Direct position estimation method based on ADBO algorithm

Abstract

The invention belongs to the technical field of satellite navigation, and particularly discloses a direct position estimation method based on an ADBO algorithm. The method comprises the steps of firstly obtaining satellite ephemeris information and auxiliary positioning information; constructing a direct position estimation cost function by combining a weighting model and an autocorrelation approximation scheme; taking the direct position estimation cost function as a fitness function of an ADBO algorithm, and continuously iteratively updating the population position by using the ADBO algorithm until an iteration termination condition is reached; and finally, calculating a navigation solution of the receiver according to the fitness value of the optimal individual of the population at the moment. According to the scheme provided by the invention, the balance between navigation precision and complexity is realized, and more efficient navigation performance is obtained.

Description

Technical Field

[0001] The present application belongs to the field of satellite navigation technology, and more specifically, to a direct position estimation method based on the ADBO (Adaptive DungBeetle Optimizer) algorithm. Background Art

[0002] The global navigation satellite system generally achieves positioning through a two-step approach (2sp): the signals of different satellites are processed separately to calculate synchronization parameters, and then these parameters are used to solve the position of the receiver, and then estimate the position, velocity and time (PVT) state of the receiver. However, in complex reception scenarios such as multipath and shielding, 2sp is easily affected by the environment, resulting in a significant decrease in positioning performance.

[0003] In recent years, the demand for high-precision positioning and navigation services in complex environments has continued to grow, and the limitations of traditional positioning technologies have become increasingly prominent. With the advancement of data transmission and computing power, the direct position estimation (DPE) method has gradually attracted people's interest. DPE is a very promising alternative to the global navigation satellite system. It jointly processes the signals of all visible satellites, models the received satellite signals as a function related only to the navigation state of the receiver, eliminates the estimation step of the intermediate parameters, and directly solves the problem in the navigation domain to obtain the maximum likelihood estimate of the PVT state of the receiver. This one-step estimation method has been proven to have higher positioning accuracy and robustness. However, due to the non-convexity, nonlinearity and huge feasible solution space of the DPE optimization problem, it is not suitable for solving using gradient optimization algorithms, so its positioning accuracy is highly dependent on the solution algorithm used. To overcome these limitations, many methods are dedicated to optimizing DPE solutions. However, the current DPE optimization solution methods either have insufficient positioning accuracy or are still too computationally intensive. Summary of the invention

[0004] In view of the above defects or improvement needs of the prior art, the present application provides a direct position estimation method based on the ADBO algorithm, which aims to solve the technical problem that the existing direct position estimation method is too complex in calculation.

[0005] To achieve the above objectives, in a first aspect, the present application provides a direct position estimation method based on the ADBO algorithm, comprising: Obtain satellite ephemeris information and auxiliary positioning information; The direct position estimation cost function is constructed by combining the weighted model and the autocorrelation approximation scheme; The direct position estimation cost function is used as the fitness function of the ADBO algorithm, and the population position is continuously updated by using the ADBO algorithm until an iteration termination condition is reached; The navigation solution of the receiver is calculated based on the fitness value of the best individual in the population at this time.

[0006] Preferably, the weighted model and the autocorrelation approximation scheme are combined to construct a direct position estimation cost function, specifically: Construct a basic direct position estimation model based on satellite ephemeris information; Optimizing the direct position estimation model using the autocorrelation approximation scheme; The satellite elevation angle and carrier-to-noise ratio weighted model is introduced to correct the direct position estimation model; Obtain the cost function for the direct position estimation model.

[0007] Preferably, the direct position estimation cost function is used as the fitness function of the ADBO algorithm, and the population position is continuously updated iteratively using the ADBO algorithm until the iteration termination condition is reached, specifically: Initialize ADBO parameters; The fitness landscape characteristics and ruggedness are calculated according to the fitness value of the current population; the population dynamic threshold, adaptive convergence factor and hybrid mutation strategy are calculated according to the fitness landscape characteristics and ruggedness; the population position is iteratively updated based on the population dynamic threshold, adaptive convergence factor and hybrid mutation update formula until the iteration termination condition is reached.

