Deep sea underwater vehicle positioning system and method

Abstract

The invention relates to the technical field of ocean detection and navigation, in particular to a deep sea underwater vehicle positioning system and method. Comprising a multi-sensor fusion positioning module for performing weighted fusion on acoustic, inertial and magnetic field sensor data by using a data fusion algorithm; the self-adaptive signal processing module integrates a noise monitoring unit and a self-adaptive signal processing algorithm in the acoustic sensor, detects the intensity and frequency characteristics of marine environment noise in real time and adjusts the coefficient of a self-adaptive filter and the gain of a signal in real time; the map matching correction module is used for establishing a submarine topography map and a geomagnetic field distribution map, matching map data with measurement data, detecting data deviation and performing positioning correction; and the underwater relay node auxiliary positioning module is used for arranging relay nodes in the deep sea, receiving signals sent by the underwater vehicle and forwarding the signals to the water surface base station, and the water surface base station performs positioning and calculation to obtain position coordinates of the underwater vehicle. According to the invention, the positioning precision, the operating distance and the anti-interference capability of the underwater vehicle can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of ocean exploration and navigation technology, and in particular to a deep-sea underwater vehicle positioning system and method. BACKGROUND

[0002] The positioning technology of underwater vehicles is a key foundation for their safe execution of tasks in the fields of ocean science research, resource exploration, and military affairs. At present, the mainstream positioning technologies for underwater vehicles mainly include acoustic positioning systems, inertial navigation systems, and satellite positioning technologies. Among them, the acoustic positioning system is based on the propagation characteristics of sound waves in seawater, and determines the position of the vehicle by measuring the sound wave propagation time or phase information; the inertial navigation system uses inertial measurement elements such as accelerometers and gyroscopes to collect acceleration and angular velocity data of the vehicle, and calculates its position through integration operation; the satellite positioning technology relies on electromagnetic signals to achieve positioning by receiving satellite signals.

[0003] However, these technologies have obvious shortcomings. The positioning accuracy of the acoustic positioning system is easily affected by seawater temperature, salinity, and pressure, and the change in sound velocity can cause positioning deviation of several meters to several tens of meters. Moreover, sound waves attenuate severely when propagating in deep sea, and the effective positioning range is only a few hundred meters to a few kilometers, and they are easily disturbed by ocean environmental noise. The inertial navigation system has cumulative error, and the error gradually increases with the increase of navigation time, making it difficult to meet the positioning accuracy requirements of long-time deep-sea navigation. The satellite positioning technology cannot be directly applied underwater due to the strong absorption of electromagnetic waves by seawater.

[0004] In summary, the existing positioning technologies cannot meet the needs of high-precision, long-distance, and stable positioning of underwater vehicles in deep-sea complex environments in terms of positioning accuracy, effective distance, and anti-interference ability. Therefore, it is of urgent practical significance to solve these problems. SUMMARY

[0005] In view of the above, the purpose of the present application is to provide a deep-sea underwater vehicle positioning system and method to solve the problems of the prior art in terms of positioning accuracy, effective distance, and anti-interference ability.

[0006] To achieve the above purpose, the technical solution of the present application is as follows: In the first aspect, a deep-sea underwater vehicle positioning system comprises:

[0007] A multi-sensor fusion positioning module, including a sensor installation and data acquisition unit, a data fusion algorithm selection unit, and a Kalman filter data fusion process unit; used for installing acoustic sensors, inertial sensors, and magnetic field sensors, and using data fusion algorithms to weight and fuse the data collected by the acoustic sensors, inertial sensors, and magnetic field sensors to obtain the fusion positioning information of the underwater vehicle;

[0008] The adaptive signal processing module comprises a marine environment noise monitoring unit, an adaptive filter algorithm selection unit, a step factor adaptive adjustment unit and an adaptive signal gain adjustment and processing unit; the noise monitoring unit and the adaptive signal processing algorithm are integrated in the acoustic sensor, and the intensity and frequency characteristics of the marine environment noise are detected in real time, and the adaptive filter coefficients and the signal gain are adjusted in real time;

