IARO-based positioning methods, systems, computer equipment, and media

Abstract

This invention provides a positioning method, system, computer device, and medium based on IARO, belonging to the field of positioning technology. The method first uses a least squares algorithm to determine an initial position estimate as the search center, and then dynamically determines the search radius by combining distance residual statistical characteristics and a horizontal accuracy factor to construct the search space. A chaotic sequence is generated through tent mapping and initialized with an artificial rabbit population via polar coordinate transformation. In the iterative optimization phase, the artificial rabbits select update strategies based on energy factors, including detouring foraging and random hiding, continuously updating their position and fitness values, and finally outputting the globally optimal solution as the target position estimate. This invention improves positioning accuracy and stability, enhances global search capabilities and local exploration efficiency, and is particularly suitable for ultra-wideband (UWB) positioning in occluded scenarios, effectively addressing signal propagation errors caused by complex factors such as obstacle occlusion.

Description

Technical Field

[0001] This invention relates to the field of positioning technology, and in particular to a positioning method, system, computer device, and medium based on IARO. Background Technology

[0002] Ultra-wideband (UWB) technology utilizes nanosecond-level non-sinusoidal narrow pulses and large bandwidth characteristics, possessing centimeter-level high resolution and strong anti-interference capabilities, making it one of the ideal choices for indoor positioning. However, in actual indoor environments, factors such as obstacle obstruction, multipath effects, and human interference often cause UWB signals to propagate non-line-of-sight (NLOS), introducing significant errors during ranging and severely affecting positioning accuracy and stability.

[0003] To mitigate NLOS errors, researchers are increasingly incorporating intelligent optimization algorithms into UWB positioning research. These algorithms simulate natural phenomena such as biological evolution, swarm intelligence, or physical laws to construct search mechanisms, possessing powerful global and local search capabilities, which helps improve the robustness of positioning systems in complex environments. Among the many factors affecting algorithm performance, establishing the search space is particularly crucial: an excessively large space reduces convergence speed, while an excessively small space easily leads to getting trapped in local optima. However, existing research mostly constructs the search space based on fixed boundaries of UWB base station groups, lacking adaptability to dynamic error environments and failing to cover potential positioning needs outside the base station area.

[0004] To address this issue, some scholars have proposed extending the base station boundary outwards by an integer multiple of the communication range to construct a dynamic search space, improving the flexibility of regional coverage. However, this method still generates an excessively large space in low-error scenarios, leading to unnecessary computational burden. Other scholars have used the Chan algorithm to dynamically determine the search range based on coordinates estimated, significantly accelerating convergence in the early stages of iteration. However, its core drawback lies in the assumption that constructing the search radius function relies on an ideal Gaussian error model, while actual ranging errors often exhibit asymmetrical distribution characteristics, making it difficult for this model to effectively reflect the error characteristics of real-world scenarios. Therefore, a new method is urgently needed to solve these problems. Summary of the Invention

[0005] The purpose of this invention is to provide a positioning method, system, computer device, and medium based on IARO, which improves positioning accuracy and stability, enhances global search capability and local exploration efficiency, and is especially suitable for UWB positioning in occluded scenarios. It can effectively address signal propagation error problems caused by complex factors such as obstacle occlusion.

[0006] To achieve the above objectives, the present invention provides a positioning method based on IARO, comprising the following steps:

[0007] Step S1: Obtain the ranging values ​​from the target tag to each UWB base station, and calculate the initial location estimate based on the ranging values ​​using the least squares (LS) algorithm.

[0008] Step S2: Dynamically determine the adaptive search radius by calculating the statistical characteristics of the distance residual and the horizontal dilution of precision (HDOP), and construct a circular search space centered on the initial position estimate.

[0009] Step S3: Generate a chaotic sequence using Tent mapping within the circular search space, and then initialize the artificial rabbit population through polar coordinate transformation;

[0010] Step S4: In the iterative optimization phase, each artificial rabbit selects an update strategy based on the value of the energy factor, updates its current position and calculates its fitness value, and updates the individual optimal solution of each artificial rabbit and the global optimal solution of the population, until the set number of iterations is reached, and outputs the global optimal solution as the target position estimation result.

