Offshore work platform multi-source fusion positioning method based on particle hierarchical optimization

Abstract

The invention provides an offshore work platform multi-source fusion positioning method based on particle hierarchical optimization, which takes system structure generation in a complex scene as a research background, aims to solve the problem of omni-directional high-precision positioning of an offshore work platform, and aims to solve the problems and defects of poor positioning precision or incapability of positioning and the like caused by influence of various positioning factors. By applying the UWB positioning technology, the multi-source fusion positioning method based on the BDS / UWB combined positioning technology and the layered optimization technology is designed, and the positioning precision is greatly improved compared with that of a traditional k-N algorithm; the function of improving the positioning precision and reliability of the offshore operation platform is achieved, and the seamless positioning requirement is met. According to the fusion positioning algorithm based on particle hierarchical optimization, positioning errors caused by mismatching, W signal accumulation and PDR error accumulation are effectively reduced, so that accurate and continuous positioning of an offshore operation platform is realized, and the problems of positioning continuity and stability in a motion state of an offshore target are well solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of fusion positioning, and in particular relates to a multi-source fusion positioning method for an offshore operating platform based on particle hierarchical optimization. Background Art

[0002] With the successful global networking of the BeiDou Navigation Satellite System (BDS), BDS has become an important space-time infrastructure, providing users with global, all-weather, high-precision position, speed and time information. However, the signal attenuation of BDS is obvious in complex marine environments, and it cannot meet the application requirements of the "last mile" of seamless high-precision positioning of offshore operating platforms. In order to build a national comprehensive positioning, navigation and timing (PNT) system with unified benchmarks, seamless coverage, security and reliability, and high efficiency and convenience, multi-source sensor fusion collaborative precise positioning will become the main solution to solve the limitations and vulnerabilities of single navigation technology. At present, multi-source sensor fusion collaborative precise positioning technology has been widely valued and developed at home and abroad, but there are still many problems that need to be solved in terms of positioning models and methods.

[0003] UWB (Ultra Wideband) is a short-range wireless communication technology developed in the 1960s. It uses nanosecond non-sinusoidal narrow wave pulses to transmit data. It is named because the spectrum it occupies ranges from 3.1 to 10.6 GHz, with a range of up to 7.5 GHz. UDB positioning technology is affected by factors such as multipath and NLOS, or has poor positioning accuracy or cannot be positioned due to insufficient number of satellites and poor spatial geometric distribution. The use of Kalman filtering to smooth the TDOA (Time Difference Of Arrival) ranging can effectively reduce the impact of noise and outliers. UWB-assisted BDS can effectively make up for the above problems and defects. At the same time, combined with the dead reckoning (PDR) positioning results, the particle filter (PF) is used to achieve the fusion positioning of the two technical methods to improve the continuous positioning accuracy of dynamic targets. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a multi-source fusion positioning method for an offshore operating platform based on particle hierarchical optimization, so as to improve the positioning accuracy and reliability of the offshore operating platform.

[0005] The technical solution adopted by the present invention to solve the above technical problems is: a multi-source fusion positioning method for an offshore operating platform based on particle hierarchical optimization, comprising the following steps:

[0006] S1: Maintain high-precision time synchronization between base stations. UWB transmits tag and base station BS signal data by sending and receiving extremely narrow pulse target signals of nanoseconds or less, and converts the delay difference between the tag and the base station BS into a distance difference based on the signal propagation speed;

[0007] S2: Based on the ranging Kalman filter smoothing process, a curve equation about the position of the tag to be measured is constructed, and the location information of the tag is solved through the BDS / UWB positioning algorithm;

[0008] S3: Perform PDR positioning, calculate the frequency, step length and heading of the target at sea in real time through the inertial sensor information in the smart portable device, and then calculate the current position information based on the target's previous position;

[0009] S4: Complementary particle filtering is used to fuse the location information obtained by BDS / UWB and PDR to solve the target positioning result.

[0010] According to the above scheme, in step S1, the specific steps are:

[0011] S11: The tag Tag transmits a pulse signal to different base stations BS;

[0012] S12: After each base station BS detects the signal transmitted by the tag Tag, it records a high-precision timestamp and sends it to the data processing center;

[0013] S13: The data processing center converts the high-precision timestamp into high-precision TDOA, and converts it into distance difference in combination with the signal propagation speed.

