Unmanned ship recovery beidou high-precision relative positioning method based on multi-source information fusion

Abstract

The present application relates to a Beidou high-precision relative positioning method for unmanned ship recovery based on multi-source information fusion, belonging to the technical field of position reference transmission, based on Beidou carrier phase differential positioning, by introducing high-precision ranging information collected by a laser range finder, a tight combination relative positioning solution model is constructed, which improves the redundancy of observation information and optimizes the positioning solution accuracy. Compared with the traditional single Beidou relative positioning method, the method can effectively avoid the limitations of Beidou relative positioning caused by satellite blocking, provide high-precision and high-reliability relative positioning technology support for unmanned ship dock recovery scene, and significantly improve the stability and success rate of the recovery task.

Description

Technical Field

[0001] This invention belongs to the field of position reference transfer technology, and in particular to a BeiDou high-precision relative positioning method for unmanned surface vessel recovery based on multi-source information fusion. Background Technology

[0002] Unmanned surface vessels (USVs), equipped with mission payloads, can perform critical tasks such as intelligence gathering, maritime patrol, maritime rescue, and convoy navigation. They are suitable for dangerous, repetitive, and other missions unsuitable for manned vessels. However, due to their limited energy reserves, they lack long-range endurance and typically require a mother ship to deploy them to a designated area. After the mission, the mother ship recovers them. Currently, three common deployment and recovery methods are used: sling-launch, stern-slip, and dock-mounted. Safe and reliable deployment and recovery are crucial for USV missions, especially the recovery process based on high-precision relative position information.

[0003] The navigation and positioning for unmanned surface vessel (USV) recovery can be divided into two stages: The first stage involves the USV navigating from a distant work location to near the mother ship, using navigation and positioning technologies to ensure safe navigation. This stage typically employs satellite navigation, inertial navigation, and compass techniques. The second stage involves the USV reaching the mother ship and recovering it using high-precision relative positioning technologies. This stage typically employs satellite navigation, visual positioning, laser ranging, and sonar techniques to achieve high-precision relative positioning. The BeiDou Navigation Satellite System, with its all-weather capability and non-accumulating error over time, is the primary technology for obtaining the USV's position information. Both the USV and the mother ship are equipped with high-precision BeiDou receivers. During the transit phase, the USV can achieve meter-level navigation and positioning accuracy. A differential data transmission link is established between the mother ship and the USV via a data transmission radio, and centimeter-level high-precision relative positioning can be achieved through carrier phase differential positioning technology.

[0004] The positioning technology for unmanned surface vessels (USVs) has evolved from traditional GNSS / INS combined positioning to the application of LiDAR, visual sensors, and acoustic sensors in integrated positioning systems. The basic principle of multi-sensor fusion applications is to leverage the strengths of different sensors to compensate for each other, resulting in measurement results with better accuracy and reliability. Based on the degree of coupling in multi-source information fusion, it can be categorized into loosely coupled, tightly coupled, and deeply coupled systems. Loosely coupled systems use the measurement information from each sensor to perform independent positioning calculations, combining the results at the positioning level. Tightly coupled systems fuse the raw observation information from each sensor to establish a unified observation model for optimal estimation of position parameters. Deeply coupled systems are mainly used to penetrate to the underlying satellite navigation receiver, assisting in signal acquisition and tracking.

[0005] In the application scenario of unmanned surface vessel (USV) dock recovery, the number of BeiDou visible satellites on the daughter ship is insufficient due to partial obstruction of the mother ship's hull, leading to reduced accuracy or even failure of BeiDou carrier phase differential positioning. Therefore, it is necessary to provide high-precision relative positioning technology during USV dock recovery so that the USV's BeiDou dynamic differential relative positioning can still provide high-precision position information relative to the mother ship even when the number of visible satellites is insufficient. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of existing technologies and propose a high-precision relative positioning method for unmanned surface vessel (USV) recovery based on multi-source information fusion using BeiDou. Based on BeiDou carrier phase differential positioning, this method introduces high-precision ranging information provided by multi-source ranging sensors to construct a relative positioning solution model closely integrated with BeiDou, thereby improving observation redundancy and relative positioning accuracy. This provides a high-precision and highly reliable relative positioning technology for USV dock recovery missions.

