Method and system for correcting inertial navigation errors based on acoustic feature matching positioning

Abstract

The present invention discloses a method and system for correcting inertial navigation errors based on acoustic feature matching positioning. The method comprises: establishing an environmental acoustic feature information vector library based on an underwater acoustic propagation model of the ocean environment and an environmental acoustic feature information library; performing acoustic feature information matching and positioning based on the environmental acoustic feature information vector library to obtain the position information of an underwater unmanned vehicle; selecting an acoustic feature matching positioning system as an auxiliary navigation system, forming a combined navigation system with an inertial navigation system, selecting a classical Kalman filter for information fusion, adopting an indirect estimation method, and using an open-loop correction method to correct the navigation parameters of the inertial navigation system based on the obtained position information of the underwater unmanned vehicle. The present invention does not require the underwater vehicle to surface or actively transmit acoustic wave signals. Instead, it only needs to passively receive sound source information from existing beacons on the water surface or underwater, and can achieve error correction of the SINS system using a single hydrophone.

Description

Technical Field

[0001] The present invention relates to the field of positioning technology, and in particular to a method for correcting inertial navigation errors based on acoustic feature matching positioning. Background Art

[0002] Autonomous navigation and positioning capabilities are essential for submarines to maintain long-term underwater navigation. Submarines are equipped with a variety of navigation instruments, but they primarily rely on inertial navigation systems (INS). The passive and autonomous nature of inertial navigation systems facilitates autonomous navigation and positioning, making them a core navigational device. However, a major drawback of inertial navigation systems is that positioning errors accumulate over time, making them incapable of meeting the requirements for long-term, high-precision navigation and positioning. To suppress the growth of navigation system position errors and improve long-term navigation accuracy, inertial navigation systems must be regularly calibrated. Therefore, research on key technologies for autonomous underwater integrated navigation and positioning is crucial for building autonomous, robust, and high-precision underwater navigation systems, and represents a key development direction for underwater integrated navigation and positioning. In practical engineering applications, other navigation systems are often used as auxiliary navigation systems, or as sub-navigation systems, to correct for errors accumulated over time by the SINS during long-duration missions. Alternatively, the vehicle periodically surfaces near the surface to use GNSS to correct for SINS errors.

[0003] Currently, conventional single-system navigation systems are unable to meet the stringent requirements for navigation and positioning in complex underwater environments. Multi-sensor integrated navigation and positioning is the future development direction. Currently, integrated navigation systems generally use SINS as the primary sensor. However, SINS also inevitably suffers from the disadvantage of gradually accumulating navigation errors over time, resulting in low navigation accuracy over long flight times.

[0004] Based on the above, other navigation systems are often used in practical engineering applications as auxiliary navigation systems to correct for errors accumulated over time during the long journeys of the SINS. The most common navigation subsystems in integrated navigation are SINS, Doppler velocity log (DVL), GNSS, and underwater acoustic positioning systems (such as ultra-short baseline (USBL) and long baseline (LBL)). In the SINS and DVL integrated navigation and positioning method, DVL measures the vehicle's ground velocity by transmitting acoustic waves to the seabed. While this method can provide highly accurate velocity information, it also suffers from the disadvantage of error accumulation over time. In the SINS and GNSS integrated navigation and positioning method, the UUV must surface close to the water, which risks exposing its concealment. In the SINS and underwater acoustic positioning system integrated navigation and positioning method, when multiple UUVs are in the same sea area, the exchange of acoustic signals for positioning and data transmission becomes complex, and active signal transmission is often required, which may expose the vehicle and lose its concealment. In LBL systems, multiple beacons are required to determine the vehicle's position. In USBL systems, multiple array elements are required for positioning, resulting in a large system size and unsuitable for small UUVs. Therefore, a method is needed to timely correct the inertial navigation system without requiring the UUV to approach the water surface. The acoustic feature matching localization system (AFMLS) assisted navigation employed in this invention offers high accuracy, eliminates the need for surface exposure, and eliminates external radiation, ultimately resolving the UUV's concealment issue.

[0005] In the field of underwater acoustic matching and positioning, traditional underwater acoustic signal matching field positioning methods assume that sound waves are plane waves and the sound field is isotropic. Based on this assumption, a variety of array signal processing methods have been developed, and matched filtering technology is used to improve processing gain. However, in real ocean environments, due to the non-uniformity of seawater and the influence of the boundary between the sea surface and the seabed, the actual sound field deviates significantly from the plane wave assumption, making it difficult for traditional matching field positioning methods to accurately locate underwater targets. Summary of the Invention

[0006] In response to the problem that the errors of underwater UUV inertial navigation systems accumulate over time and become difficult to work independently for a long time, the present invention provides a method and system for correcting inertial navigation errors based on acoustic feature matching positioning to solve the above technical problems.

[0007] The present invention discloses a method for correcting inertial navigation errors based on acoustic feature matching positioning, which comprises the following steps:

[0008] Step 1: Based on the underwater sound propagation model of the ocean environment and the environmental acoustic feature information database, establish an environmental acoustic feature information vector database;

[0009] Step 2: Based on the environmental acoustic feature information vector library, perform acoustic feature information matching and positioning to obtain the position information of the underwater unmanned vehicle;

[0010] Step 3: Select the acoustic feature matching positioning system as the auxiliary navigation system, form a combined navigation system with the inertial navigation system, select the classical Kalman filter for information fusion, adopt the indirect estimation method, and use the open-loop correction method to correct the navigation parameters of the inertial navigation system based on the obtained position information of the underwater unmanned vehicle.