[0014] Furthermore, in step S11, it is assumed that f H and f L are the upper and lower limits of the frequency corresponding to the peak attenuation of the signal power spectrum density by 10 dB, respectively. C Indicates the center frequency; the ratio of signal bandwidth to center frequency is greater than 20% or the absolute bandwidth is greater than 500MHz:

[0015]

[0016] The maximum transmission power of UWB does not exceed -41.3dBm / MHz.

[0017] Furthermore, in step S13, the data processing center obtains the TDOA measurement value by subtracting the high-precision timestamps between the base stations BS and multiplying it by the electromagnetic wave propagation speed. The specific steps are:

[0018] Assume BS1 is the master base station, BSi is the slave base station, i≠1;

[0019] Tag sends a pulse signal to each base station at the same time at T0; the time when BS1 receives the pulse signal is T1, and the time when BSi receives the pulse signal is Ti. After receiving the pulse signal, BSi sends a confirmation notification to BS1;

[0020] BS1 will synchronize the time correction information τ with each slave base station 1i The corresponding feedback is given to BSi; the high-precision timestamp information after BSi correction is:

[0021] Ti′=Ti+τ 1i ;

[0022] BSi sends the corrected high-precision timestamp information Ti′ to the data processing center through the UDP protocol; let c be the propagation speed of radio waves, and the data processing center converts the acquired precise timestamp information into TDOA measurement value by difference;

[0023] R i,1 =c(Ti′-T1).

[0024] According to the above scheme, in step S2, the specific steps are:

[0025] S21: Let Δt k is the time taken by the tag to move from the k-1th epoch to the kth epoch, Δt k-1 is the time it takes for the tag Tag to move from the k-2 epoch to the k-1 epoch, R k , R k-1 , R k-2 are the predicted values ​​of the kth, k-1th, and k-2th epochs of the TDOA measurements, respectively, ω k-1 is process noise; based on Kalman filter smoothing, the state equation is constructed using the following velocity model to predict the TDOA measurement value of UWB:

[0026]

[0027] Assume the state transfer matrix State Vector The state equation matrix form of the discrete Kalman filter constructed using the above formula is:

[0028] x k =Ax k-1 +w k-1 ;

[0029] Assume z k is the TDOA measurement value R at the kth epoch k , H is the measurement matrix, h = [1 0], v k is the observation noise; the observation equation of discrete Kalman filtering is:

[0030] z k =Hx k +v k ;

[0031] S22: Assume that there are M base stations BS, BS1 is the master base station, BSi is the slave base station, i≠1; (X i , Y i ) is the i-th base station BS i The known location of The coordinates (x, y) of the tag Tag are the locations to be estimated, the tag Tag and the i-th base station BS i The distance between them is:

[0032]

[0033] Let c be the propagation speed of radio waves, t i,1 is the TDOA measurement value; then the MS and BS i (i≠1) and the main base station BS 1 The actual distance difference R i,1 for:

[0034]

[0035] Let X i,1 =X i -X 1 ,Y i,1 =Y i -Y 1 , x,y,R 1 Assuming them as unknowns, we get a system of linear equations:

[0036]

[0037] Solve the equations to get the coordinate position of the Tag.

[0038] Furthermore, in step S21, the process noise and measurement noise covariance matrices are Q k =10 -4 m 2 / s、R k =0.01m 2 .

[0039] According to the above scheme, in step S3, the specific steps are:

[0040] Let L n is the step length of the nth step, θ n is the heading angle of the nth step; let the initial position of the known target be (x 0 ,y 0 ), by the position of the target at step n-1 (x n-1 ,yn-1 ) recursively obtain the position of the nth step (x n ,y n )for:

[0041]

[0042] Let η be a constant; t k-1 and t k are the moments corresponding to the k-1th step and the kth step respectively; a max (t) and a min (t) are the maximum and minimum acceleration values ​​within the interval t, respectively; the peak and trough detection pedometer model based on triple constraints is used to detect gait, and the step length L is estimated using an empirical model k for:

[0043]

[0044] According to the above scheme, in step S4, the specific steps are:

[0045] set up are the horizontal and vertical coordinates of the αth particle, respectively. They obey the Gaussian distribution with the horizontal and vertical coordinates obtained by the previous BDS / UWB positioning as the mean and 1 as the variance. The initial coordinate vector of the αth particle is:

