Multi-source fusion positioning method for offshore platform based on particle stratified optimization

Abstract

The application provides a multi-source fusion positioning method of an offshore operation platform based on particle hierarchical optimization, takes architecture generation in a complex scene as research background, is oriented to all-around high-precision positioning of the offshore operation platform, aims at problems and defects such as poor positioning precision or positioning failure caused by influences of multiple positioning factors, designs a multi-source fusion positioning method based on BDS / UWB combined positioning technology and hierarchical optimization technology by applying UWB positioning technology, and positioning precision is greatly improved compared with traditional k-N algorithm; functions of improving positioning precision and reliability of the offshore operation platform are realized, and seamless positioning requirements are met. The fusion positioning algorithm based on particle hierarchical optimization effectively reduces positioning errors caused by false matching, W signal accumulation and PDR error accumulation, so that accurate and continuous positioning of the offshore operation platform is realized, and the continuity and stability of positioning in the motion state of the offshore target are better 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 layered 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, velocity, and time information. However, BDS suffers from significant signal attenuation in complex maritime environments, making it unable to meet the application requirements of the "last mile" of seamless, high-precision positioning for offshore platforms. To build a national integrated positioning, navigation, and timing (PNT) system with unified benchmarks, seamless coverage, security, reliability, efficiency, and convenience, multi-source sensor fusion collaborative precise positioning will become the main solution to address the limitations and vulnerabilities of single navigation technologies. Currently, multi-source sensor fusion collaborative precise positioning technology has received widespread attention and development both domestically and internationally, but there are still many issues that need to be addressed 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-scale, non-sinusoidal, narrow pulses to transmit data. It derives its name from the fact that it occupies a spectrum ranging from 3.1 to 10.6 GHz, with a wide range of up to 7.5 GHz. UDB positioning technology is affected by factors such as multipath and NLOS, as well as by insufficient satellite numbers and poor spatial geometric distribution, resulting in poor positioning accuracy or even inability to locate objects. However, using Kalman filtering to smoothen and pre-process TDOA (Time Difference of Arrival) ranging measurements can effectively reduce the impact of noise and outliers. UWB-assisted BDS can effectively address these issues. Furthermore, combined with dead reckoning (PDR) positioning results, particle filtering (PF) is used to achieve a fusion of the two technical approaches, improving 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 layered 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 offshore operating platforms based on particle layered optimization, comprising the following steps:

[0006] S1: Maintaining high-precision time synchronization between base stations, UWB transmits tag and base station signal data by sending and receiving extremely narrow pulse target signals of nanoseconds or less. The delay difference between the tag and the base station is converted 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, using the inertial sensor information in the smart portable device to calculate the target's cadence, stride length, and heading in real time, and then calculate the target's current position based on its previous position;

[0009] S4: Complementary particle filtering is used to fuse the position 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 transmits a pulse signal to different base stations BS;

[0012] S12: After each base station BS detects the signal transmitted by the 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 based on the signal propagation speed.

[0014] Furthermore, in step S11, let 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 10dB, respectively. C Indicates the center frequency; the ratio of the signal bandwidth to the 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 as follows:

[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 time T0; the time when BS1 receives the pulse signal is T1, the time when BSi receives the pulse signal is Ti, and 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 via the UDP protocol. Let c be the propagation speed of radio waves. The data processing center converts the obtained precise timestamp information into TDOA measurement values ​​by difference calculation.

[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 Δt is the time it takes for the tag to move from the k-1th epoch to the kth epoch. k-1 R is the time it takes for the tag Tag to move from the k-2th epoch to the k-1th epoch. k 、R k-1 、R k-2 are the predicted values ​​of the kth, k-1th, and k-2th epochs of the TDOA measurements, ω k-1 is the process noise; based on the Kalman filter smoothing process, 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 location 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 actual distance difference R between the main base station BS1 i,1 for:

[0034]

[0035] Let X i,1 =X i -X1,Y i,1 =Y i -Y1, treat x, y, and R1 as unknowns and obtain the linear equation system:

[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 (x0, y0), and the position of the target in the n-1th step (x n-1 ,y n-1 ) recursively get the position of step n (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 gait is detected by the peak and trough detection pedometer model based on triple constraints, and the step length L is estimated by the 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 from 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 quantity, 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] The offshore operation platform multi-source fusion positioning system based on particle hierarchical optimization comprises a processor and a memory, the memory stores computer instructions, and the processor is used for executing the computer instructions stored in the memory, and when the computer instructions are executed by the processor, the system implements the steps of the offshore operation platform multi-source fusion positioning method based on particle hierarchical optimization.

