A sea personnel search and rescue positioning method based on integrated navigation and beidou communication

Abstract

The application discloses a kind of based on combination navigation and beidou communication's offshore personnel search and rescue positioning method, comprising: step 1, life jacket user terminal receives GNSS signal, carries out the double target satellite selection of geometric precision factor and the number of satellite selection, obtains satellite combination after satellite selection;The life jacket user terminal is located in the life jacket worn by the person who falls into water;Step 2, with satellite combination after satellite selection, the position of the person who falls into water is solved, and GNSS position information is obtained;Step 3, the position information of the person who falls into water is obtained by drift prediction, and the GNSS position information and the position information of the person who falls into water drift prediction are used for combination navigation, and the final position information of the person who falls into water is obtained.The method uses double target satellite selection based on beluga whale optimization algorithm, and uses the optimal satellite combination to carry out fast and accurate GNSS positioning, in view of the problems that GNSS and offshore drift prediction exist, based on EKF, GNSS / Offshore drift prediction combination navigation is carried out, and the continuous and reliable positioning of the person who falls into water is realized.

Description

Technical Field

[0001] This invention belongs to the technical field of satellite positioning and navigation, and particularly relates to a maritime personnel search and rescue positioning method based on integrated navigation and Beidou communication. Background Technology

[0002] With the rapid development of maritime transport and the increasing number of ships and personnel at sea, maritime accidents are becoming more frequent due to the complex and changeable marine environment, making maritime search and rescue a critical issue. The BeiDou Navigation Satellite System (BDS) provides satellite-based two-way digital message communication services. User terminals in life jackets can automatically send alarms to the ground control center, including the current real-time location. The success rate of search and rescue largely depends on the accurate location and search efficiency and speed. GNSS is rapidly developing and widely used in marine surveying and navigation. However, an excessive number of GNSS satellites can increase positioning calculation time. In maritime search and rescue applications, while meeting the required positioning accuracy, a combination of satellites with good geometric layout can be selected for positioning calculation. GNSS, due to issues such as multipath propagation at sea and positioning malfunctions, may cause inaccurate or interrupted real-time positioning of people in the water.

[0003] Traditional satellite selection algorithms typically employ ergonomic methods to choose satellite combinations with a fixed number of selected satellites, primarily using the minimum GDOP method and the maximum volume method. Traditional ergonomic methods with a fixed number of selected satellites are computationally intensive, involving complex matrix multiplication and inversion operations. This high computational complexity and slow speed make them unsuitable for emergency situations in maritime search and rescue, and they cannot provide real-time alarm positions. To reduce the time required for satellite selection, some researchers have proposed introducing intelligent optimization algorithms into satellite selection strategies to reduce GDOP computation and achieve faster selection. Mosavi and Divband proposed using an evolutionary algorithm (EA) to improve GDOP computational efficiency, but this introduced unnecessary errors, raising accuracy as a new concern. With the increasing number of available satellites in multi-constellation GNSS systems, a fixed number of selected satellites has significant limitations on the selection results. Satellite selection algorithms are gradually evolving into multi-objective optimization problems. GDOP and the number of selected satellites are used as dual objectives in satellite selection model construction. Qiu Ming et al. used the Empire competition optimization algorithm, while Xu Xiaojun et al. used the NSGA-II algorithm to solve the dual-objective satellite selection strategy problem, with the number of satellites and GDOP as objectives, which improves the flexibility of satellite selection compared to fixing the number of satellites. To improve the performance of optimization algorithms, polarization feedback mechanisms and perturbation factors have been introduced, and some adaptive update strategies have also been incorporated into algorithm improvements. However, these algorithms require adjusting many parameters and are prone to getting trapped in local optima, resulting in slow convergence speeds.

[0004] Geng Jiaying et al. proposed a model for predicting the drift location of people who have fallen into the water. This method recursively predicts the drift from one location to the next, providing auxiliary decision-making for personnel search and rescue. However, this method based on recursive integration is prone to error accumulation. As the search and rescue time accumulates, the error in drift prediction will become larger and larger.