[0008] Preferably, the direct position estimation model is optimized using an autocorrelation approximation scheme, specifically:

[0009] in, is the state of the receiver position, velocity and time, , is the location of the receiver, is the speed of the receiver, and is the receiver clock offset and drift; represents the clock offset of the i-th satellite; is the pseudorange of the ith satellite, is the speed of light, is the chip period of the PRN code; Indicates transpose; symbol Indicates taking the absolute value; symbol ;symbol Indicates candidate parameters.

[0010] Preferably, the satellite elevation angle and carrier-to-noise ratio weighted model is introduced to correct the direct position estimation model. The corrected model for:

[0011] in, Indicates total visible satellites; represents the direct position estimation model under the i-th satellite optimized by the autocorrelation approximation scheme; represents the weight of the i-th satellite, , and represent the elevation angle and carrier-to-noise ratio of the i-th satellite respectively.

[0012] Preferably, the cost function of the direct position estimation model is obtained, specifically:

[0013] The above formula represents the direct position estimation model The independent variable at its maximum value , In range Among them, represents the search space provided by the auxiliary positioning information; is the state of the receiver position, velocity and time; symbol Indicates candidate parameters; symbol Indicates an estimated value.

[0014] Preferably, the fitness landscape characteristic quantity and ruggedness are calculated according to the fitness value of the current population, specifically: Fitness landscape characteristics for:

[0015] in, express and The covariance of for The standard deviation of for The standard deviation of , represents the fitness value of each individual in the population, , represents the distance between each individual in the population and the optimal individual; Ruggedness describes the complexity of terrain, as follows:

[0016]

[0017] get , where the parameter is a constant, the minimum value that makes the terrain flat The value is called the information stability measure and is expressed as express; When , it indicates that the variable The value decreases, When , it indicates that the variable The value increases, When , it indicates that the variable The value remains unchanged; based on The entropy measure of the frequency of different symbol combinations in is defined as:

[0018] in, is the symbol value exist The probability of consecutive occurrences in yes Neutron Block The number of Roughness is the average value of information entropy under multiple observation granularities, that is:

[0019] in, express The value set of includes 8 values, 7 of which are , .

[0020] Preferably, the population dynamic threshold, adaptive convergence factor and hybrid mutation strategy are calculated according to the fitness landscape characteristic quantity and ruggedness as follows: Population dynamics threshold for:

[0021] Adaptive Convergence Factor for:

[0022] The hybrid mutation strategy is:

[0023] in, is the maximum number of iterations; is the characteristic quantity of the fitness landscape; is the current iteration number; represents random numbers generated from a standard Gaussian distribution, represents random numbers generated from a standard Cauchy distribution; Represents the current local optimal position; For the ruggedness.

[0024] Preferably, the iterative update of the population position is performed based on the population dynamic threshold, the adaptive convergence factor and the hybrid mutation update formula, specifically: According to the hybrid mutation strategy, the current iterative global optimal solution is perturbed and used as the current global optimal solution for the next cycle. ; When rolling the ball, the position is updated as follows:

[0025] in, For position, in brackets is the number of iterations; Determine whether the dung beetle deviates from its original direction; is the defect factor; is a constant; represents the global worst position, Indicates sunlight; symbol Indicates taking the absolute value; When encountering an obstacle, the position is updated as:

[0026] in, is the deflection angle; When spawning the position is updated to:

[0027] in, is the location of the ovum, and is an independent random vector variable with the same dimension as the optimization problem; and For the lower and upper bounds of the spawning area:

[0028]

[0029] and Respectively represent the maximum and minimum values; Represents the current local optimal position; and To provide lower and upper bounds for the optimization problem; is the adaptive convergence factor; The spawning areas are:

[0030] When foraging, the position is updated as:

[0031] in, represents random numbers that follow a normal distribution, The range is Random variables within ; and Respectively represent the lower and upper bounds of the optimal foraging area:

[0032]

[0033] It indicates the global optimal position; When stealing, the position is updated to:

[0034] in, is a normally distributed random vector, is a constant.

