Low-cost UWB downlink TDOA indoor positioning optimization method suitable for non-line-of-sight environment

Abstract

A low-cost UWB downlink TDOA indoor positioning optimization method suitable for a non-line-of-sight environment comprises the steps that TDOA data of UWB signals are converted into distance difference information, geometric condition verification is carried out, and data which do not conform to physical constraints are removed. A hierarchical calculation and dynamic data screening strategy is adopted, non-abnormal TDOA values are selected from the available TDOA data, and a Chan algorithm is executed to carry out preliminary positioning calculation; if the Chan algorithm cannot be analyzed, optimizing calculation by adopting an LS iterative algorithm; and if the number of the currently available TDOA data meets a preset condition, carrying out high-precision positioning by adopting a Taylor series iterative algorithm. And the Euclidean distance between the current positioning result and the last effective positioning coordinate and the Euclidean distance between the current positioning result and the initial positioning result of the Chan algorithm are calculated, and if the Euclidean distances exceed a set threshold value, the Euclidean distance is marked as abnormal and the data are returned to be screened again. And if the positioning result passes verification, outputting effective coordinates. According to the invention, through hierarchical processing and dynamic data screening, the non-line-of-sight error is effectively suppressed, and low-cost and high-precision real-time indoor positioning is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless communication and navigation positioning, and particularly to a low-cost UWB downlink TDOA indoor positioning optimization method applicable to non-line-of-sight environments. Background Art

[0002] With the rapid development of the Internet of Things, smart cities, and industrial automation, the importance of indoor positioning technology in fields such as logistics management, personnel tracking, and equipment monitoring has become increasingly prominent. Traditional satellite positioning technologies such as GPS often struggle to meet high-precision positioning requirements in indoor environments due to signal attenuation, reflection, and occlusion problems. Therefore, more and more research has turned to using ultra-wideband (UWB) technology for indoor positioning. UWB signals have the characteristics of wide pulse width, wide frequency band, and high time resolution, giving them significant advantages in ranging and positioning accuracy, enabling centimeter-level positioning accuracy and overcoming the influence of multipath effects to a certain extent.

[0003] The positioning method based on time difference of arrival (TDOA) estimates the target position by using the time differences of multiple base stations receiving the same signal. Among them, the downlink TDOA positioning scheme calculates the position by the base station transmitting signals and using the signal propagation delay. Compared with the uplink scheme, it can reduce the processing burden and power consumption of terminal devices. However, in actual indoor environments, the non-line-of-sight (NLOS) transmission phenomenon is widespread, and obstacles and complex propagation paths can lead to inconsistent signal propagation time delays, thus reducing the accuracy of TDOA positioning. Existing NLOS optimization methods often rely on expensive hardware configurations or complex signal processing technologies, resulting in a relatively high overall system cost and making it difficult to be widely applied in low-cost positioning systems.

[0004] Specifically, the disadvantages of the existing technologies are as follows:

[0005] 1. The influence of NLOS errors on TDOA positioning accuracy

[0006] In complex indoor environments, UWB signal propagation is easily blocked by walls, equipment, and personnel, resulting in the NLOS effect, increasing the deviation of TDOA measurement values, and thus affecting positioning accuracy.

[0007] 2. The stability of positioning data in dynamic environments

[0008] In practical applications, positioning targets (such as personnel and equipment) are constantly moving, and the measurement data may experience short-term fluctuations, resulting in unstable positioning results.

[0009] 3. Low-cost implementation of NLOS discrimination

[0010] Most existing NLOS discrimination methods rely on complex statistical models or machine learning methods, with high computational complexity and high equipment costs.

[0011] It should be noted that the information disclosed in the above background art section is only for understanding the background of the present application, and thus may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0012] The main object of the present invention is to overcome the defects existing in the above background art, and to provide a low-cost UWB downlink TDOA indoor positioning optimization method applicable to non-line-of-sight environments.