[0011] Preferably, in step S1, the specific steps are as follows:

[0012] It has There are 1 UWB base station, and their coordinates are as follows: , , ..., The coordinates of the target label are The corresponding measured distance is , , ..., The coordinates are obtained by solving the problem using the LS algorithm:

[0013] ;

[0014] ;

[0015] ;

[0016] ;

[0017] in, This represents the position estimate of LS. This represents the coefficient term of LS. This represents the constant term of LS. Indicates the first The x-coordinate of each UWB base station Indicates the first The vertical coordinate of each UWB base station Indicates that the UWB tag is related to the first Ranging values ​​of a UWB base station, The x-axis represents the LS estimate. This represents the ordinate estimated by LS. Indicates the matrix transpose flag.

[0018] Preferably, in step S2, the adaptive search radius is:

[0019] ;

[0020] ;

[0021] ;

[0022] ;

[0023] ;

[0024] ;

[0025] in, Indicates the adaptive search radius. This represents the mean of the distance residuals. The standard deviation of the distance residuals. Indicates the horizontal precision factor. An empirical factor representing the adjustment of the level of precision. Indicates UWB tag to the 1st Distance residual of each UWB base station Indicates the first Planar coordinates of a UWB base station Indicates that the UWB tag is related to the first Ranging values ​​of a UWB base station, This represents the normalized geometric matrix composed of the direction cosines of each UWB base station.

[0026] Preferably, in step S3, the Tent mapping satisfies the iteration rule:

[0027] ;

[0028] in, Indicates the first The Tent mapping value of the next iteration. Indicates the first The Tent mapping value of the next iteration.

[0029] Preferably, in step S3, the artificial rabbit population is initialized through polar coordinate transformation, and the specific steps are as follows:

[0030] Generate initial random numbers and use Tent mapping to perform The next iteration yields ;

[0031] Calculate the radius and angle in polar coordinates:

[0032] ;

[0033] in, Represents the radius in polar coordinates. Represents polar coordinate angles;

[0034] Convert the position of the artificial rabbit from polar coordinates to Cartesian coordinates:

[0035] ;

[0036] in, The initial x-coordinate of the artificial rabbit is represented. This represents the initial ordinate of the artificial rabbit.

[0037] Preferably, in step S4, the fitness value is calculated using the following formula:

[0038] ;

[0039] in, Indicates the first An artificial rabbit in Estimated position at time This represents the fitness value.

[0040] Preferably, in step S4, the update strategy includes a detour foraging phase and a random hiding phase. In the detour foraging phase, the Levy flight strategy is introduced, and the artificial rabbit's position update formula is as follows:

[0041] ;

[0042] ;

[0043] ;

[0044] ;

[0045] ;

[0046] ;

[0047] ;

[0048] in, Indicates the first The artificial rabbit in the first The candidate coordinates are updated during the detour foraging phase in the next iteration. Indicates the first The artificial rabbit in the first The coordinates at the next iteration Indicates the first The artificial rabbit in the first The coordinates at the next iteration , , ..., ; , This indicates the number of captive rabbits in the population. Represents the motion operator, Indicates stride length. Indicates the current iteration number. Indicates the maximum number of iterations. Indicates quantity And a sequence whose content is either 0 or 1. This indicates that a certain number of dimensions are randomly selected for updating. This represents a randomly selected number from 1 to 1. integers, Indicates 1 to A random sequence of integers, , ..., ; , ..., , The dimension of the coordinates This represents the floor function. Indicates generating 1 to A random sequence of integers, This represents the random step size generated according to the Lévy distribution. Indicates the step size parameter. This represents the shape parameters of Levi's flight. Represents the Gamma function. , All represent random numbers between (0,1). , , All represent random numbers that conform to a standard normal distribution. This represents the natural constant, with a value of approximately 2.71828;