[0046]

[0047] set up are the horizontal and vertical coordinates of the αth particle at the k-1th step respectively; is the state noise; the state transfer equation of the particle established by the PDR state equation is:

[0048]

[0049] set up is the weight of the αth particle at the kth step; is the Euclidean distance of the observation of the αth particle at the kth step; Γ is the observation noise; the BDS / UWB positioning result is used as the observation, and the particle weight is updated as follows:

[0050]

[0051] The fusion positioning result of step k is:

[0052]

[0053] A computer memory stores a computer program that can be executed by a computer processor. The computer program executes a multi-source fusion positioning method for an offshore operating platform based on particle layered optimization.

[0054] A multi-source fusion positioning system for an offshore operating platform based on particle hierarchical optimization, the system includes a processor and a memory, the memory stores computer instructions, the processor is used to execute the computer instructions stored in the memory, and when the computer instructions are executed by the processor, the system implements the steps of a multi-source fusion positioning method for an offshore operating platform based on particle hierarchical optimization.

[0055] The beneficial effects of the present invention are:

[0056] 1. The multi-source fusion positioning method for offshore working platforms based on particle hierarchical optimization of the present invention takes the system architecture generation in complex scenarios as the research background, aims at the problem of all-round high-precision positioning of offshore working platforms, and aims at the problems of poor positioning accuracy or inability to locate caused by the influence of various positioning factors. By applying UWB positioning technology, a multi-source fusion positioning method based on BDS / UWB combined positioning technology and hierarchical optimization technology is designed. The positioning accuracy is greatly improved compared with the traditional kN algorithm; the function of improving the positioning accuracy and reliability of offshore working platforms is realized, and the seamless positioning requirements are met.

[0057] 2. The fusion positioning algorithm based on particle hierarchical optimization of the present invention effectively reduces the positioning error caused by mismatching, W signal accumulation and PDR error accumulation, thereby realizing the precise and continuous positioning of the offshore operating platform and better solving the continuity and stability problems of positioning of offshore targets in motion. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a principle block diagram of an embodiment of the present invention.

[0059] Figure 2 It is a diagram of the structure of a UWB system based on TDOA according to an embodiment of the present invention.

[0060] Figure 3 It is a diagram of the TDOA measurement parameter estimation process of an embodiment of the present invention.

[0061] Figure 4 4 is a schematic diagram of the PDR positioning principle of an embodiment of the present invention. DETAILED DESCRIPTION

[0062] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0063] The UWB and BDS combined positioning system is based on TDOA positioning technology, with tags and base stations (BS) as inputs. It does not require time synchronization between tags and BSs. It calculates the distance difference by estimating the time delay difference between tags and BSs. It only needs to maintain high-precision time synchronization between base stations. The UWB system based on TDOA is composed of Figure 1The positioning algorithm uses the distance difference obtained by converting the signal propagation speed, and constructs the position curve equation of the positioning facility equipment based on the ranging Kalman filter smoothing process, and finally solves the positioning position through the equation.

[0064] See also Figure 1 , an embodiment of the present invention comprises the following steps:

[0065] Step 1: Send and receive target pulse signals and collect measurement values.

[0066] The tag transmits pulse signals to different BSs, and UWB transmits data by sending and receiving extremely narrow pulses of nanoseconds or less.

[0067]

[0068] In the formula, fH and / fL refer to the upper and lower limits of the frequency corresponding to the attenuation of the signal power spectrum density peak by 10dB, and fC represents the center frequency, that is, UWB is defined as a signal with a relative bandwidth (ratio of signal bandwidth to center frequency) greater than 20% or an absolute bandwidth greater than 500MHz. In order to prevent UWB from interfering with other communication systems, the maximum transmit power of UWB is limited to no more than -41.3dBm / MHz.

[0069] After each BS detects the signal transmitted by the Tag, it records a high-precision timestamp and sends it to the data processing center, which converts it into high-precision TDOA and converts it into distance difference with the help of signal propagation speed.