[0011] Furthermore, the step 1 includes:

[0012] Place a test sound source near the underwater unmanned vehicle to obtain prior information on the propagation of the sound source radiation signal based on the current environment;

[0013] Based on the acquired prior information on the propagation of the sound source radiation signal in the current environment and the channel characteristic parameters, an underwater acoustic propagation model suitable for the ocean environment is selected, and known ocean environmental parameters are input into the underwater acoustic propagation model; wherein the channel characteristic parameters include a sound velocity profile, environmental parameters, and underwater sound propagation characteristics; and the ocean environmental parameters include temperature, salinity, and depth;

[0014] As the position of the underwater unmanned vehicle hydrophone changes, the arrival structure of its received signal also changes, that is, the relative arrival delay and amplitude of each eigenvalue line are different, so an environmental acoustic feature information database is constructed;

[0015] In the area where the beacon sound source may appear, a search grid for the hydrophone is set, with each grid point as the assumed beacon sound source position. Using prior information and the sound ray theory model, the eigenvalue arrival time delay and amplitude of the test sound source (r, z) to the receiving hydrophone are calculated. A characteristic library of the eigenvalue arrival time delay and amplitude of the beacon sound source to the receiving hydrophone is established as a vector library of environmental acoustic feature information; the prior information includes environmental parameters and hydrophone positions.

[0016] Furthermore, the step 2 includes:

[0017] Step 21: Search for as many eigenwaves as possible and calculate all eigenwave additional information. Then, combine all the eigenwave additional information and decompose characteristic parameters of the eigenwaves from the actual received signal as matching information. The eigenwave additional information includes propagation attenuation and propagation delay, and the characteristic parameters include arrival angle and relative arrival delay.

[0018] Step 22: Perform an acoustic feature information matching operation based on the known eigenvalue arrival structure diagram, extract position feature information, and finally obtain a relative position estimate of the beacon relative to the underwater unmanned vehicle;

[0019] Step 23: Based on the relative estimated value of the beacon's position relative to the underwater unmanned vehicle and the position information of the beacon in the acoustic signal, the position information of the underwater unmanned vehicle can be calculated.

[0020] Furthermore, the step 21 includes:

[0021] Assuming that there are N eigenwaves reaching the receiving hydrophone, the channel transfer function from the beacon source to the receiving hydrophone is expressed as:

[0022]

[0023] Where z s 、z r and r represent the depth of the sound source, the depth of the receiver, and the distance between the receiver and the sound source respectively; g i is the amplitude of the ith eigenvalue, n i is the propagation delay of the i-th eigenwave;

[0024] The received signal at the receiving hydrophone is expressed as

[0025]

[0026] Where x(n) represents the received signal, s(n) represents the sound source signal, and e(n) represents the noise;

[0027] Based on the received signal at the receiving hydrophone, the arrival time of the eigenvalue line is obtained, that is, the relative delay of the eigenvalue line is estimated.

[0028] Furthermore, the step 22 includes:

[0029] By utilizing the environmental acoustic feature information vector library and directly correlating the autocorrelation function of the environmental acoustic feature information with the autocorrelation function of the hydrophone signal, the position of the sound source can be estimated.

[0030] Furthermore, the step 22 specifically includes:

[0031] Perform bandpass filtering on the received signal of the hydrophone and calculate its autocorrelation function R xx ;

[0032] Using prior information and a sound ray theory model, the arrival delay and amplitude of the eigenvalues ​​of the test sound source (r, z) to the receiving hydrophone are calculated; the prior information includes environmental parameters and the hydrophone position;

[0033] The autocorrelation function R of the environmental acoustic characteristic signal rplc (r,z) and the autocorrelation function R of the hydrophone signal xx Perform correlation processing to obtain the correlation coefficient ρ(r,z);

[0034] A search is performed within the expected beacon source location area to construct a fuzzy plane with a correlation coefficient ρ(r,z), whose peak position is the relative position estimate of the beacon source.

[0035] Furthermore, assuming that the signal and noise are uncorrelated, the autocorrelation function of the received signal x(n) is:

[0036]

[0037] Where R ee is the autocorrelation function of the noise e(n), R ss is the autocorrelation function of the sound source signal s(n).

[0038] Furthermore, the step 3 includes:

[0039] Step 31: The position information of the underwater unmanned vehicle can be obtained by solving the problem in step 2. The position information is compared with the position information of the underwater unmanned vehicle corrected at the last moment, and the possible jump or frame loss of its output data is judged. When the position information output by the acoustic feature matching positioning system is within the threshold range, the positioning result data of the acoustic feature matching positioning system is normal and can be used, and the process goes to step 32; otherwise, the navigation and positioning is continued by relying on the inertial navigation system, and the navigation information of the inertial navigation system is directly output;

[0040] Step 32: The Kalman filter uses the heading, attitude and position errors of the inertial navigation system as state estimates, and the position information provided by the acoustic feature matching positioning system as measurement information. The Kalman filter estimates the error and then feeds it back to the inertial navigation system to correct the navigation results of the inertial navigation system. At the same time, it can also smooth the navigation information of the acoustic feature matching positioning system, and finally obtain the corrected reliable navigation information of the combined navigation system.