[0009] The map matching correction module comprises a map establishment unit, a data matching unit and a data deviation detection and positioning correction unit; based on the fusion positioning information of the underwater vehicle, seabed topographic maps and geomagnetic field distribution maps are established, and the map data and the measured data are matched, the data deviation is detected and the positioning correction is performed;

[0010] The underwater relay node assisted positioning module comprises a relay node arrangement strategy unit, a signal receiving, processing, enhancing and forwarding unit and a water surface base station positioning calculation unit; according to the map matching correction module, underwater relay nodes are arranged in the deep sea area, signals emitted by the underwater vehicle are received and forwarded to the water surface base station, and the position coordinates of the underwater vehicle are obtained by the water surface base station positioning calculation.

[0011] Preferably, the sensor installation and data acquisition unit is used for installing the acoustic sensor, the inertial sensor and the magnetic field sensor on the underwater vehicle and acquiring data of the acoustic sensor, the inertial sensor and the magnetic field sensor; the acoustic sensor is installed outside the underwater vehicle, the inertial sensor and the magnetic field sensor are installed at a stable position inside the underwater vehicle, the data acquired by the acoustic sensor comprises the propagation time and the direction of arrival of the acoustic signal, the data acquired by the inertial sensor comprises the acceleration and the angular velocity of the underwater vehicle, and the data acquired by the magnetic field sensor comprises the intensity and the direction of the geomagnetic field.

[0012] Preferably, the data fusion algorithm selection unit adopts the Kalman filter algorithm for data fusion; the data fusion process of the Kalman filter in the data fusion process unit is divided into two stages of prediction and update.

[0013] Preferably, the adaptive filter coefficients adopt the formula for real-time adjustment, wherein, is the i-th coefficient of the adaptive filter at the j-th moment, is the j-th moment step factor, and the error signal , is the expected signal, , is the order of the adaptive filter, is the i-th moment step factor, , is the order of the adaptive filter, is the i-th moment step factor, The acoustic signal input at any time.

[0014] Preferably, the Using formula Adaptive adjustments are made, among which, It is the maximum step size factor. It is an adjustment factor. This represents noise power.

[0015] Preferably, the gain of the signal is expressed by the formula... Adjustments were made, including It is the maximum gain. It is the gain adjustment factor.

[0016] Preferably, the specific operations for establishing the seabed topographic map and the geomagnetic field distribution map are as follows: for establishing the seabed topographic map, a high-precision multibeam sonar system is used to conduct a comprehensive measurement of the target sea area; for establishing the geomagnetic field distribution map, a high-precision magnetic field measurement device is used.

[0017] Preferably, the water surface base station positioning calculation unit is used to receive signals forwarded by relay nodes and perform positioning calculations based on the signal's time difference of arrival or angle of arrival information.

[0018] Secondly, the present invention provides a method for locating a deep-sea underwater vehicle, comprising the following steps:

[0019] S1. An acoustic sensor, an inertial sensor, and a magnetic field sensor are installed in the multi-sensor fusion positioning module, and a data fusion algorithm is used to weight and fuse the data collected by the acoustic sensor, the inertial sensor, and the magnetic field sensor to obtain the fused positioning information of the underwater vehicle.

[0020] S2. A noise monitoring unit and an adaptive signal processing algorithm are integrated into the acoustic sensor. The adaptive signal processing module detects the intensity and frequency characteristics of marine environmental noise in real time and adjusts the adaptive filter coefficients and signal gain in real time.

[0021] S3. Based on the fused positioning information of the underwater vehicle, the map matching and correction module establishes a seabed topographic map and a geomagnetic field distribution map, and matches the map data with the measurement data to detect data deviation and perform positioning correction.

[0022] S4. According to the map matching and correction module, the underwater relay node assisted positioning module deploys underwater relay nodes in the deep sea area to receive signals emitted by the underwater vehicle and forward them to the surface base station. The surface base station calculates the positioning signal to obtain the position coordinates of the underwater vehicle.

