Warship landing positioning method, device and equipment and storage medium

Abstract

The invention provides a ship landing positioning method, device and equipment and a storage medium, and relates to the field of auxiliary ship landing guiding and positioning. Obtaining a target signal detection mode; obtaining a target hybrid positioning mode; determining a preliminary station distribution scheme according to the target hybrid positioning mode; obtaining geometric structure information of the target ship; according to the geometric structure information and the preliminary station distribution scheme, generating a target station distribution scheme applied to the target ship; and determining a target positioning scheme for the target ship according to the target signal detection mode, the target hybrid positioning mode and the target station distribution scheme. According to the invention, the problem that in the prior art, the positioning speed of a time of arrival (TOA) algorithm is slow is solved; however, a time difference of arrival (TDOA) algorithm has a positioning blind area, and the like, so that the carrier landing positioning efficiency is low.

Description

Technical Field

[0001] This application relates to the field of ship landing guidance and positioning, and in particular to a landing positioning method, device, equipment and storage medium. Background Technology

[0002] Large ships are floating platforms with limited space, and landing on them is affected by factors such as wake turbulence, atmospheric turbulence, deck winds, deck motion, and the ship's surface effect. Therefore, the requirements for target positioning on board are more stringent, necessitating strict trajectory control in disturbed environments. Current research on ultra-wideband positioning technology, both domestically and internationally, mainly focuses on indoor and short-range scenarios, with limited research on the positioning capabilities and accuracy of long-range targets. Furthermore, long-range target landing guidance based on satellite positioning, radar positioning, and photoelectric signal positioning technologies is also limited by meteorological conditions; the guidance effect is significantly reduced in rough seas and low light conditions.

[0003] Currently, the main existing positioning methods are the Time of Arrival (TOA) algorithm and the Time Difference of Arrival (TDOA) algorithm. The TOA algorithm is based on the distance from each anchor node to the target, offering high positioning accuracy, but it requires strict device time synchronization. The TDOA algorithm, on the other hand, calculates the time difference between signals arriving at different base stations for positioning, resulting in faster positioning speed, but the positioning error increases with the distance between the target and the base station.

[0004] Among existing positioning methods, the TOA algorithm is slow and the TDOA algorithm has a positioning blind zone, both of which result in low shipboard positioning efficiency. Summary of the Invention

[0005] This application provides a ship landing positioning method, apparatus, device, and storage medium to solve the problem that the TOA algorithm has a slow positioning speed and the TDOA algorithm has a positioning blind zone, resulting in low ship landing positioning efficiency.

[0006] Firstly, this application provides a shipboard positioning method, including:

[0007] The target signal detection method is obtained, wherein the target signal detection method is determined based on the channel impulse response peak detection algorithm;

[0008] Obtain the target hybrid positioning method; wherein, the target hybrid positioning method is determined based on the time-of-arrival ranging algorithm and the time difference of arrival ranging algorithm;

[0009] Based on the target hybrid positioning method, a preliminary site deployment plan is determined;

[0010] Obtain the geometric structure information of the target ship;

[0011] Based on the geometric structure information and the preliminary station deployment plan, a target station deployment plan is generated for application to the target ship.

[0012] Based on the target signal detection method, the target hybrid positioning method, and the target deployment scheme, a target positioning scheme for the target ship is determined.

[0013] In one possible design, based on the target signal detection method, the target hybrid positioning method, and the target deployment scheme, a target positioning scheme for the target ship is determined, including:

[0014] Based on the target signal detection method, the target hybrid positioning method, and the target deployment plan, a preliminary positioning plan is determined;

[0015] Based on the preliminary positioning plan, a positioning simulation was conducted on the target ship.

[0016] Based on the positioning simulation results, an optimized target scheme is generated;

[0017] Based on the target optimization scheme, the preliminary positioning scheme is optimized to obtain the target positioning scheme.

[0018] In one possible design, based on the positioning simulation results, a target optimization scheme is generated, including:

[0019] Based on the positioning simulation results, the anchor node configuration deployment error data is determined, and an optimized anchor node configuration deployment scheme is generated based on the anchor node configuration deployment error data; and / or,

[0020] Based on the positioning simulation results, the clock offset error data is determined, and a clock offset optimization scheme is generated based on the clock offset error; and / or,

[0021] Based on the positioning simulation results, the error data of the number of anchor nodes is determined, and an optimization scheme for the number of anchor nodes is generated based on the error data of the number of anchor nodes.

[0022] In one possible design, prior to acquiring the target signal detection method, the following is also included:

[0023] Set up a vector network analyzer for connecting ultra-wideband base stations and attaching tags;

[0024] The transmission coefficients were detected using a vector network analyzer.

[0025] The calculation method for the channel impulse response is determined based on the transmission coefficient.

[0026] Determine an automatic multi-scale peak detection algorithm for peak detection;

[0027] The target signal detection method is determined based on the calculation method of channel impulse response and the automatic multi-scale peak detection algorithm.

[0028] In one possible design, prior to obtaining the target hybrid localization method, the following is also included:

[0029] Configure the time-of-arrival ranging algorithm for primary base station ranging and the time difference of arrival ranging algorithm for non-primary base station ranging;

[0030] The least squares method is used to fuse the time-of-arrival ranging algorithm and the time difference of arrival ranging algorithm to obtain a hybrid target positioning method.

[0031] In one possible design, the preliminary deployment plan includes the deployment distance, deployment structure, and number of stations.

[0032] In one possible design, based on the target hybrid positioning method, a preliminary site deployment plan is determined, including:

[0033] Obtain the candidate station layout structure, candidate station distance, and candidate station quantity; among which, the candidate station layout structures include rectangular station layout structure, rhomboid station layout structure, and parallelogram station layout structure;

[0034] Based on the candidate station structure, candidate station distance, and candidate station number, a target hybrid positioning method is used for simulation to obtain the station deployment simulation results.

[0035] Based on the simulation results, a preliminary station deployment plan was determined.

[0036] Secondly, this application provides a shipboard positioning device, comprising:

[0037] The first acquisition module is used to acquire the target signal detection method, wherein the target signal detection method is determined based on the channel impulse response peak detection algorithm;

[0038] The second acquisition module is used to acquire the target hybrid positioning method; wherein, the target hybrid positioning method is determined based on the time of arrival ranging algorithm and the time difference of arrival ranging algorithm;

[0039] The first determination module is used to determine the preliminary site deployment plan based on the target hybrid positioning method;

[0040] The third acquisition module is used to acquire the geometric structure information of the target ship;

[0041] The generation module is used to generate a target deployment plan for the target ship based on the geometric structure information and the preliminary deployment plan.

[0042] The second determining module is used to determine the target positioning scheme for the target ship based on the target signal detection method, the target hybrid positioning method, and the target deployment scheme.

[0043] Thirdly, this application provides a shipboard positioning method device, including: a memory and a processor;

[0044] The memory stores instructions that the computer executes;

[0045] The processor executes computer execution instructions stored in memory, causing the processor to perform a shipboard positioning method as described in the first aspect of the invention.

[0046] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement a shipboard positioning method as described in the first aspect of the invention.

[0047] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements a shipboard positioning method according to the first aspect of the invention.