[0041] Furthermore, in said step 32:

[0042] The system state equation is expressed as:

[0043]

[0044] Where, X SINS is the state variable of the inertial navigation system, F SINS is the state matrix of the inertial navigation system, W SINS / AFMLS is the state noise of the inertial navigation system;

[0045] The difference between the position information calculated by the inertial navigation system and the acoustic feature matching positioning system is selected as the observation value, and the position information of the inertial navigation system is expressed as the sum of the true value and the error value:

[0046]

[0047]

[0048]

[0049] Similarly, the position information of the acoustic feature matching positioning system can be expressed as:

[0050]

[0051]

[0052]

[0053] The measurement equation of the system is expressed as:

[0054]

[0055] where η AFMLS is the measurement noise of the acoustic feature matching positioning system;

[0056] The measurement matrix is ​​expressed as:

[0057] H LBL =[0 3×6 I 3×3 0 3×6 ]

[0058] The acoustic array is installed on the underwater unmanned vehicle carrier. The position of the underwater unmanned vehicle in the rectangular coordinate system is (X UUV ,Y UUV ,Z UUV ), the position in longitude and latitude coordinates is (L UUV ,λ UUV ,Z UUV ); Assume that the coordinate reference of the inertial navigation system, the acoustic array coordinate system and the carrier coordinate system have been calibrated and made consistent;

[0059] According to the geodetic rectangular coordinate system position information (X ob ,Y ob ,Z ob ) or longitude and latitude coordinate information (λ ob ,L ob ,Z ob ), and the position of the beacon sound source relative to the underwater unmanned vehicle obtained by the acoustic feature matching positioning system (x AFMLS ,y AFMLS ,z AFMLS ), the position coordinates of the underwater unmanned vehicle in the earth rectangular coordinate system (XAFMLS ,Y AFMLS ,Z AFMLS ) or longitude and latitude coordinates (L AFMLS ,λ AFMLS ,Z AFMLS ), as shown below:

[0060] (X AFMLS ,Y AFMLS ,Z AFMLS )=(X ob ,Y ob ,Z ob )+(x AFMLS ,y AFMLS ,z AFMLS )

[0061] The output coordinates of the underwater unmanned vehicle from the inertial navigation system are expressed as (X SINS ,Y SINS ,Z SINS ) or (L SINS ,λ SINS ,Z SINS ); Kalman filter output combined navigation position coordinates are expressed as (X kalman ,Y kalman ,Z kalman ) or (L kalman ,λ kalman ,Z kalman );

[0062] When the inertial navigation system needs to be calibrated, the error of the underwater unmanned vehicle inertial navigation system (δX SINS ,δY SINS ,δZ SINS ) or (δL SINS ,δλ SINS ,δZ SINS ), error in the geodetic rectangular geographic coordinate system:

[0063] (δX SINS ,δY SINS ,δZ SINS )

[0064] =(X SINS ,Y SINS ,Z SINS )-(X kalman ,Y kalman ,Z kalman )

[0065] The error of the inertial navigation system in the latitude and longitude geographic coordinate system:

[0066] (δL SINS ,δλ SINS ,δZSINS )

[0067] =(L SINS ,λ SINS ,Z SINS )-(L kalman ,λ kalman ,Z kalman )

[0068] The above errors are used to correct the position information of the inertial navigation system and reset the navigation parameters.

[0069] The present invention also discloses a system for correcting inertial navigation errors based on acoustic feature matching positioning, which includes:

[0070] Acoustic feature matching positioning system, used to output the position information of underwater unmanned vehicles;

[0071] The judgment system is used to make a difference between the position information of the underwater unmanned vehicle output by the acoustic feature matching positioning system and the position information of the underwater unmanned vehicle corrected at the last moment, and to judge whether there may be jumps or frame losses in the output data. When the difference is within the threshold range, the positioning result of the acoustic feature matching positioning system is normal; otherwise, the navigation and positioning is continued by relying on the inertial navigation system, and the navigation information of the inertial navigation system is directly output;

[0072] A Kalman filter is used to receive the heading, attitude, and position errors output by the inertial navigation system and use them as state estimates. It is also used to receive the position information output by the acoustic feature matching positioning system and use it as measurement information to estimate the error of the position information.

[0073] The inertial navigation system is used to receive the error amount of the position information fed back by the Kalman filter to correct its own navigation results.

[0074] Due to the adoption of the above technical solution, the present invention has the following advantages:

[0075] This system utilizes the available acoustic signatures in the underwater environment for matching and positioning. It eliminates the need for the underwater vehicle to surface or actively transmit acoustic signals. Instead, it passively receives acoustic source information from existing beacons on the surface or underwater, using a single hydrophone to achieve error correction for the SINS system. The system is compact and suitable for long-range, concealed operations by small unmanned underwater vehicles (UUVs). BRIEF DESCRIPTION OF THE DRAWINGS

[0076] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments described in the embodiments of the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0077] Figure 1 Schematic diagram of the system structure of an embodiment of the present invention;

[0078] Figure 2 Schematic diagram of a method for correcting inertial navigation errors based on acoustic feature matching positioning assistance according to an embodiment of the present invention;

[0079] Figure 3 This is the conventional matching field positioning result of an embodiment of the present invention;

[0080] Figure 4 Schematic diagram of acoustic feature matching positioning results based on neighborhood prior environmental information;

[0081] Figure 5 This is a flow chart of correcting inertial navigation errors using acoustic feature matching and positioning according to an embodiment of the present invention. DETAILED DESCRIPTION

[0082] The present invention will be further described with reference to the accompanying drawings and embodiments. Obviously, the embodiments described are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those skilled in the art should fall within the scope of protection of the embodiments of the present invention.

[0083] The present invention obtains the prior information and channel characteristic parameters of the propagation of the sound source radiation signal in the current environment, selects an underwater acoustic propagation model suitable for the marine environment, does not transmit any signal, and only needs to passively receive the broadcast signals periodically emitted by the available beacons in the surrounding area. The acoustic feature matching positioning method based on the neighborhood prior environmental information is used to complete self-positioning. Combined with the position, heading, and attitude information output by the SINS system, with the help of Kalman filtering technology, high-precision UUV position information is obtained, thereby realizing the correction of the SINS cumulative error. This method is suitable for the concealment requirements of small UUVs when they need to obtain their own precise navigation position in public or sensitive waters. The system is simple to implement, small in size, and meets the needs of long-range navigation and UUV autonomous navigation. It can be applied to the precise navigation and positioning of small UUVs with concealment requirements, providing technical support for high-precision navigation in specific application scenarios.