[0023] The beneficial effects of this invention are as follows: Compared with existing technologies, this invention improves positioning accuracy by fusing data from acoustic sensors, inertial sensors, and magnetic field sensors using a Kalman filter algorithm to integrate the fused positioning information of underwater vehicles; it enhances the anti-interference capability of acoustic signals by employing an adaptive signal processing algorithm; it reduces cumulative positioning errors by combining seabed topographic maps with geomagnetic field distribution maps for matching and correction; and it extends the effective range by deploying underwater relay nodes. This effectively solves the problems of low positioning accuracy, weak anti-interference capability, and limited effective range in existing technologies, significantly improving the positioning stability and reliability of deep-sea underwater vehicles, and making it suitable for complex deep-sea operational scenarios. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a schematic diagram of the structure of a deep-sea underwater vehicle positioning system according to the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and beneficial effects of this invention clearer, the invention will be further described in detail below with reference to specific embodiments. It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains.

[0027] like Figure 1 As shown, the present invention provides a deep-sea underwater vehicle positioning system, comprising:

[0028] 1. Multi-sensor fusion positioning module

[0029] 1.1 Sensor Installation and Data Acquisition Unit

[0030] On deep-sea underwater vehicles, the proper installation of acoustic sensors, inertial sensors, and magnetic field sensors is crucial. Acoustic sensors are mounted externally to the underwater vehicle, positioned to maximize the reception of surrounding acoustic signals while minimizing obstruction from the vehicle's own structure. Inertial and magnetic field sensors are installed in stable locations inside the underwater vehicle to prevent vibrations and external interference from affecting measurement accuracy.

[0031] During navigation, various sensors collect data in real time. The acoustic sensors collect the propagation time of acoustic signals. and direction of arrival By measuring the propagation time of sound waves from the sound source to the sensor, combined with the known speed of sound in seawater... This allows for a preliminary estimation of the distance between the underwater vehicle and the sound source. The speed of propagation here Seawater temperature ,salinity and pressure The function can be obtained through empirical formulas. Calculations, such as the commonly used Mackenzie formula.

[0032] Inertial sensors collect the acceleration of underwater vehicles in real time. and angular velocity ,in These represent the accelerations along the three coordinate axes. These represent the angular velocities along the three coordinate axes.

[0033] Magnetic field sensor collects the intensity of the Earth's magnetic field. ,in These represent the components of the Earth's magnetic field along the three coordinate axes.

[0034] 1.2 Data Fusion Algorithm Selection Unit

[0035] The Kalman filter algorithm is used for data fusion. The Kalman filter is a recursive optimal estimation algorithm that can optimally estimate the system state based on the system's input and output observation data.

[0036] Set time The state vector of the underwater vehicle is It contains information such as the underwater vehicle's position and velocity. The system's state equation is:

[0037]

[0038] in, It is the state transition matrix, which describes the system state from time 1 to 2. At the time The transfer relationship; It is the control input matrix; It controls the input vector; It is process noise, with a mean of zero and a covariance of... The Gaussian distribution.

[0039] The observation equation is:

[0040]

[0041] in, It is the observation vector, which consists of measurement data from acoustic sensors, inertial sensors, and magnetic field sensors; It is the observation matrix; It is observation noise, which follows a pattern with a mean of zero and a covariance of . The Gaussian distribution.

[0042] 1.3 Data Fusion Process Unit of Kalman Filtering

[0043] The data fusion process of Kalman filtering is divided into two stages: prediction and update.

[0044] (1) Prediction stage

[0045] State prediction:

[0046] Covariance prediction:

[0047] in, It is a moment The predicted state, It is a moment The optimal estimated state, It is a moment The predicted covariance, It is a moment The optimal estimate of covariance, yes The transpose of .

[0048] (2) Update phase

[0049] Kalman gain calculation:

[0050] Status Update:

[0051] Covariance update:

[0052] in, It is Kalman gain. It is a moment The optimal estimated state, It is a moment The optimal estimate of covariance, It is the identity matrix. yes The transpose of .

[0053] By repeatedly predicting and updating, the Kalman filter algorithm weighted and fused data from acoustic sensors, inertial sensors, and magnetic field sensors to obtain more accurate fused positioning information for underwater vehicles. This effectively reduces the positioning error of a single sensor and improves the accuracy and reliability of positioning.