[0035] In a second aspect, the present application provides an electronic device comprising: at least one memory for storing programs; and at least one processor for executing the programs stored in the memory. When the program stored in the memory is executed, the processor is used to execute the method described in the first aspect or any possible implementation of the first aspect.

[0036] In general, the above technical solutions conceived by this application have the following beneficial effects compared with the prior art: (1) The scheme proposed in this application combines low-complexity correlation calculation technology, optimizes the direct position estimation DPE model through the autocorrelation approximation scheme, calculates the correlation value of the candidate point, avoids intensive calculation of the correlation between the signal replica generated according to the candidate PVT and the received signal, and effectively reduces the computational cost and complexity required for DPE; at the same time, on this basis, this application introduces a weight model of satellite elevation angle and carrier-to-noise ratio, and performs weighted processing on satellite signals of different qualities, thereby ensuring the accuracy of the DPE cost function model.

[0037] (2) The solution proposed in this application proposes an ADBO algorithm to achieve rapid search of DPE navigation solutions. The ADBO algorithm adjusts the ratio of foraging and egg-laying dung beetles and the size of the spatial convergence factor according to the fitness landscape characteristics, and adjusts the global search and local search capabilities of the algorithm according to the current landscape characteristics of the population.

[0038] (3) The solution proposed in this application proposes an ADBO algorithm to achieve fast search of DPE problems. The ADBO algorithm adopts a hybrid mutation strategy that combines Cauchy mutation and Gaussian mutation, and adjusts the coefficient of the hybrid mutation according to the ruggedness, further improving the algorithm's global search capability and ability to escape from local optimality.

[0039] (4) The proposed scheme in this application performs well in simulation experiments under different satellite geometric conditions. The low-complexity direct position estimation method based on the ADBO algorithm is superior to the modified range gate method (MRG) method in terms of accuracy, stability and running time. On the one hand, the ADBO algorithm focuses on searching areas with greater possibilities and avoids unnecessary searches, which significantly reduces the computational cost. On the other hand, the ADBO algorithm controls the number of evaluation points by adjusting the number of individuals and the number of iterations while ensuring that the highest peak is within the search space, thus achieving a balance between accuracy and complexity and obtaining more efficient performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a flowchart of a direct position estimation method based on the ADBO algorithm provided in an embodiment of the present application.

[0041] Figure 2 It is a flowchart of constructing a DPE cost function in an embodiment of the present application.

[0042] Figure 3 It is a flow chart of continuously iterating the optimal fitness based on the ADBO algorithm in an embodiment of the present application.

[0043] Figure 4 It is a performance comparison chart of the ADBO algorithm before and after the convergence factor is improved in the embodiment of the present application.

[0044] Figure 5 It is a comparison diagram of population distribution before and after the roughness disturbance of the ADBO algorithm is added in the embodiment of the present application.

[0045] Figure 6 This is a comparison chart of positioning error results in the simulation test of this application when the satellite geometry is good.

[0046] Figure 7 This is a comparison chart of positioning error results under the condition of poor satellite geometry in the simulation test of this application.

[0047] Figure 8 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0049] The terms "first" and "second" in the specification and claims herein are used to distinguish different objects rather than to describe a specific order of the objects. For example, a first response message and a second response message are used to distinguish different response messages rather than to describe a specific order of the response messages.

[0050] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0051] In the description of the embodiments of the present application, unless otherwise specified, "multiple" means two or more than two. For example, multiple processing units refer to two or more processing units, etc.; multiple elements refer to two or more elements, etc.

[0052] First, the technical terms involved in the embodiments of the present application are introduced.

[0053] Adaptive Dung Beetle Optimizer (ADBO) is an improved intelligent optimization algorithm based on the behavior of dung beetles in nature, inspired by the dung beetle's rolling, foraging, stealing and reproduction behaviors.

