A vertical channel navigation method for a deep-sea underwater vehicle

Abstract

The present invention discloses a vertical channel navigation method for a deep-sea underwater vehicle. After using the CTD profile to construct a pressure-depth database, the pressure measured by the IPS of the underwater vehicle is input into the database for linear interpolation to obtain the IPS interpolation depth. The IPS measurement is inevitably affected by waves and sensor noise. Then, a smoother depth is output based on the AKF filter to weaken the influence of waves and sensor noise. It can be applied in a deep-sea environment and is not affected by the depth of seawater. This scheme uses the temperature, salinity, and pressure profiles measured by CTD, combined with the seawater state equation, and has a smaller conversion error from pressure to depth, no error accumulation, and a high frequency of updating the smoothed data output by the navigation result. Navigation is performed based on the smoothed vertical velocity information, which has better practical application and promotion effects.

Description

Technical Field

[0001] The invention belongs to the field of deep-sea underwater vehicle navigation, and in particular relates to a vertical channel navigation method for a deep-sea underwater vehicle. Background Art

[0002] For the navigation technology of shallow-water underwater vehicles, DVL can be used to output vertical velocity without error accumulation, and the depth can be calculated based on the basic equation of static pressure (P=ρgh). However, for deep-sea underwater robots, the bottom tracking of DVL is invalid, so the vertical velocity cannot be output and it cannot be applied in deep-sea environments. In addition, since the density of seawater changes with temperature, salinity and pressure, the depth obtained using the basic equation of static pressure in deep-sea environments has a large error with the true value.

[0003] For vertical navigation of deep-sea carriers, the existing technologies include:

[0004] (1) LBL navigation: When LBL is used in deep-sea environments, it can output vertical position, but cannot provide vertical velocity. In addition, the data update frequency of LBL is too low, and the deployment and recovery of LBL are troublesome, expensive, and the data update frequency is low.

[0005] (2) Glass, Michael. "Combining pressure and inertial measurements for submarine depth determination." Naval engineers journal 98.6 (1986): 62-68. Michael Glass inputs the accelerometer data and pressure into a low-pass filter and a high-pass filter, and the filter outputs the depth and vertical velocity. This technology mainly filters out the influence of waves on pressure measurement, but it does not consider the error caused by the change of seawater density in the deep sea environment, and the programming of the filter is very complicated, and the actual application effect is poor;

[0006] (3) Willumsen, Are B., Ove Kent Hagen, and Per Norvald Boge. "Filtering depth measurements in underwater vehicles for improved seabed imaging." OCEANS 2007-Europe. IEEE, 2007. Are B. Willumsen input the pressure data together with the INS data into the NavLab software for processing. This technology also uses the UNESCO 1983 algorithm, but does not output the vertical velocity, and the INS has cumulative errors. In the absence of DVL and GPS assistance, its vertical velocity error and depth error diverge exponentially over time, making it unreliable. Summary of the invention

[0007] In view of the problem in the prior art that a deep-sea underwater vehicle cannot obtain vertical speed and has inaccurate depth, the present invention proposes a vertical channel navigation method for a deep-sea underwater vehicle, which provides accurate depth and vertical speed for the underwater vehicle and realizes more accurate navigation.

[0008] The present invention is implemented by adopting the following technical solution: A vertical channel navigation method for a deep-sea underwater vehicle comprises the following steps:

[0009] Step A: Before deploying the underwater vehicle, use the ship-borne CTD to measure the seawater salinity, temperature and pressure profile information in the mission area;

[0010] Step B, according to the profile information obtained in step A, the depth is calculated based on the seawater state equation, and a "pressure-depth" database is constructed;

[0011] Step C, importing the "pressure-depth" database into the navigation computer of the underwater vehicle, and when the underwater vehicle is sailing, inputting the pressure information measured by the IPS installed on the underwater vehicle into the database for linear interpolation to obtain the corresponding IPS interpolation depth;

[0012] Step D: Run the AKF filter in the underwater vehicle, input the linear interpolated depth and the vertical acceleration of the INS into the AKF filter, output the corresponding depth and vertical velocity, and realize vertical channel navigation.

