An underwater multi-station speed measurement method based on acoustic beacons

Abstract

The present invention belongs to the field of underwater target navigation and positioning, and discloses an underwater multi-station velocity measurement method based on acoustic beacons. The specific steps are as follows: 1) Establish a coordinate system, obtain measurement information, and estimate the initial position of the vehicle; 2) Perform sound speed correction and position parameter estimation point by point. After the position parameters converge through iterative calculation, perform speed parameter estimation; 3) Estimate the coefficients of the constraint polynomial and optimize the position and speed parameters of the vehicle. This technology has the characteristics that the observation process does not depend on the vehicle, the measurement accuracy is high, and the results can be obtained quasi-real-time. It realizes the reliable estimation of position and speed parameters based on polynomial constraints under complex navigation conditions, solves the problems of functional expression of observed motion and provision of continuous and smooth motion parameters under high-dynamic motion conditions, has good adaptability to the complex motion process of underwater targets, has broad application prospects for technology, and can provide data processing and analysis technology for the analysis and evaluation of the underwater motion performance of vehicles.

Description

Technical Field

[0001] The present invention belongs to the field of underwater target navigation and positioning, and particularly relates to an underwater multi-station speed measurement method based on acoustic beacons, which is a technology for measuring the underwater movement speed without relying on the conditions of the vehicle itself by using acoustic beacons and multi-station receiving devices. Background Art

[0002] Affected by factors such as R & D costs, operating conditions, and ocean environments, it is difficult to obtain motion data during underwater tests of vehicles. Currently, the main method is to install inertial sensors inside the vehicle to obtain parameters such as acceleration and angular rate, and then obtain information such as speed, position, and attitude through data processing. This method of obtaining data is relatively mature and reliable, but there are also inherent defects. The drift error of inertial devices accumulates over time, resulting in a large positioning error during long-term navigation. Another internal measurement method is to install a Doppler log on the vehicle and measure the ship's speed and cumulative voyage relative to the seabed using the Doppler frequency shift between the transmitted acoustic wave and the received seabed-reflected acoustic wave. Its technology is also relatively mature. The above two methods both rely on the vehicle itself, and the data is recorded and processed on the vehicle, which is suitable for post-analysis. When the vehicle cannot be recovered normally due to a malfunction, it is difficult to obtain its underwater movement process and state information.

[0003] In contrast, underwater acoustic positioning technology is an external observation technology that does not rely on navigation. Currently, various positioning modes such as ultra-short baseline, short baseline, and long baseline have been developed, and robust positioning performance and high positioning accuracy can be obtained under the condition of installing acoustic beacons. However, underwater acoustic positioning technology is closely related to the working conditions and environmental conditions of actual applications. Any underwater acoustic positioning system has its applicable scope and conditions. For local underwater navigation with strong mobility and high dynamics, in order to accurately measure and identify its motion characteristics, targeted technical designs need to be adopted in combination with specific working conditions. Especially for the measurement of speed parameters, if the conventional position difference method is used for calculation, the obtained sequence values will show large random fluctuations, which are difficult to meet the requirements of vehicle motion performance analysis.

[0004] In recent years, with the progress of underwater acoustic technology, the performance of acoustic beacons has been continuously improved, and technologies such as high-frame-rate positioning acoustic signal provision, robust estimation of radial velocity, and high-precision underwater station calibration have been developed, providing a technical basis for solving the problem of high-precision underwater speed measurement of vehicles under external measurement conditions. In order to solve the problem of high-reliability speed measurement for underwater performance evaluation of vehicles, it is urgent to develop an external measurement technology for underwater motion parameters based on acoustic beacons. Summary of the Invention

[0005] To solve the above problems, the present invention proposes an underwater motion speed measurement technology that uses acoustic beacons and multi-station receiving devices to achieve underwater motion speed measurement without relying on the conditions of the vehicle itself. The acoustic positioning system based on acoustic beacons provides slant range and radial velocity information, combines with the positions of the stations to complete the position and velocity solution, and then uses the polynomial constraint method to achieve the optimal estimation of the entire sequence. This technology has the characteristics of not relying on the vehicle during the observation process, high measurement accuracy, quasi-real-time acquisition of results, and smooth and continuous data. It can adapt to local complex underwater navigation processes with strong maneuverability and high dynamicity, and has practical value for the analysis and evaluation of the underwater motion performance of the vehicle.

