Shallow sea underwater acoustic jump interference resistant long baseline positioning system and fusion algorithm

Abstract

The invention discloses a long baseline positioning system and fusion algorithm for resisting shallow sea underwater acoustic jump interference, a long baseline array adopts a buoy type, a bottom type, or other composition forms, or a composite form of multiple forms, comprises a plurality of underwater acoustic beacons, and is used for receiving underwater acoustic signals emitted by an underwater vehicle; two (or more) transducers are installed at different depths (two sides of a jump layer) on a long baseline node, redundant transducers are selected through an intelligent algorithm to solve and solve the interference of the jump layer in long baseline positioning, and a data fusion algorithm is adopted to carry out fusion calculation on a navigation track. And the comprehensive underwater acoustic measurement performance, the shallow sea underwater acoustic jump interference resistance of the long baseline array and the positioning precision of the system under the complex sea condition can be greatly improved.

Description

Technical Field

[0001] The invention relates to the technical field of underwater acoustic positioning, tracking and measurement, and in particular to a long baseline positioning system and a fusion algorithm capable of resisting shallow water underwater acoustic jump interference. Background Art

[0002] Underwater positioning technology is an important part of deep-sea research and seabed missions, mainly based on acoustic positioning technology, among which the application of long baseline (LBL) system has shown great advantages. In the field of underwater vehicle positioning applications that require long distance and high precision, the long baseline system has the advantages of convenient calibration, large single array range, and high long-distance positioning accuracy. Compared with other positioning methods, the long baseline system has a very high performance in accuracy and reliability.

[0003] In terms of specific working principles, when the long baseline system is working, at least three nodes need to be deployed on the seabed to form a seabed node array in a certain formation. Then, by measuring the propagation delay of the acoustic signal between the underwater vehicle and the seabed node, the spherical intersection method is used to solve the geodetic coordinates of the underwater vehicle. Once calibrated, the system can continuously provide accurate positioning data.

[0004] However, there are also certain problems in the long baseline array positioning process. Due to the stratification of underwater acoustic velocity and the different depths between the underwater vehicle and the transducer, especially in shallow seas, lakes and reservoirs, the propagation of underwater acoustic signals is often interfered by the transition layer, which will affect the accuracy of signal processing and positioning tracking.

[0005] At present, long baseline positioning is mainly divided into two modes: buoy type and bottom type. However, both modes are based on a single transducer. If the depth difference between the underwater vehicle and the transducer is large, especially when it is above or below the transition layer, the propagation of the hydroacoustic signal will be disturbed by the transition layer, which will cause the spherical intersection method to fail during positioning solution, and thus fail to achieve accurate positioning.

[0006] Therefore, future research needs to solve the problem of interference of the transition layer on signal propagation. Possible measures include improving signal processing technology or changing the layout of the transducer to reduce the depth difference with the vehicle. In this way, we can give full play to the potential of the long baseline system in underwater positioning and provide more accurate positioning services for deep-sea research and seabed missions. Summary of the invention

[0007] In order to solve the defects and shortcomings of the above-mentioned prior art, the inventor has provided a new solution through improved design, the purpose of which is to avoid the failure of the spherical intersection method during positioning solution and achieve accurate positioning. Specifically, the present invention is implemented as follows:

[0008] A long baseline positioning system resistant to shallow water acoustic jump interference comprises: a long baseline array adopts a buoy type, a bottom type, or other composition forms, or a composite form of multiple forms, including multiple hydroacoustic beacons, used to receive hydroacoustic signals emitted by underwater vehicles, which comprises: a buoy is connected to an electronic cabin, at least one upper transducer, and at least one lower transducer in sequence through a transmission cable, and the end is an anchor block; a wireless transmission device is connected to the electronic cabin; wherein adjacent upper transducers and lower transducers are arranged at different depths and the distance between them is greater than the jump layer depth; the hydroacoustic signal sent by the underwater vehicle can be received and analyzed, and sent to the positioning tracking measurement software through the wireless transmission device; the underwater vehicle is used to navigate in the measurement waters, and carries a cooperative beacon to transmit the hydroacoustic signal in the measurement waters; the positioning tracking measurement software is used to obtain the positioning data sent by the long baseline array, and calculate, track, and locate the motion trajectory of the underwater vehicle based on the received data, perform data fusion and filtering processing, and further improve the positioning accuracy and stability.