[0013] To achieve the above object, the present invention adopts the following technical solutions:

[0014] A low-cost UWB downlink TDOA indoor positioning optimization method applicable to non-line-of-sight environments, comprising the following steps:

[0015] S1. Data acquisition and preprocessing: Collect TDOA data and RSSI data of UWB signals, convert the TDOA data into distance difference information and perform geometric condition verification, and eliminate data that does not meet physical constraints;

[0016] S2. Hierarchical calculation and dynamic data screening: First, select anomaly-free TDOA values that meet preset conditions from the available TDOA data, and eliminate the value with the largest RSSI change in the remaining TDOA data, and then perform preliminary positioning calculation using the Chan algorithm; if the Chan algorithm cannot be resolved, further eliminate the value with the largest RSSI change from the current available TDOA data, and use the least squares (LS) iterative algorithm for optimization calculation; if the number of current available TDOA data meets the preset conditions, eliminate the TDOA value with the largest RSSI change in the current available data again, and use the Taylor series iterative algorithm for high-precision positioning;

[0017] S3. Dynamic data verification and anomaly elimination: After each calculation stage in step S2 is completed, calculate the Euclidean distance between the current positioning result and the previous valid positioning coordinate. If it exceeds the set first threshold, it is marked as an anomaly; at the same time, calculate the Euclidean distance between the current positioning result and the preliminary positioning result of the Chan algorithm. If it exceeds the set second threshold, it is determined as unreliable data; if the data is marked as an anomaly, return to the corresponding calculation stage in step S2 to re-screen the data and recalculate;

[0018] S4. Output the final positioning result: If the positioning result after iterative calculation passes the verification in step S3, output the valid coordinates.

[0019] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the low-cost UWB downlink TDOA indoor positioning optimization method applicable to non-line-of-sight environments.

[0020] A computer program product includes a computer program which, when executed by a processor, implements the low-cost UWB downlink TDOA indoor positioning optimization method applicable to non-line-of-sight environments as described above.

[0021] The present invention has the following beneficial effects:

[0022] The present invention proposes a low-cost UWB downlink TDOA indoor positioning optimization method applicable to non-line-of-sight environments. By fusing ultra-wideband (UWB) downlink TDOA and RSSI data, and combining the dynamic screening and optimization strategies of multi-level algorithms (Chan, least squares method, and Taylor series iteration), the indoor positioning accuracy and stability are significantly improved in non-line-of-sight (NLOS) environments. Aiming at the problems of traditional positioning technologies such as multipath effects, signal occlusion, and high hardware costs, the present invention innovatively uses the RSSI signal strength to dynamically eliminate low-quality TDOA data, and combines geometric condition verification to ensure the physical rationality of the data, effectively suppressing the time delay error caused by NLOS. Through a hierarchical calculation strategy, the low-complexity Chan algorithm is preferentially used for rapid positioning, and the high-precision Taylor algorithm is only called for iterative optimization when necessary, taking into account both the operation efficiency and the positioning accuracy, while reducing the hardware resource requirements, and realizing real-time high-precision positioning of low-cost devices. In addition, a historical positioning data comparison mechanism and a dynamic threshold judgment are introduced to filter out abnormally fluctuating data, enhancing the robustness of the system in dynamic and complex environments. Experiments show that the trajectory error of the method of the present invention in an occlusion scenario is significantly reduced compared with the benchmark algorithm, and the error cumulative distribution is more concentrated in a low range, verifying its comprehensive advantages in improving positioning reliability, stability, and economy.

[0023] Other beneficial effects in the embodiments of the present invention will be further described below. Description of the Drawings

[0024] Figure 1 It is a flowchart of the low-cost UWB downlink TDOA indoor positioning optimization method applicable to non-line-of-sight environments of the present invention.

[0025] Figure 2 It is a comparison chart of the trajectory accuracy of the RSSI-assisted and non-assisted positioning algorithms in an occlusion environment.

[0026] Figure 3 It is a comparison chart of the cumulative distribution of positioning errors based on RSSI assistance in a non-line-of-sight environment. Detailed Embodiments

[0027] The following makes a detailed description of the embodiments of the present invention. It should be emphasized that the following description is merely exemplary and not intended to limit the scope of the present invention and its applications.