[0084] The present invention provides a system for correcting inertial navigation errors based on acoustic feature matching positioning assistance, which includes:

[0085] Acoustic feature matching positioning system, used to output the position information of underwater unmanned vehicles;

[0086] The judgment system is used to make a difference between the position information of the underwater unmanned vehicle output by the acoustic feature matching positioning system and the position information of the underwater unmanned vehicle corrected at the last moment, and to judge whether there may be jumps or frame losses in the output data. When the difference is within the threshold range, the positioning result of the acoustic feature matching positioning system is normal; otherwise, the navigation and positioning is continued by relying on the inertial navigation system, and the navigation information of the inertial navigation system is directly output;

[0087] A Kalman filter is used to receive the heading, attitude, and position errors output by the inertial navigation system and use them as state estimates. It is also used to receive the position information output by the acoustic feature matching positioning system and use it as measurement information to estimate the error of the position information.

[0088] The inertial navigation system is used to receive the error amount of the position information fed back by the Kalman filter to correct its own navigation results.

[0089] Specifically, the above system structure can be designed as shown in the attached Figure 1 As shown, it is equipped with an external data interface to access external sensor information to improve the accuracy of the system's integrated navigation. The main structure is a cylindrical structure, which is roughly divided into five parts, namely: auxiliary navigation correction control module, inertial navigation module, depth meter, underwater acoustic signal processing module, and sound head. The sound head part at the bottom of the main body contains a transceiver and a hydrophone array element base array. An underwater acoustic transducer is installed at the center of the array, which can emit sound signals. When used for acoustic feature matching and positioning, it only needs to receive underwater acoustic signals and no signals are emitted. The top of the main body is provided with external interfaces, including a control and debugging interface, a data interface, and a power interface.

[0090] The system solution for correcting inertial navigation errors based on acoustic feature matching positioning in this method is:

[0091] Aiming at the demand for autonomous, concealed, and precise navigation and positioning of small unmanned vehicles in underwater space, the inertial navigation error is corrected based on acoustic feature matching positioning. When the UUV does not surface and actively transmits signals, the accumulated inertial navigation error is corrected, thereby improving the UUV's underwater navigation accuracy and meeting the requirements of UUV's concealed operations. The implementation process of the inertial navigation error correction based on acoustic feature matching positioning involved in the present invention is shown in the attached figure. Figure 2 The specific description is as follows:

[0092] The system process framework includes the SINS system, the acoustic feature matching positioning system, and the Kalman filter. The Kalman filter is used to estimate the SINS navigation error, transmit the navigation error estimate to the SINS to correct the SINS system error, reset the navigation parameters, and then use the corrected navigation parameters as the latest initial conditions for the navigation equations.

[0093] (1) SINS system

[0094] The strapdown inertial navigation system collects digital signals from the gyroscopes and accelerometers within the system and then feeds them into a designated navigation computer for computation. Ultimately, this information yields navigation information such as the attitude, velocity, and position of the UUV's carrier coordinate system relative to a specific navigation reference coordinate system. As the platform moves, the installed gyroscopes measure the UUV's angular velocity relative to the inertial reference system. Based on this information, the transformation matrix between the sound source coordinate system and the positioning coordinate system can be calculated. This allows the acceleration variables obtained by the three-axis accelerometer to be fed back to the positioning coordinate system. Finally, through the final computations performed by the computer, specific navigation information is obtained.

[0095] SINS errors are primarily caused by errors in the inertial sensor, errors in the computer calculation process, errors caused by the mathematical model, and errors caused during initial alignment. The errors caused by the inertial sensor are the primary error, increasing over time and representing the error that needs to be corrected in this invention.

[0096] (2) Acoustic feature matching positioning system

[0097] Based on acoustic feature matching positioning, the concept of inverse matching field positioning is adopted. Combining the characteristics of ocean waveguides, it fully utilizes the complex structure of the ocean environment and the classic sound propagation model to extract the signal propagation field structure in the ocean waveguide. Using the known absolute position of the beacon sound source, the relative position of the beacon and the UUV is obtained through feature matching positioning technology, ultimately obtaining the absolute position of the UUV. After the beacon has been accurately calibrated in absolute position, the UUV only needs to obtain the relative coordinate relationship between it and the beacon to achieve self-positioning. The positioning principle can be expressed as follows:

[0098] X UUV =X T +X UT

[0099] Where X UUV Indicates the absolute coordinates of the underwater vehicle, X T Indicates the absolute coordinates of the beacon after precise calibration, X UT Indicates the coordinates of the vehicle relative to the beacon in the geographic coordinate system.