[0054] 2. Adaptive signal processing module

[0055] 2.1 Marine Environmental Noise Monitoring Unit

[0056] Integrating a noise monitoring unit into an acoustic sensor allows for real-time monitoring of the intensity and frequency characteristics of marine environmental noise. In the deep-sea underwater environment, marine environmental noise exhibits complex characteristics. Noise primarily originates from marine biological activity, wave impact, and human activities. Its intensity and frequency distribution vary with time and space. Typically, the power spectral density of marine environmental noise... It can be represented as a frequency The function.

[0057] Based on actual measurements and research, within a certain frequency range, the power spectral density of marine environmental noise can be approximated as a power-law distribution:

[0058]

[0059] in, It is a constant related to the marine environment. It is the power law exponent, and its value usually takes the range of 1000-1000. arrive Between different marine regions and environmental conditions, and The value will be different.

[0060] 2.2 Adaptive Filtering Algorithm Selection Unit

[0061] Based on the noise monitoring unit results, an adaptive signal processing algorithm, namely an adaptive filtering algorithm, is integrated into the acoustic sensor. This paper adopts the adaptive least mean square (LMS) filtering algorithm to effectively suppress the interference of marine environmental noise on the acoustic signal. The LMS filtering algorithm is an adaptive filtering algorithm based on gradient descent. It continuously adjusts the coefficients of the adaptive filter to minimize the mean square error between the output of the adaptive filter and the desired signal.

[0062] Let the input acoustic signal be The noise signal is The received mixed signal is The output of the adaptive filter is ,in Is the adaptive filter in the 1st... The first moment One coefficient, It is the order of the adaptive filter.

[0063] Error signal ,in It is the desired signal. The LMS filtering algorithm updates the adaptive filter coefficients iteratively:

[0064]

[0065] in, It is the first The step size factor controls the update speed of the adaptive filter coefficients. The choice of step size factor is crucial; an excessively large step size factor can lead to filter instability, while an excessively small step size factor will slow down the convergence speed of the adaptive filter.

[0066] 2.3 Adaptive Adjustment Unit for Step Size Factor

[0067] To enable the LMS filtering algorithm to better adapt to changes in marine environmental noise, the step size factor was adjusted. Adaptive adjustments are made based on the power spectral density of marine environmental noise monitored in real time by the marine environmental noise monitoring unit. The step size factor is dynamically adjusted.

[0068] Let the noise power at the current moment be... Define an adaptive step size factor adjustment function:

[0069]

[0070] in, It is the maximum step size factor. This is an adjustment coefficient used to control the rate of change of the step size factor with respect to noise power. When the noise power... When the step size factor increases, This reduces the noise level, making the adaptive filter more stable under high noise conditions; when the noise power... When the step size is reduced, the step size factor increases, enabling the adaptive filter to track signal changes more quickly.

[0071] 2.4 Adaptive Signal Gain Adjustment and Processing Unit

[0072] In addition to adjusting the adaptive filter coefficients in real time, the LMS filtering algorithm also needs to adaptively adjust the gain of the acoustic signal. Let the initial amplitude of the received acoustic signal be... Dynamically adjust gain based on noise intensity .

[0073] Define a gain adjustment function:

[0074]

[0075] in, It is the maximum gain. It is the gain adjustment factor. By adjusting the gain, the signal amplitude is appropriately enhanced when the noise is strong, thus improving the signal's detectability; and when the noise is weak, the signal is prevented from being over-amplified and causing distortion.

[0076] The acoustic sensor integrates a noise monitoring unit and an adaptive signal processing algorithm. The noise monitoring unit can be used to detect the intensity and frequency characteristics of marine environmental noise in real time; the adaptive signal processing algorithm adjusts the adaptive filter coefficients and signal gain in real time based on the noise monitoring results.

[0077] Received mixed signal The signal is input into an adaptive filter, and after filtering and gain adjustment, an enhanced acoustic signal is output. This effectively improves the acoustic signal's anti-interference capability, ensures its quality, and provides a reliable data foundation for subsequent positioning processing.