[0054] Direct Position Estimation (DPE) is a new navigation receiver technology that can directly infer the receiver's position from the received sampled data. Direct position estimation uses the maximum likelihood estimation method to directly infer the receiver's position from the received signal.

[0055] The embodiments of the present application are described below in conjunction with the drawings in the embodiments of the present application.

[0056] Embodiment 1: like Figure 1 As shown, the embodiment of the present application includes the following steps: S01 obtains the sampling signal of the Global Positioning System (GPS) and the auxiliary information of the Assisted GPS (A-GPS); S02 combines the weighted model and the autocorrelation approximation scheme to construct a direct position estimation cost function; Figure 2 As shown, it includes the following sub-steps: S021 builds a basic direct position estimation model based on satellite ephemeris information; Basic DPE question: Considering that there are The receiver captures the line-of-sight signal of the visible satellite at time The sampled received complex baseband signal can be expressed as: (1) The subscript Represents the visible satellite number, and Respectively The signal amplitude of the satellite and the known Pseudo-Random Noise (PRN) code. and Respectively represent The time delay and Doppler shift of the satellites. Represents noise, which is generally assumed to be additive white Gaussian noise with a mean of zero.

[0057] If the receiver observes snapshots, the signal model can be expressed in matrix form: (2) in, yes The received signal vector, yes The complex signal amplitude vector, yes The noise vector is yes The local signal replica matrix of and Each component Defined by the delayed and Doppler-shifted signal envelope: .

[0058] Traditional 2SP receivers process each satellite signal independently and perform a two-dimensional search to estimate and And other parameters. snapshots, the optimization problem is: (3) in represents the conjugate transpose operation, symbol Represents a vector After obtaining the measurement parameters, the traditional receiver calculates its position through trilateration. Symbol .

[0059] DPE exploits the relationship between the receiver's PVT state and the time delay and Doppler shift of the received signal ( and Can be expressed as PVT state ), that is:

[0060]

[0061] in, is the PVT status of the receiver, is the location of the receiver, is the speed of the receiver, and are the clock bias and drift of the receiver respectively. Therefore, the time-frequency parameterized model (2) of the received signal can be obtained by using the parameter vector of DPE It is expressed as: (4) By calculating the and , a signal replica is generated for each candidate PVT and correlated with the received signal. The correlation values ​​of all candidate points are combined into a correlation graph, and the result with the highest correlation value is the maximum likelihood estimate of the PVT. Symbol represents the candidate parameters, and the corresponding optimization problem is: (5) S022 optimizes the direct position estimation model using the autocorrelation approximation scheme; Autocorrelation scheme: In order to reduce the computational cost of the above signal correlation, the autocorrelation function is used as an alternative. Through the signal model, that is, equation (2), the 8-dimensional optimization problem in equation (5) can be decomposed into two 4-dimensional problems: the correlation function of time delay and Doppler frequency shift.

[0062] (6) (7) in, and Represent the candidate points and The deviation of the time delay and Doppler shift between satellites relative to their true values. Indicates the chip period of the PRN code.

[0063] In static or low-speed scenarios, when the receiver's velocity and clock drift are assumed to be fixed, the maximum likelihood estimates of the position and time bias can be obtained from (6). By exploiting the relationship between the code phase of the GNSS satellite signal and the candidate position and clock bias, the correlation function can be approximated by the pseudorange correlation function to optimize the calculation: (8) in, is the state of the receiver position, velocity and time, , is the location of the receiver, is the receiver speed, and is the receiver clock offset and drift; represents the clock offset of the i-th satellite; is the pseudorange of the ith satellite, is the speed of light, is the chip period of the PRN code; Indicates transpose; symbol Indicates taking the absolute value; symbol ;symbol Indicates candidate parameters.

[0064] S023 introduces satellite elevation angle and carrier-to-noise ratio weighted model to correct the direct position estimation model; Introducing a weighted model: In DPE, by analyzing the changes in pseudorange error, satellite elevation angle, and carrier-to-noise ratio (C / N0), a method for allocating weights to line-of-sight signal measurements is further proposed. The weights are: (9) in and Respectively represent The elevation angle and carrier-to-noise ratio of the satellites. Introducing these weights into the model, the low-computation correlation technology is as follows: (10) in, Indicates total visible satellites; represents the direct position estimation model under the i-th satellite optimized by the autocorrelation approximation scheme; S024 obtains the cost function of the direct position estimation model.