[0049] In the random hiding phase, a non-linearly decaying hiding step size is used. The position update formula for the artificial rabbit is as follows:

[0050] ;

[0051] ;

[0052] ;

[0053] ;

[0054] in, Indicates the first The artificial rabbit in the first The candidate coordinates are updated during the random hidden phase in the next iteration. Indicates the first The artificial rabbit in the first The cave is randomly selected in the next iteration. Indicates (1, A random number between ) Indicates the first The artificial rabbit in the first The first iteration generated during the nth iteration A cave, , ..., , This indicates that the step size is hidden. Indicates quantity And a sequence whose content is either 0 or 1. This indicates that a certain number of dimensions are randomly selected for updating. Represents a random number between (0, 1). This represents a random number that conforms to a standard normal distribution.

[0055] The present invention also provides an IARO-based positioning system, comprising:

[0056] The data acquisition module is used to obtain the ranging values ​​from the target tag to each UWB base station;

[0057] The initial estimation module is used to calculate the initial position estimate based on the distance measurement value using the LS algorithm;

[0058] The search space construction module is used to dynamically determine the adaptive search radius by calculating the statistical characteristics of the distance residual and the horizontal precision factor, and to construct a circular search space centered on the initial position estimate.

[0059] The population initialization module is used to generate chaotic sequences in a circular search space using Tent mapping and to initialize the artificial rabbit population through polar coordinate transformation.

[0060] The iterative optimization module is used to control the artificial rabbits to select an update strategy based on the value of the energy factor during the iterative optimization phase, update the current position and calculate the fitness value, update the individual optimal solution of each artificial rabbit and the global optimal solution of the population, until the set number of iterations is reached, and output the global optimal solution as the target position estimation result.

[0061] The iterative optimization module specifically includes:

[0062] Energy factor calculation unit, used to calculate the energy factor of artificial rabbits;

[0063] The update strategy selection unit is used to select the appropriate update strategy based on the value of the energy factor.

[0064] The location update unit is used to update the location of the artificial rabbit according to the selected update strategy.

[0065] Fitness value calculation unit, used to calculate the fitness value of artificial rabbits;

[0066] The optimal solution update unit is used to update the individual optimal solution of each artificial rabbit and the global optimal solution of the population.

[0067] The present invention also provides a computer device, including a memory and a processor, wherein the memory is used to store instructions and the processor is used to execute the instructions to implement the IARO-based localization method for occlusion-oriented scenarios as described above.

[0068] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the IARO-based localization method for occlusion scenarios as described above.

[0069] Therefore, the present invention employs the above-described IARO-based positioning method, system, computer equipment, and medium, and the beneficial technical effects are as follows:

[0070] (1) An adaptive search space construction method that relies solely on UWB ranging values ​​and base station coordinates is proposed. This method first uses the LS algorithm to determine the search center, and then dynamically adjusts the search radius by combining the statistical characteristics of the distance residual and the horizontal precision factor, which significantly enhances the adaptability of the search space and the convergence efficiency of the optimization algorithm.

[0071] (2) Based on the original Artificial Rabbit Optimization (ARO) algorithm, several improvement strategies are introduced to enhance the algorithm's global search capability and local exploration efficiency, proposing an improved artificial rabbit optimization (IARO) algorithm. Specifically, these include: utilizing Tent mapping to increase the diversity of the initial population, incorporating Lévy flight to strengthen the global search capability, and improving the hidden step size update formula to increase convergence efficiency. These improvements significantly enhance the algorithm's global search capability and local exploration efficiency in complex localization problems. Attached Figure Description

[0072] Figure 1 This is a flowchart of a positioning method based on IARO according to the present invention;

[0073] Figure 2 For comparing the positioning error with the search radius;

[0074] Figure 3 This is the floor plan of the interior lobby;

[0075] Figure 4 Comparison of CDF curves for different algorithms;

[0076] Figure 5 The fitness convergence curves of different algorithms in the localization optimization process;

