Heave measurement method based on complementary filtering

Abstract

The invention discloses a heave measurement method based on complementary filtering, belongs to the field of inertial navigation measurement and ship motion measurement, and is used for measuring the heave condition of a ship. The method comprises the following steps: based on the idea of complementary filtering, indirectly realizing a complementary high-pass filter by designing a low-pass filter; the attitude information calculated by the inertial system is used for calculating an attitude matrix among a carrier system, a navigation system and a semi-fixed system, and then heaving acceleration information under the navigation system is projected to the semi-fixed system; setting a sliding window for recording heaving acceleration information, and solving dominant frequency of acceleration in the window through FFT (Fast Fourier Transform) for updating a high-pass filter; the acceleration information is filtered and integrated to obtain the heave displacement of the ship, and certain amplitude compensation is carried out on the heave displacement. According to the invention, the parameters of the filter can be adaptively adjusted according to the change of the sea condition, no obvious phase advance phenomenon exists, the method is easy to implement, and the heave measurement precision is high.

Description

Technical Field

[0001] The invention belongs to the field of inertial navigation measurement and ship motion measurement, and in particular relates to a heave measurement method based on complementary filtering. Background Art

[0002] When a ship is sailing or working stationary on the sea, it will produce angular motion and linear motion in three directions due to the influence of sea conditions such as sea breeze, waves, and currents. The large-scale high-frequency heave motion will have a significant impact on the ship's navigation, seabed topography detection, offshore drilling platform work, etc., so the measurement of the ship's heave motion is extremely critical.

[0003] The traditional ship heave measurement method is based on a high-pass filter. This method can effectively filter out the influence of low-frequency signals, but there is a certain phase advance phenomenon in the measurement of ship heave motion, and it is not possible to monitor the heave motion in real time, which will lead to large measurement errors. Therefore, a ship heave measurement method based on complementary filtering is proposed. This method can effectively track the heave motion of the ship and has high measurement accuracy. Summary of the invention

[0004] The object of the present invention is to provide a heave measurement method based on complementary filtering to solve the problem in the prior art that the accuracy of ship heave measurement is low due to phase advance generated by a traditional high-pass filter.

[0005] The purpose of the present invention is achieved through the following technical solutions:

[0006] A heave measurement method based on complementary filtering comprises the following steps:

[0007] Step 1: After the inertial system completes the initial alignment, collect the angular velocity and acceleration information of the three axes and perform inertial navigation solution;

[0008] Step 2: Design the analog low-pass filter H L (s), using the complementary idea, we can get the analog high-pass filter H H (s); then use the bilinear Z transform to convert the analog high-pass filter into a digital high-pass filter H H (z);

[0009] Step 3: Based on the attitude information and acceleration information calculated by the inertial navigation system in step 1, calculate the attitude transformation matrix from the carrier system B to the navigation system N Attitude transformation matrix from navigation system N to semi-fixed system D By using the specific force equation The projection on the celestial axis gives the heave acceleration a d ;

[0010] Step 4: Design a sliding window to record the heave acceleration a obtained in step 3 d , through fast Fourier transform analysis, the dominant frequency of the real-time sea condition is obtained, which is used to update the cutoff frequency of the digital high-pass filter in 2, where the update time of the sliding window is t;

[0011] Step 5: By calculating the heave acceleration a in step 3 d Perform filtering, integration and heave amplitude compensation to obtain real-time heave motion information of the ship;

[0012] Step 6: During the multi-beam seafloor topography detection process, accurate heave information is obtained for motion compensation of the ship.

[0013] Furthermore, the initial alignment of the inertial system in step 1 mainly refers to the initial alignment of the static base, including coarse alignment and fine alignment processes, and the inertial navigation solution includes inertial device error compensation, geographic information update, speed update, position update and attitude update.

[0014] Furthermore, the analog low-pass filter H designed in step 2 L (s):

[0015]

[0016] Among them, ω c is the filter cutoff frequency, which is determined by the heave dominant frequency; ξ is the filter damping coefficient, which is s is the complex frequency domain variable of the continuous-time system;

[0017] The analog high-pass filter H is obtained by the complementary idea H (s):

[0018]

[0019] Finally, according to the bilinear Z transform, the digital high-pass filter H is obtained. H (z):

[0020]

[0021] Where T is the sampling period, Ω = ω c T.

[0022] Furthermore, in step 3, the heave acceleration a d The calculation process is:

[0023]

[0024] in, It is the relative force information under the navigation system, that is, the comprehensive acceleration; is the projection of the integrated acceleration in the semi-fixed system; is the comprehensive acceleration Projection onto the celestial axis.

[0025] Furthermore, in step 4, the length of the sliding window data is L.

[0026] Furthermore, the step 5 is specifically as follows:

[0027] Let heave acceleration be a, heave velocity be v, and heave displacement be h;

[0028] a(k)=H H (z)a(k)

[0029]

[0030] v(k)=H H (z)v(k)

[0031]

[0032] h(k)=H H (z)h(k)

[0033] h(k)=mh(k)

[0034] Among them, k represents the current moment of the motion information; (k-1) represents the previous moment of the motion information; and m is the heave displacement amplitude compensation coefficient.