[0065] Low-complexity DPE cost function: Without loss of generality, the a priori feasible solution space provided by A-GPS is regarded as a Cartesian space with the same dimension as the parameter, denoted as By replacing the cost function in equation (5) with the cost function in equation (10), the position-time related optimization problem can be formalized as: (11) The above formula represents the direct position estimation model The independent variable at its maximum value , In range Among them, represents the search space provided by the auxiliary positioning information; is the state of the receiver position, velocity and time; symbol Indicates candidate parameters; symbol Indicates an estimated value.

[0066] S03 uses the direct position estimation cost function as the fitness function of the ADBO algorithm, and uses the ADBO algorithm to continuously iterate and update the population position until the iteration termination condition is reached; Figure 3 As shown, it includes the following sub-steps: S031 initialize ADBO parameters; Initialize the maximum number of iterations , population size , fitness function , search space and upper and lower boundaries , , roughness parameter .

[0067] S032 Calculate the fitness landscape characteristic (Fitness Distance Correlation, FDC) based on the fitness value of the current population and ruggedness; Given a population The vector corresponding to the fitness value . The optimal individual in the identified population is denoted as , corresponding to the highest fitness value. The distance between each individual and the optimal individual is expressed as ,in . is defined as follows: (12) in, express and The covariance of , for The standard deviation of for The standard deviation of , represents the fitness value of each individual in the population, , represents the distance between each individual in the population and the optimal individual; symbol The FDC metric ranges from −1 (indicating perfect anticorrelation) to 1 (indicating perfect correlation).

[0068] Ruggedness describes the complexity of the terrain. and the fitness vector , the differential sequence is derived by the following formula ,in . Defined relative to The symbolic function of is: (13) get , where the parameter is a constant, the minimum value that makes the terrain flat The value is called the information stability measure and is expressed as express; When , it indicates that the variable The value decreases, When , it indicates that the variable The value increases, When , it indicates that the variable The value remains unchanged; based on The entropy measure of the frequency of different symbol combinations in is defined as: (14) in, is the symbol value exist The probability of consecutive occurrences in yes Neutron Block The number of The roughness defined in this algorithm is the average value of information entropy under multiple observation granularities, that is: (15) S033 calculates population dynamic thresholds, adaptive convergence factors and hybrid mutation strategies based on fitness landscape characteristics and ruggedness; Based on FDC, a dynamic threshold was designed to determine the behavioral selection in combination with the ovipositor and foraging dung beetle populations: (16) In order to dynamically adjust the upper and lower bounds of the search space, an adaptive convergence factor is designed: (17) like Figure 4The following is a comparison of the performance of the ADBO algorithm before and after the adaptive convergence factor is improved. It can be seen that the algorithm converges faster after increasing the adaptive convergence factor.

[0069] According to the terrain ruggedness, an intuitive hybrid mutation strategy is proposed: (18) in represents random numbers generated from a standard Gaussian distribution, represents a random number generated from a standard Cauchy distribution. Figure 5 This is a comparison of the early population distribution before and after the disturbance of increasing ruggedness. It can be seen that the population distribution is more evenly distributed after the disturbance of increasing ruggedness.

[0070] S034 performs iterative updates of population positions based on population dynamic threshold, adaptive convergence factor and hybrid mutation update formula; According to the hybrid mutation strategy proposed in formula (18), the current iterative global optimal solution is perturbed and used as the current optimal solution for the next cycle. .

[0071] Ball rolling behavior: In nature, dung beetles use the sun to navigate and keep a straight track of their rolling dung balls. The position updates of ball rolling dung beetles are as follows:

[0072] (19) in Indicates the current iteration number. Represents iteration The position of the dung beetle at that time. Determines whether the dung beetle deviates from its original direction (1 means no deviation, −1 means deviation). is the defect factor, the initial value is set to 0.1, is a constant. represents the global worst position, Simulates sunlight. The larger the value, the farther the dung beetle is from the light source.