[0007] The technical problem solved by this invention is achieved through the following technical solution: A method for high-precision relative positioning of unmanned surface vessels (USVs) based on multi-source information fusion using BeiDou navigation satellites includes the following steps: Step 1: Construct an unmanned surface vessel dock recovery system; Step 2: Based on the structure of the unmanned surface vessel dock recovery system, construct a measurement model for the relative position information of the unmanned surface vessel and the mother vessel; Step 3: Based on the relative position information measurement model between the unmanned surface vessel and the mother vessel, construct a tight combination relative positioning model of BeiDou / multi-source ranging information, and perform positioning through the constructed positioning model.

[0008] Furthermore, the unmanned surface vessel dock recovery system in step 1 includes an unmanned surface vessel, a mother vessel dock, and a mother vessel. The system integrates multi-source sensors and hardware devices to achieve absolute positioning, relative positioning, and data communication functions. The multi-source sensors and hardware devices include a Beidou antenna, a Beidou receiver, a laser rangefinder, and a data transmission radio.

[0009] Furthermore, the specific implementation method of step 1 is as follows: the satellite navigation receiving equipment of the unmanned surface vessel (USV) and the mother vessel simultaneously receive signals from the BeiDou satellite navigation system to generate pseudorange and carrier phase raw observation data information; the USV and the mother vessel are equipped with laser rangefinders to achieve high-precision measurement of the relative distance between the USV and the mother vessel; the USV's multi-source information fusion processing module receives raw observation data information sent by the mother vessel's satellite navigation system through the data link in real time, and receives high-precision ranging information output by the laser rangefinder in real time. By tightly combining the observation information, multi-source fusion relative positioning calculation is performed to obtain accurate relative position information between the antennas of the mother vessel and the USV in real time.

[0010] Furthermore, the specific implementation method of step 2 is as follows: Step 2.1: The relative position information measurement model includes the BeiDou carrier phase differential measurement model and the multi-source ranging information measurement model; Step 2.2: The BeiDou carrier phase differential measurement model is based on carrier phase double-difference operation, which is used to eliminate receiver clock error and satellite clock error, and reduce tropospheric and ionospheric errors. Step 2.3: The multi-source ranging information measurement model is based on the ranging information of the ranging sensor, and the ranging information is converted to the Beidou antenna position reference through pole arm compensation.

[0011] Furthermore, the BeiDou carrier phase differential relative positioning model is as follows: in, For inter-station and inter-satellite double difference operators, L For BeiDou carrier phase observations, p This is the geometric distance between the antenna and the receiver. T For tropospheric delay, I For tropospheric delay, l For carrier wavelength, N For ambiguity, e L For observation noise that is not modeled, carrier phase double difference can eliminate receiver clock error and satellite clock error, and also weaken the impact of tropospheric and ionospheric errors on BeiDou relative positioning to a certain extent.

[0012] Furthermore, the multi-source ranging information measurement model is as follows: in, D For the ranging information of the multi-source ranging sensor, ( Dx BDS , Dy BDS , Dz BDS () represents the three-dimensional position coordinate difference between the unmanned surface vessel and the BeiDou antenna of the mother ship. dx BDS,D , dy BDS,D , dz BDS,D ) represents the relative position coordinate increment of the lever arm. e D This refers to observation noise that has not been modeled.