[0077] Figure 6 Box plots of RMSE for 20 independent runs of different algorithms under different iteration settings. Detailed Implementation

[0078] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0079] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0080] Example 1

[0081] like Figure 1 As shown, this invention provides a positioning method based on IARO, comprising the following steps:

[0082] Step S1: Obtain the ranging values ​​from the target tag to each UWB base station, and calculate the initial location estimate based on the ranging values ​​using the LS algorithm. The specific steps are as follows:

[0083] It has There are 1 UWB base station, and their coordinates are as follows: , , ..., The coordinates of the target label are The corresponding measured distance is , , ..., The coordinates are obtained by solving the problem using the LS algorithm:

[0084] ;

[0085] ;

[0086] ;

[0087] ;

[0088] in, This represents the position estimate of LS. This represents the coefficient term of LS. This represents the constant term of LS. Indicates the first The x-coordinate of each UWB base station Indicates the first The vertical coordinate of each UWB base station Indicates that the UWB tag is related to the first Ranging values ​​of a UWB base station, The x-axis represents the LS estimate. This represents the ordinate estimated by LS. Indicates the matrix transpose flag.

[0089] Step S2: Dynamically determine the adaptive search radius by calculating the statistical characteristics of the distance residual and the horizontal precision factor, and construct a circular search space centered on the initial position estimate.

[0090] The adaptive search radius is:

[0091] ;

[0092] ;

[0093] ;

[0094] ;

[0095] ;

[0096] ;

[0097] in, Indicates the adaptive search radius. This represents the mean of the distance residuals. The standard deviation of the distance residuals. Indicates the horizontal precision factor. An empirical factor representing the adjustment of the level of precision. Indicates UWB tag to the 1st Distance residual of each UWB base station Indicates the first Planar coordinates of a UWB base station Indicates that the UWB tag is related to the first Ranging values ​​of a UWB base station, This represents the normalized geometric matrix composed of the direction cosines of each UWB base station.

[0098] Figure 2 The diagram shows the positioning error (black curve) of LS estimation at different locations and the corresponding adaptive search radius (red curve). The results show that the adaptive radius is larger than the LS error in most locations, ensuring broad search coverage; at the same time, the two are highly consistent in their changing trends, demonstrating good error adaptability and dynamic response capability.

[0099] Step S3: Generate a chaotic sequence using Tent mapping within the circular search space, and then initialize the artificial rabbit population through polar coordinate transformation.

[0100] Tent mappings satisfy the iteration rule:

[0101] ;

[0102] in, Indicates the first The Tent mapping value of the next iteration. Indicates the first The Tent mapping value of the next iteration.

[0103] The specific steps for initializing an artificial rabbit population using polar coordinate transformation are as follows:

[0104] Generate initial random numbers and use Tent mapping to perform The next iteration yields ;

[0105] Calculate the radius and angle in polar coordinates:

[0106] ;

[0107] in, Represents the radius in polar coordinates. Represents polar coordinate angles;

[0108] Convert the position of the artificial rabbit from polar coordinates to Cartesian coordinates:

[0109] ;

[0110] in, The initial x-coordinate of the artificial rabbit is represented. This represents the initial ordinate of the artificial rabbit.

[0111] Step S4: In the iterative optimization phase, each artificial rabbit selects an update strategy based on the value of the energy factor, updates its current position and calculates its fitness value, and updates the individual optimal solution of each artificial rabbit and the global optimal solution of the population until the set number of iterations is reached, and outputs the global optimal solution as the target position estimation result.

[0112] The formula for calculating fitness value is as follows:

[0113] ;

[0114] in, Indicates the first An artificial rabbit in Estimated position at time This represents the fitness value.