[0055] The offshore operation platform multi-source fusion positioning method based on particle hierarchical optimization of the present application has the following beneficial effects:

[0056] 1. The offshore operation platform multi-source fusion positioning method based on particle hierarchical optimization of the present application takes the architecture generation in a complex scene as the research background, is oriented to the all-around high-precision positioning of the offshore operation platform, and solves the problems of poor positioning accuracy or the inability to position caused by the influence of various positioning factors by applying the UWB positioning technology, designing a multi-source fusion positioning method based on the BDS / UWB combined positioning technology and hierarchical optimization technology, and greatly improving the positioning accuracy compared with the traditional k-N algorithm.

[0057] 2. The fusion positioning algorithm based on particle hierarchical optimization effectively reduces the positioning errors caused by the mis-matching, W signal accumulation and PDR error accumulation, thereby realizing the accurate and continuous positioning of the offshore operation platform and better solving the continuity and stability problems of the positioning under the motion state of the offshore target. BRIEF DESCRIPTION OF DRAWINGS

[0058] Figure 1 is the principle block diagram of the embodiment of the present application.

[0059] Figure 2 is the UWB system composition diagram based on TDOA of the embodiment of the present application.

[0060] Figure 3 is the TDOA measurement parameter estimation process diagram of the embodiment of the present application.

[0061] Figure 4 is the PDR positioning principle diagram of the embodiment of the present application. DETAILED DESCRIPTION

[0062] The present application will be further described in detail below in combination with the drawings and specific embodiments.

[0063] The UWB and BDS combined positioning system is based on the TDOA positioning technology, takes the tag (Tag) and the base station (Base Station, BS) as the input, does not need the time synchronization between the Tag and the BS, calculates the distance difference by estimating the time delay difference between the Tag and the BS, only needs to keep the high-precision time synchronization between the base stations, and the UWB system composition based on TDOA is as follows: Figure 1The positioning algorithm converts the distance difference obtained by the signal propagation speed, and constructs the position curve equation of the positioning facility device based on the ranging Kalman filter smoothing processing. Finally, the positioning position is calculated by the equation.

[0064] Referring to Figure 1 Embodiments of the present application include the following steps:

[0065] Step one: transmits and receives target pulse signals and collects 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 indicate the upper and lower limits of the frequency corresponding to the 10dB attenuation of the signal power spectrum density peak, and fC represents the center frequency, that is, UWB is defined as a signal with a relative bandwidth (signal bandwidth to center frequency ratio) greater than 20% or an absolute bandwidth greater than 500MHz. In order to prevent UWB from causing interference to other communication systems, the maximum transmission power of UWB is limited to not more than -41.3dBm / MHz.

[0069] After each BS detects the signal transmitted by the Tag, it records the high-precision time stamp and sends it to the data processing center, which converts it to high-precision TDOA, supplemented by the signal propagation speed to convert to distance difference.

[0070] The TDOA measurement value is obtained by taking the difference between the high-precision time stamp information between BSs and multiplying it by the electromagnetic wave propagation speed. As shown in Figure 2 BS1-5 represent UWB base stations, of which BS1 is the master base station and BS2-5 are the slave base stations. Assuming that the Tag sends pulse signals to multiple BSs at time T0, the BS1-5 receive the pulse signals at times T1, T2, T3, T4, and T5, respectively; when all BSs receive the pulse signals, BS2-5 send confirmation notifications to BS1, and BS1 feeds back time synchronization correction information τ 12 , τ 13 , τ 14 , τ 15 to each slave base station BS2-5 at this time, the corrected high-precision time stamp information of the five BSs is T1', T2', T3', T4', and T5', respectively, which is then transmitted to the data processing center through the UDP protocol. Subsequently, the data processing center converts the obtained precise time stamp information into TDOA measurement parameters by taking the 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 calculate 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 a state equation using the following velocity model:

[0080]

[0081] Where Δt k is the time it takes for the tag to move from the k-1th epoch to the kth epoch. Similarly, Δt k-1 is the time it takes for the tag to move from the k-2th epoch to the k-1th 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 the discrete Kalman filter 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 The covariance matrices of observation noise, process noise and measurement noise are Q k =10 -4 m 2 / s、R k =0.01m 2 .