[0013] Moreover, in step 3, the BeiDou / multi-source ranging information tightly combined relative positioning model uses Kalman filtering to achieve observation information fusion and optimal parameter estimation; The system state equation and measurement equation are as follows: in, for k Moment n A state vector containing parameters to be estimated regarding the relative position of the antenna; for k Moment m The measurement vector includes raw satellite observation information and laser ranging observation information; for k -1 to k The system's one-step transition matrix at time t; for k System noise at time -1; Let be the system noise matrix, which characterizes the noise generated by... k -1 hour arrives k The noise of each system at each time point affects k The degree of each state at any given moment; for k Measurement matrix at time; for k Moment m Dimensional measurement noise; The Kalman filter equation for relative positioning calculation using a tight combination of multi-source information is as follows: in, It is a state Kalman filter estimation; It is to utilize The calculated pair A one-step prediction; The filter gain is selected based on the estimated value. The mean square error matrix is ​​minimized; For valuation The mean square error matrix.

[0014] The advantages and positive effects of this invention are: This invention is based on BeiDou carrier phase differential positioning. By introducing high-precision ranging information collected by a laser rangefinder, a tightly coupled relative positioning solution model is constructed, which improves the redundancy of observation information while optimizing the positioning solution accuracy. Compared with traditional relative positioning methods that rely solely on BeiDou, the method proposed in this invention can effectively avoid the limitations of BeiDou relative positioning caused by satellite obstruction, providing high-precision and high-reliability relative positioning technology support for unmanned surface vessel dock recovery scenarios, and significantly improving the stability and success rate of recovery missions. Attached Figure Description

[0015] Figure 1 This is a diagram showing the overall architecture of the unmanned surface vessel dock recovery system of the present invention; Figure 2 This is a flowchart of the multi-source ranging information combination estimation process of the present invention. Detailed Implementation

[0016] The present invention will be further described in detail below with reference to the accompanying drawings.

[0017] In the application scenario of unmanned surface vessel (USV) dock recovery, the insufficient number of BeiDou visible satellites on the daughter ship due to partial obstruction of the mother ship's hull leads to reduced or even failed BeiDou carrier phase differential positioning accuracy. To address this issue, this invention proposes a high-precision relative positioning method based on multi-source information fusion. This method is based on BeiDou carrier phase differential positioning and introduces high-precision ranging information provided by multi-source ranging sensors to construct a relative positioning solution model tightly integrated with BeiDou, thereby improving observation redundancy and ultimately enhancing relative positioning accuracy. This provides a high-precision and highly reliable relative positioning technology for USV dock recovery missions.

[0018] This invention establishes a general scheme for the recovery of an unmanned surface vessel (USV) dock, including the installation method of the BeiDou antenna and the installation method of the multi-source ranging sensor. The high-precision relative positioning of the USV dock during recovery is divided into two stages. The first stage, outside the working range of the ranging sensor, utilizes BeiDou carrier phase differential positioning to achieve high-precision relative positioning. The second stage, under close-range conditions where the USV's BeiDou visual satellites are partially obscured by the mother ship, utilizes a BeiDou / ranging fusion positioning method to achieve high-precision relative positioning.

[0019] A method for high-precision relative positioning of BeiDou for unmanned surface vessel recovery based on multi-source information fusion, such as Figure 2 As shown, it includes the following steps: Step 1: Construct an unmanned surface vessel dock recovery system.

[0020] like Figure 1 As shown, the unmanned surface vessel (USV) dock recovery system in step 1 includes an USV, a mothership dock, and a mothership. The system integrates multi-source sensors and hardware devices to achieve absolute positioning, relative positioning, and data communication functions. The multi-source sensors and hardware devices include a BeiDou antenna, a BeiDou receiver, a laser rangefinder, and a data transmission radio.

[0021] The satellite navigation receiving equipment of both the unmanned surface vessel (USV) and the mother vessel simultaneously receives signals from the BeiDou Navigation Satellite System, generating raw observation data information of pseudorange and carrier phase. The USV and the mother vessel are equipped with laser rangefinders to achieve high-precision measurement of the relative distance between the USV and the mother vessel. The USV's multi-source information fusion processing module receives raw observation data information sent by the mother vessel's satellite navigation system through a data link in real time, and also receives high-precision ranging information output by the laser rangefinder in real time. By tightly combining the observation information, multi-source fusion relative positioning calculation is performed to obtain accurate relative position information between the antennas of the mother vessel and the USV in real time.