[0006] The technical solution of the present invention is as follows:

[0007] An underwater multi-station speed measurement method based on acoustic beacons. First, a unified coordinate system is established, and information such as the positions of the stations and the sound speed profile is registered to obtain the estimated values of the arrival time delay and the radial velocity, and the initial position of the vehicle is estimated. Secondly, through the iterative operation of sound speed correction and position parameter estimation, the point-by-point solution results of the position and velocity parameters of the vehicle are obtained. Finally, a reasonable polynomial constraint function is selected, and the function coefficients are identified using the point-by-point solution results of the position and velocity parameters, and an optimized sequence of the position and velocity parameters of the vehicle is obtained based on the polynomial expression.

[0008] The specific steps are as follows:

[0009] Step 1: Establish the initial measurement information

[0010] (1.1) Establish the coordinate system

[0011] Establish a station coordinate system. Select a theoretical position point in the measurement area, set its elevation zero point as the origin O, the OX axis points east, the OY axis points north, and the OZ axis is vertically upward. Let the target position of the vehicle to be measured be X = [x, y, z], and the speed be Both the measurement information and the parameter estimation results are matched to this coordinate framework.

[0012] (1.2) Obtain the measurement information

[0013] Under the condition of multi-station marine deployment, through technical means such as satellite navigation and positioning, inertial sensors, and underwater acoustic positioning, obtain the position information of each station. The coordinates of station j are N is the number of stations (N≥3).

[0014] Obtain the sound speed profile c(z) through on-site measurement. The specific information includes the sound speed values varying with depth stratification and the observation positions.

[0015] Under the condition of an acoustic beacon, the receivers carried by each station are used to detect, process, and record the arriving acoustic signals. Using the prior information designed for positioning the acoustic signals and combining the acoustic velocity information measured on-site, the time delay of arrival and the radial velocity are estimated to obtain the time delay vector and the radial velocity vector

[0016] (1.3) Initial position estimation

[0017] From the N stations, 3 stations are selected, and their numbers are set as 1 # -3 # , where the 3 # stations are reference stations to make a preliminary estimate of the target position.

[0018] The slant range between the target and the station is:[[]]

[0019]

[0020] 1 # 、2 # The baseline lengths between the stations and the 3 # station are:[[]]

[0021]

[0022] The initial position estimate value X 0 =[x 0 ,y 0 ,z 0 T :

[0023]

[0024] Where:[[]]

[0025]

[0026] cosθ = λ1λ2 + μ1μ2 + ν1ν2, sinθ=(1 - cos 2 θ) 1 / 2

[0027] Step 2: Solve the position and velocity parameters point by point

[0028] (2.1) Acoustic velocity correction and position parameter estimation

[0029] Based on the time delay vector the estimated value of the slant range between the target and the station can be calculated according to the acoustic velocity profile c(z) by the following formula:[[]]

[0030]

[0031] Where:[[]]​

[0032]

[0033]

[0034] Then the target position is estimated as follows:

[0035]

[0036] in:

[0037]

[0038] (2.2) Iterative calculation of position parameters

[0039] Get the estimated target position After that, ||ΔX||2 is used as the criterion to determine whether the predetermined convergence accuracy has been reached; if not, the position estimate is updated to the initial position value X of the next iteration. 0 , iteratively calculate according to the method described in (2.1) until it converges to the required accuracy.

[0040] (2.3) Speed ​​parameter estimation

[0041] Get the target position estimate that meets the convergence accuracy Then, the estimated value of the speed parameter is obtained as follows:

[0042]

[0043] Step 3: Estimate constraint polynomial coefficients and optimize position and velocity parameters

[0044] (3.1) Polynomial coefficient estimation

[0045] In a navigation sequence, the method of step 2 is used to solve point by point to obtain the estimated values ​​of the parameter sequence of position and speed. and (Wherein, the sequence number of the time series k = 1, 2, ..., K).

[0046] Taking into account the inevitable random errors in point-by-point observation data, which causes random "jitter" in the data, the position and velocity sequences are expressed as functions constrained by polynomials as follows:

[0047]

[0048] Where m (m≥2), p (p≥2), and q (q≥2) are the polynomial orders.