[0035] A computer device / equipment / system comprises a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of a heave measurement method based on complementary filtering.

[0036] A computer-readable storage medium stores a computer program / instruction thereon, which, when executed by a processor, implements the steps of a heave measurement method based on complementary filtering.

[0037] The beneficial effects of the present invention are:

[0038] The present invention incorporates the idea of ​​complementary filtering to achieve ship heave measurement based on traditional high-pass filters, designs a simple and controllable second-order complementary high-pass filter, and effectively solves the problem of obvious phase advance in traditional high-pass filters; for the amplitude error generated by the complementary filter, a corresponding heave displacement amplitude compensation coefficient is designed, which can achieve high-precision ship heave measurement, and the error can be controlled within a measurement error of 5 cm or 5%. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 Design a flow chart for the solution of the present invention;

[0040] Figure 2 This is the heave displacement output curve diagram for sea state 1;

[0041] Figure 3 This is the heave displacement error curve for sea condition 1;

[0042] Figure 4 This is the heave displacement output curve diagram for sea state 2;

[0043] Figure 5 This is the heave displacement error curve for sea state 2;

[0044] Figure 6 This is the heave displacement error curve of the fixed frequency complementary filter. DETAILED DESCRIPTION

[0045] The present invention is further described below in conjunction with the accompanying drawings.

[0046] like Figure 1 As shown, the present invention discloses a heave measurement method based on complementary filtering, and the specific steps include the following:

[0047] Step 1: After the inertial system completes the initial alignment, the angular velocity information and acceleration information of the three axes are collected and the inertial navigation solution is performed; this includes the coarse alignment and fine alignment processes. The inertial navigation solution includes inertial device error compensation, geographic information update, velocity update, position update, and attitude update.

[0048] Step 2: Design the analog low-pass filter H L (s), using the complementary idea, we can get the analog high-pass filter H H (s); then use the bilinear Z transform to convert the analog high-pass filter into a digital high-pass filter H H (z);

[0049] First, design an analog low-pass filter H L (s):

[0050]

[0051] Among them, ω c is the cutoff frequency of the filter, and its value can be determined by the dominant frequency of heave; ξ is the damping coefficient of the filter, and its value is s is the complex frequency domain variable of the continuous-time system;

[0052] Then, the analog high-pass filter H is obtained by the complementary idea. H (s):

[0053]

[0054] Finally, according to the bilinear Z transform, the digital high-pass filter H can be obtained. M (z):

[0055]

[0056] Where T is the sampling period, Ω = ω c T.

[0057] Step 3: Based on the attitude information and acceleration information calculated by the inertial navigation system, the attitude matrix from the carrier system B to the navigation system N can be calculated. Attitude matrix from navigation system N to semi-fixed system D By using the specific force equation The projection on the celestial axis gives the heave acceleration a d ;

[0058] Define the attitude transformation matrix from the carrier system to the navigation system for:

[0059]

[0060] Define the attitude transformation matrix from the navigation system to the semi-fixed system for:

[0061]

[0062] Calculate the comprehensive acceleration based on the specific force equation for:

[0063]

[0064] Among them, φ is the yaw angle of the carrier, θ is the longitudinal yaw angle of the carrier, and γ is the lateral yaw angle of the carrier; is the specific force vector under the load system; is the error caused by the earth's rotation and the Coriolis acceleration; g n is the projection of gravitational acceleration in the navigation system; is the comprehensive acceleration Projection onto the celestial axis.

[0065] Step 4: Design a sliding window to record the heave acceleration a d ,Through fast Fourier transform analysis, the dominant frequency of the real-time sea condition can be obtained, which can be used to update the high-pass filter, where the update time of the sliding window is t;

[0066] The frequency of the ship's heave motion is roughly between 0.05Hz and 0.2Hz, and its period is between 5s and 20s. In order to make the FFT result more accurate, the length L of the sliding window should be an integer power of 2, and the update time t should be greater than the maximum heave period.

[0067] Step 5: By calculating the heave acceleration a in step 3 d By performing three high-pass filtering, two integrations and heave amplitude compensation, the real-time heave motion information of the ship can be obtained;

[0068] Note the heave acceleration a d is a, the heave velocity is v, and the heave displacement is h;

[0069] a(k)=H H (z)a(k)

[0070]

[0071] v(k)=H H (z)v(k)

[0072]

[0073] h(k)=H H (z)h(k)

[0074] h(k))=mh(k)

[0075] Among them, k represents the current moment of the motion information; (k-1) represents the previous moment of the motion information; and m is the heave displacement amplitude compensation coefficient.