[0073] If it encounters an obstacle, the dung beetle needs to reposition itself by dancing on the dung ball, updating its position as shown below: (20) in is the deflection angle, when indicates that the position is not updated.

[0074] Oviposition behavior: Dung beetles roll dung balls to a safe area for female dung beetles to lay eggs and protect their offspring. The safe area is defined as: (twenty one) in represents the current local optimal position, and are the lower and upper bounds of the optimization problem. and are the lower and upper bounds of the spawning area, See formula (17), which is the adaptive convergence factor designed based on FDC.

[0075] The female dung beetle will then lay one egg per iteration within this safe area, with the location updated to: (twenty two) If the position exceeds the safe area, adjust as follows: (twenty three) in For the The egg-laying ball is in The position at the iteration, and is an independent random vector variable of the same dimension as the optimization problem.

[0076] Foraging behavior: The eggs develop into dung beetles and emerge from the ground to forage for food. Each dung beetle selects an optimal foraging area, defined as follows: (twenty four) in It indicates the global optimal position. and Respectively represent the lower and upper bounds of the optimal foraging area. After determining the optimal foraging area, the position of the dung beetle is updated to: (25) in For the A little dung beetle in the The position at the iteration, represents random numbers that follow a normal distribution, The range is The random variables within .

[0077] It should be noted that the spawning behavior and foraging behavior of the ADBO algorithm are consistent with the dynamic threshold of formula (16) related.

[0078] Stealing behavior: Some dung beetles engage in stealing behavior, stealing dung balls from other dung beetles. The stealing dung beetles will be close to the best stealing position, that is, the global optimal position. The position of the stealing dung beetles is updated as follows: (26) in, Indicates Only the thieving dung beetle is The position at the iteration is, is a normally distributed random vector, is a constant.

[0079] S035 repeats steps S032 to S034 until the iteration termination condition is reached.

[0080] S04 calculates the navigation solution of the receiver according to the fitness value of the best individual in the population at this time; The fitness value of the best individual obtained according to the ADBO algorithm And the best fitness value , further calculate and output the navigation solution .

[0081] Embodiment 2: This embodiment introduces the performance of the solution of the present application through simulation experiments.

[0082] Hardware configuration: Intel(R) i7-12700H CPU, 16GB RAM; Software configuration: 64-bit Windows 11 operating system, MATLAB (R2024a) programming environment.

[0083] This embodiment compares and analyzes ten algorithms, including the traditional 2SP method, the MRG method, the ADBO method proposed in the present invention, and seven popular swarm intelligence optimization algorithms: Particle Swarm Optimization (PSO), Sparrow Search Algorithm (SSA), Grey Wolf Optimizer (GWO), Harris Hawks Optimization (HHO), Pigeon-Inspired Optimization (PIO), Parrot Optimizer (PO), Dung Beetle Optimizer (DBO). Since satellite geometry affects the search difficulty of the objective function, the simulation considers two scenarios: one is the case of good satellite geometry, and the other is the case of poor satellite geometry. Assuming that the receiver is static in both cases, the experiment only focuses on the search in the position-time domain.

[0084] Under good satellite geometry conditions, consider an open sky reception scenario with eight satellites. The PRN numbers, azimuths and elevations of the satellites are shown in Table 1. C Tested under / N0.

[0085] Table 1

[0086] The positioning error, clock bias error, evaluation points and execution time of the ten algorithms are compared, and the results are shown in Table 2.

[0087] Table 2

[0088] Due to the different solutions of 2SP and the fact that this experiment focuses on comparing the efficiency of various search algorithms, the execution time of 2SP is not considered.