[0013] Furthermore, in step B, it is specifically implemented in the following manner:

[0014] (1) The “pressure-depth” database is stored in a “key-value” format, where CTD pressure is the “key” and the depth calculated using the seawater state equation is the “value”;

[0015] (2) The complete formula of the seawater state equation is as follows:

[0016]

[0017] Where p is the seawater pressure, z is the seawater depth, S is the seawater salinity, T is the seawater temperature, V(35, 0, p) is the seawater specific volume under standard ocean conditions, g0 is the gravitational acceleration at the seawater surface, μ is the latitude, γ p is the average vertical gravity gradient, and δ(S, T, p) is the specific volume anomaly.

[0018] (3) Convert the integral form of the seawater state equation into the cumulative form:

[0019]

[0020] The unit of pressure is "dBar", p i is the pressure corresponding to the i-th row of data in the database, p i+1 It is the pressure corresponding to the i+1th row of data in the database.

[0021] Furthermore, in step C, considering that the "pressure-depth" database stores a series of known, discrete "key-values", when the pressure measured by the IPS is not in the database, a linear interpolation method is used to obtain a depth that matches the known "pressure-depth" database to obtain the IPS interpolation depth.

[0022] Further, in step D:

[0023] (1) The system state equation of the AKF filter is:

[0024] X k =A k *X k-1 +ω k-1

[0025] Write it in matrix form

[0026]

[0027] Where x is the depth estimated by the AKF filter, v x is the vertical velocity estimated by the AKF filter, a x is the vertical acceleration estimated by the AKF filter, Δt is the time interval for the filter execution, ω k-1 is the system noise;

[0028] (2) The observation equation of the AKF filter is:

[0029] Z k =H k *Xk +v k

[0030] Write it in matrix form

[0031]

[0032] Among them, d IPS is the IPS interpolation depth, a INS is the vertical acceleration of INS, v k To observe the noise, if the depth gauge data is updated, then otherwise

[0033] Furthermore, the AKF filter is based on variance scaling technology, using the scaling factor S k Prior estimation of the covariance matrix in the AKF filter To scale, The scaling factor S k The calculation formula is as follows:

[0034]

[0035] in is the prior state estimate of the AKF filter.

[0036] Compared with the prior art, the advantages and positive effects of the present invention are:

[0037] After this scheme uses the CTD profile to build a "pressure-depth" database, the pressure measured by the IPS of the underwater vehicle is input into the database for linear interpolation to obtain the IPS interpolation depth. The IPS measurement is inevitably affected by waves and sensor noise. Then, a smoother depth is output based on the AKF filter to weaken the influence of waves and sensor noise. It can be applied in deep-sea environments and is not affected by the depth of seawater. This scheme uses CTD to obtain seawater temperature, salinity, and pressure profiles. Combined with the seawater state equation, the conversion error from pressure to depth is smaller, there is no error accumulation, the navigation result output is smooth, the data update frequency is high, and vertical navigation is performed based on the smoothed vertical velocity information, which has better practical application and promotion effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is a schematic diagram of a linear interpolation process according to an embodiment of the present invention;

[0039] Figure 2 This is a schematic diagram of the overall flow of the navigation method according to an embodiment of the present invention;

[0040] Figure 3It is a schematic diagram of the north speed and east speed curve of the INS during the sea trial of the embodiment of the present invention;

[0041] Figure 4 It is a schematic diagram of the estimated depth output by AKF and the depth curve calculated using the basic equation of static pressure in an embodiment of the present invention;

[0042] Figure 5 This is the vertical velocity estimated by using AKF when the underwater vehicle in the embodiment of the present invention floats up. DETAILED DESCRIPTION

[0043] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described below in conjunction with the accompanying drawings and embodiments. In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention can also be implemented in other ways different from those described herein, and therefore, the present invention is not limited to the specific embodiments disclosed below.