[0009] Another aspect of the present invention discloses a fusion algorithm of the long baseline positioning system based on the above-mentioned anti-shallow sea water acoustic jump interference, comprising the following steps:

[0010] Step S1, deploying a long baseline array in the measuring water area, and calibrating each node of the long baseline array; when installing the hydroacoustic transducer to deploy the buoy node, ensure that the two hydroacoustic transducers are deployed at different depths and the distance between them is greater than the depth of the transition layer; the long baseline array adopts a buoy type, a bottom type, or other composition forms, or a composite form of multiple forms;

[0011] Step S2: Use an underwater vehicle to navigate in the long baseline array area, carry a cooperative beacon to transmit a hydroacoustic signal in the measurement waters, and the hydroacoustic transducer at each node of the long baseline array receives and analyzes the hydroacoustic signal, and then sends the analysis result to the positioning tracking measurement software through a data transmission link;

[0012] Step S3, using the positioning tracking measurement software to perform positioning tracking measurement on the underwater vehicle, analyzing the data of each node, and solving the navigation trajectory of the underwater vehicle through the data fusion algorithm; the positioning tracking measurement software uses the acquired coordinates of each node and the hydroacoustic positioning delay data to calculate the navigation trajectory of the underwater vehicle according to the intersection principle, and draws and displays it in the interface.

[0013] The fusion algorithm comprises the following steps:

[0014] Step A1: Each node can receive the underwater acoustic delay of m transducers, and there are n nodes, where m≥2 and n≥3; when all nodes and transducers can receive the signal and solve the delay, there are at least Combination methods can form at least A self-organizing array positioning base array;

[0015] Step A2, performing delay filtering on the signals received by each transducer, comparing the delay data with the previously received data, and removing the data that is much larger than the average value;

[0016] Step A3, performing positioning calculation on the base arrays formed by various combinations according to the intersection equation;

[0017] Step A4: selecting the long baseline array nodes and transducers required for the solution according to the navigation depth of the underwater vehicle, further reducing the dimension of the positioning solution result, or weighting or down-weighting the solution results of the transducers located on both sides of the transition layer at different depths;

[0018] Step A5: performing data fusion on each positioning solution result; the data fusion algorithm includes but is not limited to Kalman filtering, weighted fusion, data association, Bayesian probability fusion and probabilistic multi-hypothesis tracking;

[0019] Step A6: Draw and display the navigation trajectory after data fusion in the positioning tracking measurement software.

[0020] The working principle of the present invention mainly includes the following aspects:

[0021] Multi-point layout: multiple beacon buoys are deployed on the seabed, and a seabed node array is constructed in a certain formation. These beacons can receive the hydroacoustic signals emitted by underwater vehicles, and send the received signals to the positioning and tracking measurement software through wireless transmission equipment, thereby tracking and locating underwater vehicles;

[0022] Double-layer transducer layout: In the buoy node, the electronic cabin, upper transducer, and lower transducer are connected in sequence through transmission cables. The distance between the upper transducer and the lower transducer at different depths is greater than the depth of the transition layer. This is an important design to avoid the failure of the spherical intersection method during positioning solution due to the interference of the transition layer.

[0023] Data fusion algorithm: The positioning tracking measurement software obtains the positioning data sent by the long baseline array and uses the data fusion algorithm for calculation, tracking and positioning. The data fusion algorithm can be Kalman filtering, weighted fusion, data association, Bayesian probability fusion and probabilistic multi-hypothesis tracking. The purpose of data fusion is to integrate the data of each beacon to improve the accuracy and stability of positioning.