[0089] In order to intuitively demonstrate the performance of the ADBO algorithm, the positioning error curve is given as follows: Figure 6 As shown in the figure, it can be seen that many algorithms such as PSO, HHO, PIO, and PO are prone to fall into local optimality, resulting in stagnation. DBO converges quickly and is not prone to fall into local optimality. Compared with DBO, ADBO proposed in the present invention further improves the performance, is superior to other algorithms in continuous search capability, and finally achieves the minimum positioning error.

[0090] Under conditions of poor satellite geometry, an indoor environment near a window is simulated, characterized by C The / N0 level is as low as 25 dB / Hz and only satellites with elevation angles between 20° and 75° and azimuth angles between 0° and 180° are used. The corresponding PRN code numbers, azimuth angles and elevation angles of the satellites are shown in Table 3.

[0091] Table 3

[0092] The positioning error, clock deviation error, evaluation points and execution time of the ten algorithms are compared, and the results are shown in Table 4.

[0093] Table 4

[0094] The positioning error curves of different algorithms under poor satellite geometry conditions are as follows: Figure 7As shown in the figure. Due to the more challenging optimization problem caused by poor satellite geometry, all algorithms require more iterations to converge. Many algorithms seem to converge successfully in the figure, but in fact they are just stuck in the local optimal solution with large positioning error. The ADBO algorithm is equipped with a better update strategy, which effectively escapes the local optimal solution and successfully converges to the correct position, achieving the minimum positioning error.

[0095] Based on the method in the above embodiment, the embodiment of the present application provides an electronic device, such as Figure 8 As shown, it may include: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus 40. The processor may call the logic instructions in the memory to execute the method in the above embodiment.

[0096] In addition, the logic instructions in the above-mentioned memory can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application.

[0097] Based on the method in the above embodiment, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a processor, the processor executes the method in the above embodiment.

[0098] Based on the method in the above embodiment, an embodiment of the present application provides a computer program product. When the computer program product runs on a processor, the processor executes the method in the above embodiment.

[0099] It is understandable that the processor in the embodiment of the present application may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. The general-purpose processor may be a microprocessor or any conventional processor.

[0100] The method steps in the embodiments of the present application can be implemented by hardware or by a processor executing software instructions. The software instructions can be composed of corresponding software modules, and the software modules can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, mobile hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an ASIC.

[0101] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions may be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)), etc.

[0102] It should be understood that the various numerical numbers involved in the embodiments of the present application are only used for the convenience of description and are not used to limit the scope of the embodiments of the present application.

[0103] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A direct position estimation method based on ADBO algorithm, characterized in that: include: Obtain satellite ephemeris information and auxiliary positioning information; The direct position estimation cost function is constructed by combining the weighted model and the autocorrelation approximation scheme; The direct position estimation cost function is used as the fitness function of the ADBO algorithm, and the population position is continuously updated by using the ADBO algorithm until an iteration termination condition is reached; The navigation solution of the receiver is calculated based on the fitness value of the best individual in the population at this time.

2. The direct position estimation method according to claim 1, characterized in that: The joint weighted model and autocorrelation approximation scheme construct the direct position estimation cost function, which is: Construct a basic direct position estimation model based on satellite ephemeris information; Optimizing the direct position estimation model using the autocorrelation approximation scheme; The satellite elevation angle and carrier-to-noise ratio weighted model is introduced to correct the direct position estimation model; Obtain the cost function for the direct position estimation model.

3. The direct position estimation method according to claim 1, characterized in that: The direct position estimation cost function is used as the fitness function of the ADBO algorithm, and the population position is continuously updated by the ADBO algorithm until the iteration termination condition is reached, specifically: Initialize ADBO parameters; Calculate the fitness landscape characteristics and ruggedness according to the fitness value of the current population; Calculate the population dynamic threshold, adaptive convergence factor and mixed mutation strategy according to the fitness landscape characteristics and ruggedness; The population position is iteratively updated based on the population dynamic threshold, adaptive convergence factor and hybrid mutation update formula until the iteration termination condition is reached.