[0028] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0029] See also Figure 1 The embodiment of the present invention provides a low-cost UWB downlink TDOA indoor positioning optimization method suitable for non-line-of-sight environment, comprising the following steps:

[0030] Step S1. Data collection and preprocessing: Collect TDOA data and RSSI data of UWB signals, convert TDOA data into distance difference information and perform geometric condition verification, and eliminate data that does not meet physical constraints. Among them, TDOA represents the time difference of arrival of signals from different base stations, which can be converted into distance as long as the signal propagation speed is known. RSSI represents the signal strength received by the base station and can be associated with the target distance.

[0031] In some embodiments, the geometric condition verification is specifically: based on the triangle principle, determine whether the distance difference between the two sides is greater than the length of the third side. If it is greater, mark the corresponding TDOA data as invalid and discard it.

[0032] Step S2. Hierarchical calculation and dynamic data screening: First, select the TDOA values ​​without abnormalities that meet the preset conditions from the available TDOA data, and eliminate the values ​​with the largest RSSI changes in the remaining TDOA data, and then execute the Chan algorithm to perform preliminary positioning calculations; if the Chan algorithm cannot be resolved, further eliminate the values ​​with the largest RSSI changes from the currently available TDOA data, and use the least squares (LS) iterative algorithm for optimization calculations; if the number of currently available TDOA data meets the preset conditions, eliminate the TDOA values ​​with the largest RSSI changes in the currently available data again, and use the Taylor series iterative algorithm for high-precision positioning.

[0033] In some embodiments, selecting the TDOA value without abnormality that meets the preset condition is selecting the first two TDOA values ​​without abnormality.

[0034] In some embodiments, the number of available TDOA data meets a preset condition that the number of available TDOA data reaches or exceeds three.

[0035] In some embodiments, the elimination operation based on RSSI screening is performed step by step, that is, after each calculation stage is completed, the value with the largest RSSI change in the currently available TDOA data is eliminated to gradually reduce the impact of NLOS errors.

[0036] In some embodiments, the rejection operation based on RSSI screening specifically includes:

[0037] If the Chan algorithm parsing is valid, the value with the largest RSSI change is removed from the remaining TDOA data;

[0038] If the Chan algorithm parsing is invalid, the value with the largest RSSI change is removed from the first two TDOA data;

[0039] The value with the largest RSSI change is determined by traversing the currently available TDOA data and comparing their RSSI differences, so as to preferentially suppress the signal error caused by non-line-of-sight propagation.

[0040] In some embodiments, the hierarchical calculation and dynamic data screening further include:

[0041] If the verification results of both the Chan algorithm and the LS algorithm are valid, the calculation result of the LS algorithm is used as the initial value, and the Taylor series iteration algorithm is executed for positioning optimization;

[0042] If any of the verification results of the Chan algorithm or the LS algorithm is invalid, a data rejection operation is triggered, the TDOA values that do not meet the signal quality requirements are screened and removed from the currently available TDOA data, and the hierarchical calculation process is restarted;

[0043] The termination conditions of the hierarchical calculation process include: the Taylor series iteration algorithm is verified to be valid, the preset maximum number of loops is reached, or the number of available TDOA data is insufficient.

[0044] Step S3. Dynamic data verification and abnormal rejection: After each calculation stage in step S2 is completed, calculate the Euclidean distance between the current positioning result and the previous valid positioning coordinates. If it exceeds the set first threshold (value1), it is marked as abnormal; at the same time, calculate the Euclidean distance between the current positioning result and the preliminary positioning result of the Chan algorithm. If it exceeds the set second threshold (value2), it is determined as unreliable data; if the data is marked as abnormal, return to the corresponding calculation stage in step S2 to re-screen the data and recalculate.

[0045] Step S4. Output the final positioning result: If the positioning result after iterative calculation passes the verification in step S3, output the valid coordinates.

[0046] In some embodiments, if the verification cannot be passed after the set number of iterations, restart the data acquisition and processing process.