[0024] Optimization strategy for depth difference: Since the depth difference between the underwater vehicle and the transducer may be large, this technical solution selects the long baseline array nodes and transducers required for the solution based on the navigation depth of the underwater vehicle, further reducing the dimension of the positioning solution result, or weighting or down-weighting the solution results of the transducers located on both sides of the transition layers at different depths to reduce the interference caused by the depth difference.

[0025] Resisting shallow sea jump layer interference: Each node can receive the water sound delay of m transducers, and n nodes (m≥2, n≥3) are calculated in combination. In this way, the jump layer interference problem can be better dealt with in environmental conditions such as shallow sea, lakes and reservoirs.

[0026] The beneficial technical effects of the present invention include the following points:

[0027] 1. High-precision positioning: Through multi-node settings, optimized transducer layout and data fusion algorithm, the present invention can achieve high-precision underwater positioning, greatly improving the positioning accuracy of seabed missions and deep-sea research.

[0028] 2. Resisting the interference of transition layers: The present invention can effectively resist the interference of transition layers in shallow sea environments through the deep layout of transducers and data processing technology, thereby improving the reliability of positioning.

[0029] 3. Strong system stability: Through data fusion and filtering processing, the stability of the positioning system can be further enhanced and the impact of environmental changes on positioning accuracy can be reduced.

[0030] 4. Flexible application: The present invention has taken into account different deployment modes (such as buoy type, bottom type, etc.), and can be combined and selected in various ways based on actual needs and environmental conditions, with high flexibility and adaptability.

[0031] 5. Easy to operate: The present invention takes into account detailed layout steps and operation processes, as well as supporting software support, so that the operation process is simple and easy to understand, and the technical requirements for the operator are relatively low. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a flow chart of a long baseline design and positioning fusion algorithm for resisting shallow water acoustic jump interference of the present invention;

[0033] Figure 2 It is a schematic diagram of the long baseline array buoy deployment and system of the present invention;

[0034] Figure 3 It is a schematic diagram of the long baseline array buoy structure of the present invention;

[0035] Figure 4 It is a flowchart of the positioning fusion algorithm of the present invention;

[0036] Figure 5 Schematic diagram of the extended Kalman state space model in an embodiment of the present invention. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings. It should be understood that these descriptions are only exemplary and are not intended to limit the scope of the present invention. In addition, in the following description, the description of well-known structures and technologies is omitted to avoid unnecessary confusion of the concept of the present invention.

[0038] Embodiment 1: A long baseline design and positioning fusion algorithm for resisting shallow water acoustic jump interference, including a long baseline array, an underwater vehicle, positioning tracking measurement software, etc.; when in use, the underwater vehicle navigates within the long baseline array area; the long baseline array adopts a buoy type, a bottom type, or other composition forms, or a composite form of multiple forms; the positioning tracking measurement software is used to perform positioning tracking measurement on the underwater vehicle and solve its navigation trajectory; the intersection algorithm includes but is not limited to spherical intersection, circular intersection, and hyperbolic intersection; the positioning tracking measurement software uses the coordinates of each node and the acoustic positioning time delay data to calculate the navigation trajectory of the underwater vehicle according to the intersection principle, and specifically includes the following steps (such as Figure 1 shown):

[0039] The long baseline array needs to be deployed in advance in the measurement waters (such as Figure 2 After the deployment is completed, the nodes of the long baseline array are calibrated, mainly for the hydroacoustic transducers under the transition layer in the node; when installing the nodes (buoys), try to ensure that the distance between the two hydroacoustic transducers is greater than the depth of the transition layer, and place them at different depths (such as Figure 3 shown);

[0040] When the system is working, the underwater vehicle carries the cooperative beacon to transmit the hydroacoustic signal in the measurement water area; the hydroacoustic transducer of each node of the long baseline array receives and analyzes the hydroacoustic signal, and then sends the analysis result to the positioning and tracking measurement software through the data transmission link;

[0041] The positioning, tracking and measurement software analyzes the data of each node and calculates the navigation trajectory of the underwater vehicle through a data fusion algorithm.