4. The direct position estimation method according to claim 2, characterized in that: The autocorrelation approximation scheme is used to optimize the direct position estimation model, specifically: in, is the state of the receiver position, velocity and time, , is the location of the receiver, is the receiver speed, and is the receiver clock offset and drift; represents the clock offset of the i-th satellite; is the pseudorange of the ith satellite, is the speed of light, is the chip period of the PRN code; Indicates transpose; symbol Indicates taking the absolute value; symbol ;symbol Indicates candidate parameters.

5. The direct position estimation method according to claim 2, characterized in that: The satellite elevation angle and carrier-to-noise ratio weighted model is introduced to correct the direct position estimation model. The corrected model for: in, Indicates total visible satellites; represents the direct position estimation model under the i-th satellite optimized by the autocorrelation approximation scheme; represents the weight of the i-th satellite, , and represent the elevation angle and carrier-to-noise ratio of the i-th satellite respectively.

6. The direct position estimation method according to claim 2, characterized in that: The cost function of the direct position estimation model is obtained as follows: The above formula represents the direct position estimation model The independent variable at its maximum value , In range Among them, represents the search space provided by the auxiliary positioning information; is the state of the receiver position, velocity and time; symbol Indicates candidate parameters; symbol Indicates an estimated value.

7. The direct position estimation method according to claim 3, characterized in that: The fitness landscape characteristics and ruggedness are calculated based on the fitness value of the current population, specifically: Fitness landscape characteristics for: in, express and The covariance of for The standard deviation of for The standard deviation of , represents the fitness value of each individual in the population, , represents the distance between each individual in the population and the optimal individual; Ruggedness describes the complexity of terrain, as follows: get , where the parameter is a constant, the minimum value that makes the terrain flat The value is called the information stability measure and is expressed as express; When , it indicates a variable The value decreases, When , it indicates a variable The value increases, When , it indicates a variable The value remains unchanged; based on The entropy measure of the frequency of different symbol combinations in is defined as: in, is the symbol value exist The probability of consecutive occurrences in yes Neutron Block The number of Roughness is the average value of information entropy under multiple observation granularities, that is: in, express The value set of includes 8 values, 7 of which are , .

8. The direct position estimation method according to claim 3, characterized in that: The population dynamic threshold, adaptive convergence factor and hybrid mutation strategy are calculated based on the fitness landscape characteristics and ruggedness as follows: Population dynamics threshold for: Adaptive Convergence Factor for: The hybrid mutation strategy is: in, is the maximum number of iterations; is the characteristic quantity of the fitness landscape; is the current iteration number; represents random numbers generated from a standard Gaussian distribution, represents random numbers generated from a standard Cauchy distribution; Represents the current local optimal position; For the ruggedness.

9. The direct position estimation method according to claim 3, characterized in that: The population position is iteratively updated based on the population dynamic threshold, adaptive convergence factor and hybrid mutation update formula, specifically: According to the hybrid mutation strategy, the current iterative global optimal solution is perturbed and used as the current global optimal solution for the next cycle. ; When rolling the ball, the position is updated as follows: in, For position, in brackets is the number of iterations; Determine whether the dung beetle deviates from its original direction; is the defect factor; is a constant; represents the global worst position, Indicates sunlight; symbol Indicates taking the absolute value; When encountering an obstacle, the position is updated as: in, is the deflection angle; When spawning the position is updated to: in, is the location of the ovum, and is an independent random vector variable with the same dimension as the optimization problem; and For the lower and upper bounds of the spawning area: and Respectively represent the maximum and minimum values; Represents the current local optimal position; and To provide lower and upper bounds for the optimization problem; is the adaptive convergence factor; The spawning areas are: When foraging, the position is updated as: in, represents random numbers that follow a normal distribution, The range is Random variables within ; and Respectively represent the lower and upper bounds of the optimal foraging area: It indicates the global optimal position; When stealing, the position is updated to: in, is a normally distributed random vector, is a constant.

10. An electronic device, characterized in that: include: at least one memory for storing a computer program; At least one processor is used to execute the program stored in the memory. When the program stored in the memory is executed, the processor is used to execute the method according to any one of claims 1 to 9.

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