[0005] In summary, the existing technology has the following problems:

[0006] a) Current GNSS positioning for maritime search and rescue targets is difficult to balance computational efficiency and positioning accuracy. Existing satellite selection strategies require adjustment of many parameters and are prone to getting stuck in local optima. The convergence speed is not fast enough, and it is difficult to achieve a balance between global search and local optimization, making it difficult to achieve rapid real-time location alerts for people who have fallen into the water during maritime search and rescue.

[0007] b) Currently, GNSS positioning of people who fall into the sea suffers from insufficient positioning accuracy due to multipath interference at sea, as well as positioning discontinuity, while sea drift prediction suffers from error accumulation.

[0008] Terminology Explanation:

[0009] GNSS (Global Navigation Satellite System)

[0010] GPS (Global Positioning System)

[0011] Beidou Navigation Satellite System (BDS)

[0012] GDOP (Geometric Dilution of Precision)

[0013] Beluga Whale Optimization (BWO) algorithm

[0014] EKF Extended Kalman Filter Summary of the Invention

[0015] Purpose of the invention: The technical problem to be solved by the present invention is to provide a maritime personnel search and rescue positioning method based on integrated navigation and Beidou communication, which addresses the shortcomings of the existing technology.

[0016] To address the aforementioned technical problems, this invention discloses a maritime personnel search and rescue positioning method based on integrated navigation and BeiDou communication, comprising:

[0017] Step 1: The life jacket user terminal receives GNSS signals, performs dual-target satellite selection based on geometric precision factor and number of selected satellites, and obtains the selected satellite combination; the life jacket user terminal is located inside the life jacket worn by the person who fell into the water.

[0018] Step 2: Use the selected satellite combination to calculate the location of the person who fell into the water and obtain GNSS location information;

[0019] Step 3: Obtain the location information of the person who fell into the water through drift prediction, and use the GNSS location information and the drift prediction location information of the person who fell into the water for combined navigation to obtain the final location information of the person who fell into the water.

[0020] Furthermore, step 1 employs a dual-target star selection method based on the Beluga optimization algorithm, considering both the geometric precision factor and the number of selected stars, including:

[0021] Step 1.1: Extract and encode all visible satellites, and initialize the parameters of the beluga optimization algorithm, which include the number of beluga satellites N and the maximum number of iterations T;

[0022] Step 1.2: Initialize the population, calculate the fitness value, and obtain the beluga whale individual with the best fitness value;

[0023] Step 1.3, calculate the balance factor;

[0024] Step 1.4: Select the exploration phase and the development phase;

[0025] Step 1.5, select the whale fall stage;

[0026] Step 1.6: Recalculate the fitness and optimal solution;

[0027] Step 1.7: Determine whether the iteration is complete. After the iteration is complete, output the best individual. The selected satellites corresponding to the best individual form the satellite combination after the selection.

[0028] Furthermore, the population initialization in step 1.2 includes:

[0029] Number all visible satellites sequentially from 1, 2, ..., S, with each satellite corresponding to a number, for a total of S satellites; generate an initial population, where each beluga whale is a candidate solution for a satellite combination; randomly select m satellites from the S visible satellites to combine them, generating N different beluga whales to form a population, where m∈[5,S].

[0030] The position of the i-th beluga whale individual X i :

[0031] X i =[x1,x2,...,x j ,...,x S ]

[0032] x j =0 or 1, j = 1, 2, ..., S

[0033]

[0034] In the formula, X i Let m be the initial position of the i-th beluga whale, 1≤i≤N, m be the number of satellites selected, and x be the initial position of the beluga whale. j =0 indicates that the j-th satellite was not selected, x j =1 indicates that the j-th satellite is selected. Initially, the X value of each beluga whale is... i Randomly generated;

[0035] Step 1.2 calculates the fitness value to obtain the beluga whale individual with the optimal fitness value. This includes a bi-objective joint decision fitness function composed of the number of star selections and GDOP. The fitness value JA for each beluga whale individual is:

[0036]

[0037] In the formula, w1 and w2 are the weights corresponding to GDOP and the number of selected satellites, respectively, representing the weight allocation of the two optimization objectives considered in the satellite selection, w1+w2=1; the GDOP value of each beluga whale individual is calculated based on the selected satellite combination; min1 and max1 are the minimum and maximum GDOP values ​​of N beluga whale individuals, respectively; min2 and max2 are the minimum and maximum number of selected satellites of N beluga whale individuals, respectively.