[0022] Step 2: Based on the structure of the unmanned surface vessel dock recovery system, construct a measurement model for the relative position information of the unmanned surface vessel and the mother vessel.

[0023] Step 2.1: The relative position information measurement model includes the BeiDou carrier phase differential measurement model and the multi-source ranging information measurement model.

[0024] The BeiDou carrier phase differential relative positioning model is as follows: in, For inter-station and inter-satellite double difference operators, L For BeiDou carrier phase observations, p This is the geometric distance between the antenna and the receiver. T For tropospheric delay, I For tropospheric delay, l For carrier wavelength, N For ambiguity, e L For observation noise that is not modeled, carrier phase double difference can eliminate receiver clock error and satellite clock error, and also weaken the impact of tropospheric and ionospheric errors on BeiDou relative positioning to a certain extent.

[0025] The multi-source ranging information measurement model is as follows: in, D For the ranging information of the multi-source ranging sensor, ( Dx BDS , Dy BDS , Dz BDS () represents the three-dimensional position coordinate difference between the unmanned surface vessel and the BeiDou antenna of the mother ship. dx BDS,D , dy BDS,D , dz BDS,D ) represents the relative position coordinate increment of the lever arm. e D This refers to observation noise that has not been modeled.

[0026] Step 2.2: The BeiDou carrier phase differential measurement model is based on carrier phase double-difference operation, which is used to eliminate receiver clock error and satellite clock error, and reduce tropospheric and ionospheric errors.

[0027] Step 2.3: The multi-source ranging information measurement model is based on the ranging information of the ranging sensor, and the ranging information is converted to the Beidou antenna position reference through pole arm compensation.

[0028] Step 3: Based on the relative position information measurement model between the unmanned surface vessel and the mother vessel, construct a tight combination relative positioning model of BeiDou / multi-source ranging information, and perform positioning through the constructed positioning model.

[0029] The BeiDou / multi-source ranging information compact combination relative positioning model uses Kalman filtering to achieve observation information fusion and optimal parameter estimation; The system state equation and measurement equation are as follows: in, for k Moment n A state vector containing parameters to be estimated regarding the relative position of the antenna; for k Moment m The measurement vector includes raw satellite observation information and laser ranging observation information; for k -1 to k The system's one-step transition matrix at time t; for k System noise at time -1; Let be the system noise matrix, which characterizes the noise generated by... k -1 hour arrives k The noise of each system at each time point affects k The degree of each state at any given moment; for k Measurement matrix at time; for k Moment m Dimensional measurement noise; The Kalman filter equation for relative positioning calculation using a tight combination of multi-source information is as follows: in, It is a state Kalman filter estimation; It is to utilize The calculated pair A one-step prediction; The filter gain is selected based on the estimated value. The mean square error matrix is ​​minimized; For valuation The mean square error matrix.

[0030] It should be emphasized that the embodiments described in this invention are illustrative rather than limiting. Therefore, this invention includes, but is not limited to, the embodiments described in the specific implementation. Any other implementations derived by those skilled in the art based on the technical solutions of this invention are also within the scope of protection of this invention.

Claims

1. A method for high-precision relative positioning of unmanned surface vessels (USVs) based on multi-source information fusion using BeiDou, characterized by: Includes the following steps: Step 1: Construct an unmanned surface vessel dock recovery system; Step 2: Based on the structure of the unmanned surface vessel dock recovery system, construct a measurement model for the relative position information of the unmanned surface vessel and the mother vessel; Step 3: Based on the relative position information measurement model between the unmanned surface vessel and the mother vessel, construct a tight combination relative positioning model of BeiDou / multi-source ranging information, and perform positioning through the constructed positioning model.

2. The method for high-precision relative positioning of unmanned surface vessels based on multi-source information fusion using BeiDou as described in claim 1, characterized in that: The unmanned surface vessel (USV) dock recovery system in step 1 includes an USV, a mother vessel dock, and a mother vessel. The system integrates multi-source sensors and hardware devices to achieve absolute positioning, relative positioning, and data communication functions. The multi-source sensors and hardware devices include a BeiDou antenna, a BeiDou receiver, a laser rangefinder, and a data transmission radio.