[0047] The following further describes the specific embodiments of the present invention, its algorithm examples and experimental verification.

[0048] A low-cost UWB downlink TDOA indoor positioning optimization algorithm applicable to non-line-of-sight environments.

[0049] The solution of the present invention:

[0050] Combine RSSI (Received Signal Strength) information to assist in judging the quality of TDOA data and eliminate NLOS measurement values with large errors.

[0051] Adopt a TDOA hybrid positioning method, dynamically select appropriate TDOA combinations according to data quality, and reduce the influence brought by NLOS errors.

[0052] Hierarchical calculation strategy:

[0053] 1. Give priority to using the Chan algorithm, only select the first two anomaly-free TDOA values for calculation to improve calculation efficiency.

[0054] 2. If the Chan algorithm cannot be parsed, then adopt the LS iterative algorithm (least squares method) and use all available TDOA data for optimization calculation.

[0055] 3. When the number of TDOA is large (≥3), further adopt the Taylor series iterative algorithm for fine calculation to improve accuracy.

[0056] Data screening mechanism:

[0057] Judge the data quality through RSSI values, gradually eliminate unreliable TDOA data, reduce redundant calculations, and improve efficiency.

[0058] Use historical location information for screening:

[0059] Only retain the data that meets the above conditions, improve positioning stability, and avoid the influence of instantaneous outliers on the final result.

[0060] Calculate the Euclidean distance (value2) between the current result and the positioning result of the Chan algorithm in this round. If the deviation is too large, there may be errors.

[0061] Calculate the Euclidean distance between the current positioning result and the previous valid positioning coordinates. If it exceeds the set threshold (value1), it is considered that the data may be unreliable.

[0062] NLOS discrimination strategy based on RSSI:

[0063] Calculate the RSSI values of all available TDOA data, give priority to retaining the TDOA data with higher signal strength, and eliminate weaker signal sources.

[0064] In different calculation steps (Chan, LS, Taylor), gradually screen the TDOA data with the largest RSSI change to reduce the influence of NLOS errors.

[0065] By using a simple rule discrimination method (instead of a complex statistical learning method), reduce the computational complexity and hardware cost, and achieve efficient and low-cost NLOS error suppression.

[0066] The main innovative contributions and technical advantages of this invention are:

[0067] Precise error compensation

[0068] TDOA and RSSI fusion: Use RSSI data to evaluate the quality of TDOA measurement results, and eliminate data with weak signals and possible NLOS interference.

[0069] Multi-level positioning algorithm:

[0070] First, adopt the Chan algorithm and use the selected first two high-quality TDOA values for preliminary positioning;

[0071] When the Chan algorithm cannot meet the geometric analysis requirements, enable the LS iterative algorithm and integrate all available TDOA data;

[0072] When the number of available TDOA data reaches a certain amount (≥3), adopt the Taylor iterative algorithm for refined positioning.

[0073] Data screening and condition judgment mechanism: Set condition judgments in each level of the algorithm, and filter out abnormal data by comparing the deviation from historical positioning data or preset thresholds (such as Euclidean distance thresholds), so as to effectively compensate for NLOS errors.

[0074] Low-cost real-time positioning

[0075] Hierarchical calculation strategy: First, adopt the Chan algorithm with low computational complexity for rapid positioning, and only call the LS or Taylor algorithm with higher precision but larger computational complexity when necessary to reduce the overall computational burden.

[0076] Simplify hardware requirements: Realize error compensation through software algorithms without the support of additional expensive hardware, enabling the system to achieve high-precision real-time positioning on low-cost devices.

[0077] Dynamic data screening: Introduce condition judgments such as RSSI and geometric constraints in each positioning step to effectively reduce redundant calculations and incorrect data, and improve the operation efficiency.

[0078] Enhanced system robustness

[0079] Historical data comparison mechanism: By comparing the Euclidean distance between the current positioning result and the last valid positioning result, mutant data is automatically eliminated to improve data stability.

[0080] Multi-algorithm fusion and redundancy check: Combining the results of multiple algorithms such as Chan, LS, and Taylor, and ensuring the consistency and robustness of the final output positioning data through conditional judgment and data validity detection.