[0076] To verify the effectiveness of the present invention, simulation tests were conducted on navigation and positioning at sea based on the SINS / GPS combined navigation system. The data measured by the inertial device will also participate in the Kalman filter after error compensation and inertial navigation solution. Therefore, the low-frequency noise of the celestial integrated acceleration calculated by the combined navigation system has been filtered out by the Kalman filter. The key parameters of the system and track are: gyro constant zero bias 0.01° / h, gyro angle random walk The zero bias of the constant value of the meter is 100μg, and the speed of the meter is random walk. The ship first stayed on the sea surface for 300 seconds to complete the initial alignment, and then sailed northward at a constant speed of 10m / s for 1500 seconds;

[0077] Sea condition 1: Assume that the ideal heave displacement generated by the ship is multi-signal sin(2pi*1 / 10*t)+0.4sin(2pi*1 / 8*t)+0.2sin(2pi*1 / 5*t);

[0078] Sea condition two: Assume that the ideal heave displacement signal generated by the ship in the first half of the time is sin(2pi*1 / 10*t)+0.4sin(2pi*1 / 8*t)+0.2sin(2pi*1 / 5*t), and the ideal heave signal generated in the second half of the time is 0.8sin(2pi*1 / 8*t)+0.3sin(2pi*1 / 6*t)+0.1sin(2pi*1 / 5*t).

[0079] like Figure 2 and Figure 3 As shown, a composite signal based on sea conditions simulating real sea conditions is filtered by the complementary filter designed by the present invention. After the filter converges, the error of heave measurement is within 0.04m, which has extremely high measurement accuracy.

[0080] like Figure 4 and Figure 5 As shown in the figure, according to the composite signal of sea condition 2 simulating the real sea condition, when the dominant frequency changes, the filter will overshoot, and there will be a convergence time of about 90s at the transition point, but the wind and waves on the sea surface are mostly relatively stable, and the default dominant frequency will not change frequently; and from the error, it can be seen that the filter designed in this paper has extremely high measurement accuracy after stabilization, and the heave error of the front time is within 0.04m, and the heave error of the back time is within 0.02m.

[0081] like Figure 6 As shown, in the case of sea state 2, if the complementary filter does not have the frequency adaptive function of the present invention, the heave motion measurement error is within 0.04m, and the back-end error will be twice as high as that of the adaptive filter.

[0082] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A heave measurement method based on complementary filtering, characterized in that: The following steps are involved: Step 1: After the inertial system completes the initial alignment, collect the angular velocity and acceleration information of the three axes and perform inertial navigation solution; Step 2: Design the analog low-pass filter H L (s), using the complementary idea, we can get the analog high-pass filter H H (s); then use the bilinear Z transform to convert the analog high-pass filter into a digital high-pass filter H H (z); Step 3: Based on the attitude information and acceleration information calculated by the inertial navigation system in step 1, calculate the attitude transformation matrix from the carrier system B to the navigation system N Attitude transformation matrix from navigation system N to semi-fixed system D By using the specific force equation The projection on the celestial axis gives the heave acceleration a d ; Step 4: Design a sliding window to record the heave acceleration a obtained in step 3 d , through fast Fourier transform analysis, the dominant frequency of the real-time sea condition is obtained, which is used to update the cutoff frequency of the digital high-pass filter in 2, where the update time of the sliding window is t; Step 5: By calculating the heave acceleration a in step 3 d Perform filtering, integration and heave amplitude compensation to obtain real-time heave motion information of the ship; Step 6: During the multi-beam seafloor topography detection process, accurate heave information is obtained for motion compensation of the ship.

2. A heave measurement method based on complementary filtering according to claim 1, characterized in that: The initial alignment of the inertial system in step 1 mainly refers to the initial alignment of the static base, including coarse alignment and fine alignment processes. The inertial navigation solution includes inertial device error compensation, geographic information update, speed update, position update and attitude update.

3. The heave measurement method based on complementary filtering according to claim 1, characterized in that: The analog low-pass filter H designed in step 2 L (s): Among them, w c is the filter cutoff frequency, the value of which is determined by the heave dominant frequency; ξ is the filter damping coefficient, the value of which is s is the complex frequency domain variable of the continuous-time system; The analog high-pass filter H is obtained by the complementary idea H (s): Finally, according to the bilinear Z transform, the digital high-pass filter H is obtained. H (z): Where T is the sampling period, Ω = w c T.

4. The heave measurement method based on complementary filtering according to claim 1, characterized in that: In step 3, the heave acceleration a d The calculation process is: in, It is the relative force information under the navigation system, that is, the comprehensive acceleration; is the projection of the integrated acceleration in the semi-fixed system; is the comprehensive acceleration Projection onto the celestial axis.

5. The heave measurement method based on complementary filtering according to claim 1, characterized in that: The sliding window data length in step 4 is L.

6. The heave measurement method based on complementary filtering according to claim 1, characterized in that: The step 5 is specifically as follows: Let heave acceleration be a, heave velocity be v, and heave displacement be h; a(k)=H H (z)a(k) v(k)=H H (from)to(to) h(k)=H H (z)h(k) h(k)=mh(k) Among them, k represents the current moment of the motion information; (k-1) represents the previous moment of the motion information; and m is the heave displacement amplitude compensation coefficient.

7. A computer device / equipment / system comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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