[0042] The positioning fusion algorithm includes but is not limited to Kalman filtering, weighted fusion, data association, Bayesian probability fusion, and the specific process (such as Figure 4 As shown) as follows:

[0043] For each node, m (m ≥ 2) transducers can receive the underwater acoustic delay, and there are n (n ≥ 3) nodes. When all nodes and transducers can receive the signal and calculate the delay, there are at least Combination methods (according to the spherical intersection equation), that is, at least A positioning array (self-organized array); however, since the horizontal spacing between the transducers on the same node cannot meet the baseline length requirement of the long baseline array (insufficient positioning accuracy), the combination result can be further reduced;

[0044] The signals received by each transducer are subjected to time delay filtering, the time delay data is compared with the previously received data, and outliers are eliminated (data that is much larger than the average value is removed);

[0045] The base arrays formed by various combinations are positioned and solved according to the intersection equation;

[0046] According to the navigation depth of the underwater vehicle, the long baseline array nodes and transducers required for the solution are selected to further reduce the dimension of the positioning solution result, or the solution results of the transducers at different depths (on both sides of the transition layer) are weighted or down-weighted;

[0047] Perform data fusion on each positioning solution result; data fusion algorithms include but are not limited to Kalman filtering, weighted fusion, data association, Bayesian probability fusion, probabilistic multi-hypothesis tracking, etc.;

[0048] The navigation trajectory after data fusion is drawn and displayed in the positioning tracking measurement software.

[0049] This patent proposes a long baseline array design and positioning fusion algorithm that is resistant to shallow water acoustic jump interference: two (or more) transducers are installed at different depths (on both sides of the jump layer) on the long baseline node, and redundant transducers are selected through an intelligent algorithm to solve the jump layer interference in long baseline positioning, and a data fusion algorithm is used to fuse and solve the navigation trajectory, which can greatly improve the system's comprehensive hydroacoustic measurement performance under complex sea conditions, the long baseline array's ability to resist shallow water acoustic jump interference and positioning accuracy.

[0050] Example 2: Practical Operation

[0051] The long baseline array (buoy) is deployed in the survey waters, and after the deployment is completed, a running ship calibration is performed;

[0052] Installing a cooperation beacon on the underwater vehicle and making it navigate in the survey waters;

[0053] The long baseline array receives and processes the hydroacoustic signals and transmits the processing results to the positioning tracking measurement software;

[0054] The positioning tracking measurement software uses a data fusion algorithm to calculate the navigation trajectory of the underwater vehicle based on the information of each transducer, and draws and displays it in the interface.

[0055] Example 3

[0056] The long baseline array uses the principle of hydroacoustic positioning based on ranging intersection to realize the positioning and solution of the target. It uses the hydroacoustic propagation speed, the time delay of the target sound source to reach the receiver and the array position parameters to realize the positioning of the target and solve the target position. The positioning equation group is as follows:

[0057]

[0058] In the formula, (x, y, z) is the target position to be determined; (x i ,y i ,z i ) is the coordinate of the base array node; c is the speed of sound, t i It is the time when the underwater acoustic signal arrives at each array node.

[0059] After dimension reduction, the positioning equation group is simplified to the form of Ax=b. It can be seen from the equation group that the number of equations is much larger than the number of unknowns. Therefore, the least square method is used for optimization and solution, and the optimized coordinates of the underwater target to be measured x=(A T A) -1 A T b. The solution result is expressed as the measurement vector of the fusion algorithm, which represents the position and speed of the target, in the form of:

[0060]

[0061] The matrix measurement fusion vector composed of multiple combinations is:

[0062] z=[s1 s2 s i… ] T (i=3,4).