[0038] Sort the fitness values ​​JA of N beluga whale individuals, and the beluga whale individual with the smallest fitness value is the optimal beluga whale individual. The position corresponding to the optimal beluga whale individual is the optimal position in the beluga whale population.

[0039] Furthermore, let B be the balance factor in step 1.3. f In step 1.4, when B f When B > 0.5, select the exploration phase and update the location of all beluga whale individuals during the exploration phase; when B f When the value is ≤0.5, select the development phase and update the location of all individual beluga whales during the development phase:

[0040]

[0041] In the above formula, r is the position of the i-th beluga whale updated in the (t+1)th iteration, 1≤i≤N, 1≤t≤T; r3 and r4 are random numbers between (0,1); and These are the positions of the i-th beluga whale and the random beluga whale at the t-th iteration, respectively. Let C1 be the optimal position in the beluga whale population at the t-th iteration; C1 represents the intensity of random jumps during Levy's flight; F is the Levy flight function.

[0042] In step 1.5, the whale's falling behavior is simulated in each iteration, and the probability of the whale falling is denoted as W. f If B f <W f Then the whale fall phase begins, during which the positions of all individual beluga whales are updated;

[0043] Step 1.6 includes: If B f <W f After updating the whale fall location, recalculate the fitness value and select a new best individual; if B f ≥W f If the fitness value is not found, the best individual will be selected again.

[0044] Step 1.7 includes: if t < T, repeat steps 1.3 to 1.6; if t = T, the maximum number of iterations is reached, the iteration is completed, and the best individual and the optimal fitness value are output. The selected satellites corresponding to the best individual form the satellite combination after the selection.

[0045] Further, step 2 includes using the selected satellite combination to calculate the location of the person who fell into the water based on pseudorange single-point positioning, obtaining GNSS position information, and denoting the GNSS position information obtained at time k as P. g (k).

[0046] Furthermore, step 3 includes:

[0047] Step 3.1: Obtain the location information of the person who fell into the water through drift prediction;

[0048] Step 3.2: Based on extended Kalman filtering, perform fusion navigation of the GNSS position information and the predicted drift position information of the person who fell into the water to obtain the error correction value of the position information of the person who fell into the water;

[0049] Step 3.3: Correct the predicted drift position information of the person who fell into the water based on the error correction value to obtain the final position information of the person who fell into the water.

[0050] Furthermore, step 3.1 includes the location information P of the person who fell into the water. f (k) is as follows:

[0051]

[0052] In the formula, P0 is the initial position obtained by GNSS at the first moment after the person falls into the water; P f(k) represents the position of the person who fell into the water at time k, predicted by drift; V1(k) and V2(k) are the surface velocity and wind-induced drift velocity at time k, respectively.

[0053] Further, step 3.2 includes: establishing a first-order Markov process model, where the state vector can be represented as:

[0054] Y k =[δp δv δφ] T

[0055] In the formula, δp, δv and δφ represent the position, velocity and attitude error vectors in the navigation system, respectively;

[0056] The observation vector is:

[0057] Z k =[P f (k)-P g (k)]

[0058] The extended Kalman filter consists of two parts: prediction and update. The steps are as follows:

[0059] Prediction phase:

[0060]

[0061] In the formula, This is the estimated state vector value for epoch k-1; Φ is the one-step prediction value of the state vector at epoch k; k,k-1 P is the state transition matrix from epoch k-1 to k. k-1 P is the error covariance matrix of the filtered estimate of the state vector at epoch k-1; k,k-1 Q is the one-step prediction of the error covariance matrix of the filtered estimate of the state vector at epoch k; k Let be the covariance matrix of the process noise;

[0062] Update phase:

[0063]

[0064] In the formula, The filtered estimate of the state vector for epoch k includes error correction values ​​for the location information of the person who fell into the water; P k Let K be the error covariance matrix of the filtered estimate of the state vector at epoch k; k R is the gain matrix; k To measure the covariance matrix of the noise; H k This is the observation matrix used in filtering.

[0065] Furthermore, it also includes step 4, which involves sending the final location information of the person who fell into the water to the ground control center in real time via the BeiDou short message communication link.

