A heave measurement method based on complementary filtering

Abstract

The present invention discloses a heave measurement method based on complementary filtering, which belongs to the field of inertial navigation measurement and ship motion measurement and is used to measure the heave of a ship. The method comprises: based on the concept of complementary filtering, indirectly realizing its complementary high-pass filter by designing a low-pass filter; using the attitude information solved by the inertial system to calculate the attitude matrix between the carrier system, the navigation system, and the semi-fixed system, and then projecting the heave acceleration information under the navigation system to the semi-fixed system; setting a sliding window for recording the heave acceleration information, and calculating the dominant frequency of the acceleration in the window through FFT to update the high-pass filter; filtering and integrating the acceleration information to obtain the heave displacement of the ship, and performing a certain amplitude compensation on the heave displacement. The present invention can adaptively adjust the filter parameters according to changes in sea conditions, without obvious phase advance phenomenon, is easy to implement, and has high heave measurement accuracy.

Description

Technical Field

[0001] The invention belongs to the fields of inertial navigation measurement and ship motion measurement, and particularly 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 operations, etc., so the measurement of the ship's heave motion is extremely critical.

[0003] Traditional ship heave measurement methods rely on high-pass filters, which effectively filter out the influence of low-frequency signals. However, they suffer from a phase lead in the measurement of ship heave motion, preventing real-time monitoring of heave motion and leading to large measurement errors. Therefore, a ship heave measurement method based on complementary filtering is proposed. This method can effectively track ship heave motion with 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 of low accuracy of ship heave measurement due to phase advance generated by traditional high-pass filters.

[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 initial alignment, collect angular velocity and acceleration information of the three axes and perform inertial navigation calculations;

[0008] Step 2: Design the analog low-pass filter H L (s), using the complementary idea to obtain the analog high-pass filter H H (s); then use 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 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 state 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 seabed 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, velocity 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 cutoff frequency of the filter, and its value is 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;

[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 comparative force information under the navigation system, that is, the comprehensive acceleration; is the projection of the integrated acceleration in the semi-fixed frame; is the integrated 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] Wherein, k represents the current moment of motion information; (k-1) represents the previous moment of motion information; and m is the heave displacement amplitude compensation coefficient.

[0035] A computer device / apparatus / 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 concept of complementary filtering to solve the problem of ship heave measurement based on traditional high-pass filters, designs a simple and controllable second-order complementary high-pass filter, and effectively solves the obvious phase lead phenomenon of traditional high-pass filters. For the amplitude error generated by the complementary filter, a corresponding heave displacement amplitude compensation coefficient is designed, which can realize high-precision ship heave measurement, and the error can be controlled within 5cm or 5% of the measurement error. 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 for sea state 1;

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

[0042] Figure 4 This is the heave displacement output curve 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 will be further described below with reference to the accompanying drawings.

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

[0047] Step 1: After the inertial system completes initial alignment, collect angular velocity and acceleration information for the three axes and perform inertial navigation solutions. This includes coarse 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 to obtain the analog high-pass filter H H (s); then use 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 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] Obtain the comprehensive acceleration according to 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 integrated 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 conditions 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] Wherein, k represents the current moment of motion information; (k-1) represents the previous moment of 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 a SINS / GPS integrated navigation system for navigation and positioning at sea. The data measured by the inertial device, after error compensation and inertial navigation calculation, also participates in the Kalman filter. Therefore, the low-frequency noise of the celestial integrated acceleration calculated by the integrated 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 Add the table constant zero bias 100μg, add the table speed 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 10 m / 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 2: 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 displacement 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, based on the composite signal of sea state 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. However, the wind and waves on the sea surface are mostly relatively stable, and the default dominant frequency does 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, with the heave error of the front time within 0.04m and the heave error of the back time 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, its 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 foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A heave measurement method based on complementary filtering, characterized by: The following steps are involved: Step 1: After the inertial system completes initial alignment, collect angular velocity and acceleration information of the three axes and perform inertial navigation calculations; Step 2: Design the analog low-pass filter H L (s), using the complementary idea to obtain the analog high-pass filter H H (s); then use bilinear Z transform to convert the analog high-pass filter into a digital high-pass filter H H (z); Among them, ω c is the cutoff frequency of the filter, and its value is 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; T is the sampling period, Ω = ω c T; 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 the specific force equation The projection on the celestial axis gives the heave acceleration in, It is the comparative force information under the navigation system, that is, the comprehensive acceleration; is the projection of the integrated acceleration in the semi-fixed frame; is the integrated acceleration Projection on the celestial axis; 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 state 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 filtering, integrating and compensating the heave amplitude of the heave acceleration in step 3, the real-time heave acceleration a, heave velocity v and heave displacement h of the ship are obtained; a(k)=H H (z)a(k) υ(k)=H H (z)v(k) h(k)=H H (z)h(k) h(k)=mh(k) Wherein, k represents the current moment of motion information; (k-1) represents the previous moment of motion information; m is the heave displacement amplitude compensation coefficient; Step 6: During the multi-beam seabed topography detection process, accurate heave information is obtained for motion compensation of the ship.

2. The 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, velocity update, position update and attitude update.

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

4. A computer device / apparatus / 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 3.

5. 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 3 are implemented.

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