[0044] Terminology explanation:

[0045] UV:Underwater Vehicle

[0046] CTD: Conductivity-Temperature-Depth sensors

[0047] IPS: Intelligent Pressure Sensor

[0048] INS: Inertial Navigation System

[0049] DVL: Doppler Velocity Log

[0050] AKF: Adaptive Kalman Filter, adaptive AKF filter

[0051] LBL: Long Baseline Acoustic Positioning System, long baseline acoustic positioning system

[0052] This embodiment provides a vertical channel navigation method for a deep-sea underwater vehicle, such as Figure 2 As shown, the specific steps include:

[0053] Step A: Before deploying the underwater vehicle, use the ship-borne CTD to measure the seawater salinity, temperature and pressure profile information in the mission area;

[0054] Step B, according to the profile information obtained in step A, the depth is calculated based on the seawater state equation, and a "pressure-depth" database is constructed;

[0055] (1) The “pressure-depth” database is stored in a “key-value” format, where the CTD pressure is the “key” and the depth calculated using the seawater state equation is the “value”.

[0056] Table 1 is a partial example of the "pressure-depth" database. The first column of Table 1 is the pressure profile measured by CTD, and the second column is the depth calculated using the seawater state equation. The pressure is arranged in ascending order, and its unit is "dBar" and the unit of depth is "meter (m)". The file format of the database is txt.

[0057] Table 1 Some examples of the “pressure-depth” database

[0058]

[0059]

[0060] (2) The seawater state equation is calculated using the UNESCO 1983 algorithm, and its complete formula is as follows:

[0061]

[0062] Where p is the seawater pressure, z is the seawater depth, S is the seawater salinity, T is the seawater temperature, V(35,0,p) is the seawater specific volume under the standard ocean environment (salinity is 35psu, temperature is 0°C), g0 is the gravitational acceleration at the seawater surface, μ is the latitude, γ p is the average vertical gravity gradient, and δ(S, T, p) is the specific volume anomaly.

[0063] (3) In order to perform digital calculations, this embodiment converts the integral form of the seawater state equation into a cumulative form:

[0064]

[0065] The unit of pressure is "dBar", p i is the pressure corresponding to the i-th row of data in the database, p i+1 It is the pressure corresponding to the i+1th row of data in the database.

[0066] Step C: importing the "pressure-depth" database into the navigation computer of the underwater vehicle. The underwater vehicle is equipped with an IPS. When the underwater vehicle is navigating, the pressure information measured by the IPS is input into the database, and linear interpolation is performed to obtain the corresponding IPS interpolation depth.

[0067] Here, linear interpolation is required for the following reasons: the "pressure-depth" database stores a series of known, discrete "key-values". The pressure measured by the IPS may not be in the database, so the linear interpolation method is used to obtain the depth that matches the known "pressure-depth" database. For example: if the pressure measured by the IPS is 586.3900dBar, according to the database, there is no record with a "key" of 586.3900 in the data at this time, then linear interpolation can be used to obtain the IPS interpolation depth corresponding to the pressure of 586.3900dBar. The interpolation flow chart is as follows: Figure 1 shown.

[0068] Step D: Run the AKF filter in the underwater vehicle, input the linear interpolation depth and the vertical acceleration of the INS into the AKF filter, and output the vertical velocity, which is specifically:

[0069] (1) The system state equation of the AKF filter is:

[0070] X k =A k *X k-1 +ω k-1

[0071] Write it in matrix form

[0072]

[0073] Where x is the depth estimated by the AKF filter, v x is the vertical velocity estimated by the AKF filter, a x is the vertical acceleration estimated by the AKF filter, Δt is the time interval for the filter execution, ω k-1 is the system noise.

[0074] (2) The observation equation of the AKF filter is:

[0075] Z k =H k *X k +v k

[0076] Rewrite it into matrix form

[0077]

[0078] Among them, d IPS is the IPS interpolation depth, a INS is the vertical acceleration of INS, v k To observe the noise, if the depth gauge data is updated, then otherwise

[0079] The AKF filter used in this embodiment is based on variance scaling technology, using a scaling factor S k Prior estimation of the covariance matrix in the AKF filter To scale, The scaling factor S k The calculation formula is as follows:

[0080]

[0081] in is the prior state estimate of the AKF filter.