[0078] 3. Map Matching and Correction Module

[0079] 3.1 Map Building Unit

[0080] In deep-sea environments, in order to achieve map-matching-based positioning correction based on the fusion positioning information of underwater vehicles, it is necessary to pre-construct accurate seabed topographic maps and geomagnetic field distribution maps.

[0081] To create seabed topographic maps, a high-precision multibeam sonar system is used to comprehensively measure the target sea area. The multibeam sonar emits a fan-shaped beam towards the seabed, and the time from emission to return is measured. Combined with the speed of sound in seawater It can calculate the seabed depth corresponding to each beam. The calculation formula is: Depth data from different locations are integrated, and interpolation algorithms (such as Kriging interpolation) are used to construct a continuous 3D mesh model of the seabed topography. The data is... ,in Represents planar coordinates.

[0082] The creation of a geomagnetic field distribution map relies on high-precision magnetic field measurement equipment. Multiple measurement points are set up in the target sea area, and measurements are taken at each point. geomagnetic field strength and direction To describe the spatial distribution of the Earth's magnetic field, spherical harmonic analysis is used, representing the Earth's magnetic field as a linear combination of a series of spherical harmonic functions:

[0083]

[0084] in, To measure the distance from a point to the Earth's center, Remainder latitude, Longitude and The spherical harmonic coefficients, To establish Legendre polynomials, Let represent the order of the spherical harmonic expansion, and , Indicates the degree of the spherical harmonic expansion, and , This represents the highest order of the spherical harmonic expansion. By fitting the measured data, the spherical harmonic coefficients are determined, thus obtaining the geomagnetic field distribution model, whose data are... ,in Represents three-dimensional coordinates.

[0085] 3.2 Data Matching Unit

[0086] During underwater navigation, the vehicle uses its onboard topographic sensors (such as single-beam sonar) and magnetic field sensors to acquire real-time seabed topographic data of its current location. and geomagnetic field data ,in To measure the position of underwater vehicles in real time.

[0087] (1) Matching of seabed topographic data

[0088] To determine the discrepancy between the measurement data and the map data, the normalized cross-correlation coefficient of the seabed topography data was used. Perform a matching degree calculation. For seabed topography data, The calculation formula is:

[0089]

[0090] in, This is a matching window used for comparing seabed topographic data. Three-dimensional network model data representing the seabed topographic map, This represents the seabed topography data measured in real time by the underwater vehicle. and The mean values ​​within the matching window are the 3D network model data of the seabed topography map and the seabed topography data measured by the underwater vehicle.

[0091] (2) Geomagnetic field data matching

[0092] Real-time acquisition of geomagnetic field data at the current location using a magnetic field sensor Normalized cross-correlation coefficients of geomagnetic field data were used. Perform a matching degree calculation. Similarly, for geomagnetic field data, The calculation formula is:

[0093]

[0094] in, This represents data from a geomagnetic field distribution model. This represents the geomagnetic field data measured in real time by the underwater vehicle. This is a matching window used for comparing geomagnetic field data. and These are the mean values ​​within the matching window for the geomagnetic field distribution model data and the geomagnetic field data measured in real time by the underwater vehicle, respectively.

[0095] 3.3 Data Deviation Detection and Positioning Correction Unit

[0096] (1) Deviation detection

[0097] when or Below the set threshold or If a discrepancy is found between the measurement data and the map data, positioning correction is required.

[0098] (2) Positioning correction

[0099] ①Seabed topography matching correction

[0100] Once a deviation is detected, the least squares method is used for positioning correction. Assume the initial positioning error of the underwater vehicle is... , and The corrected position is .

[0101] Taking seabed topography matching as an example, an error function is constructed. :

[0102]

[0103] Through the Taking the partial derivatives and setting them to zero, we obtain the system of equations:

[0104]

[0105] Solving the system of equations yields and The optimal estimate.

[0106] ② Geomagnetic field matching correction

[0107] Similarly, for geomagnetic field matching, an error function is constructed. :

[0108]

[0109] By solving , and The system of equations formed yields , and The optimal estimate.

[0110] Finally, the obtained error estimate is applied to the positioning results of the underwater vehicle to achieve positioning correction and improve the long-term stability and accuracy of positioning.