[0066] Furthermore, the GDOP value in step 1.2 is calculated as follows:

[0067] GNSS positioning uses a dual-constellation navigation system of Global Positioning System (GPS) and BDS. The system observation matrix is ​​represented as follows:

[0068]

[0069] In the above formula, the subscripts GPS and BDS represent the GPS and BDS systems, respectively; H GPS and H BDS These are the first three columns of the observation matrix under the corresponding satellite navigation system;

[0070]

[0071] Beneficial effects:

[0072] a) To address the issues of insufficient efficiency and accuracy in positioning and alarming for maritime search and rescue personnel, this invention employs a dual-target satellite selection based on the Beluga optimization algorithm and uses the optimal satellite combination for rapid and accurate GNSS positioning.

[0073] b) To address the shortcomings of both GNSS and sea drift prediction, GNSS / sea drift prediction combined navigation is performed based on EKF to achieve continuous and reliable positioning of people who have fallen into the water.

[0074] c) The real-time location information of the person who fell into the water is sent to the ground control center via Beidou short message communication. Attached Figure Description

[0075] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, and the advantages of the present invention in the above and / or other aspects will become clearer.

[0076] Figure 1 This is a schematic flowchart of a maritime personnel search and rescue positioning method based on integrated navigation and BeiDou communication, provided as an embodiment of this application. Detailed Implementation

[0077] The embodiments of the present invention will now be described with reference to the accompanying drawings.

[0078] This application discloses a maritime personnel search and rescue positioning method based on integrated navigation and BeiDou communication. When a person falls into the water, the life jacket user terminal receives Global Navigation Satellite System (GNSS) signals, performs a GNSS satellite selection algorithm based on the White Whale optimization algorithm, constructs a dual objective of geometric accuracy factor and satellite selection number as fitness values, selects the optimal set of satellites for positioning calculation, and performs integrated navigation based on Extended Kalman Filter (EKF) with the maritime personnel drift prediction positioning results. Finally, the location and alarm rescue information are sent to the ground control center via the BeiDou short message communication link.

[0079] Figure 1 The flowchart of an embodiment of the present invention is as follows:

[0080] Step 1: The life jacket user terminal receives GNSS signals, performs dual-target satellite selection based on geometric precision factor and number of selected satellites, and obtains the selected satellite combination; the life jacket user terminal is located inside the life jacket worn by the person who fell into the water.

[0081] First, the user terminal on the life jacket begins real-time positioning after falling into the water. To quickly and accurately select a satellite combination with a good ensemble structure from all visible stars from multiple satellite navigation constellations, this embodiment of the invention designs a dual-target satellite selection algorithm based on the Whale Optimization Algorithm, and uses the optimized satellite combination for positioning. The specific process of the dual-target satellite selection algorithm based on the Whale Optimization Algorithm is as follows:

[0082] Step 1.1: Extract and encode all visible satellites, and initialize the parameters of the beluga optimization algorithm. The parameters of the beluga optimization algorithm include: the number of beluga satellites is N=30, and the maximum number of iterations is T=200.

[0083] Step 1.2, initialize the population, including: number all visible satellites sequentially from 1, 2, ..., S, with each satellite corresponding to a number, for a total of S satellites; generate the initial population, where each beluga whale is a candidate solution for a satellite combination, randomly select m satellites from the S visible satellites to combine them, and generate N different beluga whales to form a population. Since GPS and BeiDou dual-system satellite positioning requires at least 5 satellites, m∈[5,S].

[0084] The position of the i-th beluga whale individual X i :

[0085] X i =[x1,x2,...,x j ,...,x S ]

[0086] x j=0 or 1, j = 1, 2, ..., S

[0087]

[0088] In the formula, X i Let m be the initial position of the i-th beluga whale, 1≤i≤N, m be the number of satellites selected, and x be the initial position of the beluga whale. j =0 indicates that the j-th satellite was not selected, x j =1 indicates that the j-th satellite is selected. Initially, the X value of each beluga whale is... i Randomly generated;

[0089] The fitness value is calculated, and the beluga whale individual with the best fitness value is determined through comparison. The calculation process for the fitness value JA is as follows:

[0090] GDOP is closely related to positioning accuracy; therefore, GDOP is selected as one of the targets for satellite selection. In this embodiment of the invention, GNSS positioning uses a dual constellation navigation system of Global Positioning System (GPS) and BDS. The system observation matrix is ​​represented as follows:

[0091]

[0092] In the formula, the subscripts GPS and BDS represent the GPS and BDS systems, respectively; H GPS and H BDS These are the first three columns of the observation matrix under the corresponding satellite navigation system.