[0049] The polynomial coefficients are estimated using the estimated values ​​of the position and velocity parameter sequences obtained by point-by-point calculation:

[0050]

[0051] Wherein:

[0052]

[0053] Thus, the polynomial coefficients C in the X, Y, and Z directions are obtained. X = [a0, a1, …, a m , C Y = [b0, b1, …, b p , C Z = [c0, c1, …, c q .

[0054] (3.2) Position and Velocity Parameter Optimization

[0055] Using the functions x(t), y(t), z(t), of the position and velocity polynomial constraints described in (3.1), substitute the polynomial coefficients C X , C Y and C Z to obtain the optimized position and velocity parameter sequences and (where the sequence number k of the time series is k = 1, 2, …, K).

[0056] Advantages of the present invention: This method is a technique for measuring underwater motion speed without relying on the conditions of the vehicle itself by using acoustic beacons and multi-station receiving devices. It fully considers the influence of the underwater acoustic channel on the measurement and the resulting fluctuations in the sequence data, realizes reliable estimation of position and velocity parameters based on polynomial constraints under complex navigation conditions, solves the problem of functional expression of the observed motion and the provision of continuous and smooth motion parameters under high-dynamic motion conditions, has good adaptability to the complex motion process of underwater targets, has broad technical application prospects, and can provide data processing and analysis techniques for the analysis and evaluation of the underwater motion performance of vehicles. Description of the Drawings

[0057] Figure 1 is the basic flow of the method proposed by the present invention.

[0058] Figure 2 is the distribution diagram of the target trajectory and the station positions.

[0059] Figure 3 is the slant range sequence curve of the four stations of the regular quadrilateral measurement array, where the horizontal axis is the time series and the vertical axis is the slant range value.

[0060] Figure 4It is the radial velocity sequence curve of 4 measuring stations of a regular quadrilateral measuring array, where the horizontal axis is the time series and the vertical axis is the radial velocity value.

[0061] Figure 5 The three-dimensional trajectory of the vehicle is given, and the point-by-point solution results, parameter estimation results and reference values are compared in the figure.

[0062] Figure 6(a) and Figure 6(b) are the position and velocity sequence curves of the vehicle respectively, where the horizontal axis is the time series and the vertical axes are the position value and velocity value respectively. The point-by-point motion results, parameter estimation results and reference values are compared in the figure. Detailed implementation manner

[0063] The following further describes the specific implementation manner of the method proposed by the present invention in combination with the accompanying drawings and technical solutions.

[0064] The main steps of the present invention are as Figure 1 shown. The following takes a group of underwater vehicles moving vertically as an example, and the goal is to obtain the velocity value of the vehicle at the characteristic moment. By processing and calculating according to the above steps, the rationality and effectiveness of the proposed method are illustrated.

[0065] (1) Select a regular quadrilateral measurement formation, with 4 measuring stations located at the seabed vertices of the quadrilateral, and measure a group of vertically moving targets. The movement trajectory of the vehicle and the distribution of the measuring station positions are shown in Figure 2 . After the vehicle starts to move, positioning acoustic signals are synchronously transmitted. Each measuring station receives the acoustic signal, and the time delay vector and the radial velocity vector are obtained through signal processing. During this period, the measurement of the sound speed profile is synchronously completed. In the measurement coordinate system, the above measurement information is uniformly processed, and the initial value X of the target is obtained according to the method described in (1.3) 0 =[x 0 ,y 0 ,z 0 T .

[0066] (2) According to the above measurement information, the sound speed correction and the iterative solution of the position parameters are completed according to the methods described in (2.1) and (2.2). The slant range sequence after the iteration is shown in Figure 3 . According to this group of slant ranges, the target position estimation value sequence is calculated point by point. Then, according to the Figure 4 shown radial velocity data, it is calculated according to the method described in (2.3), and the target velocity estimation value sequence