[0048] This application provides a ship landing positioning method, apparatus, equipment, and storage medium, which acquires a target signal detection method; acquires a target hybrid positioning method; determines a preliminary station deployment scheme based on the target hybrid positioning method; acquires the geometric structure information of the target ship; generates a target station deployment scheme applied to the target ship based on the geometric structure information and the preliminary station deployment scheme; and determines a target positioning scheme for the target ship based on the target signal detection method, the target hybrid positioning method, and the target station deployment scheme. Compared with existing technologies, the TOA algorithm has strict requirements for device time synchronization and a slow positioning speed; while the TDOA algorithm has a positioning blind zone, and the positioning error increases with the distance between the target and the base station. These problems lead to low ship landing positioning efficiency. This application, based on a typical ultra-wideband positioning system, determines a signal detection method based on CIR and a hybrid positioning method based on TOA and TDOA, which can accurately achieve signal detection and positioning. Simulations were conducted using the hybrid positioning method to determine a preliminary positioning scheme, and the optimal station deployment scheme suitable for the target ship landing scenario was sought based on the geometric structure of the target ship. This improves positioning accuracy and target guidance reliability, thereby improving ship landing positioning efficiency. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of the present 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 some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1This is a schematic diagram of the system architecture of the ship landing and positioning method provided in the embodiments of this application;

[0051] Figure 2 A schematic flowchart of a shipboard positioning method provided in this application embodiment. Figure 1 ;

[0052] Figure 3 A simplified schematic diagram of the deck-mountable station area structure provided in this application embodiment;

[0053] Figure 4 A schematic flowchart of a shipboard positioning method provided in this application embodiment. Figure 2 ;

[0054] Figure 5 A schematic flowchart of a shipboard positioning method provided in this application embodiment. Figure 3 ;

[0055] Figure 6 A schematic flowchart of a shipboard positioning method provided in this application embodiment. Figure 4 ;

[0056] Figure 7 This is a schematic diagram of the structure of a shipboard positioning device provided in an embodiment of this application;

[0057] Figure 8 This is a schematic diagram of the structure of a shipboard positioning method and device provided in an embodiment of this application;

[0058] Figure 9 This is a schematic diagram illustrating the principle of peak detection method for input and output waveforms provided in an embodiment of this application.

[0059] Figure 10 A schematic diagram illustrating the impact of different station deployment distances on positioning accuracy, provided in an embodiment of this application.

[0060] Figure 11 This is a schematic diagram illustrating the distribution of hybrid positioning error with the number of nodes provided in an embodiment of this application.

[0061] Figure 12 A schematic diagram of the error distribution of different station deployment methods on the island provided in the embodiments of this application;

[0062] Figure 13 A schematic diagram illustrating the positioning estimation error when the main base station is selected at different locations, as provided in an embodiment of this application.

[0063] Figure 14 A schematic diagram comparing the error distribution between the base station and the tag with a Gaussian distribution provided in the embodiments of this application;

[0064] Figure 15A schematic diagram showing the deviation between the average distance error of 1-100 measurements provided in this application embodiment and the average distance error of all data.

[0065] Figure 16 A schematic diagram illustrating the change of distance over time obtained by averaging every 20 sets of data provided in this application embodiment;

[0066] Figure 17 This application provides a schematic diagram illustrating how the positioning error of the hybrid algorithm varies with the number of anchor nodes under different noise variances in its embodiments.

[0067] Figure 18 This is a schematic diagram of the base station deployment at the island superstructure provided in this application embodiment;

[0068] Figure 19 This is a schematic diagram of the AMPD detection results provided in an embodiment of this application;

[0069] Figure 20 This is a schematic diagram of the TOA positioning error distribution provided in an embodiment of this application;

[0070] Figure 21 A schematic diagram illustrating how the TOA positioning error varies with the distance between the carrier-based aircraft and the ship, as provided in this embodiment of the application.

[0071] Figure 22 This is a schematic diagram of the error distribution of the TDOA algorithm provided in the embodiments of this application;

[0072] Figure 23 This is a schematic diagram of the error distribution of the hybrid positioning algorithm provided in the embodiments of this application;

[0073] Figure 24 This is a schematic diagram of the rectangular station deployment error distribution of the hybrid algorithm provided in the embodiments of this application;

[0074] Figure 25 This is a schematic diagram of the error distribution of the hybrid algorithm diamond-shaped station layout provided in the embodiments of this application;

[0075] Figure 26 This is a schematic diagram of the parallelogram-shaped station layout error distribution provided in the embodiments of this application;

[0076] Figure 27 A schematic diagram of the fitting function when the noise standard deviation is 0.01, provided for embodiments of this application;

[0077] Figure 28 A schematic diagram of the fitting function when the noise standard deviation is 0.03, provided for embodiments of this application;

[0078] Figure 29 A schematic diagram of the fitting function when the noise standard deviation is 0.05, provided for embodiments of this application;

[0079] Figure 30 A schematic diagram of the x-axis positioning error curves of the three-dimensional station layout scheme at different island heights provided in the embodiments of this application;

[0080] Figure 31 A schematic diagram of the y-axis positioning error curves for three-dimensional station deployment schemes at different island heights provided in the embodiments of this application;

[0081] Figure 32 A schematic diagram of the z-axis positioning error curves of the three-dimensional station layout scheme at different island heights provided in the embodiments of this application;

[0082] Figure 33 A schematic diagram of positioning error curves for three-dimensional station deployment schemes at different island heights provided in the embodiments of this application;

[0083] Figure 34 A schematic diagram of the pitch angle error curves for the three-dimensional station deployment scheme at different island heights provided in the embodiments of this application;

[0084] Figure 35 This is a schematic diagram of the yaw angle error curves for the three-dimensional station deployment scheme at different island heights provided in the embodiments of this application. Detailed Implementation

[0085] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0086] In the embodiments of this application, the terms "first" and "second" are used to distinguish identical or similar items with substantially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and that "first" and "second" do not necessarily imply difference. It should be noted that in the embodiments of this application, words such as "exemplary" or "for example" are used to indicate that something is being used as an example, illustration, or description. Any embodiment or design scheme described as "exemplary" or "for example" in this application should not be construed as being better or more advantageous than other embodiments or design schemes. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner. In the embodiments of this application, "at least one" refers to one or more, and "more than one" refers to two or more.

[0087] It should be noted that the phrase "at...time" in the embodiments of this application can refer to the instant at which a certain situation occurs, or to a period of time after the occurrence of a certain situation; the embodiments of this application do not specifically limit this. Furthermore, the ship landing positioning method provided in the embodiments of this application is merely an example, and ship landing positioning methods may include more or fewer elements.

[0088] To facilitate a clear description of the technical solutions in the embodiments of this application, some terms and technologies involved in the embodiments of this application will be briefly introduced below:

[0089] Ultra-wideband (UWB) positioning technology is a high-precision positioning technology based on extremely wide frequency band wireless signals. It features centimeter-level positioning accuracy, strong anti-multipath interference capability, and high real-time performance. In recent years, it has received much attention in fields such as indoor positioning, the Internet of Things, and smart devices.

[0090] UWB technology achieves positioning by transmitting and receiving non-sinusoidal pulse signals in an extremely wide frequency band (typically 3.1–10.6 GHz, with a bandwidth exceeding 500 MHz), and calculates the target position using the time difference or phase difference of the signals.

[0091] Channel Impulse Response (CIR): The impulse response detection method is used for ultra-wideband signal identification. It determines the tag's position coordinates by measuring the peak value of the CIR, thus achieving high-precision positioning. Especially in non-line-of-sight environments, the CIR detection method, compared to traditional peak detection methods based on input-output waveforms, can more accurately identify signal peaks, effectively improving positioning accuracy.

[0092] Non-Line of Sight (NLOS): In the fields of wireless communication and positioning, NLOS refers to a situation where a signal does not propagate in a straight line during transmission. Instead, due to the presence of obstacles, the signal reaches the receiver through reflection, refraction, or diffraction. This situation is common in complex environments such as indoor spaces and urban canyons, and can significantly impact positioning accuracy and communication quality.

[0093] Line of sight (LOS): In wireless communication and positioning, LOS refers to the straight-line propagation path of a signal between the transmitter and receiver, without any obstructions. This propagation path typically provides better signal quality and higher positioning accuracy because the signal experiences less interference and attenuation during propagation.

[0094] A Programmable Vector Network Analyzer (PNA) is a precision test instrument used to measure network parameters of electronic components, circuits, and systems, such as S-parameters (scattering parameters), reflection coefficients, and transmission coefficients. These parameters are crucial for evaluating and optimizing the performance of systems such as wireless communications, radar, and satellite communications. PNAs feature high precision, wide dynamic range, and programmability, enabling them to meet the needs of various application scenarios.

[0095] Automatic multiscale-based peak detection (AMPD) is an algorithm for detecting peaks in periodic and quasi-periodic signals. It utilizes a multi-scale sliding window to compare signals on both sides and find local maxima, performing well even with noisy data. AMPD requires almost no hyperparameters, is highly adaptable, and has good noise resistance. It exhibits good signal adaptability, with its only assumption being that the signal is periodic or quasi-periodic, and the requirement for periodicity is not very high. In wireless communication and positioning technologies, AMPD can be used for ultra-wideband signal identification, determining the tag's location coordinates by measuring the peak value of the impulse response (CIR), thus achieving high-precision positioning.