[0093]

[0094] The number of selected satellites and GDOP form a bi-objective joint decision fitness function. The number of selected satellites is also related to positioning accuracy. A joint decision fitness value is designed, and the fitness value JA for each individual beluga whale is:

[0095]

[0096] In the formula, w1 and w2 are the weights corresponding to GDOP and the number of selected stars, respectively, representing the weight allocation of the two optimization objectives considered in star selection. w1 + w2 = 1, and in this embodiment, w1 = 0.5 and w2 = 0.5 are taken. The GDOP value of each beluga whale individual is calculated based on the number of selected stars. min1 and max1 are the minimum and maximum GDOP values ​​of N beluga whale individuals, respectively. min2 and max2 are the minimum and maximum number of selected stars of N beluga whale individuals, respectively.

[0097] The joint decision fitness values ​​(JA) of N beluga whale individuals are ranked, and the beluga whale individual with the smallest joint decision fitness value is the optimal beluga whale individual.

[0098] Step 1.3, Balance Factor Calculation

[0099] Balance factor B f Used to determine whether a population can transition from the exploration phase to the development phase:

[0100]

[0101] In the formula, B0 is a value that is randomly generated in each iteration, B0∈(0,1). As the number of iterations t gradually increases, B... f Gradually decrease.

[0102] Step 1.4, Choosing between the exploration and development phases

[0103] Based on the balance factor B f Judgment stage selection:

[0104]

[0105] In the formula, flag=1 represents the exploration phase, and flag=2 represents the development phase.

[0106] The beluga whale's location has been updated as follows during the exploration phase:

[0107]

[0108] In the formula, For the (t+1)th iteration, the position of the i-th individual in the j-th dimension. p and r are random integers, where p is a random integer in d dimensions, and d represents the dimension of the problem variable in the White Whale Optimization Algorithm, initialized to d = 2, p ∈ [1, d], r ∈ [1, N]; Let represent the position of the i-th individual in the random dimension p at the t-th iteration. Let r represent the position of the random individual r on the random dimension p at the t-th iteration; r1 and r2 are random numbers in the interval (0,1), which are random operators in the enhanced exploration phase; sin(2πr2) and cos(2πr2) represent the mirrored beluga whale fins facing the water surface, reflecting the synchronous or mirrored swimming of the beluga whale.

[0109] During the development phase, beluga whales will share location information for hunting; therefore, the best individual will influence the movement and foraging of other individuals. Simultaneously, beluga whales will employ a levy flight strategy to capture prey, the mathematical model of which is as follows:

[0110]

[0111]

[0112] In the formula, r is the position of the i-th beluga whale updated in iteration t+1; r3 and r4 are random numbers between (0,1); and These are the positions of the i-th beluga whale and the random beluga whale at the t-th iteration, respectively. Let C1 be the optimal position in the beluga whale population at the t-th iteration; C1 represents the intensity of random jumps during Levy's flight; and F is the Levy flight function.

[0113]

[0114]

[0115] Γ(x) = (x-1)!

[0116] In the formula, u and v are normally distributed random numbers, u,v ~ N(0,1); β is a default constant with a value of 1.5.

[0117] Step 1.5, Selection of Whale Fall Stage

[0118] In each iteration, the whale's falling behavior is simulated, and the probability of the whale falling is W. f for:

[0119]

[0120] As the number of iterations increases, the probability of whale fall gradually decreases from 0.1 to 0.05.

[0121] If B f <W f Then, the whale fall phase begins. Assuming the population size remains constant, a whale fall descent step size is set, and a whale fall location update formula is established:

[0122]

[0123] In the formula, r5, r6, and r7 are all random numbers between (0,1). The drop step size is calculated as follows:

[0124]

[0125] C2 = 2W f ×N

[0126] In the formula, u b and l b C1 represents the upper and lower bounds of the beluga whale individual location variable; C2 is the step factor, which is related to the population size and the probability of whale fall.

[0127] Step 1.6, recalculate fitness and optimal individuals.

