of the i-th hidden node ij Weights from the ith hidden node to the jth output node, phi 0 A hidden threshold value for the RBF neural network;
in theory, any function can be expressed as a weighted sum of a set of radial basis functions by adjusting Center during RBF neural network training i And w ij Realizing function approximation, therefore, the designed RBF neural network can realize the function approximation by trainingAnd->Approximation of the relation function, after training, as a speed predictor, to +.>For input pair +.>The prediction is carried out, and as the designed RBF neural network has a simple structure, a good function approximation effect can be obtained only by a small quantity of training samples;
step 3: embedding the designed RBF neural network into an SINS/DVL integrated navigation system to compensate the flow rate;
after the RBF neural network is embedded into the SINS/DVL integrated navigation system, two working modes are provided: training mode and compensation mode, corresponding to normal and abnormal conditions of DVL, respectively: when DVL works normally, it can output at the same timeAnd-> For combined navigation on the one hand and +.>Training RBF neural network together, and making RBF neural network approach +.>And->Is a function of the relationship of (2); when the DVL bottom tracking is out of lock, using RBF neural network as a speed predictor, outputting +.>As input, predict +.>And assisting the SINS to carry out integrated navigation, so as to realize flow velocity compensation.
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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111174774A (en) * 2020-01-21 2020-05-19 河海大学 Navigation information fusion method and system under water level mode at certain depth
AU2020103939A4 (en) * 2020-12-08 2021-02-11 Xi'an University Of Architecture And Technology Polar Integrated Navigation Algorithm of SINS / GPS Based on Grid Framework
CN112729291A (en) * 2020-12-29 2021-04-30 东南大学 SINS/DVL ocean current velocity estimation method for deep-submergence long-endurance submersible

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11532168B2 (en) * 2019-11-15 2022-12-20 Nvidia Corporation Multi-view deep neural network for LiDAR perception

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111174774A (en) * 2020-01-21 2020-05-19 河海大学 Navigation information fusion method and system under water level mode at certain depth
AU2020103939A4 (en) * 2020-12-08 2021-02-11 Xi'an University Of Architecture And Technology Polar Integrated Navigation Algorithm of SINS / GPS Based on Grid Framework
CN112729291A (en) * 2020-12-29 2021-04-30 东南大学 SINS/DVL ocean current velocity estimation method for deep-submergence long-endurance submersible

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
"Robust energy harvest balancing optimization with V2X-SWIPT over MISO secrecy channel";Zhengyu Zhu et al.;《Computer Networks》;全文 *
"INS/DVL/PS Tightly Coupled Underwater Navigation Method With Limited DVL Measurements";刘沛佳等;《IEEE SENSORS JOURNAL》;第18卷(第7期);全文 *
"径向基函数神经网络快速算法及其应用";许敏等;《统计与决策》(第１６期);正文第1-3节 *
基于RBF神经网络的SINS/DVL组合导航流速补偿方法;牛雷;《2019年上海-西安声学学会第六届声学学术交流会议》;正文第0-3节，图1 *

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