[0096] In maritime operations of large ships, landing guidance for long-range targets is a crucial aspect, directly impacting the safe landing of carrier-based aircraft and the overall combat effectiveness of the ship. Current research on ultra-wideband positioning technology, both domestically and internationally, primarily focuses on indoor and short-range scenarios, with limited research on the positioning capabilities and accuracy for long-range targets. Large ships, being limited floating platforms, are subject to interference from factors such as wake turbulence, atmospheric turbulence, deck winds, deck motion, and surface effects. Therefore, the requirements for target positioning on board are far more stringent, necessitating strict trajectory control even in turbulent environments.

[0097] In addition, long-range target landing guidance using satellite positioning, radar positioning, and photoelectric signal positioning technologies is also limited by the weather environment: when the sea conditions are bad and the light is weak, the guidance effect will be greatly reduced.

[0098] Existing positioning methods mainly include the TOA (Time of Arrival) algorithm and the TDOA (Time Difference of Origin) algorithm. The TOA algorithm is based on the distance from each anchor node to the target, and has high positioning accuracy, but it has strict requirements for device time synchronization and is relatively slow. The TDOA algorithm, on the other hand, calculates the time difference of signal arrival at different base stations for positioning, and has a faster positioning speed, but it has a positioning blind zone, and the positioning error increases with the distance between the target and the base station.

[0099] Based on this, embodiments of this application provide a ship landing positioning method, apparatus, device, and storage medium, which can be used in the field of assisting ship landing guidance and positioning, and are intended to solve the above-mentioned technical problems of the prior art.

[0100] To address the aforementioned issues, the inventors discovered during the landing positioning and guidance process for large ships that, among existing positioning methods, the TOA algorithm, based on the distance from each anchor node to the target, offers high positioning accuracy but requires strict equipment time synchronization and is relatively slow. The TDOA algorithm, on the other hand, calculates the time difference of signal arrival at different base stations for positioning, offering faster positioning speed but suffers from blind spots, and the positioning error increases with the distance between the target and the base station. To resolve these problems, the inventors, based on a typical ultra-wideband positioning system, determined a CIR-based signal detection method and a hybrid positioning method based on TOA and TDOA, which can accurately achieve signal detection and positioning. Simulations were conducted using the hybrid positioning method to determine a preliminary positioning scheme, and the optimal station deployment scheme suitable for the target ship's geometry was sought. This improved positioning accuracy, increased the reliability of target guidance, and enhanced landing positioning efficiency.

[0101] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0102] Figure 1 This is a schematic diagram of the system architecture for the shipboard positioning method provided in an embodiment of this application. Figure 1 In the above architecture, at least one of data acquisition device 101, processing device 102 and display device 103 is included.

[0103] It is understood that the structure illustrated in the embodiments of this application does not constitute a specific limitation on the system architecture of the ship landing and positioning method. In other feasible embodiments of this application, the above architecture may include more or fewer components than illustrated, or combine some components, or divide some components, or arrange different components, which can be determined according to the actual application scenario and is not limited here. Figure 1 The components shown can be implemented in hardware, software, or a combination of both.

[0104] In the specific implementation process, the data acquisition device 101 may include an input / output interface or a communication interface. The data acquisition device 101 can be connected to the processing device through the input / output interface or the communication interface and acquire relevant data.

[0105] The processing device 102 can process the collected data and formulate a target positioning plan for the target ship.

[0106] The display device 103 can also be a touch screen or the screen of a terminal device, used to receive user commands while displaying the above-mentioned content, so as to realize interaction with the user.

[0107] It should be understood that the aforementioned processing device can be implemented by a processor reading instructions from memory and executing those instructions, or it can be implemented by a chip circuit.

[0108] Furthermore, the network architecture and business scenarios described in the embodiments of this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided in the embodiments of this application. As those skilled in the art will know, with the evolution of network architecture and the emergence of new business scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0109] The technical solution of this application will be described in detail below with reference to specific embodiments:

[0110] Figure 2 A schematic flowchart of a shipboard positioning method provided in this application embodiment. Figure 1 The method includes:

[0111] S201. Obtain the target signal detection method, wherein the target signal detection method is determined based on the channel impulse response peak detection algorithm.

[0112] S202, Obtain the target hybrid positioning method; wherein, the target hybrid positioning method is determined based on the time of arrival ranging algorithm and the time difference of arrival ranging algorithm.

[0113] S203. Determine the preliminary site deployment plan based on the target hybrid positioning method.

[0114] The preliminary station deployment plan includes station distance, station structure, and number of stations.

[0115] S204. Obtain the geometric structure information of the target ship.

[0116] S205. Based on the geometric structure information and the preliminary station deployment plan, generate a target station deployment plan to be applied to the target ship.

[0117] Due to the geometric limitations of large ships, only certain areas are suitable for base station deployment. Areas obstructed by the ship's runway and island superstructure are unsuitable. When ultra-wideband base stations can be deployed on the island superstructure, base station deployment should be an optimization solution in three-dimensional space.

[0118] In one possible embodiment, Figure 3A simplified schematic diagram of the deck-mountable station area structure provided in this application embodiment is shown below. Figure 3 As shown, based on the existing layout of large ships, a suitable deployment area D is obtained, which is usually composed of multiple areas.

[0119] The station deployment area can be described using the following mathematical language:

[0120] D∈{D1∪D2∪D3}

[0121]

[0122] Furthermore, all line segments are represented using the form ax + by + c ≤ 0. When the station coordinates belong to the station layout space, their values ​​are always less than or equal to zero. When the station coordinates are within the station layout space, they satisfy the constraint conditions regardless of their location. All coordinate points within a reasonable interval should be treated equally. Therefore, the constraint function is defined as follows:

[0123]

[0124]

[0125] Where m and n are the number of line segments that make up the two regions, respectively.

[0126] Specifically, the constraint function can be used to intuitively determine whether the base station coordinates are within a reasonable deployment area. Only when the constraint function value is zero are the site coordinates within the deployment area. The larger the constraint function value, the farther the site coordinates are from the deployment area.

[0127] In one possible embodiment, solving the above constraint equations, in Figure 3 Based on this, we can obtain Figure 18 , Figure 18 This is a general schematic diagram of the base station deployment at the island superstructure provided in this application embodiment, as shown below. Figure 18 As shown, the red-marked locations indicate the approximate locations of the base stations deployed on the island superstructure.

[0128] Optionally, Scheme 1, based on a base station deployment with an approximate maximum geometric distribution, has the following base station coordinates:

[0129] S1=(100,15,H), S2=(350,0,0), S3=(200,35,0), S4=(60,45,0)

[0130] Optionally, considering the larger island superstructure, a 3D base station deployment scheme 2 can be used to deploy two base stations. The coordinates of the base stations are as follows:

[0131] S1=(100,15,H), S2=(350,0,0), S3=(200,35,0), S4=(80,20,H)

[0132] Furthermore, in a ship-guided scenario, the error distribution of different station deployment methods on the island superstructure is simulated.

[0133] In one possible embodiment, Figure 12 This is a schematic diagram illustrating the error distribution of different station deployment methods on the island superstructure provided in this application embodiment, such as... Figure 12 As shown, the positioning error of the 3D base station deployment scheme 2, which can deploy two base stations on a relatively large island, is very large. This is because the two base stations on the island are too close together, resulting in a reduced geometric configuration and thus a large positioning error. Therefore, the positioning error of the scheme based on the approximate maximum geometric distribution, which deploys one base station on the island, is smaller and can meet the requirements for precise positioning.

[0134] Furthermore, considering shipboard landing scenarios, when the base station is located on the island superstructure, simulations of positioning errors at different island superstructure heights can yield simulation curves of the positioning errors for the 3D base station deployment scheme at different island superstructure heights:

[0135] In one possible embodiment, Figure 30 This is a schematic diagram of the x-axis positioning error curves for the three-dimensional station layout scheme at different island heights provided in the embodiments of this application.

[0136] In one possible embodiment, Figure 31 This is a schematic diagram of the y-axis positioning error curves for three-dimensional station deployment schemes at different island heights provided in the embodiments of this application.