[0128] In the previous step, if Bf <W f If so, the whale fall phase begins. After updating the whale fall location, the fitness value is recalculated, and a new best individual is selected. If B f ≥W f If the fitness value is not found, the fitness value will be recalculated and the best individual will be selected again.

[0129] Step 1.7: After the iteration is completed, the best individual is output. The selected satellites corresponding to the best individual form the satellite combination after the selection.

[0130] If t < T, return to step 1.3 and repeat steps 1.3 to 1.6. If t = T, the maximum number of iterations has been reached, the iteration is complete, the iteration termination condition is met, and the best individual (optimal solution) and the optimal fitness value are output. The satellites selected by the best individual form the satellite combination after the selection.

[0131] Step 2: Use the selected satellite combination to calculate the location of the person who fell into the water and obtain GNSS location information;

[0132] Using the optimal satellite combination selected after satellite selection, the location of the person who fell into the water was calculated based on pseudorange single-point positioning. The GNSS positioning result obtained at time k is P. g (k).

[0133] Step 3: Obtain the location information of the person who fell into the water through drift prediction, and use the GNSS location information and the drift prediction location information of the person who fell into the water for combined navigation to obtain the final location information of the person who fell into the water.

[0134] GNSS-based positioning suffers from multipath issues at sea and situations where positioning is impossible. To improve the continuity and accuracy of positioning, this embodiment of the invention uses EKF for GNSS / drift prediction combined navigation.

[0135] Step 3.1: Obtain the location information of the person who fell into the water through drift prediction;

[0136] In the drift prediction model, the volume of a person falling into the water above the water surface is small, so the influence of waves on drift is ignored. The main factors affecting drift speed are the effects of wind and ocean currents.

[0137] V = V1 + V2

[0138] In the formula, V1 is the surface velocity of the water; V2 is the wind-induced drift velocity, which can be obtained from meteorological and oceanographic databases.

[0139] The equation of motion for the personnel is:

[0140]

[0141] In the formula, P0 is the initial position obtained by GNSS at the first moment after the person falls into the water; Pf (k) represents the position of the person who fell into the water at time k, predicted by drift; V1(k) and V2(k) represent the surface velocity and wind-induced drift velocity at time k, respectively.

[0142] Step 3.2: Based on extended Kalman filtering, perform fusion navigation of the GNSS position information and the predicted drift position information of the person who fell into the water to obtain the error correction value of the position information of the person who fell into the water;

[0143] Based on EKF, GNSS / drift prediction fusion navigation is performed. Navigation information from both systems is input into the EKF and loosely combined to obtain personnel state estimates. A first-order Markov process model is established, and the state vector can be represented as:

[0144] Y k =[δp δv δφ] T

[0145] In the formula, δp, δv and δφ represent the position, velocity and attitude error vectors in the navigation system, respectively.

[0146] The observation vector is:

[0147] Z k =[P f (k)-P g (k)]

[0148] Among them, P f (k) and P g (k) represents the drift prediction and the position information measured by GNSS, respectively.

[0149] The extended Kalman filter consists of two parts: prediction and update. The steps are as follows:

[0150] Prediction phase:

[0151]

[0152] In the formula, This is the estimated state vector value for epoch k-1; Φ is the one-step prediction value of the state vector at epoch k; k,k-1 P is the state transition matrix from epoch k-1 to k; k-1 P is the error covariance matrix of the filtered estimate of the state vector at epoch k-1; k,k-1 Q is the one-step prediction value of the error covariance matrix of the filtered estimate of the state vector at epoch k; k Let be the covariance matrix of the process noise.

[0153] Update phase:

[0154]

[0155] In the formula, The filtered estimate of the state vector for epoch k includes error correction values ​​for the location information of the person who fell into the water; P k Let K be the error covariance matrix of the filtered estimate of the state vector at epoch k; k R is the gain matrix; k To measure the covariance matrix of the noise; H k This is the observation matrix used in filtering.

[0156] Step 3.3: Correct the predicted drift position information of the person who fell into the water based on the error correction value to obtain the final position information of the person who fell into the water.

[0157] Step 4: The location is transmitted to the ground control center in real time via the BeiDou short message communication link. The ground control center is existing technology, and this embodiment of the invention does not limit its scope.