[0063] An application example of this embodiment is a fusion algorithm based on extended Kalman filtering, in which the extended Kalman state space model in information fusion is as follows: Figure 5 As shown;

[0064] The initial fusion data S1 and the state estimate x at the last moment (k-1) k-1 and error covariance p k-1 , construct the state space and calculate the state estimate x 0 k and error covariance p k ; Treat the data to be fused S2 as the observation space and the state estimation value x 0 k and error covariance p k Construct the observation equation and update the observation matrix 1 to calculate the state estimate x 1 k and Kalman gain K1; the data to be fused S3 is regarded as a new observation space, and the state estimation value x 1 kand error covariance p k Construct the observation equation and update the observation matrix 2 to calculate the state estimate x 2 k and Kalman gain K2; continue to perform the fusion calculation of the data to be fused, each time a new data to be fused s n The state estimate x is obtained by updating the observation matrix n. n k and the corresponding Kalman gain K n ; Finally, the Kalman gain K1...K n Construct the error covariance matrix at the current moment;

[0065] After all the data to be fused complete the above steps, the state estimate x k and error covariance P k are updated to accurately reflect the current state k; the estimated state value x after all data fusion is n k The final output fusion result at the current moment k is used as the state estimation value at the next moment k+1, and together with the error covariance matrix, it is used as the input parameter for the fusion solution at the next moment k+1;

[0066] The initial data and data to be fused in the figure are vectors to be fused.

[0067] z=[s1 s2 s i … ] T (i=3,4…), that is, the positioning results calculated by various combination arrays

[0068] s i (i=1,2,3 … )'s collection.

[0069] The output fusion result is the optimal estimate of the final positioning result.

[0070] The observation space model constructed by the positioning results of each base array is:

[0071]

[0072] In the formula, Represents the system state transition observation coefficient of different fusion data at time k.

[0073] In the fusion positioning process based on the extended Kalman filter technology, the positioning result of one array is first selected as the initial fusion data to construct the state space equation; then the positioning results of other arrays are regarded as the observation space to construct the observation equation. Then, multi-array fusion positioning based on the extended Kalman filter is realized.

[0074] It should be understood that the above specific embodiments of the present invention are only used to illustrate or explain the principles of the present invention, and do not constitute a limitation of the present invention. Therefore, any modifications, equivalent substitutions, improvements, etc. made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. In addition, the appended claims of the present invention are intended to cover all changes and modifications that fall within the scope and boundaries of the appended claims, or the equivalent forms of such scope and boundaries.

Claims

1. A long baseline positioning system that is resistant to shallow water acoustic jump interference, characterized in that include: The long baseline array adopts a buoy type, a bottom type, or other composition forms, or a composite form of multiple forms, including a plurality of hydroacoustic beacons, and is used to receive hydroacoustic signals emitted by underwater vehicles, including: A buoy, which is connected to an electronic cabin, at least one upper transducer, and at least one lower transducer in sequence through a transmission cable, and has an anchor block at the end; a wireless transmission device is connected to the electronic cabin; wherein adjacent upper transducers and lower transducers are arranged at different depths and the distance between them is greater than the depth of the transition layer; and the buoy can receive and analyze the hydroacoustic signal sent by the underwater vehicle, and send it to the positioning tracking measurement software through the wireless transmission device; An underwater vehicle used to navigate in the survey waters and carry a cooperative beacon to transmit hydroacoustic signals in the survey waters; The positioning tracking measurement software is used to obtain the positioning data sent by the long baseline array, and calculate, track, and locate the motion trajectory of the underwater vehicle based on the received data, perform data fusion and filtering processing, and further improve positioning accuracy and stability.

2. A fusion algorithm for a long baseline positioning system resistant to shallow water acoustic jump interference according to claim 1, characterized in that The following steps are involved: Step S1, deploying a long baseline array in the measuring water area, and calibrating each node of the long baseline array; when installing the hydroacoustic transducer to deploy the buoy node, ensure that the two hydroacoustic transducers are deployed at different depths and the distance between them is greater than the depth of the transition layer; the long baseline array adopts a buoy type, a bottom type, or other composition forms, or a composite form of multiple forms; Step S2: Use an underwater vehicle to navigate in the long baseline array area, carry a cooperative beacon to transmit a hydroacoustic signal in the measurement waters, and the hydroacoustic transducer at each node of the long baseline array receives and analyzes the hydroacoustic signal, and then sends the analysis result to the positioning tracking measurement software through a data transmission link; Step S3, using the positioning tracking measurement software to perform positioning tracking measurement on the underwater vehicle, analyzing the data of each node, and solving the navigation trajectory of the underwater vehicle through the data fusion algorithm; the positioning tracking measurement software uses the acquired coordinates of each node and the hydroacoustic positioning delay data to calculate the navigation trajectory of the underwater vehicle according to the intersection principle, and draws and displays it in the interface.