[0115] The update strategy includes:

[0116] During the detour foraging phase, the Levy flight strategy was introduced, and the artificial rabbit's position update formula is as follows:

[0117] ;

[0118] ;

[0119] ;

[0120] ;

[0121] ;

[0122] ;

[0123] ;

[0124] in, Indicates the first The artificial rabbit in the first The candidate coordinates are updated during the detour foraging phase in the next iteration. Indicates the first The artificial rabbit in the first The coordinates at the next iteration Indicates the first The artificial rabbit in the first The coordinates at the next iteration , , ..., ; , This indicates the number of captive rabbits in the population. Represents the motion operator, Indicates stride length. Indicates the current iteration number. Indicates the maximum number of iterations. Indicates quantity And a sequence whose content is either 0 or 1. This indicates that a certain number of dimensions are randomly selected for updating. This represents a randomly selected number from 1 to 1. integers, Indicates 1 to A random sequence of integers, , ..., ; , ..., , The dimension of the coordinates This represents the floor function. Indicates generating 1 to A random sequence of integers, This represents the random step size generated according to the Lévy distribution. Indicates the step size parameter. This represents the shape parameters of Levi's flight. Represents the Gamma function. , All represent random numbers between (0,1). , , All represent random numbers that conform to a standard normal distribution. This represents the natural constant, with a value of approximately 2.71828;

[0125] In the random hiding phase, a non-linearly decaying hiding step size is used. The position update formula for the artificial rabbit is as follows:

[0126] ;

[0127] ;

[0128] ;

[0129] ;

[0130] in, Indicates the first The artificial rabbit in the first The candidate coordinates are updated during the random hidden phase in the next iteration. Indicates the first The artificial rabbit in the first The cave is randomly selected in the next iteration. Indicates (1, A random number between ) Indicates the first The artificial rabbit in the first The first iteration generated during the nth iteration A cave, , ..., , This indicates that the step size is hidden. Indicates quantity And a sequence whose content is either 0 or 1. This indicates that a certain number of dimensions are randomly selected for updating. Represents a random number between (0, 1). This represents a random number that conforms to a standard normal distribution.

[0131] To balance exploration and utilization, the artificial rabbit needs to exhibit high randomness in the early stages of the global search, then reduce randomness in later stages to improve accuracy and stability. The energy factor used for the simulation conversion is calculated as follows:

[0132] ;

[0133] in, Indicates energy factor, This represents a random number in (0,1).

[0134] When energy factor When the value is greater than 0, the artificial rabbit will take a detour to find food; otherwise, it will hide randomly.

[0135] Before the end of each iteration, IARO uses a greedy criterion to determine the current position of each artificial rabbit to ensure that the overall fitness of the population continues to be optimized.

[0136] If the current iteration is in the detour foraging phase, then the location update rule is as follows:

[0137] ;

[0138] If the current iteration is in the random hiding phase, the update rule for this position is as follows:

[0139] ;

[0140] in, This represents the fitness function.

[0141] The invention will be further illustrated by specific experiments below.

[0142] An indoor hall environment with typical obstruction characteristics was selected for the experiment. A load-bearing column in the center of the experimental area could obstruct signal propagation, thus dividing the positioning area into a complex environment where line-of-sight (LOS) and no-no-sight (NLOS) conditions coexist: some areas maintain complete LOS with all UWB base stations, while other areas experience localized obstruction. For example... Figure 3 As shown, four UWB base stations (A0-A3, DW1000 modules) were deployed in a rectangular layout within the site, each with an antenna height of 1.8 m and a base station boundary dimension of 5.5 m (width) × 8 m (length). To simulate dynamic interference factors in a real-world scenario, several personnel were arranged to walk randomly within the site during the experiment, thereby introducing dynamic occlusion effects and enhancing the impact of NLOS. The test personnel, carrying UWB tags, moved within the area at a constant speed along a predetermined trajectory.

[0143] To verify the performance of the proposed algorithm, nine representative localization algorithms were selected for comparative experiments, including the LS algorithm and eight intelligent optimization algorithms, including ARO, Particle Swarm Optimization (PSO), Sparrow Search Algorithm (SSA), Grey Wolf Optimizer (GWO), Northern Goshawk Optimization (NGO), Arithmetic Optimization Algorithm (AOA), AliBaba and the Forty Thieves (AFT), and Crow Search Algorithm (CSA).