[0158] In its specific implementation, this application provides a computer storage medium and a corresponding data processing unit. The computer storage medium is capable of storing a computer program, which, when executed by the data processing unit, can run the invention's content regarding a maritime personnel search and rescue positioning method based on integrated navigation and BeiDou communication, as well as some or all of the steps in various embodiments. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0159] Those skilled in the art will clearly understand that the technical solutions in the embodiments of the present invention can be implemented using computer programs and their corresponding general-purpose hardware platforms. Based on this understanding, the technical solutions in the embodiments of the present invention, or the parts that contribute to the prior art, can be embodied in the form of computer programs, i.e., software products. These computer program software products can be stored in a storage medium and include several instructions to cause a device containing a data processing unit (which may be a personal computer, server, microcontroller, MUU, or network device, etc.) to execute the methods described in various embodiments or certain parts of the embodiments of the present invention.

[0160] This invention provides a maritime personnel search and rescue positioning method based on integrated navigation and BeiDou communication. Many methods and approaches exist for implementing this technical solution; the above description is merely a specific embodiment of this invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this invention, and these improvements and modifications should also be considered within the scope of protection of this invention. All components not explicitly stated in this embodiment can be implemented using existing technologies.

Claims

1. A maritime personnel search and rescue positioning method based on integrated navigation and BeiDou communication, characterized in that, include: Step 1: The life jacket user terminal receives GNSS signals, performs dual-target satellite selection based on geometric precision factor and number of selected satellites, and obtains the selected satellite combination; the life jacket user terminal is located inside the life jacket worn by the person who fell into the water. Step 2: Use the selected satellite combination to calculate the location of the person who fell into the water based on pseudorange single-point positioning to obtain GNSS location information; Step 3: Obtain the location information of the person who fell into the water through drift prediction; use the GNSS location information and the drift prediction location information of the person who fell into the water for combined navigation to obtain the final location information of the person who fell into the water; including: Step 3.1: Obtain the drift prediction position information of the person who fell into the water through drift prediction. The drift prediction position information is determined by the initial position obtained from the GNSS position information of the person who fell into the water at the first moment of falling into the water, the water surface flow velocity, and the wind-induced drift velocity. Step 3.2: Based on extended Kalman filtering, perform fusion navigation of the GNSS position information and the predicted drift position information of the person who fell into the water to obtain the error correction value of the position information of the person who fell into the water; Step 3.3: Correct the predicted drift position information of the person who fell into the water based on the error correction value to obtain the final position information of the person who fell into the water; Step 4: Send the final location information of the person who fell into the water to the ground control center in real time via the BeiDou short message communication link; Step 1 employs a dual-objective star selection method based on the Beluga optimization algorithm, considering both the geometric precision factor and the number of selected stars. This includes: Step 1.1: Extract and encode all visible satellites, and initialize the parameters of the beluga optimization algorithm, which include the number of beluga satellites N and the maximum number of iterations T; Step 1.2: Initialize the population, calculate the fitness value, and obtain the beluga whale individual with the best fitness value; Step 1.3, calculate the balance factor B. f ; Step 1.4, according to the balance factor B f Select either the exploration or development phase, and update the location of all individual beluga whales. Step 1.5: Simulate whale falling behavior in each iteration, and denote the probability of whale falling as W. f ,like Then the whale fall phase begins, during which the positions of all individual beluga whales are updated; Step 1.6, recalculate the fitness and optimal solution, if After updating the whale fall location, recalculate the fitness value and select a new best individual; if If the fitness value is not found, the best individual will be selected again. Step 1.7, if Repeat steps 1.3 to 1.6; if Once the maximum number of iterations is reached, the iteration is complete, and the best individual and the optimal fitness value are output. The selected satellites corresponding to the best individual form the satellite combination after the selection.

2. The maritime personnel search and rescue positioning method based on integrated navigation and BeiDou communication according to claim 1, characterized in that, Step 1.2, initializing the population, includes: numbering all visible satellites sequentially from 1, 2, ..., S, with each satellite corresponding to a number, for a total of S satellites; generating an initial population, where each beluga whale is a candidate solution for a satellite combination; randomly selecting m satellites from the S visible satellites for combination to generate N different beluga whales forming a population. The position X of the i-th beluga whale individual i : ,x j =0 or 1, j=1, 2, …S; ， In the formula, For the first The initial positions of the beluga whales are given, 1 ≤ i ≤ N. x is the number of satellites selected. j =0 indicates that the j-th satellite was not selected, x j =1 indicates that the j-th satellite is selected. Initially, X for each beluga whale... i Randomly generated.