3. The method for high-precision relative positioning of unmanned surface vessels based on multi-source information fusion using BeiDou as described in claim 1, characterized in that: The specific implementation method of step 1 is as follows: the satellite navigation receiving equipment of the unmanned surface vessel (USV) and the mother vessel simultaneously receive signals from the BeiDou satellite navigation system to generate pseudorange and carrier phase raw observation data information; the USV and the mother vessel are equipped with laser rangefinders to achieve high-precision measurement of the relative distance between the USV and the mother vessel; the USV's multi-source information fusion processing module receives raw observation data information sent by the mother vessel's satellite navigation system through the data link in real time, and receives high-precision ranging information output by the laser rangefinder in real time. By tightly combining the observation information, multi-source fusion relative positioning calculation is performed to obtain accurate relative position information between the antennas of the mother vessel and the USV in real time.

4. The method for high-precision relative positioning of unmanned surface vessels based on multi-source information fusion using BeiDou as described in claim 1, characterized in that: The specific implementation method of step 2 is as follows: Step 2.1: The relative position information measurement model includes the BeiDou carrier phase differential measurement model and the multi-source ranging information measurement model; Step 2.2: The BeiDou carrier phase differential measurement model is based on carrier phase double-difference operation, which is used to eliminate receiver clock error and satellite clock error, and reduce tropospheric and ionospheric errors. Step 2.3: The multi-source ranging information measurement model is based on the ranging information of the ranging sensor, and the ranging information is converted to the Beidou antenna position reference through pole arm compensation.

5. The method for high-precision relative positioning of unmanned surface vessels based on multi-source information fusion using BeiDou as described in claim 4, characterized in that: The BeiDou carrier phase differential relative positioning model is as follows: ； in, For inter-station and inter-satellite double difference operators, L For BeiDou carrier phase observations, ρ This is the geometric distance between the antenna and the receiver. T For tropospheric delay, I For tropospheric delay, λ For carrier wavelength, N For ambiguity, ε L For observation noise that is not modeled, carrier phase double difference can eliminate receiver clock error and satellite clock error, and also weaken the impact of tropospheric and ionospheric errors on BeiDou relative positioning to a certain extent.

6. The method for high-precision relative positioning of unmanned surface vessels based on multi-source information fusion using BeiDou as described in claim 4, characterized in that: The multi-source ranging information measurement model is as follows: ； in, D For the ranging information of the multi-source ranging sensor, ( Δx BDS , Δy BDS , Δz BDS () represents the three-dimensional position coordinate difference between the unmanned surface vessel and the BeiDou antenna of the mother ship. δx BDS,D , δy BDS,D , δz BDS,D ) represents the relative position coordinate increment of the lever arm. ε D This refers to observation noise that has not been modeled.

7. The method for high-precision relative positioning of unmanned surface vessels based on multi-source information fusion using BeiDou as described in claim 1, characterized in that: In step 3, the BeiDou / multi-source ranging information tightly combined relative positioning model uses Kalman filtering to achieve observation information fusion and optimal parameter estimation. The system state equation and measurement equation are as follows: ； in, for k Moment n A state vector containing parameters to be estimated regarding the relative position of the antenna; for k Moment m The measurement vector includes raw satellite observation information and laser ranging observation information; for k -1 to k The system's one-step transition matrix at time t; for k System noise at time -1; Let be the system noise matrix, which characterizes the noise generated by... k -1 hour arrives k The noise of each system at each time point affects k The degree of each state at any given moment; for k Measurement matrix at time; for k Moment m Dimensional measurement noise; The Kalman filter equation for relative positioning calculation using a tight combination of multi-source information is as follows: ； ； ； ； ； in, It is a state Kalman filter estimation; It is to utilize The calculated pair A one-step prediction; The filter gain is selected based on the estimated value. The mean square error matrix is ​​minimized; For valuation The mean square error matrix.

Images