[0137] In one possible embodiment, Figure 32 This is a schematic diagram of the z-axis positioning error curves for the three-dimensional station layout scheme at different island heights provided in the embodiments of this application.

[0138] In one possible embodiment, Figure 33 This is a schematic diagram of the positioning error curves of the three-dimensional station deployment scheme at different island heights provided in the embodiments of this application.

[0139] In one possible embodiment, Figure 34 This is a schematic diagram of the pitch angle error curves for the three-dimensional station deployment scheme at different island heights provided in the embodiments of this application.

[0140] In one possible embodiment, Figure 35 This is a schematic diagram of the yaw angle error curves for the three-dimensional station deployment scheme at different island heights provided in the embodiments of this application.

[0141] The simulation results show that when the base station is deployed on the island, the higher the island height, the better the positioning error of the hybrid algorithm. The island height has little impact on the positioning error of the hybrid algorithm on the x and y axes, and also has little impact on the yaw angle error. However, the higher the island height, the smaller the positioning error of the hybrid algorithm on the z axis, and the smaller the pitch angle error.

[0142] S206. Based on the target signal detection method, the target hybrid positioning method, and the target deployment scheme, determine the target positioning scheme for the target ship.

[0143] It should be noted that existing ranging methods for UWB mainly rely on peak detection. This method can be further divided into two types: one detects the peak value of the input and output waveforms, and the other detects the peak value of the channel's impulse response.

[0144] In one possible embodiment, Figure 9 This is a schematic diagram illustrating the principle of the peak detection method for input and output waveforms provided in the embodiments of this application, as shown below. Figure 9 As shown, the peak detection method based on input and output waveforms measures distance according to the time difference between corresponding peak values ​​of the input and output waveforms. The input waveform is the transmitted waveform of the ultra-wideband base station, and the output waveform is the received waveform of the tag. When locating the target, a threshold can be set based on the normalized maximum peak value, and the first peak value greater than this threshold is taken as the detected peak value.

[0145] Optionally, ranging can be achieved by measuring the time difference between the peak value of the signal received by the tag and the peak value of the signal transmitted by the ultra-wideband base station, where the distance r is... i As shown in the following formula:

[0146] r i =(t i -t0)c

[0147] Where t0 is the time of the peak value measured after the ultra-wideband base station transmits the signal; t i The peak value received by the i-th attached tag starting from the start of signal transmission from the ultra-wideband base station.

[0148] However, peak detection methods based on input and output waveforms require identifying the peak values ​​of the two corresponding waveforms. Due to the large size of shipborne aircraft and the limited area where they can be deployed, ultra-wideband base stations are easily obstructed. Furthermore, the corresponding peak values ​​cannot be easily identified in NLOS (Normally In Slowly Oriented) environments using this method, and it is often used in LOS (Lowest Slowly Oriented) environments.

[0149] Optionally, to address this problem, this application uses a CIR peak detection algorithm for target localization. Then, before step S201, the following steps are included:

[0150] Optionally, a vector network analyzer can be configured to connect to an ultra-wideband base station and attach tags.

[0151] Optionally, the transmission coefficient can be detected using a vector network analyzer.

[0152] Optionally, the calculation method for the channel impulse response can be determined based on the transmission coefficient.

[0153] Optionally, determine an automatic multi-scale peak detection algorithm for detecting peaks.

[0154] Optionally, the target signal detection method can be determined based on the calculation method of the channel impulse response and the automatic multi-scale peak detection algorithm.

[0155] Specifically, the distance between the UWB base station and the attached tag is determined based on the peak value of the channel impulse response transmitted between them. Each UWB base station and the attached tag are connected via a PNA, and the transmission coefficient S21 of each tag is measured by the PNA. Then, the channel impulse response is obtained from S21, which is achieved by performing an inverse fast Fourier transform on S21.

[0156] More specifically, suppose there are N propagation paths between an ultra-wideband base station and a tag, and the magnitude of these N paths is α. k Phase is The time delay is τ k Therefore, its channel impulse response should be as follows:

[0157]

[0158] More specifically, the direct path is obtained by the peak detection algorithm.

[0159] Optionally, for the LOS case, its maximum peak value can be used as the time value for TOA estimation.

[0160] Optionally, for the NLOS case, since other multipath components may be greater than the value of the direct path, the strongest path cannot be used for estimation, and a certain algorithm is needed for distance measurement.

[0161] Specifically, considering the high level of environmental noise and the complexity of the signal, the Automatic Multi-Scale Peak Detection (AMPD) method can be used for detection.

[0162] More specifically, considering that the shape and amplitude of the peak may vary depending on the application, the signal is decomposed into sub-signals at multiple scales, and local maxima are sought at each scale. By comparing the local maxima at different scales, the true peak location can be determined, and the influence of baseline noise can be eliminated.

[0163] Furthermore, simulation verification is performed to generate signals. Peak detection is performed on it to generate a detection signal map.

[0164] In one possible embodiment, Figure 19 This is a schematic diagram of the AMPD detection results provided in an embodiment of this application, as shown below. Figure 19 As shown, further processing of the generated detection signal can yield the AMPD detection result and determine whether the correct peak value can be detected.

[0165] Specifically, such as Figure 19 As shown, two peaks can be detected, at 5 and 25 respectively, indicating that the correct peaks can be detected.

[0166] Furthermore, CIR detection methods are used for ultra-wideband signal identification.

[0167] This embodiment provides a ship landing positioning method, which involves: acquiring a target signal detection method; acquiring a target hybrid positioning method; determining a preliminary station deployment scheme based on the target hybrid positioning method; acquiring the geometric structure information of the target ship; generating a target station deployment scheme applied to the target ship based on the geometric structure information and the preliminary station deployment scheme; and determining a target positioning scheme for the target ship based on the target signal detection method, the target hybrid positioning method, and the target station deployment scheme. Compared to existing technologies, the TOA algorithm has strict requirements for device time synchronization and a slow positioning speed; while the TDOA algorithm has a positioning blind zone, and the positioning error increases with the distance between the target and the base station. These problems lead to low ship landing positioning efficiency. This application, based on a typical ultra-wideband positioning system, determines a CIR-based signal detection method and a hybrid positioning method based on TOA and TDOA, which can accurately achieve signal detection and positioning. Simulations were conducted using the hybrid positioning method to determine a preliminary positioning scheme, and the optimal station deployment scheme suitable for the target ship landing scenario was sought based on the target ship's geometry. This improves positioning accuracy and target guidance reliability, thereby improving ship landing positioning efficiency.

[0168] Figure 4 A schematic flowchart of a shipboard positioning method provided in this application embodiment. Figure 2 ,like Figure 4 As shown, the specific implementation steps of S206 above include:

[0169] S401. Determine the preliminary positioning scheme based on the target signal detection method, the target hybrid positioning method, and the target deployment scheme.

[0170] S402. Based on the preliminary positioning plan, conduct positioning simulation of the target ship.

[0171] It should be noted that the systematic errors of UWB positioning systems usually involve factors such as manufacturing uncertainties within UWB devices, and the stability of hardware and clocks. This may lead to certain differences in positioning systems between different devices or at different points in time, mainly including anchor node configuration deployment errors and clock offset errors.

[0172] S403. Based on the positioning simulation results, determine the anchor node configuration deployment error data, and generate an anchor node configuration deployment optimization scheme based on the anchor node configuration deployment error data; and / or.

[0173] The deployment of UWB anchor nodes is also a crucial factor affecting positioning accuracy; a proper deployment method can improve measurement accuracy. A well-designed anchor node deployment can optimize signal transmission paths and reception quality to the greatest extent possible, thereby improving positioning accuracy and reliability. When determining the deployment locations of anchor nodes, the distance and layout between them should cover the entire monitoring area and maintain sufficient overlap to ensure comprehensive positioning coverage throughout the region. The height and location of the anchor nodes also need to be considered. Generally, anchor nodes should be placed at the edges or corners of the monitoring area to ensure that the signal propagation path passes through as much of the monitoring area as possible, reducing signal obstruction and attenuation.