[0070] The TDOA measurement value is obtained by subtracting the high-precision timestamp information between BSs and multiplying it by the electromagnetic wave propagation speed. Figure 2 As shown in the figure, BS1~5 represent UWB base stations, where BS1 is the master base station and BS2~5 are slave base stations. Assume that at time T0, Tag sends a pulse signal to multiple BSs at the same time, and the times when BS1~5 receive the pulse signal are T1, T2, T3, T4, and T5 respectively; when all BSs receive the pulse signal, BS2~5 sends a confirmation notification to BS1, and BS1 then sends the time synchronization correction information τ 12 , τ 13 , τ 14 , τ 15 Feedback to each slave base station BS2~5, at this time, the high-precision timestamp information of the 5 BSs after correction is T1', T2', T3', T4', T5', and then transmitted to the data processing center through the UDP protocol. The data processing center then converts the obtained precise timestamp information into TDOA measurement parameters by difference.

[0071] The specific calculation process is as follows:

[0072] (1) The time when the tag transmits the pulse signal is T0, and the initial time when each BS receives the pulse signal is T1, T2, T3, T4, and T5 respectively.

[0073] (2) The slave base stations BS2-5 communicate with the master base station BS1, and BS1 calculates and obtains the synchronization correction information τ with other base stations. 12 , τ 13 , τ 14 , τ 15 , and fed back to BS2~5, the high-precision timestamp information after correction from each base station is:

[0074] T2′=T2+τ 12 ,T3′=T3+τ 13 ,T4′=T4+τ 14 ,T5′=T5+τ 15

[0075] (3) BS1-5 sends the timestamp information to the data processing center via the UDP protocol, and the data processing center converts it into a TDOA measurement value in meters.

[0076] R 2,1 =c(T2′-T1),R 3,1 =c(T3′-T1),R 4,1 =c(T4′-T1),R 5,1 =c(T5′-T1)

[0077] Step 2: BDS / UWB positioning.

[0078] Construct a set of curve equations about the tag position to be measured, and solve the tag position through a specific algorithm. The specific positioning process is as follows: Figure 3 shown.

[0079] (1) Based on Kalman filter smoothing, the UWB TDOA measurement value can be predicted by constructing the state equation using the following velocity model:

[0080]

[0081] In the formula, Δt k is the time taken by the tag to move from the k-1th epoch to the kth epoch. Similarly, Δt k-1 is the time consumed by the tag to move from the k-2 epoch to the k-1 epoch; the state equation matrix form of the discrete Kalman filter constructed by the above formula is:

[0082] x k =Ax k-1 +w k-1

[0083] In the formula, the state transfer matrix State Vector w k-1 is the process noise. The observation equation of discrete Kalman filtering is:

[0084] z k =Hx k +v k

[0085] In the above formula, z k is the TDOA measurement value R at the kth epoch k , H is the measurement matrix, h = [1 0], v k is the observation noise, process noise and measurement noise covariance matrix Q k =10 -4 m 2 / s、R k =0.01m 2 .

[0086] (2) Constructing the Tag Position Curve Equation

[0087] Assume there are M BSs, Tag(x,y) is the position to be estimated, (X i , Y i ) is the known location of the i-th BS. Tag and BS i The distance between them is:

[0088]

[0089] in,

[0090] Let R i,1 Indicates MS and BS i (i≠1) and BS 1 (the actual distance difference between the serving base station and the

[0091]

[0092] Where c is the propagation speed of radio waves, t i，1 It is the TDOA measurement value, unit: second.

[0093] Finally, we can get:

[0094]

[0095] Among them, X i,1 =X i -X 1 ,Y i,1 =Y i -Y 1 , x,y,R1 Considered as unknown variables, the above formula is a linear equation system. Solving the equation system can get the coordinate position of the Tag.

[0096] Step 3: PDR positioning.

[0097] PDR uses the inertial sensor information in the smart portable device to calculate the cadence, step length and heading of the target at sea in real time, and then calculates the current position information based on the target's previous position.

[0098] Assume that the initial position of the target is known to be (x 0 ,y 0 ). From the position of the target at step n-1 (x n-1 ,y n-1 ) Recursively, we can get the position of the nth step as (x n ,y n )

[0099]

[0100] Where, L n is the step length of the nth step; θ n is the heading angle of the nth step.

[0101] The peak and valley detection pedometer model based on triple constraints is used to accurately detect gait. The step length estimation adopts the empirical model:

[0102]

[0103] Where η is a constant; t k-1 and t k are the moments corresponding to the k-1th step and the kth step respectively; a max (t) and a min (t) are the maximum and minimum acceleration values ​​within the interval t, respectively.