[0081] Dynamic threshold control: Adjusting data screening conditions and judgment thresholds according to real-time environmental changes (such as dynamic occlusion, multipath effect, etc.), enabling the system to maintain high positioning accuracy and stability in complex and dynamic indoor environments.

[0082] Embodiment 1

[0083] A low-cost hybrid positioning algorithm for TDOA positioning system design is designed, which combines TDOA (Time Difference of Arrival) and RSSI (Received Signal Strength Indication) data to filter out non-line-of-sight (NLOS) signals during the positioning process, thereby improving positioning accuracy.

[0084] Combination of algorithm and application field:

[0085] The algorithm is applied to a positioning system, especially in occasions where high positioning accuracy is required but the cost of traditional positioning systems needs to be reduced. The algorithm determines the target position by analyzing TDOA and RSSI data points, which are crucial for calculating the distances between the target and multiple base stations.

[0086] Main steps of algorithm implementation:

[0087] Initial data collection:

[0088] TDOA data processing:

[0089] All TDOA data is converted into distance differences (i.e., distance differences calculated based on time differences). The triangle principle is used to verify the rationality of these distance differences. Specifically, assuming that the physical distance between the base station and the target is known, the distance difference between the two sides cannot be greater than the length of the third side (i.e., the physical distance between the two base stations). This means that the present invention can exclude data that does not meet the actual physical conditions by performing geometric judgment on TDOA data.

[0090] Condition verification:

[0091] Geometric condition verification:

[0092] For all TDOA data that meet the distance difference verification, further geometric condition judgment is performed. If the geometric constraints or other validity conditions required for positioning are not met (such as unreasonable calculation of distance differences), these TDOA data are marked as invalid and discarded.

[0093] The minimum RSSI filtering algorithm shown in Table 1 filters the distance difference data based on the result validity of the Chan algorithm and the RSSI value. This algorithm is an RSSI filtering algorithm based on result validity. If Chan_result_valid is reasonable, considering that the Chan algorithm only uses the first two groups of distance difference operations, the data with the largest RSSI change in the remaining distance difference array is removed to avoid low-signal errors. If Chan_result_valid is unreasonable, considering that non-line-of-sight propagation may exist in the first two data, the data with the largest RSSI change is removed from the first two data to reduce interference and improve the calculation accuracy.

[0094] The TDOA positioning algorithm shown in Table 2 improves the positioning accuracy based on the result validity of the Chan and LS algorithms. This algorithm can be called the DL TDOA solution algorithm. If both Chan_result_valid and LS_result_valid are reasonable, the LS result is used as the initial value of the Taylor algorithm for iterative calculation to improve the accuracy. If a value is 0, it indicates that it may be affected by Nlos, and then enter "Algorithm1" to remove unreasonable distance difference data. After that, recalculate in a loop until taylor_result_valid is reliable, the number of super loops is reached, or the available data is less than 2 and then terminate.

[0095] RSSI-based filtering: As presented in the algorithm pseudocode in Table 1, in order to avoid the impact of data with poor signal quality (such as being far from the base station or having interference) on the positioning accuracy, the TDOA data with the largest RSSI change is deleted to ensure data quality. This algorithm filters the distance difference data based on the result validity of the Chan algorithm and the RSSI value. If Chan_result_valid is reasonable, considering that the Chan algorithm only uses the first two groups of distance difference operations, the data with the largest RSSI change in the remaining distance difference array is removed to avoid low-signal errors. If Chan_result_valid is unreasonable, considering that non-line-of-sight propagation may exist in the first two data, the data with the largest RSSI change is removed from the first two data to reduce interference and improve the calculation accuracy.