[0144] To ensure fair comparison, the search spaces of the eight intelligent optimization algorithms were expanded by a factor of 1.25 relative to the base station boundary to eliminate error suppression issues caused by limited search boundaries under severe NLOS conditions. The population size for all intelligent optimization algorithms was uniformly set to 5, and the maximum number of iterations was fixed at 50. The cumulative distribution function (CDF) and root mean square error (RMSE) were used as evaluation metrics for the positioning accuracy of each algorithm.

[0145] Experimental results and analysis.

[0146] Figure 4 The CDF curves of the errors of various positioning algorithms are shown. The IARO algorithm proposed in this invention has the highest probability of positioning errors less than 0.5 m, reaching 91.8%, and the maximum error is only 1.378 m. Its overall accuracy and error distribution concentration are superior to those of the comparative algorithms such as LS (90.5%, 1.990 m), ARO (75.9%, 4.851 m), SSA (89.8%, 2.919 m), and GWO (88.3%, 4.927 m). The results show that the IARO algorithm still has strong adaptability and robustness under the conditions of small population size and limited number of iterations. In contrast, most intelligent optimization algorithms have more dispersed error distributions and significantly larger maximum errors, making it difficult to maintain stable convergence.

[0147] Figure 5 The graph shows the fitness convergence curves for random locations affected by obstruction from load-bearing columns. The IARO algorithm achieves stable convergence after 7 iterations. Although GWO and AOA also converge after approximately 14 iterations, they converge to relatively high fitness values, indicating that they are trapped in local optima, thus limiting positioning accuracy. The remaining optimization algorithms exhibit slower convergence trends, requiring more iterations to gradually approach the optimal solution.

[0148] To systematically evaluate the stability and robustness of each algorithm, this embodiment sets the maximum number of iterations to 50 and 100 respectively, and runs each algorithm independently 20 times. Figure 6 The box plot distributions of RMSE under two iteration settings are shown, and Table 1 lists the corresponding median and maximum RMSE values. In the case of 50 iterations, the IARO algorithm has the lowest median RMSE and the smallest interquartile range, demonstrating superior positioning accuracy and stability compared to other intelligent optimization algorithms. Its overall positioning accuracy is 16.9% higher than that of the LS algorithm. Furthermore, IARO shows significantly greater convergence in error variance compared to ARO, indicating stronger convergence stability in complex ranging environments.

[0149] Table 1. Median and maximum RMSE of different algorithms in 20 runs under different iteration settings.

[0150] ;

[0151] After increasing the number of iterations to 100, the median RMSE of IARO decreased only slightly by 0.003 m, while ARO and CSA decreased by 0.134 m and 0.199 m, respectively. This indicates that IARO can achieve near-optimal performance with fewer iterations, exhibiting higher computational efficiency and lower computational cost. Although NGO showed a median RMSE close to IARO under both iteration conditions, its larger maximum error revealed insufficient robustness. The results show that IARO converges stably with fewer iterations and maintains robustness in both global search and local refinement.

[0152] Example 2

[0153] An IARO-based positioning system includes:

[0154] The data acquisition module is used to obtain the ranging values ​​from the target tag to each UWB base station;

[0155] The initial estimation module is used to calculate the initial position estimate based on the distance measurement value using the LS algorithm;

[0156] The search space construction module is used to dynamically determine the adaptive search radius by calculating the statistical characteristics of the distance residual and the horizontal precision factor, and to construct a circular search space centered on the initial position estimate.

[0157] The population initialization module is used to generate chaotic sequences in a circular search space using Tent mapping and to initialize the artificial rabbit population through polar coordinate transformation.

[0158] The iterative optimization module is used to control the artificial rabbits to select an update strategy based on the value of the energy factor during the iterative optimization phase, update the current position and calculate the fitness value, update the individual optimal solution of each artificial rabbit and the global optimal solution of the population, until the set number of iterations is reached, and output the global optimal solution as the target position estimation result.