[0104] Step 4: Particle filter positioning fusion.

[0105] In order to improve the heading estimation accuracy, complementary filtering is used to fuse the target positioning results calculated by the above two methods. Specifically, PF is used to fuse the BDS / UWB positioning results and the PDR positioning results. The initial coordinate vector of the αth particle is set to

[0106]

[0107] In the formula, are the horizontal and vertical coordinates of the αth particle, and they obey the Gaussian distribution with the horizontal and vertical coordinates obtained from the previous BDS / UWB positioning as the mean and 1 as the variance.

[0108] The state transfer equation of the particle is established by the PDR state equation:

[0109]

[0110] In the formula, are the horizontal and vertical coordinates of the αth particle at the k-1th step respectively; is the state noise.

[0111] Take the BDS / UWB positioning results as observations and update the particle weights:

[0112]

[0113] In the formula, is the weight of the αth particle at the kth step; is the Euclidean distance of the observation of the αth particle at the kth step; Γ is the observation noise. The fusion positioning result of the kth step can be obtained as follows:

[0114]

[0115] Based on the above method and steps, the high-precision position coordinates of the offshore operating platform can be effectively determined, and the needs of seamless and precise positioning can be met to achieve multi-source fusion positioning.

[0116] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0117] The above embodiments are only used to illustrate the design ideas and features of the present invention, and their purpose is to enable those skilled in the art to understand the content of the present invention and implement it accordingly. The protection scope of the present invention is not limited to the above embodiments. Therefore, any equivalent changes or modifications made based on the principles and design ideas disclosed by the present invention are within the protection scope of the present invention.

Claims

1. A multi-source fusion positioning method for offshore operating platforms based on particle hierarchical optimization, characterized by: The following steps are involved: S1: Maintain high-precision time synchronization between base stations. UWB transmits tag and base station BS signal data by sending and receiving extremely narrow pulse target signals of nanoseconds or less, and converts the delay difference between the tag and the base station BS into a distance difference based on the signal propagation speed; S2: Construct a curve equation about the position of the tag to be measured based on the ranging Kalman filter smoothing process, and calculate the location information of the tag through the BDS / UWB positioning algorithm; S3: Perform PDR positioning, calculate the frequency, step length and heading of the target at sea in real time through the inertial sensor information in the smart portable device, and then calculate the current position information based on the target's previous position; S4: Complementary particle filtering is used to fuse the location information obtained by BDS / UWB and PDR to solve the target positioning result.

2. The multi-source fusion positioning method for offshore operating platforms based on particle hierarchical optimization according to claim 1 is characterized in that: In the step S1, the specific steps are: S11: The tag Tag transmits a pulse signal to different base stations BS; S12: After each base station BS detects the signal transmitted by the tag Tag, it records a high-precision timestamp and sends it to the data processing center; S13: The data processing center converts the high-precision timestamp into high-precision TDOA, and converts it into distance difference in combination with the signal propagation speed.

3. The multi-source fusion positioning method for offshore operating platforms based on particle layered optimization according to claim 2 is characterized in that: In the step S11, it is assumed that f H and f L are the upper and lower limits of the frequency corresponding to the peak attenuation of the signal power spectrum density by 10 dB, respectively. C Indicates the center frequency; the ratio of signal bandwidth to center frequency is greater than 20% or the absolute bandwidth is greater than 500MHz: The maximum transmission power of UWB does not exceed -41.3dBm / MHz.

4. The multi-source fusion positioning method for offshore operating platforms based on particle layered optimization according to claim 2 is characterized in that: In step S13, the data processing center obtains the TDOA measurement value by subtracting the high-precision time stamps between the base stations BS and multiplying it by the electromagnetic wave propagation speed. The specific steps are: Assume BS1 is the master base station, BSi is the slave base station, i≠1; Tag sends a pulse signal to each base station at the same time at T0; the time when BS1 receives the pulse signal is T1, and the time when BSi receives the pulse signal is Ti. After receiving the pulse signal, BSi sends a confirmation notification to BS1; BS1 will synchronize the time correction information τ with each slave base station 1i The corresponding feedback is given to BSi; the high-precision timestamp information after BSi correction is: Ti′=Ti+τ 1i ; BSi sends the corrected high-precision timestamp information Ti′ to the data processing center via the UDP protocol; Let c be the propagation speed of radio waves. The data processing center converts the acquired precise time stamp information into TDOA measurement value by difference; R i,1 = c(Ti′-T1).