[0174] The orientation of the anchor node and the direction of the antenna are also factors that need to be considered. By correctly setting the orientation of the anchor node and the direction of the antenna, the signal reception quality and positioning accuracy can be optimized to the maximum extent. Finally, it is also necessary to consider the obstacles and interference factors that may exist in the monitoring area, as well as the complexity of the surrounding environment. These factors will affect the deployment method and effectiveness of the anchor node.

[0175] For example, the compensation method is as follows:

[0176] First, for an ultra-wideband (UWB) base station to achieve positioning functionality, it needs to simultaneously obtain the distance relationships between at least three base stations and the target to calculate a unique solution. However, in UWB positioning algorithms, different choices of master base stations lead to different positioning errors. Therefore, this section analyzes the noise distribution under different master base station selections.

[0177] Secondly, the locations of four base stations are fixed, and all combinations of base stations with a number of 3 are selected. The location estimate is then calculated for each combination to obtain the error result.

[0178] In one possible embodiment, Figure 13 This is a schematic diagram illustrating the positioning estimation error when the main base station is selected at different locations, as provided in the embodiments of this application. Figure 13 As shown, the left side represents the positioning estimation error when the main base station is selected as (0,0), and the right side represents the positioning estimation error when the main base station is selected as (100,100).

[0179] When the selected main base station coordinates are (0,0), the average measurement error exhibits a non-uniform distribution. The average measurement error is smaller closer to the main base station, and larger further away.

[0180] Specifically, when the overall noise variance of the environment is constant, the average measurement error is also constant. Regardless of whether the main base station coordinates are (0,0) or (100,100), the average measurement error is approximately the same at the same distance. It can be considered that, given a sufficiently large number of measurements, the average measurement error is only related to the distance from the main base station and the magnitude of noise in the environment.

[0181] Finally, since the landing trajectory of the target aircraft carrier is known, selecting the main base station that is as close as possible to the landing trajectory and averaging the results can reduce the impact of the main base station selection.

[0182] S404. Based on the positioning simulation results, determine the clock offset error data, and generate a clock offset optimization scheme based on the clock offset error; and / or.

[0183] The basic principle of distance measurement in UWB positioning systems is based on calculating the signal transmission time from the transmitter to the receiver and multiplying it by the speed of electromagnetic waves in the medium to obtain the distance measurement value. Although UWB devices typically use high-precision crystal oscillators to generate clock signals, imperfections in the crystal manufacturing process, coupled with the effects of temperature changes and power supply voltage fluctuations, can cause slight fluctuations in the crystal frequency, leading to clock drift. This instability of the internal clock components accumulates over long-term use, eventually resulting in clock offset errors. Furthermore, UWB positioning systems consist of two units: tag nodes and anchor nodes. Tag nodes are usually in motion and may be affected by external environmental interference. For example, temperature changes, mechanical vibrations, and electromagnetic interference can all affect the internal clock components, leading to clock offset errors. Since electromagnetic waves travel at extremely high speeds in space, often approaching the speed of light, even small clock deviations can cause significant distance measurement errors in the system.

[0184] Specifically, while keeping the relative positions of the base station and the tag unchanged, several sets of data are collected. The ranging information obtained is subtracted from the actual distance to obtain the ranging error caused by the clock offset. The distribution characteristics are statistically analyzed, and a probability distribution curve is plotted and compared with the Gaussian distribution.

[0185] In one possible embodiment, Figure 14 This is a schematic diagram comparing the error distribution between the base station and the tag with a Gaussian distribution provided in an embodiment of this application, as shown below. Figure 14 As shown, the error distribution of the base station due to clock skew basically conforms to a Gaussian distribution.

[0186] Furthermore, the method of averaging multiple sets of data is used to reduce ranging error.

[0187] The more data collected, the smaller the average error. However, considering the real-time nature of positioning, it is necessary to find a critical number of data collections, that is, the fewest number of data collections possible while ensuring acceptable error. In the experiment, the distance error after averaging data from 1 to 100 measurements was used to find the minimum acceptable number of data collections.

[0188] In one possible embodiment, Figure 15 This is a schematic diagram illustrating the deviation between the average distance error of 1-100 measurements and the average distance error of all data provided in this application embodiment. Figure 15 As shown, after averaging approximately 20 sets of measurements, the error between the average distance and the average distance of all data is less than 1 cm, which can be considered as approximately equal. This means that averaging every 20 sets of measurements yields good performance.

[0189] Furthermore, by averaging every 20 sets of test data, the distribution of the average distance over time during the entire experimental testing process was obtained.

[0190] In one possible embodiment, Figure 16 This is a schematic diagram illustrating the change in distance over time obtained by averaging every 20 sets of data provided in this application embodiment, as shown below. Figure 16 As shown, the average distance obtained by taking the average of every 20 sets of data fluctuated by no more than 3cm throughout the entire experiment, which can be considered as good performance.

[0191] Furthermore, we chose to calculate the target distance by averaging every 20 sets of data.

[0192] S405. Based on the positioning simulation results, determine the error data of the number of anchor nodes, and generate an optimization scheme for the number of anchor nodes based on the error data of the number of anchor nodes.

[0193] Among them, using the TOA and TDOA hybrid algorithm requires at least three anchor nodes for two-dimensional positioning.

[0194] Specifically, the positioning error is adjusted by changing the number of anchor nodes.

[0195] In one possible embodiment, Figure 17 This application provides a schematic diagram illustrating the variation of the hybrid algorithm positioning error with the number of anchor nodes under different noise variances in its embodiments. Figure 17 As shown, the left side represents noise variance of 0.01, and the right side represents noise variance of 0.03.

[0196] Among these findings, the positioning error of the hybrid algorithm gradually decreases as the number of anchor nodes increases. When the noise variance is 0.01, the error is relatively large when the number of anchor nodes is less than or equal to 5. However, as the number of anchor nodes increases, it can be seen that when the number of anchor nodes is 6, the positioning error is significantly improved compared to when the number of anchor nodes is 5, with a substantial reduction in positioning error from a distance of 1000 meters to 3000 meters.

[0197] Specifically, when the noise variance is increased, it can be seen that when the noise variance is 0.03, the overall positioning error of the hybrid algorithm is larger compared to when the noise variance is 0.01. Furthermore, the positioning deviation is greater when using the same number of anchor nodes for different distance ranges. However, when the number of anchor nodes continues to increase from 6, it can be observed that the positioning error of the hybrid algorithm does not change significantly with the increase in the number of anchor nodes. Therefore, 6 anchor nodes are selected for positioning.

[0198] S406. Based on the target optimization scheme, optimize the preliminary positioning scheme to obtain the target positioning scheme.

[0199] In this embodiment, the target ship is simulated for positioning based on the target signal detection method, the target hybrid positioning method, and the target deployment scheme. Based on the positioning simulation results, the anchor node configuration deployment error and clock offset error are analyzed and optimized. Then, the positioning error is adjusted by changing the number of anchor nodes, and a target optimization scheme is generated to improve the positioning accuracy and the reliability of target guidance, thereby improving the landing positioning efficiency.

[0200] Figure 5 A schematic flowchart of a shipboard positioning method provided in this application embodiment. Figure 3 ,like Figure 5 As shown, the procedure before S202 above also includes:

[0201] It should be noted that, considering the positioning space is three-dimensional, and the positioning tag coordinates are (x, y, z), the number of positioning base stations n in the three-dimensional space must satisfy n > 3. The position of each positioning base station is known to be (x, y, z). i ,y i ,z i ), where i is the i-th base station, and the transmission time t can be obtained from the signal arrival timestamp. i Then the distance between its tag and the base station is:

[0202] d i =t i c

[0203] Furthermore, for the i-th positioning base station, the positioning equation can be written as:

[0204]

[0205] Furthermore, the positioning equations of each base station are combined:

[0206]

[0207] Furthermore, subtracting the expression in the first line from all expressions except the first line, we get:

[0208]

[0209] Furthermore, by simultaneously solving the positioning equations of all base stations and transforming them into matrix form, we obtain:

[0210] Ax = b

[0211]

[0212] Here, A is the measurement matrix with a dimension of n×3, while the vector b has a known dimension of n×1.