[0159] The iterative optimization module specifically includes:

[0160] Energy factor calculation unit, used to calculate the energy factor of artificial rabbits;

[0161] The update strategy selection unit is used to select the appropriate update strategy based on the value of the energy factor.

[0162] The location update unit is used to update the location of the artificial rabbit according to the selected update strategy.

[0163] Fitness value calculation unit, used to calculate the fitness value of artificial rabbits;

[0164] The optimal solution update unit is used to update the individual optimal solution of each artificial rabbit and the global optimal solution of the population.

[0165] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0166] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0167] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0168] It is worth noting that all contents not described in detail in this invention are existing technologies and are well known to those skilled in the art.

[0169] Therefore, the present invention adopts the above-mentioned IARO-based positioning method, system, computer equipment and medium, which improves positioning accuracy and stability, enhances global search capability and local exploration efficiency, and is especially suitable for UWB positioning in occluded scenarios. It can effectively cope with signal propagation error problems caused by complex factors such as obstacle occlusion.

[0170] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A positioning method based on IARO, characterized in that, Includes the following steps: Step S1: Obtain the ranging values ​​from the target tag to each UWB base station, and calculate the initial position estimate based on the ranging values ​​using the least squares algorithm; Step S2: Dynamically determine the adaptive search radius by calculating the statistical characteristics of the distance residual and the horizontal precision factor, and construct a circular search space centered on the initial position estimate. Step S3: Generate a chaotic sequence using Tent mapping within the circular search space, and then initialize the artificial rabbit population through polar coordinate transformation; Step S4: In the iterative optimization phase, each artificial rabbit selects an update strategy based on the value of the energy factor, updates its current position and calculates its fitness value, and updates the individual optimal solution of each artificial rabbit and the global optimal solution of the population until the set number of iterations is reached, and outputs the global optimal solution as the target position estimation result. The update strategy includes a detour foraging phase and a random hiding phase. During the detour foraging phase, the Levy flight strategy is introduced, and the artificial rabbit's position update formula is as follows: ； ； ； ； ； ； ； in, Indicates the first The artificial rabbit in the first The candidate coordinates are updated during the detour foraging phase in the next iteration. Indicates the first The artificial rabbit in the first The coordinates at the next iteration Indicates the first The artificial rabbit in the first The coordinates at the next iteration , =1, ..., ; ≠ , This indicates the number of captive rabbits in the population. Represents the motion operator, Indicates stride length. Indicates the current iteration number. Indicates the maximum number of iterations. Indicates quantity And a sequence whose content is either 0 or 1. This indicates that a certain number of dimensions are randomly selected for updating. This represents a randomly selected number from 1 to 1. integers, Indicates 1 to A random sequence of integers, =1, ..., ; =1, ...,⌈ ⋅ ⌉, The dimension of the coordinates This represents the floor function. Indicates generating 1 to A random sequence of integers, This represents the random step size generated according to the Lévy distribution. Indicates the step size parameter. This represents the shape parameters of Levi's flight. Represents the Gamma function. , All represent random numbers between (0,1). , , All represent random numbers that conform to a standard normal distribution. Represents the natural constant; In the random hiding phase, a non-linearly decaying hiding step size is used. The position update formula for the artificial rabbit is as follows: ； ； ； ； in, Indicates the first The artificial rabbit in the first The candidate coordinates are updated during the random hidden phase in the next iteration. Indicates the first The artificial rabbit in the first The cave is randomly selected in the next iteration. Indicates (1, A random number between ) Indicates the first The artificial rabbit in the first The first iteration generated during the nth iteration A cave, =1, ..., , This indicates that the step size is hidden. Indicates quantity And a sequence whose content is either 0 or 1. This indicates that a certain number of dimensions are randomly selected for updating. Represents a random number between (0, 1). This represents a random number that conforms to a standard normal distribution.