[0100] The challenge faced by acoustic feature matching positioning is that the mismatch of ocean environmental parameters leads to a significant decrease in positioning performance. To address this problem, the present invention adopts an acoustic feature matching positioning method based on neighborhood prior environmental information. The specific process is as follows:

[0101] 1) Establish an environmental acoustic feature information vector library

[0102] a) Place a test sound source within the UUV area to obtain prior information on the propagation of the sound source radiation signal based on the current environment;

[0103] b) Based on the acquired prior information on the propagation of the sound source radiation signal in the current environment and the channel characteristic parameters (sound velocity profile, environmental parameters, underwater sound propagation characteristics, etc.), select an underwater sound propagation model suitable for the ocean environment, and input the known ocean environment parameters (temperature, salinity, depth, etc.) into the model;

[0104] c) As the position of the UUV hydrophone changes, the arrival structure of its received signal also changes, that is, the relative arrival delay and amplitude of each eigenvalue are different. Based on this feature, an environmental acoustic feature information library is constructed. In the area where the beacon sound source may appear, a search grid for the hydrophone is set, and each grid point is used as the assumed beacon sound source location. Using known prior information such as environmental parameters and hydrophone position, the eigenvalue arrival delay and amplitude of the test sound source (r, z) to the receiving hydrophone are calculated using the sound ray theory model. A feature library of the eigenvalue arrival delay and amplitude of the beacon sound source to the receiving hydrophone is established as the environmental acoustic feature information vector library;

[0105] d) Calculate the autocorrelation function R of the environmental acoustic characteristic signal rplc (r,z).

[0106] 2) Acoustic feature information matching and positioning

[0107] Search for as many eigenwaves as possible and calculate all the additional information of the eigenwaves, including propagation attenuation, propagation delay, etc., and then combine them together. The steps of the acoustic feature information matching and positioning process are as follows:

[0108] a) Search for as many eigenwaves as possible and calculate all their additional information, including propagation attenuation and propagation delay. These information is then combined to decompose characteristic parameters such as the arrival angle and relative arrival delay of the eigenwaves from the actual received signal as matching information.

[0109] Assuming that there are N eigenwaves reaching the receiving hydrophone, the channel transfer function from the beacon source to the receiving hydrophone can be expressed as

[0110]

[0111] Where z s , z r and r represent the depth of the sound source, the depth of the receiver, and the distance between the receiver and the sound source respectively; g i is the amplitude of the i-th eigenwave (including additional phase information), which is a function of the signal frequency for broadband signals; n i is the propagation delay of the i-th eigenwave. The signal transfer function is represented by the superposition of several impulse functions. Each impulse function represents an eigenwave, which represents a propagation path of the signal. Under this channel model, the received signal at the receiving hydrophone can be expressed as

[0112]

[0113] Where x(n) represents the received signal, s(n) represents the source signal, and e(n) represents the noise. This gives the arrival time of the eigenwave ray. Therefore, theoretically, the relative delay of the eigenwave ray can be estimated using the signal received by a single hydrophone.

[0114] b) Perform acoustic feature information matching calculations based on the pre-calculated eigenvalue arrival structure diagram, extract position feature information, and finally obtain the relative position estimate of the beacon relative to the UUV.

[0115] According to the sound ray theory, the location information of the sound source is clearly contained in the arrival parameters of the eigenvalues. These parameters need to be estimated from the received signal, and then the location of the sound source is estimated using the eigenvalues ​​arrival structure matching method. The following objective function is defined:

[0116]

[0117] Where w i is the weighting factor, τ data is the relative arrival delay of the eigenvalues ​​estimated using measured data, τ rplc In step 1), using prior information such as known environmental parameters and hydrophone locations, the ray theory model is used to determine the relative arrival delay of the eigenwave ray from the test sound source (r, z) to the receiving hydrophone. In this invention, the beacon location is a known source signal, so a delay estimation algorithm based on prior information can be used.

[0118] Assuming that the signal and noise are uncorrelated, the autocorrelation function of the received signal x(n) is

[0119]

[0120] Where R ee is the autocorrelation function of the noise e(n), R ss is the autocorrelation function of the sound source signal s(n). If s(n) is a broadband white noise that obeys independent and identical distribution, then its autocorrelation function Rss (m) There is only one peak at m=0.

[0121] By using the environmental acoustic feature information vector library constructed in step 1), the autocorrelation function of the environmental acoustic feature information is directly correlated with the autocorrelation function of the hydrophone signal to estimate the sound source position. The specific algorithm steps are as follows:

[0122] Perform bandpass filtering on the received signal of the hydrophone. The bandpass filter is required to have a wide passband. Calculate its autocorrelation function R xx ;

[0123] Using known prior information such as environmental parameters and hydrophone positions, the arrival delay and amplitude of the eigenvalues ​​of the test sound source (r, z) to the receiving hydrophone are calculated using the sound ray theory model.

[0124] The autocorrelation function R of the environmental acoustic characteristic signal rplc (r,z) and the autocorrelation function R of the hydrophone signal xx After correlation processing, the correlation coefficient ρ(r,z) is obtained:

[0125]

[0126] A search is performed within the expected beacon source location area to construct a fuzzy plane with a correlation coefficient ρ(r,z), whose peak position is the relative position estimate of the beacon source.

[0127] c) Based on the calculated relative position between the beacon sound source and the UUV, and the absolute position information of the beacon in the sound signal, the absolute position of the UUV can be calculated.

[0128] Under the condition of complex ocean environment parameter mismatch, the simulation distance from the UUV is 2000m, and the beacon position positioning results at a depth of 10m are shown in the attached Figure 3 and attached Figure 4 It can be seen that in complex environments, the acoustic feature matching positioning algorithm based on neighborhood prior environmental information has superior positioning performance and good robustness.

[0129] (3) Correction of inertial navigation error based on acoustic feature matching positioning assistance

[0130] When an inertial navigation system is used alone for navigation, its errors accumulate over time, causing positioning accuracy to drift and diverge. Using a strapdown inertial navigation system as the primary reference navigation system and an acoustic signature matching positioning navigation system as a supplement can effectively correct for this inertial navigation accuracy divergence.