[0213] In one possible embodiment, Figure 20 This is a schematic diagram of the TOA positioning error distribution provided in the embodiments of this application, such as... Figure 20 As shown, by solving the matrix equation for the above positioning using the least squares method, we can obtain the TOA positioning error distribution, where the coordinates of the tag are given.

[0214] The TOA algorithm locates the target directly based on the distance from each anchor node (i.e., a circle is drawn with the anchor node as the center and the distance from the anchor node to the target as the radius, and the point where multiple circles intersect is the predicted position of the target). The positioning error is small near each anchor node and large at positions far from the anchor node. The error distribution is relatively uniform, and the positioning accuracy is high.

[0215] In one possible embodiment, Figure 21 This is a schematic diagram illustrating the variation of TOA positioning error with the distance between the carrier-based aircraft and the ship, as provided in the embodiments of this application. Figure 21 As shown, the TOA algorithm is simulated for far-field targets. The simulation reveals how the TOA positioning error changes with the distance between the carrier-based aircraft and the large ship. The simulation results show that the TOA algorithm has a small positioning error and high positioning accuracy in the carrier landing scenario. However, the TOA algorithm has very strict requirements for device time synchronization and relies on multiple communications between tags and base stations, resulting in a slow positioning speed. Therefore, it cannot meet the real-time requirements for precise positioning of carrier-based aircraft in the carrier landing scenario.

[0216] Furthermore, after base station deployment calibration, the relative positions of the four base stations are (x... i ,y i ,zi (i = 1, 2, 3, 4), the location of the moving target is unknown, let the target's coordinates be (x, y, z). In the TDOA algorithm, it is assumed that: base station 1 is a fixed station, and all distance differences are based on base station 1. Define the following distance relationship expression:

[0217]

[0218] Furthermore, the distance difference relationship expression is as follows:

[0219]

[0220] Furthermore, it can be deduced that...

[0221]

[0222] in, After formula derivation and transformation, we have the following expression:

[0223]

[0224] Where, x i,1 =x i -x1, therefore it is clear that r i,1 K i x i,1 All coordinates are known. Assume that the coordinates of the ground base stations have been predefined: base station 1 is (0,0,0), base station 2 is (x2,0,0), base station 3 is (x3,y3,0), and base station 4 is (x4,y4,z4). This definition can significantly reduce computational complexity and speed up the operation.

[0225] Specifically, the above positioning equation can be simplified as follows:

[0226]

[0227] More specifically, the above formula can be expressed as:

[0228]

[0229] Among them, the coordinate equation must also satisfy x 2 d + xe + f = 0.

[0230]

[0231] Furthermore, solving the above equations yields the three-dimensional coordinates of the positioning point. Since TDOA positioning requires at least four anchor nodes to complete three-dimensional positioning, multiple positioning base stations are placed on the deck. Shipborne aircraft carry positioning tags to transmit ultra-wideband signals. After receiving the ultra-wideband signals, each positioning base station calculates its time difference of arrival and then uses the TDOA algorithm to solve for the coordinates.

[0232] The positioning error of TDOA is mainly determined by the site error and the distance error. The distance error is equal to the product of the speed of light and the time difference of arrival. Since the speed of light is constant, the distance error is equivalent to the time difference of arrival. Because the time differences of arrival at each auxiliary monitoring station contain the same error factor, the distance measurement error is correlated. Therefore, the main factors affecting positioning accuracy are the time difference of arrival measurement error and the site error. The positional relationship between the target radiation source and each monitoring station is related to the positioning accuracy. The time difference of arrival measurement error and the site error depend on the equipment accuracy and can be regarded as inherent errors of the TDOA positioning system. Simulation is performed to obtain the error distribution of the TDOA algorithm.

[0233] In one possible embodiment, Figure 22 This is a schematic diagram of the error distribution of the TDOA algorithm provided in the embodiments of this application, as shown below. Figure 22 As shown, the positioning error of TDOA increases with the distance between the target and each anchor node. Simulation results show that the positioning error is small only in the petal-shaped region centered on the anchor node. Outside this region, the positioning error is large, and positioning is almost impossible. This indicates that the TDOA algorithm has a positioning blind zone. To address this problem, a hybrid positioning method is proposed:

[0234] S501. Configure the time-of-arrival ranging algorithm for ranging of the main base station and the time difference of arrival ranging algorithm for ranging of the non-main base station.

[0235] In this embodiment, a Time of Arrival (TOA) ranging algorithm is configured for ranging of the primary base station; and a Time Difference of Arrival (TDOA) ranging algorithm is configured for ranging of the non-primary base station.

[0236] Generally, the base station with the smallest ranging error in the vicinity is selected as the main base station.

[0237] It should be noted that this hybrid algorithm can take advantage of the small ranging error around the main base station, combined with the high positioning accuracy of the TOA algorithm, and at the same time allow other base stations to use the TDOA algorithm to avoid the problem of waiting for clock synchronization when a large number of base stations are deployed at the same time.

[0238] It should also be noted that due to the high speed of carrier-based aircraft, the TOA algorithm requires multiple signal transmission and reception processes between the tag and the base station when the tag is mounted on the aircraft, resulting in a slow positioning frequency and difficulty in achieving real-time positioning. However, with the hybrid algorithm, in the first time slot, the main base station first sends a UWB signal to the tag. Then, in the second time slot, the tag continuously broadcasts signals to all base stations. Other base stations receive this broadcast signal, thus obtaining the time difference between the base stations. During this process, due to the high speed of the carrier-based aircraft, the distance measured by the TOA algorithm has a certain distance deviation compared to the positioning equation formed by the distance difference measured by the TDOA algorithm in the second time slot. This distance deviation can be compensated by calculating the time difference between the tag's signal transmission and reception in the first and second time slots, thereby obtaining a more accurate positioning result and achieving real-time positioning.

[0239] Specifically, assume the coordinates of the target to be measured are u = (x, y). T The entire positioning system has M positioning anchor nodes, whose coordinates are a, b, c, and d. i =(x i ,y i ) T .

[0240] The distance from anchor node i to the target to be measured can be expressed as:

[0241]

[0242] Furthermore, by subtracting the obtained distances from the distance from anchor node 1 to the target, we can obtain:

[0243] r i,1 =r1-r i

[0244] Furthermore, assume that the positioning error of TDOA is e i,1 Then the TDOA measurement values ​​from the remaining anchor nodes to anchor node 1 can be expressed as:

[0245] ct i,1 =d i,1 =r i,1 +e i,1

[0246] Among them, t i,1 This represents the TDOA measurement value, where c is the speed of light and d is the speed of light. i,1 r represents the distance measured by TDOA. i,1 It is the actual distance difference, e i,1 The distance error is caused by TDOA measurement error.

[0247] Specifically, the TOA measurement value from anchor node 1 to the label can be expressed as:

[0248] ct1 = d1 = r1 + e1

[0249] Where t1 is the TOA measurement value, d1 is the distance value measured by TOA, r1 is the actual distance from anchor node 1 to the label, and e1 is the distance error caused by the TOA measurement error.

[0250] More specifically:

[0251]

[0252] More specifically, let x i,1 =(x1-x i ), y i,1 =(y1-y i ), Then we can get:

[0253]

[0254] in:

[0255]

[0256] Where, vector This can be viewed as the deviation between h and Gw, from which we can deduce:

[0257]

[0258] Furthermore, based on the previous analysis of the deviation, we know that:

[0259]

[0260] Among them, due to r i >>e i Therefore, the quadratic term can be ignored. To minimize... The hybrid positioning problem can then be equivalent to the following quadratic programming problem:

[0261]

[0262] Specifically, TDOA and TOA ranging errors can be considered as independent, identically distributed zero-mean Poisson distributions. Let k represent the deviation ratio between the TOA and TDOA positioning errors. The positioning errors then have the following statistical characteristics:

[0263] E(e i ) = 0

[0264] Cov(e i e j ) = 0

[0265]

[0266]

[0267] More specifically, the error can be seen from the above formula. Since it does not comply with the Gauss-Markov assumption, it is impossible to directly obtain an unbiased estimate with the minimum variance. Therefore, we consider using the least squares method to solve it.