3. The fusion algorithm according to claim 2, characterized in that: The fusion algorithm further comprises the following steps: Step A1: Each node can receive the underwater acoustic delay of m transducers, and there are n nodes, where m≥2 and n≥3; when all nodes and transducers can receive the signal and solve the delay, there are at least Combination methods can form at least A self-organizing array positioning base array; Step A2, performing delay filtering on the signals received by each transducer, comparing the delay data with the previously received data, and removing the data that is much larger than the average value; Step A3, performing positioning calculation on the base arrays formed by various combinations according to the intersection equation; Step A4: selecting the long baseline array nodes and transducers required for the solution according to the navigation depth of the underwater vehicle, further reducing the dimension of the positioning solution result, or weighting or down-weighting the solution results of the transducers located on both sides of the transition layer at different depths; Step A5: data fusion is performed on each positioning solution result; the data fusion algorithm includes but is not limited to Kalman filtering, weighted fusion, data association, Bayesian probability fusion and probabilistic multi-hypothesis tracking; Step A6: Draw and display the navigation trajectory after data fusion in the positioning tracking measurement software.

4. The fusion algorithm according to claim 3, characterized in that: The long baseline array adopts the principle of hydroacoustic positioning based on ranging intersection to realize the positioning and solution of the target, and utilizes the hydroacoustic propagation speed, the time delay of the sound source of the measured target to reach the receiver and the array position parameters to realize the positioning of the target and solve the target position; its positioning equation group is in the form of: In the formula, (x, y, z) is the target position to be determined; (x i ,y i ,z i ) is the coordinate of the base array node; c is the speed of sound, t i It is the time when the underwater acoustic signal arrives at each array node.

5. The fusion algorithm according to claim 4, characterized in that: The positioning equation group is optimized and solved by the least square method to obtain the optimized underwater target coordinate x=(A T A) -1 A T b; The solution result is expressed as the measurement vector of the fusion algorithm, which represents the position and speed of the target, in the form of: The matrix measurement fusion vector composed of multiple combinations is: z=[s1 s2 s i …] T (i=3,4)。 6. The fusion algorithm according to claim 5, characterized in that: The fusion algorithm is a fusion algorithm based on extended Kalman filtering, which includes the following steps: The initial fusion data S1 and the state estimate x at the last moment (k-1) k-1 and error covariance p k-1 , construct the state space and calculate the state estimate x 0 k and error covariance p k ; Treat the data to be fused S2 as the observation space and the state estimation value x 0 k and error covariance p k Construct the observation equation and update the observation matrix 1 to calculate the state estimate x 1 k and Kalman gain K1; the data to be fused S3 is regarded as a new observation space, and the state estimation value x 1 k and error covariance p k Construct the observation equation and update the observation matrix 2 to calculate the state estimate x 2 k and Kalman gain K2; continue to perform the fusion calculation of the data to be fused, each time a new data to be fused s n The state estimate x is obtained by updating the observation matrix n. n k and the corresponding Kalman gain K n ; Finally, the Kalman gain K1...K n Construct the error covariance matrix at the current moment; After all the data to be fused complete the above steps, the state estimate x k and error covariance P k are updated to accurately reflect the current state k; the estimated state value x after all data fusion is n k The final output fusion result at the current moment k is used as the state estimation value at the next moment k+1, and together with the error covariance matrix, it is used as the input parameter for the fusion solution at the next moment k+1; The initial data and the data to be fused are the vector to be fused z=[s1 s2 s i …] T (i=3,4…).

7. The fusion algorithm according to claim 6, characterized in that: The observation space model constructed by the positioning results of each base array is: In the formula, Represents the system state transition observation coefficient of different fusion data at time k.

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