2. The positioning method based on IARO according to claim 1, characterized in that, In step S1, the specific steps are as follows: It has There are 1 UWB base station, and their coordinates are as follows: , , ..., The coordinates of the target label are The corresponding measured distance is , , ..., The coordinates are obtained by solving the least squares algorithm: ； ； ； ； in, This represents the least squares position estimate. This represents the coefficient term of least squares. This represents the constant term in least squares. Indicates the first The x-coordinate of each UWB base station Indicates the first The vertical coordinate of each UWB base station Indicates that the UWB tag is related to the first Ranging values ​​of a UWB base station, The x-axis represents the least squares estimate. The ordinate represents the least squares estimation. Indicates the matrix transpose flag.

3. The positioning method based on IARO according to claim 2, characterized in that, In step S2, the adaptive search radius is: ； ； ； ； ； ； in, Indicates the adaptive search radius. This represents the mean of the distance residuals. The standard deviation of the distance residuals. Indicates the horizontal precision factor. An empirical factor representing the adjustment of the level of precision. Indicates UWB tag to the 1st Distance residual of each UWB base station Indicates the first Planar coordinates of a UWB base station Indicates that the UWB tag is related to the first Ranging values ​​of a UWB base station, This represents the normalized geometric matrix composed of the direction cosines of each UWB base station.

4. The positioning method based on IARO according to claim 3, characterized in that, In step S3, the Tent mapping satisfies the iteration rule: ； in, Indicates the first The Tent mapping value of the next iteration. Indicates the first The Tent mapping value of the next iteration.

5. The positioning method based on IARO according to claim 4, characterized in that, In step S3, the artificial rabbit population is initialized through polar coordinate transformation. The specific steps are as follows: Generate initial random numbers and use Tent mapping to perform The next iteration yields ; Calculate the radius and angle in polar coordinates: ； in, Represents the radius in polar coordinates. Represents polar coordinate angles; Convert the position of the artificial rabbit from polar coordinates to Cartesian coordinates: ； in, The initial x-coordinate of the artificial rabbit is represented. This represents the initial ordinate of the artificial rabbit.

6. The positioning method based on IARO according to claim 5, characterized in that, In step S4, the fitness value is calculated using the following formula: ； in, Indicates the first An artificial rabbit in Estimated position at time This represents the fitness value.

7. A positioning system based on IARO, characterized in that, For performing the IARO-based positioning method as described in any one of claims 1-6, comprising: The data acquisition module is used to obtain the ranging values ​​from the target tag to each UWB base station; The initial estimation module is used to calculate the initial position estimate based on the distance measurement value using the least squares algorithm; The search space construction module is used to dynamically determine the adaptive search radius by calculating the statistical characteristics of the distance residual and the horizontal precision factor, and to construct a circular search space centered on the initial position estimate. The population initialization module is used to generate chaotic sequences in a circular search space using Tent mapping and to initialize the artificial rabbit population through polar coordinate transformation. The iterative optimization module is used to control the artificial rabbits to select an update strategy based on the value of the energy factor during the iterative optimization phase, update the current position and calculate the fitness value, update the individual optimal solution of each artificial rabbit and the global optimal solution of the population, until the set number of iterations is reached, and output the global optimal solution as the target position estimation result. The iterative optimization module specifically includes: Energy factor calculation unit, used to calculate the energy factor of artificial rabbits; The update strategy selection unit is used to select the appropriate update strategy based on the value of the energy factor. The location update unit is used to update the location of the artificial rabbit according to the selected update strategy. Fitness value calculation unit, used to calculate the fitness value of artificial rabbits; The optimal solution update unit is used to update the individual optimal solution of each artificial rabbit and the global optimal solution of the population.

8. A computer device, characterized in that, It includes a memory and a processor, the memory being used to store instructions, and the processor being used to execute the instructions to implement the IARO-based localization method for occlusion-oriented scenarios as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the IARO-based localization method for occlusion-oriented scenarios as described in any one of claims 1-6.

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