[0268] Furthermore, making The smallest expression for w is:

[0269] w = (G T WG) -1 G T Wh

[0270] Among them, W can be derived from The covariance matrix is ​​obtained as follows:

[0271]

[0272] in, yes The reciprocal of the covariance matrix, k, can be obtained from the prior probability statistics of the system ranging error.

[0273] Furthermore, scaling W does not affect the result of the least squares method, so W can be rewritten as:

[0274]

[0275] In one possible embodiment, Figure 23 This is a schematic diagram of the error distribution of the hybrid positioning algorithm provided in the embodiments of this application, as shown below. Figure 23 As shown, only the deviations between TOA and TDOA positioning need to be known beforehand, and the least squares method can be used to solve the problem. Simulating the hybrid positioning algorithm yields the error distribution of the hybrid positioning algorithm.

[0276] Specifically, the error distribution pattern of the hybrid positioning algorithm is similar to that of the TOA algorithm, with a slightly lower error value, but a significantly improved positioning accuracy compared to TDOA. Given a fixed noise standard deviation, the greater the distance between the carrier-based aircraft and the ship, the larger the positioning error of the hybrid algorithm. However, compared to the TDOA positioning algorithm, its positioning error is much smaller, greatly enhancing the feasibility of the method.

[0277] S502. The least squares method is used to integrate the time-of-arrival ranging algorithm and the time difference of arrival ranging algorithm to obtain a hybrid target positioning method.

[0278] In this embodiment, based on the hybrid positioning method, a time-of-arrival (TOA) ranging algorithm for the main base station ranging and a time difference of arrival (TDOA) ranging algorithm for the non-main base station ranging are configured. The least squares method is then used to fuse the TOA and TDOA algorithms to obtain a hybrid target positioning method, which improves positioning accuracy, enhances real-time performance, and thus improves shipboard positioning efficiency.

[0279] Figure 6 A schematic flowchart of a shipboard positioning method provided in this application embodiment. Figure 4 ,like Figure 6 As shown, the specific implementation steps of S203 above include:

[0280] S601. Obtain the station layout structure to be selected, the station layout distance to be selected, and the number of stations to be selected; wherein, the station layout structure to be selected includes rectangular station layout structure, rhomboid station layout structure, and parallelogram station layout structure.

[0281] S602. Based on the candidate station structure, candidate station distance, and candidate station quantity, a target hybrid positioning method is used for simulation to obtain the station deployment simulation results.

[0282] Specifically, the positioning error distribution of the TDOA algorithm is not uniform. The error is relatively large in the ideal landing trajectory direction, potentially leading to positioning blind spots. Therefore, it is necessary to study the positioning error distribution of the hybrid algorithm. Thus, simulations were performed on the positioning error distribution of the hybrid algorithm in different directions under different station deployment methods (without considering the deployment area of ​​large ships). The resulting errors were calculated for rectangular, rhomboid, and parallelogram-shaped deployments of the hybrid algorithm.

[0283] In one possible embodiment, Figure 24 This is a schematic diagram of the error distribution of the rectangular station deployment using the hybrid algorithm provided in this application embodiment.

[0284] In one possible embodiment, Figure 25 This is a schematic diagram of the error distribution of the hybrid algorithm diamond-shaped station layout provided in the embodiments of this application.

[0285] In one possible embodiment, Figure 26 This is a schematic diagram of the parallelogram-shaped station layout error distribution provided in the embodiments of this application.

[0286] Among them, the improved hybrid algorithm needle has a more uniform distribution of positioning error in each direction under different deployment methods, and there is no positioning blind spot.

[0287] Furthermore, the impact of different station deployment distances on positioning error during hybrid algorithm deployment is simulated.

[0288] In one possible embodiment, Figure 10 This is a schematic diagram illustrating the impact of different station deployment distances on positioning accuracy, as provided in the embodiments of this application. Figure 10 As shown, from left to right, the simulation results of the impact of base station deployment on positioning accuracy are 50m×50m, 100m×50m, 200m×25m, 200m×50m, 400m×25m, and 400m×50m. It can be seen that when the coverage area of ​​the ultra-wideband base station is as large as possible, the positioning accuracy of the hybrid positioning algorithm will be higher. Therefore, when deploying base stations, try to place them in various corners of the deck to maximize the coverage area.

[0289] Furthermore, the variation of the hybrid positioning algorithm error with the number of nodes was simulated.

[0290] In one possible embodiment, Figure 11 This is a schematic diagram illustrating the distribution of hybrid positioning error with the number of nodes provided in an embodiment of this application, as shown below. Figure 11 As shown, the positioning error of the hybrid algorithm increases with distance. At a fixed distance, the positioning error decreases as the number of anchor nodes increases. When the number of anchor nodes increases from 4 to 6, the error decreases rapidly. However, when the number of anchor nodes reaches 6, the positioning error of the hybrid algorithm does not increase significantly with the number of nodes.

[0291] It should be noted that by fitting a function to the variation of the hybrid positioning algorithm error with the number of nodes, several fitting function graphs under different noise standard deviations can be obtained:

[0292] In one possible embodiment, Figure 27 A schematic diagram of the fitting function provided for an embodiment of this application when the noise standard deviation is 0.01.

[0293] In one possible embodiment, Figure 28 A schematic diagram of the fitting function when the noise standard deviation is 0.03, provided for an embodiment of this application.

[0294] In one possible embodiment, Figure 29 A schematic diagram of the fitting function when the noise standard deviation is 0.05, provided for an embodiment of this application.

[0295] like Figure 27 , Figure 28 as well as Figure 29 As shown, fitting the base station distribution characteristics reveals that it follows a Gaussian distribution. In reality, the standard deviation of the noise is approximately 0.03. Based on the simulation results, the proposed hybrid positioning algorithm can be considered to provide uniform positioning in all directions, eliminate blind spots, and exhibit very small positioning errors with a linear growth rate.

[0296] S603. Based on the site deployment simulation results, determine the preliminary site deployment plan.

[0297] In this embodiment, a target hybrid positioning method is used for simulation based on the candidate station structure, candidate station distance, and candidate station quantity to obtain station deployment simulation results. Based on these results, a preliminary station deployment plan is determined. This improves positioning accuracy, and by adjusting the station distance and quantity, it can flexibly adapt to positioning needs of different scales, enhancing system flexibility and thus improving shipboard positioning efficiency.

[0298] Figure 7 This is a schematic diagram of the structure of a shipboard positioning device provided in an embodiment of this application, as shown below. Figure 7 As shown, the device includes: a first acquisition module 71, a second acquisition module 72, a first determination module 73, a third acquisition module 74, a generation module 75, and a second determination module 76.

[0299] The first acquisition module 71 is used to acquire the target signal detection method, wherein the target signal detection method is determined based on the channel impulse response peak detection algorithm;

[0300] The second acquisition module 72 is used to acquire the target hybrid positioning method; wherein, the target hybrid positioning method is determined based on the time of arrival ranging algorithm and the time difference of arrival ranging algorithm;

[0301] The first determining module 73 is used to determine a preliminary site deployment plan based on the target hybrid positioning method;

[0302] The third acquisition module 74 is used to acquire the geometric structure information of the target ship;

[0303] The generation module 75 is used to generate a target deployment scheme for the target ship based on the geometric structure information and the preliminary deployment scheme.

[0304] The second determining module 76 is used to determine the target positioning scheme for the target ship based on the target signal detection method, the target hybrid positioning method, and the target deployment scheme.

[0305] In one possible design, based on the target signal detection method, the target hybrid positioning method, and the target deployment scheme, a target positioning scheme for the target ship is determined, including:

[0306] The second determining module 76 is also used to determine a preliminary positioning scheme based on the target signal detection method, the target hybrid positioning method, and the target deployment scheme;

[0307] Based on the preliminary positioning plan, a positioning simulation was conducted on the target ship.

[0308] Based on the positioning simulation results, an optimized target scheme is generated;

[0309] Based on the target optimization scheme, the preliminary positioning scheme is optimized to obtain the target positioning scheme.

[0310] In one possible design, based on the positioning simulation results, a target optimization scheme is generated, including:

[0311] The second determining module 76 is further configured to determine the anchor node configuration deployment error data based on the positioning simulation results, and generate an anchor node configuration deployment optimization scheme based on the anchor node configuration deployment error data; and / or,

[0312] Based on the positioning simulation results, the clock offset error data is determined, and a clock offset optimization scheme is generated based on the clock offset error; and / or,

[0313] Based on the positioning simulation results, the error data of the number of anchor nodes is determined, and an optimization scheme for the number of anchor nodes is generated based on the error data of the number of anchor nodes.