5. The multi-source fusion positioning method for offshore operating platforms based on particle hierarchical optimization according to claim 1 is characterized in that: In the step S2, the specific steps are: S21: Let Δt k is the time taken by the tag to move from the k-1th epoch to the kth epoch, Δt k-1 is the time it takes for the tag Tag to move from the k-2 epoch to the k-1 epoch, R k , R k-1 , R k-2 are the predicted values ​​of the kth, k-1th, and k-2th epochs of the TDOA measurements, respectively, ω k-1 is process noise; based on Kalman filter smoothing, the state equation is constructed using the following velocity model to predict the TDOA measurement value of UWB: Let the state transfer matrix State Vector The state equation matrix form of the discrete Kalman filter constructed using the above formula is: x k =Ax k-1 +w k-1 ; Assume z k is the TDOA measurement value R at the kth epoch k , H is the measurement matrix, h = [1 0], v k is the observation noise; the observation equation of discrete Kalman filtering is: z k =Hx k +v k ; S22: Assume that there are M base stations BS, BS1 is the master base station, BSi is the slave base station, i≠1; (X i , Y i ) is the i-th base station BS i The known location of The coordinates (x, y) of the tag Tag are the locations to be estimated, the tag Tag and the i-th base station BS i The distance between them is: Let c be the propagation speed of radio waves, t i,1 is the TDOA measurement value; then the MS and BS i (i≠1) and the actual distance difference R between the main base station BS1 i,1 for: Let X i,1 =X i -X1,Y i,1 =Y i -Y1, treat x, y, R1 as unknowns, and obtain the linear equation system: Solve the equations to get the coordinate position of the Tag.

6. The multi-source fusion positioning method for offshore operating platforms based on particle layered optimization according to claim 5 is characterized in that: In step S21, the process noise and measurement noise covariance matrices are Q k =10 -4 m 2 / s、R k =0.01m 2 .

7. The multi-source fusion positioning method for offshore operating platforms based on particle layered optimization according to claim 1 is characterized in that: In the step S3, the specific steps are: Let L n is the step length of the nth step, θ n is the heading angle of the nth step; suppose the initial position of the target is known to be (x0, y0), and the position of the target at the n-1th step (x n-1 ,y n-1 ) recursively obtain the position of the nth step (x n ,y n )for: Let η be a constant; t k-1 and t k are the moments corresponding to the k-1th step and the kth step respectively; a max (t) and a min (t) are the maximum and minimum acceleration values ​​within the interval t, respectively; the peak and trough detection pedometer model based on triple constraints is used to detect gait, and the step length L is estimated using an empirical model k for:

8. The multi-source fusion positioning method for offshore operating platforms based on particle layered optimization according to claim 1 is characterized in that: In the step S4, the specific steps are: set up are the horizontal and vertical coordinates of the αth particle, respectively. They obey the Gaussian distribution with the horizontal and vertical coordinates obtained by the previous BDS / UWB positioning as the mean and 1 as the variance. The initial coordinate vector of the αth particle is: set up are the horizontal and vertical coordinates of the αth particle at the k-1th step respectively; is the state noise; the state transfer equation of the particle established by the PDR state equation is: set up is the weight of the αth particle at the kth step; is the Euclidean distance of the observation of the αth particle at the kth step; Γ is the observation noise; the BDS / UWB positioning result is used as the observation, and the particle weight is updated as follows: The fusion positioning result of step k is:

9. A computer memory, characterized in that: A computer program executable by a computer processor is stored therein, and the computer program executes the multi-source fusion positioning method for an offshore operating platform based on particle layered optimization as described in any one of claims 1 to 8.

10. A multi-source fusion positioning system for an offshore operating platform based on particle hierarchical optimization, the system comprising a processor and a memory, characterized in that: The memory stores computer instructions, and the processor is used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the system implements the steps of the multi-source fusion positioning method for offshore working platforms based on particle layered optimization as described in any one of claims 1 to 8.

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