[0131] Integrated navigation systems are categorized into loose, tight, and ultra-tight combinations, depending on the degree of integration of each sensor's navigation parameters. In the loose integration mode, the present invention selects acoustic feature matching positioning navigation as the auxiliary navigation system, which is combined with the SINS to form an integrated navigation system. Classical Kalman filtering is used as the information fusion method, an indirect estimation method is employed, and an open-loop correction method is used to correct the SINS navigation parameters.

[0132] The entire system is coordinated under the control of the calculation module. The process is as shown in the attached Figure 5 The workflow of the system navigation position correction part is as follows:

[0133] 1) Initialization: After the UUV is launched into the water, it first rises to the surface, extends the rigid antenna, receives the GNSS signal at the initial point, and then enters normal stealth;

[0134] 2) After initialization, the UUV can dive normally. The system performs underwater navigation and positioning. The SINS outputs the attitude, velocity, and position information of the UUV. As time goes by, the position error output by the SINS increases.

[0135] 3) Based on the acoustic feature matching positioning system (AFMLS), the UUV position information can be obtained through the solution of step (2). The position information is compared with the UUV position information corrected at the last moment, and the possible jump or frame loss of its output data is judged. When the AFMLS output position information is within the threshold range (the threshold is related to the UUV navigation speed, the positioning system sampling interval, etc.), the AFMLS positioning result data is normal and can be used, and the process goes to step 4); otherwise, the navigation positioning is continued by relying on SINS to avoid the adverse effects of abnormal AFMLS data on navigation accuracy, and the navigation information of the SINS system is directly output to give full play to its high accuracy in a short time.

[0136] 4) The Kalman filter uses the heading, attitude and position errors of the SINS as state estimates and the position information provided by the AFMLS as measurement information. The error can be estimated through the filter and then fed back to the SINS to correct the SINS navigation results. At the same time, the AFMLS navigation information can also be smoothed to finally obtain the corrected reliable navigation information of the integrated navigation system.

[0137] The system state equation can be expressed as:

[0138]

[0139] Where, X SINS is the state variable of SINS, F SINS is the state matrix of SINS, W SINS / AFMLSis the state noise of SINS.

[0140] The difference between the positions calculated by SINS and AFMLS (latitude, longitude, and depth) is selected as the observation value. The SINS position information can be expressed as the sum of the true value and the error value:

[0141]

[0142]

[0143]

[0144] Similarly, AFMLS location information can be expressed as:

[0145]

[0146]

[0147]

[0148] The measurement equation of the system can be expressed as:

[0149]

[0150] where η AFMLS is the measurement noise of AFMLS.

[0151] The measurement matrix can be expressed as:

[0152] H LBL =[0 3×6 I 3×3 0 3×6 ]

[0153] The acoustic array is installed on the UUV carrier, and the position of the UUV in the rectangular coordinate system is (X UUV ,Y UUV ,Z UUV ), the position in longitude and latitude coordinates is (L UUV ,λ UUV ,Z UUV ). Assume that the SINS system coordinate reference, the acoustic array coordinate system and the carrier coordinate system reference have been calibrated and made consistent.

[0154] According to the absolute position information of the earth rectangular coordinate system carried by the beacon sound source information (X ob ,Y ob ,Z ob ) or longitude and latitude coordinate information (λ ob ,L ob ,Z ob), and the position of the beacon sound source relative to the UUV obtained by the acoustic feature matching positioning system (x AFMLS ,y AFMLS ,z AFMLS ), the absolute position coordinates of the UUV in the rectangular coordinate system (X AFMLS ,Y AFMLS ,Z AFMLS ) or longitude and latitude coordinates (L AFMLS ,λ AFMLS ,Z AFMLS ), as shown below:

[0155] (X AFMLS ,Y AFMLS ,Z AFMLS )=(X ob ,Y ob ,Z ob )+(x AFMLS ,y AFMLS ,z AFMLS )

[0156] The SINS output UUV coordinates are expressed as (X SINS ,Y SINS ,Z SINS ) or (L SINS ,λ SINS ,Z SINS ); Kalman filter output combined navigation position coordinates are expressed as (X kalman ,Y kalman ,Z kalman ) or (L kalman ,λ kalman ,Z kalman )

[0157] When the SINS needs to be calibrated, the UUV inertial navigation system error (δX SINS ,δY SINS ,δZ SINS ) or (δL SINS ,δλ SINS ,δZ SINS ), error in the geodetic rectangular geographic coordinate system:

[0158] (δX SINS ,δY SINS ,δZ SINS )=(X SINS ,Y SINS ,Z SINS )-(X kalman ,Y kalman ,Z kalman )

[0159] The error of the inertial navigation system in the latitude and longitude geographic coordinate system:

[0160] (δL SINS ,δλ SINS ,δZ SINS )=(L SINS ,λ SINS ,Z SINS )-(L kalman ,λ kalman ,Z kalman )

[0161] The above errors are used to correct the position information of the inertial navigation system and reset the navigation parameters.

[0162] The method and system for correcting inertial navigation errors based on acoustic feature matching positioning assistance described in the present invention correct the accumulated inertial navigation errors when the UUV does not surface or transmit signals, thereby improving the UUV's underwater navigation accuracy and being suitable for the UUV's covert operation requirements.