[0314] In one possible design, prior to acquiring the target signal detection method, the following is also included:

[0315] Set up a vector network analyzer for connecting ultra-wideband base stations and attaching tags;

[0316] The transmission coefficients were detected using a vector network analyzer.

[0317] The calculation method for the channel impulse response is determined based on the transmission coefficient.

[0318] Determine an automatic multi-scale peak detection algorithm for peak detection;

[0319] The target signal detection method is determined based on the calculation method of channel impulse response and the automatic multi-scale peak detection algorithm.

[0320] In one possible design, prior to obtaining the target hybrid localization method, the following is also included:

[0321] Configure the time-of-arrival ranging algorithm for primary base station ranging and the time difference of arrival ranging algorithm for non-primary base station ranging;

[0322] The least squares method is used to fuse the time-of-arrival ranging algorithm and the time difference of arrival ranging algorithm to obtain a hybrid target positioning method.

[0323] In one possible design, the preliminary deployment plan includes the deployment distance, deployment structure, and number of stations.

[0324] In one possible design, based on the target hybrid positioning method, a preliminary site deployment plan is determined, including:

[0325] The first determining module 73 is also used to obtain the site layout structure to be selected, the site layout distance to be selected, and the number of sites to be selected; wherein, the site layout structure to be selected includes a rectangular site layout structure, a rhombus site layout structure, and a parallelogram site layout structure;

[0326] Based on the candidate station structure, candidate station distance, and candidate station number, a target hybrid positioning method is used for simulation to obtain the station deployment simulation results.

[0327] Based on the simulation results, a preliminary station deployment plan was determined.

[0328] The ship landing positioning device provided in this embodiment can execute a ship landing positioning method of the above embodiment. Its implementation principle and technical effect are similar, and will not be described again here.

[0329] In a specific implementation of the aforementioned ship landing and positioning method, each module can be implemented as a processor. The processor can execute computer execution instructions stored in the memory, thereby enabling the processor to execute the aforementioned ship landing and positioning method.

[0330] Figure 8 This is a schematic diagram of the structure of a shipboard positioning method and equipment device provided in an embodiment of this application. Figure 8 As shown, the ship landing and positioning method device 80 includes at least one processor 81 and a memory 82. The ship landing and positioning method device 80 also includes a communication component 83. The processor 81, memory 82, and communication component 83 are connected via a second bus 84.

[0331] In the specific implementation process, at least one processor 81 executes computer execution instructions stored in memory 82, causing at least one processor 81 to execute a ship landing positioning method as described above on the ship landing positioning method equipment side.

[0332] The specific implementation process of processor 81 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0333] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0334] The memory may include high-speed RAM, and may also include non-volatile storage (NVM), such as at least one disk storage.

[0335] The second bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0336] The above description of the functions implemented by the ship landing positioning method device and the main control device has introduced the solution provided by the embodiments of the present invention. It is understood that, in order to achieve the above functions, the ship landing positioning method device or the main control device includes hardware structures and / or software modules corresponding to the execution of each function. By combining the units and algorithm steps of the various examples described in the embodiments of the present invention, the embodiments of the present invention can be implemented in hardware or a combination of hardware and computer software. Whether a certain function is executed by hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the technical solution of the embodiments of the present invention.

[0337] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the above-described ship landing and positioning method.

[0338] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0339] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Alternatively, the readable storage medium can be an integral part of the processor. Both the processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the shipboard positioning method device or the main control device.

[0340] This application also provides a computer program product, which includes: a computer program stored in a readable storage medium, at least one processor of the ship landing and positioning method device can read the computer program from the readable storage medium, and the at least one processor executes the computer program to cause the ship landing and positioning method device to perform the solution provided in any of the above embodiments.

[0341] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disk, or optical disk.

[0342] The technical solutions of this application have been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it is readily understood by those skilled in the art that the scope of protection of this application is obviously not limited to these specific embodiments. The above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A shipboard positioning method, characterized in that, include: The target signal detection method is obtained, wherein the target signal detection method is determined based on the channel impulse response peak detection algorithm; Obtain the target hybrid positioning method; wherein, the target hybrid positioning method is determined based on the time-of-arrival ranging algorithm and the time difference of arrival ranging algorithm; Based on the target hybrid positioning method, a preliminary site deployment plan is determined; Obtain the geometric structure information of the target ship; Based on the geometric structure information and the preliminary deployment plan, a target deployment plan is generated for the target ship. Based on the target signal detection method, the target hybrid positioning method, and the target deployment scheme, a target positioning scheme for the target ship is determined.

2. The method according to claim 1, characterized in that, The step of determining a target positioning scheme for the target ship based on the target signal detection method, the target hybrid positioning method, and the target deployment scheme includes: Based on the target signal detection method, the target hybrid positioning method, and the target deployment scheme, a preliminary positioning scheme is determined; Based on the preliminary positioning scheme, a positioning simulation was performed on the target ship; Based on the positioning simulation results, an optimized target scheme is generated; Based on the target optimization scheme, the preliminary positioning scheme is optimized to obtain the target positioning scheme.

3. The method according to claim 2, characterized in that, The step of generating a target optimization scheme based on the positioning simulation results includes: Based on the positioning simulation results, the anchor node configuration deployment error data is determined, and an optimized anchor node configuration deployment scheme is generated based on the anchor node configuration deployment error data; and / or, Based on the positioning simulation results, clock offset error data is determined, and a clock offset optimization scheme is generated based on the clock offset error; and / or, Based on the positioning simulation results, the error data of the number of anchor nodes is determined, and an optimization scheme for the number of anchor nodes is generated based on the error data of the number of anchor nodes.

4. The method according to any one of claims 1 to 3, characterized in that, Prior to the method for acquiring the target signal detection, the method further includes: Set up a vector network analyzer for connecting ultra-wideband base stations and attaching tags; The transmission coefficients are detected using the vector network analyzer. Based on the transmission coefficients, determine the calculation method for the channel impulse response; Determine an automatic multi-scale peak detection algorithm for peak detection; The target signal detection method is determined based on the channel impulse response calculation method and the automatic multi-scale peak detection algorithm.

5. The method according to any one of claims 1 to 3, characterized in that, Before obtaining the target hybrid positioning method, the method further includes: Configure the time-of-arrival ranging algorithm for primary base station ranging and the time difference of arrival ranging algorithm for non-primary base station ranging; The least squares method is used to fuse the time-of-arrival ranging algorithm and the time difference of arrival ranging algorithm to obtain a hybrid target positioning method.

6. The method according to any one of claims 1 to 3, characterized in that, The preliminary station deployment plan includes station distance, station structure, and number of stations.

7. The method according to claim 6, characterized in that, The step of determining the preliminary site deployment plan based on the target hybrid positioning method includes: Obtain the candidate station layout structure, candidate station distance, and candidate station quantity; wherein, the candidate station layout structure includes rectangular station layout structure, rhomboid station layout structure, and parallelogram station layout structure; Based on the candidate station layout structure, candidate station distance, and candidate station number, the target hybrid positioning method is used for simulation to obtain the station layout simulation results. Based on the simulation results, a preliminary deployment plan is determined.

8. A shipboard positioning device, characterized in that, include: The first acquisition module is used to acquire the target signal detection method, wherein the target signal detection method is determined based on the channel impulse response peak detection algorithm; The second acquisition module is used to acquire the target hybrid positioning method; wherein, the target hybrid positioning method is determined based on the time-of-arrival ranging algorithm and the time difference of arrival ranging algorithm; The first determining module is used to determine a preliminary site deployment plan based on the target hybrid positioning method; The third acquisition module is used to acquire the geometric structure information of the target ship; The generation module is used to generate a target deployment scheme for the target ship based on the geometric structure information and the preliminary deployment scheme. The second determining module is used to determine a target positioning scheme for the target ship based on the target signal detection method, the target hybrid positioning method, and the target deployment scheme.

9. A shipboard positioning method and equipment, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-7.

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