[0067] ​(3) According to the motion characteristics of the vehicle, a third-order polynomial is constructed in each of the three directions of X, Y, and Z, that is, m = 3, p = 3, and q = 3. Using the estimated values of the target position and velocity sequences obtained by point-by-point calculation, the estimated values of the constraint polynomial system coefficients are obtained according to the method described in (3.1), as shown in Table 1. Substitute the polynomial coefficients into the expressions for the motion parameters in (3.1) to finally obtain the optimized position and velocity parameter sequences. and Figure 5 Figure 6 respectively shows the three-dimensional trajectory and the curves of the position and velocity sequences during the motion of the vehicle. In the figure, the point-by-point motion results, parameter estimation results, and reference values are compared. It can be seen from the figure that the technology proposed in the present invention can accurately describe the high-dynamic motion process, effectively eliminate the data "jitter" of point-by-point calculation, and maintain the continuity and smoothness of the parameter sequence.

[0068] Table 1 Estimated values of constraint polynomial coefficients

[0069]

Claims

1. An underwater multi-station speed measurement method based on acoustic beacons, characterized in that: Here are the steps: Step 1: Establish initial measurement information (1.1) Establishing the coordinate system Establish the station coordinate system, select a theoretical position point in the measurement area, set its elevation zero point as the origin O, the OX axis points to the east, the OY axis points to the north, and the OZ axis is vertically upward; Assume that the target position of the vehicle to be tested is X = [x, y, z], and the speed is The measurement information and parameter estimation results are matched to this coordinate framework; (1.2) Obtaining measurement information Under the condition of multiple stations deployed at sea, the location information of each station is obtained through satellite navigation positioning, inertial sensors, and hydroacoustic positioning technology. The coordinates of station j are N is the number of measuring stations, N≥3; The sound velocity profile c(z) is obtained through field measurements, including the sound velocity values ​​that vary with depth and the observation location; Under the condition of acoustic beacon, the receivers carried by each measuring station are used to detect, process and record the arrival acoustic signals. The arrival delay and radial velocity are estimated by using the prior information of the positioning acoustic signal design and combining the sound speed information measured on site, and the delay vector is obtained. and the radial velocity vector (1.3) Initial position estimation From N measuring stations, select 3 of them and set their numbers to 1 # -3 # , of which 3 # The station is a reference station, which makes a preliminary estimate of the target location; The slope distance between the target and the measuring station is: 1 # , 2 # Station and 3 # The baseline length of the station is: The initial position estimate X is obtained as follows: 0 =[x 0 ,y 0 ,z 0 ] T : in: cosθ=λ1λ2+μ1μ2+ν1ν2,sinθ=(1-cos 2 i) 1 / 2 Step 2: Solve the position and speed parameters point by point (2.1) Sound speed correction and position parameter estimation According to the delay vector Combined with the sound velocity profile c(z), the estimated slant distance between the target and the measuring station is calculated as follows: in: Then the target position is estimated as follows: in: (2.2) Iterative calculation of position parameters Get the estimated target position After that, ||ΔX||2 is used as the criterion to determine whether the predetermined convergence accuracy has been reached; if not, the position estimate is updated to the initial position value X of the next iteration. 0 , iteratively calculate according to the method described in (2.1) until it converges to the required accuracy; (2.3) Speed ​​parameter estimation Get the target position estimate that meets the convergence accuracy After that, the estimated value of the speed parameter is obtained as follows: Step 3: Estimate constraint polynomial coefficients and optimize position and velocity parameters (3.1) Polynomial coefficient estimation In a navigation sequence, the method of step 2 is used to solve point by point to obtain the estimated values ​​of the parameter sequence of position and speed. and Wherein, the sequence number of the time series k = 1, 2, ..., K; Represent the position and velocity sequence as a function constrained by a polynomial: Where m, p, q are the polynomial orders, m≥2, p≥2, q≥2; The polynomial coefficients are estimated using the estimated values ​​of the position and velocity parameter sequences obtained by point-by-point calculation: in: Thus, we get the polynomial coefficients C in the three directions of X, Y, and Z X =[a0,a1,…,a m ]、C Y =[b0,b1,…,b p ]、C Z =[c0,c1,…,c q ]; (3.2) Position and speed parameter optimization Using the position and velocity polynomial constraints described in (3.1), y(t), z(t), Substitute the polynomial coefficients C X , C Y and C Z , get the optimized position and speed parameter sequence and Among them, the sequence number of the time series is k=1,2,…,K.

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