[0163] The system process framework of the present invention based on acoustic feature matching positioning to assist in correcting inertial navigation errors is as shown in the attached Figure 2 As shown, it includes a SINS system, an acoustic feature matching positioning system, and a Kalman filter, wherein the Kalman filter is used to estimate the SINS navigation error, transmit the navigation error estimate to the SINS to correct the SINS system error, reset the navigation parameters, and then use the corrected navigation parameters as the latest initial conditions of the navigation equation;

[0164] The acoustic feature matching positioning method described in the present invention utilizes the known absolute position information of the beacon sound source to obtain the relative position information of the beacon and the UUV through feature matching positioning technology, and finally obtains the absolute position information of the UUV;

[0165] The acoustic feature matching positioning method of the present invention fully utilizes the complex structure of the ocean environment and the classic sound propagation model, adopts an acoustic feature matching positioning method based on the neighborhood prior environmental information, and obtains the prior information of the propagation of the sound source radiation signal based on the current environment;

[0166] The acoustic feature matching positioning method described in the present invention uses prior information such as known environmental parameters, prior information on the propagation of sound source radiation signals, and channel characteristic parameters to select an underwater acoustic propagation model suitable for the ocean environment. The known ocean environmental parameters are input into the model to establish a library of eigenvalue arrival delays and amplitude characteristics of the beacon sound source reaching the receiving hydrophone, which serves as a library of environmental acoustic feature information vectors.

[0167] The present invention describes an acoustic feature matching-based positioning system to assist in correcting inertial navigation errors. The acoustic feature matching positioning system obtains the position information of the UUV through calculation, compares the position information with the UUV position information corrected by the navigation system at the previous moment, and judges the possible jumps or frame losses in its output data. When the position information output by the AFMLS is within the threshold range, the positioning result data is normally available. The heading, attitude and position error of the SINS are used as state estimates, and the position information provided by the AFMLS is used as measurement information. The SINS error is estimated through a filter and fed back to the SINS to correct the SINS navigation result. At the same time, the AFMLS navigation information can also be smoothed, and finally reliable navigation information corrected by the combined navigation system is obtained.

[0168] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A method for correcting inertial navigation errors based on acoustic feature matching positioning, characterized in that: The following steps are involved: Step 1: Based on the underwater sound propagation model of the ocean environment and the environmental acoustic feature information database, establish an environmental acoustic feature information vector database; Step 2: Based on the environmental acoustic feature information vector library, perform acoustic feature information matching and positioning to obtain the position information of the underwater unmanned vehicle; Step 3: Select the acoustic feature matching positioning system as the auxiliary navigation system, and form a combined navigation system with the inertial navigation system. Select the classical Kalman filter for information fusion. Adopt the indirect estimation method. Based on the obtained position information of the underwater unmanned vehicle, use the open-loop correction method to correct the navigation parameters of the inertial navigation system. The step 1 comprises: Place a test sound source near the underwater unmanned vehicle to obtain prior information on the propagation of the sound source radiation signal based on the current environment; Based on the acquired prior information on the propagation of the sound source radiation signal in the current environment and the channel characteristic parameters, an underwater acoustic propagation model suitable for the ocean environment is selected, and known ocean environmental parameters are input into the underwater acoustic propagation model; wherein the channel characteristic parameters include a sound velocity profile, environmental parameters, and underwater sound propagation characteristics; and the ocean environmental parameters include temperature, salinity, and depth; As the position of the underwater unmanned vehicle hydrophone changes, the arrival structure of its received signal also changes, that is, the relative arrival delay and amplitude of each eigenvalue line are different, so an environmental acoustic feature information database is constructed; In the area where the beacon sound source may appear, a search grid for the hydrophone is set, with each grid point as the assumed beacon sound source location. Using prior information and a sound ray theory model, the eigenvalue arrival time delay and amplitude of the test sound source (r, z) to the receiving hydrophone are calculated, and a characteristic library of the eigenvalue arrival time delay and amplitude of the beacon sound source to the receiving hydrophone is established as a vector library of environmental acoustic characteristic information; the prior information includes environmental parameters and hydrophone location; The step 2 includes: Step 21: Search for as many eigenwaves as possible and calculate all eigenwave additional information. Then, combine all the eigenwave additional information and decompose characteristic parameters of the eigenwaves from the actual received signal as matching information. The eigenwave additional information includes propagation attenuation and propagation delay, and the characteristic parameters include arrival angle and relative arrival delay. Step 22: Perform an acoustic feature information matching operation based on the known eigenvalue arrival structure diagram, extract position feature information, and finally obtain a relative position estimate of the beacon relative to the underwater unmanned vehicle; Step 23: Based on the relative estimated value of the beacon's position relative to the underwater unmanned vehicle and the position information of the beacon in the acoustic signal, the position information of the underwater unmanned vehicle can be calculated.

2. The method according to claim 1, characterized in that The step 21 includes: Assuming that there are N eigenwaves reaching the receiving hydrophone, the channel transfer function from the beacon source to the receiving hydrophone is expressed as: Where z s 、z r and r represent the depth of the sound source, the depth of the receiver, and the distance between the receiver and the sound source respectively; g i is the amplitude of the ith eigenvalue, n i is the propagation delay of the i-th eigenwave; The received signal at the receiving hydrophone is expressed as Where x(n) represents the received signal, s(n) represents the sound source signal, and e(n) represents the noise; Based on the received signal at the receiving hydrophone, the arrival time of the eigenvalue line is obtained, that is, the relative delay of the eigenvalue line is estimated.

3. The method according to claim 1, characterized in that The step 22 includes: By utilizing the environmental acoustic feature information vector library and directly correlating the autocorrelation function of the environmental acoustic feature information with the autocorrelation function of the hydrophone signal, the position of the sound source can be estimated.

4. The method according to claim 3, characterized in that The step 22 specifically includes: Perform bandpass filtering on the received signal of the hydrophone and calculate its autocorrelation function R xx ; Using prior information and a sound ray theory model, the arrival delay and amplitude of the eigenvalues ​​of the test sound source (r, z) to the receiving hydrophone are calculated; the prior information includes environmental parameters and the hydrophone position; The autocorrelation function R of the environmental acoustic characteristic signal rplc (r,z) and the autocorrelation function R of the hydrophone signal xx Perform correlation processing to obtain the correlation coefficient ρ(r,z); A search is performed within the expected beacon source location area to construct a fuzzy plane with a correlation coefficient ρ(r,z), whose peak position is the relative position estimate of the beacon source.