3. The maritime personnel search and rescue positioning method based on integrated navigation and BeiDou communication according to claim 2, characterized in that, Step 1.2 calculates the fitness value to obtain the beluga whale individual with the optimal fitness value. This includes a bi-objective joint decision fitness function composed of the number of star selections and GDOP. The fitness value JA for each beluga whale individual is: ， In the formula, w1 and w2 are the weights corresponding to GDOP and the number of selected satellites, respectively, representing the weight allocation of the two optimization objectives considered in the satellite selection, w1+w2=1; the GDOP value of each beluga whale individual is calculated based on the selected satellite combination; min1 and max1 are the minimum and maximum GDOP values ​​of N beluga whale individuals, respectively; min2 and max2 are the minimum and maximum number of selected satellites of N beluga whale individuals, respectively. Sort the fitness values ​​JA of N beluga whale individuals, and the beluga whale individual with the smallest fitness value is the optimal beluga whale individual. The position corresponding to the optimal beluga whale individual is the optimal position in the beluga whale population.

4. A maritime personnel search and rescue positioning method based on integrated navigation and BeiDou communication according to claim 3, characterized in that, In step 1.4, when B f When B > 0.5, select the exploration phase and update the location of all beluga whale individuals during the exploration phase; when B f When the value is ≤0.5, select the development phase and update the location of all individual beluga whales during the development phase: ， In the above formula, r3 represents the position of the i-th beluga whale updated in iteration t+1, where 1 ≤ i ≤ N and 1 ≤ t ≤ T; r3 and r4 are respectively... Random numbers between; and These are the positions of the i-th beluga whale and the random beluga whale at the t-th iteration, respectively. Let C1 be the optimal position in the beluga whale population at the t-th iteration; C1 represents the intensity of random jumps during Levy's flight; and F is the Levy flight function.

5. A maritime personnel search and rescue positioning method based on integrated navigation and BeiDou communication according to claim 1, characterized in that, Step 3.1 includes the location information P of the person who fell into the water. f (k) is as follows: ， In the formula, P0 is the initial position obtained by GNSS at the first moment after the person falls into the water; P f (k) represents the position of the person who fell into the water at time k, predicted by drift; V1(k) and V2(k) are the surface velocity and wind-induced drift velocity at time k, respectively.

6. A maritime personnel search and rescue positioning method based on integrated navigation and BeiDou communication according to claim 5, characterized in that, Step 3.2 includes: establishing a first-order Markov process model, with the state vector represented as: ， In the formula, , and These represent the position, velocity, and attitude error vectors in the navigation system, respectively. The observation vector is: ， In the formula, P g (k) indicates that the GNSS position information was obtained at time k; The extended Kalman filter consists of two parts: prediction and update. The steps are as follows: Prediction phase: ， In the formula, This is the estimated state vector value for epoch k-1; This is the one-step prediction value of the state vector at epoch k; Let k be the state transition matrix from epoch k-1 to k. Let be the error covariance matrix of the filtered estimate of the state vector at epoch k-1; The error covariance matrix of the filtered estimate of the state vector at epoch k is the one-step predicted value. Let be the covariance matrix of the process noise; Update phase: ， In the formula, The filtered estimate of the state vector for epoch k includes the error correction value for the location information of the person who fell into the water; Let be the error covariance matrix of the filtered estimate of the state vector at epoch k; This is the gain matrix; To measure the covariance matrix of the noise; This is the observation matrix used in filtering.

7. A maritime personnel search and rescue positioning method based on integrated navigation and BeiDou communication according to claim 6, characterized in that, The GDOP value in step 1.2 is calculated as follows: GNSS positioning uses a dual-constellation navigation system of Global Positioning System (GPS) and BDS. The system observation matrix is ​​represented as follows: ， In the above formula, the subscripts GPS and BDS represent the GPS and BDS systems, respectively; H GPS and H BDS These are the first three columns of the observation matrix under the corresponding satellite navigation system; 。

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