[0082] After obtaining the estimated depth and vertical velocity, the control system of the underwater vehicle makes corresponding decisions based on the depth and vertical velocity estimated by the AKF filter, thereby realizing vertical channel navigation.

[0083] In order to further verify the effect of the present invention, a sea trial was conducted in the South China Sea in December 2020. The result diagram obtained using the scheme of the present invention is as follows:

[0084] In a real sea trial environment, the vertical position and vertical speed of the INS are set to zero. Figure 3 The north and east speeds of the INS during the sea trial. It is obvious that the speed of the INS diverges when the DVL and GPS are invalid underwater. If the vertical speed and vertical position of the INS are not set to zero, its vertical speed and vertical position will definitely diverge; Figure 4 The broken line with steps in the figure is the depth calculated using the basic equation for static pressure (P = ρgh). Figure 4 The smooth curve in the figure is the estimated depth output by AKF. The reason for the step is that the data update frequency of IPS is lower than the operating frequency of AKF. It can be seen that after using AKF, relatively smooth depth data can be output even when IPS is not updated. Figure 5 The vertical velocity estimated by AKF when the underwater vehicle is ascending.

[0085] The above description is only a preferred embodiment of the present invention and does not limit the present invention in other forms. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A vertical channel navigation method for a deep-sea underwater vehicle, characterized in that: The following steps are involved: Step A: Before deploying the underwater vehicle, use the ship-borne CTD to measure the seawater salinity, temperature and pressure profile information in the mission area; Step B, according to the profile information obtained in step A, the depth is calculated based on the seawater state equation, and a "pressure-depth" database is constructed; Step C, importing the "pressure-depth" database into the navigation computer of the underwater vehicle, and when the underwater vehicle is sailing, inputting the pressure information measured by the IPS installed on the underwater vehicle into the database for linear interpolation to obtain the corresponding linear interpolation depth; Step D: The navigation system of the underwater vehicle runs an AKF filter, inputs the linearly interpolated depth and the vertical acceleration of the INS into the AKF filter, outputs the corresponding depth and vertical velocity, and thus realizes vertical channel navigation; (1) The system state equation of the AKF filter is: X k =A k *X k-1 +oh k-1 Write it in matrix form Where x is the depth estimated by the AKF filter, υ x is the vertical velocity estimated by the AKF filter, a x is the vertical acceleration estimated by the AKF filter, Δt is the time interval for the filter execution, ω k-1 is the system noise; (2) The observation equation of the AKF filter is: Z k =H k *X k +υ k Write it in matrix form Among them, d IPS is the depth of linear interpolation of IPS, a INS is the vertical acceleration of INS, υ k To observe the noise, if the depth gauge data is updated, then otherwise The AKF filter is based on variance scaling technology, using the scaling factor S k Prior estimation of the covariance matrix in the AKF filter To scale, The scaling factor S k The calculation formula is as follows: in is the prior state estimate of the AKF filter.

2. The vertical channel navigation method of a deep-sea underwater vehicle according to claim 1, characterized in that: In the step B, it is specifically implemented in the following manner: (1) The "pressure-depth" database is stored in a "key-value" format, where CTD pressure is the "key" and the depth calculated using the seawater state equation is the "value"; (2) The complete formula of the seawater state equation is as follows: Where p is the seawater pressure, z is the seawater depth, S is the seawater salinity, T is the seawater temperature, V(35, 0, p) is the seawater specific volume under standard ocean conditions, g0 is the gravitational acceleration at the seawater surface, μ is the latitude, γ p is the average vertical gravity gradient, δ(S, T, p) is the specific volume anomaly; (3) Convert the integral form of the seawater state equation into the cumulative form: Among them, p i is the pressure corresponding to the i-th row of data in the database, p i+1 It is the pressure corresponding to the i+1th row of data in the database.

3. The vertical channel navigation method of a deep-sea underwater vehicle according to claim 1, characterized in that: In step C, considering that the "pressure-depth" database stores a series of known, discrete "key-values", when the pressure measured by the IPS is not in the database, a linear interpolation method is used to obtain a depth that matches the known "pressure-depth" database to obtain a linearly interpolated depth.

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