5. The method according to claim 4, characterized in that Assuming that the signal and noise are uncorrelated, the autocorrelation function of the received signal x(n) is: Where R ee is the autocorrelation function of the noise e(n), R ss is the autocorrelation function of the sound source signal s(n).

6. The method according to claim 1, characterized in that The step 3 comprises: Step 31: The position information of the underwater unmanned vehicle can be obtained by solving the problem in step 2. The position information is compared with the position information of the underwater unmanned vehicle corrected at the last moment, and the possible jump or frame loss of its output data is judged. When the position information output by the acoustic feature matching positioning system is within the threshold range, the positioning result data of the acoustic feature matching positioning system is normal and can be used, and the process goes to step 32; otherwise, the navigation and positioning is continued by relying on the inertial navigation system, and the navigation information of the inertial navigation system is directly output; Step 32: The Kalman filter uses the heading, attitude and position errors of the inertial navigation system as state estimates, and the position information provided by the acoustic feature matching positioning system as measurement information. The Kalman filter estimates the error and then feeds it back to the inertial navigation system to correct the navigation results of the inertial navigation system. At the same time, it can also smooth the navigation information of the acoustic feature matching positioning system, and finally obtain the corrected reliable navigation information of the combined navigation system.

7. The method according to claim 6, characterized in that In said step 32: The system state equation is expressed as: Where, X SINS is the state variable of the inertial navigation system, F SINS is the state matrix of the inertial navigation system, W SINS / AFMLS is the state noise of the inertial navigation system; The difference between the position information calculated by the inertial navigation system and the acoustic feature matching positioning system is selected as the observation value, and the position information of the inertial navigation system is expressed as the sum of the true value and the error value: Similarly, the position information of the acoustic feature matching positioning system can be expressed as: The measurement equation of the system is expressed as: where η AFMLS is the measurement noise of the acoustic feature matching positioning system; The measurement matrix is ​​expressed as: H LBL =[0 3×6 I 3×3 0 3×6 ] The acoustic array is installed on the underwater unmanned vehicle carrier. The position of the underwater unmanned vehicle in the rectangular coordinate system is (X UUV ,Y UUV ,Z UUV ), the position in longitude and latitude coordinates is (L UUV ,λ UUV ,Z UUV ); Assume that the coordinate reference of the inertial navigation system, the acoustic array coordinate system and the carrier coordinate system have been calibrated and made consistent; According to the geodetic rectangular coordinate system position information (X ob ,Y ob ,Z ob ) or longitude and latitude coordinate information (λ ob ,L ob ,Z ob ), and the position of the beacon sound source relative to the underwater unmanned vehicle obtained by the acoustic feature matching positioning system (x AFMLS ,y AFMLS ,z AFMLS ), the position coordinates of the underwater unmanned vehicle in the earth rectangular coordinate system (X AFMLS ,Y AFMLS ,Z AFMLS ) or longitude and latitude coordinates (L AFMLS ,λ AFMLS ,Z AFMLS ), as shown below: (X AFMLS ,Y AFMLS ,Z AFMLS )=(X ob ,Y ob ,Z ob )+(x AFMLS ,y AFMLS ,z AFMLS ) The output coordinates of the underwater unmanned vehicle from the inertial navigation system are expressed as (X SINS ,Y SINS ,Z SINS ) or (L SINS ,λ SINS ,Z SINS ); Kalman filter output combined navigation position coordinates are expressed as (X kalman ,Y kalman ,Z kalman ) or (L kalman ,λ kalman ,Z kalman ); When the inertial navigation system needs to be calibrated, the error of the underwater unmanned vehicle inertial navigation system (δX SINS ,δY SINS ,δZ SINS ) or (δL SINS ,δλ SINS ,δZ SINS ), error in the geodetic rectangular geographic coordinate system: (δX SINS ,δY SINS ,δZ SINS ) =(X SINS ,Y SINS ,Z SINS )-(X kalman ,Y kalman ,Z kalman ) The error of the inertial navigation system in the latitude and longitude geographic coordinate system: (δL SINS ,sl SINS ,δZ SINS ) =(L SINS ,λ SINS ,WITH SINS )-(L kalman ,λ kalman ,WITH kalman ) The above errors are used to correct the position information of the inertial navigation system and reset the navigation parameters.

8. A system for correcting inertial navigation errors based on acoustic feature matching positioning, implementing the method for correcting inertial navigation errors based on acoustic feature matching positioning according to any one of claims 1 to 7, characterized in that: include: Acoustic feature matching positioning system, used to output the position information of underwater unmanned vehicles; The judgment system is used to make a difference between the position information of the underwater unmanned vehicle output by the acoustic feature matching positioning system and the position information of the underwater unmanned vehicle corrected at the last moment, and to judge whether there may be jumps or frame losses in the output data. When the difference is within the threshold range, the positioning result of the acoustic feature matching positioning system is normal; otherwise, the navigation and positioning is continued by relying on the inertial navigation system, and the navigation information of the inertial navigation system is directly output; A Kalman filter is used to receive the heading, attitude, and position errors output by the inertial navigation system and use them as state estimates. It is also used to receive the position information output by the acoustic feature matching positioning system and use it as measurement information to estimate the error of the position information. The inertial navigation system is used to receive the error amount of the position information fed back by the Kalman filter to correct its own navigation results.

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