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

[0087] Assume that 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 the actual distance difference between BS1 (serving base station), then

[0091]

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

[0093] Finally, we can get:

[0094]

[0095] Among them, X i,1 =X i -X1,Y i,1 =Y i -Y1, consider x, y, and R1 as unknowns. The above formula is a linear equation system. Solving this equation system can obtain 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, stride 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 (x0, y0). The position of the target at step n-1 (x n-1 ,y n-1 ) Recursively, we can get the position of step n 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] A triple-constrained peak-valley detection pedometer model is used to accurately detect gait. Step length estimation uses an 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] Where, 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 layer of 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] Where, are the horizontal and vertical coordinates of the αth particle at the k-1th step respectively; is the state noise.

[0111] Use the BDS / UWB positioning results as observations to update the particle weights:

[0112]

[0113] Where, 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 this application.

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

Claims

1. A multi-source fusion positioning method for offshore platforms based on particle layered optimization, characterized by: The following steps are involved: S1: Maintaining high-precision time synchronization between base stations, UWB transmits tag and base station signal data by sending and receiving extremely narrow pulse target signals of nanoseconds or less. The delay difference between the tag and the base station is converted into a distance difference based on the signal propagation speed. 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; S3: Perform PDR positioning, using the inertial sensor information in the smart portable device to calculate the target's cadence, stride length, and heading in real time, and then calculate the target's current position based on its previous position; S4: Complementary particle filtering is used to fuse the position information obtained by BDS / UWB and PDR to solve the target positioning result.

2. The multi-source fusion positioning method for offshore platforms based on particle layered optimization according to claim 1 is characterized by: In the step S1, the specific steps are: S11: The tag transmits a pulse signal to different base stations BS; S12: After each base station BS detects the signal transmitted by the 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 based on the signal propagation speed.

3. The multi-source fusion positioning method for offshore platforms based on particle layered optimization according to claim 2 is characterized by: In the step S11, let 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 10dB, respectively. C Indicates the center frequency; the ratio of the signal bandwidth to the 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 platforms based on particle layered optimization according to claim 2 is characterized by: 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 as follows: 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 time T0; the time when BS1 receives the pulse signal is T1, the time when BSi receives the pulse signal is Ti, and 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 UDP protocol; Let c be the propagation speed of radio waves. The data processing center converts the obtained 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 platforms based on particle layered optimization according to claim 1 is characterized in that: In the step S2, the specific steps are: S21: Let Δt k Δt is the time it takes for the tag to move from the k-1th epoch to the kth epoch. k-1 R is the time it takes for the tag Tag to move from the k-2th epoch to the k-1th epoch. k 、R k-1 、R k-2 are the predicted values ​​of the kth, k-1th, and k-2th epochs of the TDOA measurements, ω k-1 is the process noise; based on the Kalman filter smoothing process, the state equation is constructed using the following velocity model to predict the TDOA measurement value of UWB: Assume 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 location 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, and 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 platforms based on particle layered optimization according to claim 5 is characterized by: 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 platforms based on particle layered optimization according to claim 1 is characterized by: 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; let the initial position of the known target be (x0, y0), and the position of the target in the n-1th step (x n-1 ,y n-1 ) recursively get the position of step n (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 gait is detected by the peak and trough detection pedometer model based on triple constraints, and the step length L is estimated by the empirical model k for:

8. The multi-source fusion positioning method for offshore platforms based on particle layered optimization according to claim 1 is characterized by: 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 from 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 quantity, 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 that can be executed by a computer processor is stored therein, and the computer program executes the multi-source fusion positioning method for offshore operating platforms based on particle layered optimization as described in any one of claims 1 to 8.

10. A multi-source fusion positioning system for offshore platforms based on particle layered optimization, 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 operating platforms based on particle hierarchical optimization as described in any one of claims 1 to 8.

Images