[0111] 4. Underwater relay node auxiliary positioning module

[0112] 4.1 Relay Node Deployment Strategy Unit

[0113] According to the map matching and correction module, when deploying underwater relay nodes in deep-sea areas, it is necessary to comprehensively consider signal transmission distance, positioning accuracy requirements, and the complex characteristics of the deep-sea environment. Let the maximum effective signal transmission distance of the relay node be... This is related to the propagation characteristics of acoustic signals in seawater, and is affected by seawater temperature. ,salinity and pressure Impact. According to empirical formulas, the propagation speed of acoustic signals in seawater... It can be approximated as:

[0114]

[0115] in , , , These are constant coefficients, which can be determined experimentally. The distance between relay nodes. Should meet To ensure reliable signal transmission, relay nodes should form a reasonable topology, such as a grid or honeycomb layout, to improve positioning accuracy. Assuming relay nodes are deployed within a two-dimensional planar area to achieve coverage and positioning of underwater vehicles, let the length of this area be . Width is Then the number of relay nodes It can be roughly estimated as follows:

[0116]

[0117] 4.2 Signal Reception, Processing, Enhancement and Transmission Unit

[0118] After receiving the acoustic signal from the underwater vehicle, the relay node first preprocesses the signal. Let the received original signal be... ,in The time variable is used. Due to interference from marine environmental noise, the signal contains noise components. ,but ,in The signal is considered useful. Relay nodes use adaptive filtering algorithms to filter the signal and remove noise. For example, using the LMS filtering algorithm, the filter output... It can be represented as:

[0119]

[0120] in Let the order be the filter order. For the filter's first Each weight coefficient is updated using the following formula:

[0121]

[0122] in Step size factor For error signals, This is the desired signal.

[0123] The preprocessed signal may still be weak and requires signal amplification. The relay node uses a power amplifier to amplify the signal; let the amplifier gain be... The enhanced signal for:

[0124]

[0125] The relay node forwards the enhanced signal to the surface base station. During forwarding, the signal encoding and modulation methods need to be considered to improve transmission efficiency and reliability. Orthogonal Frequency Division Multiplexing (OFDM) modulation technology is used to modulate the signal onto multiple subcarriers for transmission. Let the number of subcarriers be... , No. The frequency of each subcarrier is The modulated signal It can be represented as:

[0126]

[0127] in For the first Modulation symbols on each subcarrier.

[0128] 4.3 Water Surface Base Station Positioning Calculation Unit

[0129] After receiving signals relayed by multiple relay nodes, the surface base station performs positioning calculations based on information such as the Time Difference of Arrival (TDOA) or Angle of Arrival (AOA). Assuming the TDOA positioning method is used, let the time when the underwater vehicle sends the signal be... , No. The time when each relay node receives the signal is The signal then travels from the underwater vehicle to the first... Propagation time of each relay node Let the position coordinates of the underwater vehicle be... , No. The location coordinates of each relay node are: Then, based on the propagation speed of the acoustic signal The equation can be obtained as follows:

[0130]

[0131] The position coordinates of the underwater vehicle can be obtained by solving a simultaneous equation involving multiple relay nodes. In practical calculations, optimization algorithms such as the least squares method can be used to improve positioning accuracy and obtain the position coordinates of the underwater vehicle.

[0132] The present invention also provides a method for locating a deep-sea underwater vehicle, which includes the following steps:

[0133] S1. An acoustic sensor, an inertial sensor, and a magnetic field sensor are installed in the multi-sensor fusion positioning module, and a data fusion algorithm is used to weight and fuse the data collected by the acoustic sensor, the inertial sensor, and the magnetic field sensor to obtain the fused positioning information of the underwater vehicle.

[0134] S2. A noise monitoring unit and an adaptive signal processing algorithm are integrated into the acoustic sensor. The adaptive signal processing module detects the intensity and frequency characteristics of marine environmental noise in real time and adjusts the adaptive filter coefficients and signal gain in real time.

[0135] S3. Based on the fused positioning information of the underwater vehicle, the map matching and correction module establishes a seabed topographic map and a geomagnetic field distribution map, and matches the map data with the measurement data to detect data deviation and perform positioning correction.

[0136] S4. According to the map matching and correction module, the underwater relay node assisted positioning module deploys underwater relay nodes in the deep sea area to receive signals emitted by the underwater vehicle and forward them to the surface base station. The surface base station calculates the positioning signal to obtain the position coordinates of the underwater vehicle.

[0137] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of the invention is limited to these examples; within the framework of the invention, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of the different aspects of the invention as described above, which are not provided in detail for the sake of brevity.

[0138] This invention aims to cover all such substitutions, modifications, and variations that fall within the scope of protection. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A deep sea underwater vehicle positioning system, characterized by, The application relates to an underwater vehicle positioning system. The multi-sensor fusion positioning module comprises a sensor installation and data acquisition unit, a data fusion algorithm selection unit and a Kalman filter data fusion process unit. The acoustic sensor, the inertial sensor and the magnetic field sensor are installed, and data collected by the acoustic sensor, the inertial sensor and the magnetic field sensor is weighted and fused by using a data fusion algorithm to obtain fusion positioning information of the underwater vehicle. The adaptive signal processing module comprises a marine environment noise monitoring unit, an adaptive filter algorithm selection unit, a step factor adaptive adjustment unit and an adaptive signal gain adjustment and processing unit. The map matching correction module comprises a map establishment unit, a data matching unit and a data deviation detection and positioning correction unit. The underwater relay node auxiliary positioning module comprises a relay node arrangement strategy unit, a signal receiving, processing, enhancing and forwarding unit and a water surface base station positioning calculation unit.

2. The system of claim 1, wherein, The sensor installation and data acquisition unit is used for installing the acoustic sensor, the inertial sensor and the magnetic field sensor on the underwater vehicle and collecting data of the acoustic sensor, the inertial sensor and the magnetic field sensor.

3. The system of claim 1, wherein, The data fusion algorithm selection unit adopts a Kalman filter algorithm for data fusion.

4. The system of claim 1, wherein, The adaptive filter coefficients are given by the formula are adjusted in real time, wherein is the coefficient of the adaptive filter at the time instant, is the coefficient of the adaptive filter at the time instant, is the step factor at the time instant, the error signal is the desired signal, , is the order of the adaptive filter, is the acoustic signal input at the time instant.

5. The system of claim 4, wherein, The Adaptive adjustment is performed using the formula wherein, is a maximum step factor, is an adjustment factor, is the noise power.

6. The system of claim 1, wherein, The gain of the signal is given by the formula is adjusted, wherein is the maximum gain, is the gain adjustment factor.

7. The system of claim 1, wherein, The Kalman filter data fusion process in the Kalman filter data fusion process unit is divided into two stages of prediction and update.

8. The system of claim 1, wherein, The water surface base station positioning calculation unit is used for receiving the signal forwarded by the relay node and performing positioning calculation according to the time difference of arrival or the angle of arrival information of the signal.

9. A method of positioning a deep-sea underwater vehicle based on the positioning system of any one of claims 1-8, characterized in that, The application relates to an underwater vehicle positioning system. S1, installing the acoustic sensor, the inertial sensor and the magnetic field sensor by using the multi-sensor fusion positioning module and weighting and fusing data collected by the acoustic sensor, the inertial sensor and the magnetic field sensor by using a data fusion algorithm to obtain fusion positioning information of the underwater vehicle. S2, integrating noise monitoring unit and adaptive signal processing algorithm in the acoustic sensor, detecting the intensity and frequency characteristics of the marine environmental noise in real time by the adaptive signal processing module, and adjusting the adaptive filter coefficients and the gain of the signal in real time; S3, according to the fusion positioning information of the underwater vehicle, the map matching correction module establishes the seabed topographic map and the geomagnetic field distribution map, and matches the map data with the measured data to detect the data deviation and correct the positioning; S4, according to the map matching correction module, the underwater relay node auxiliary positioning module arranges underwater relay nodes in the deep sea area, receives the signals sent by the underwater vehicle and forwards them to the water surface base station, and the water surface base station locates and solves the signals to obtain the position coordinates of the underwater vehicle.

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