[0096] Pseudocode of the minimum RSSI filtering algorithm in Table 1

[0097]

[0098] Hybrid algorithm execution: As presented in the algorithm pseudocode in Table 2, the positioning accuracy is improved based on the results of the Chan and LS algorithms. If both Chan_result_valid and LS_result_valid are reasonable, the LS result is used as the initial value for the Taylor algorithm for iterative calculation to improve the accuracy; if any value is 0, it indicates that it may be affected by Nlos, and "Algorithm 1" is entered to eliminate unreasonable distance difference data. Then, the loop calculation is restarted until taylor_result_valid is reliable, the number of super loops is reached, or the available data is less than 2, at which point it terminates.

[0099] Output the final coordinates: In each iteration, if the calculated coordinates meet the preset range and relevant judgment conditions, then these coordinates are used as the final output result. If after multiple iterations, the coordinates still do not meet the requirements, they are marked as invalid and further data screening is performed.

[0100] Table 2 Pseudocode of TDOA positioning algorithm

[0101]

[0102] This algorithm interacts with several key functions in the positioning technology, especially:

[0103] Positioning accuracy: By combining TDOA and RSSI data, the positioning accuracy under NLOS conditions is improved.

[0104] Data filtering and verification: Low-quality data is effectively verified and screened through RSSI and geometric conditions to ensure the reliability of the calculated coordinates.

[0105] The interaction of these technical features has significantly improved the positioning accuracy in harsh environments, especially in environments with complex signal propagation conditions.

[0106] Experimental tests

[0107] The experiment aims to test the positioning accuracy of two different algorithms in a human occlusion environment. By comparing the experimental data of the two algorithms, their applicability and stability in complex environments are analyzed.

[0108] Experimental method

[0109] Equipment and environment settings

[0110] Select two positioning tags and apply different positioning algorithms respectively.

[0111] The tags are fixed at the same position in the jeans pocket to simulate a human occlusion environment. The height is approximately 50 cm lower than that of the base station.

[0112] Use 5 base stations, distributed within a 5×7m room range.

[0113] The reference line is set within the experimental area to ensure path standardization.

[0114] The refresh rate of the positioning system is 10 Hz.

[0115] Data acquisition process

[0116] The subject walks uniformly along the reference line for two laps to ensure uniform data sampling.

[0117] The reference line trajectory is preset, with 3,200 high-precision discrete points corresponding to each lap.

[0118] During the acquisition process, the position information of the tag is recorded and the timestamp is stored.

[0119] Data processing

[0120] Timestamp alignment

[0121] Match the experimental data with the simulation trajectory points through timestamps.

[0122] Ensure that the experimental data can be accurately mapped to the corresponding positions in the preset trajectory.

[0123] Delay elimination

[0124] Select the nearest point on the reference line at each moment as the actual alignment point.

[0125] Reduce the error caused by system delay through this method to ensure high consistency between the experimental data and the actual movement trajectory.

[0126] The environments and experimental settings of the comparative experiments are exactly the same. The only difference lies in the tag-side solution algorithm: in one group of experiments, RSSI information is used for auxiliary judgment, while in the other group, it is not used.

[0127] Experimental results

[0128] As Figure 2 shown, in the comparison of trajectory points, the method of the present invention is closer to the reference line than the baseline algorithm in most areas, indicating higher positioning accuracy in occluded environments. Due to the lack of use of RSSI data, the positioning trajectory of the baseline algorithm has more obvious deviations in specific occluded areas.

[0129] As Figure 3 shown, in the comparison of the cumulative distribution function (CDF) of errors, the method of the present invention shows a better error distribution compared to the baseline algorithm, and the errors of more points remain in a lower range. This indicates that the method of the present invention can effectively reduce errors and improve the reliability and stability of positioning.

[0130] In summary, the present invention improves data credibility and algorithm robustness through distance difference judgment based on TDOA and geometric condition verification, ensuring that TDOA data conforms to physical constraints, thereby improving positioning accuracy. The first two TDOA values are used in combination with the Chan algorithm for preliminary calculation to reduce computational complexity and improve positioning speed. Subsequently, the least squares method (LS) optimizes the initial result to reduce errors and enhance robustness. At the same time, RSSI value filtering is combined to eliminate data with poor signal quality and improve the reliability of positioning. In addition, the present invention integrates the Chan, LS, and Taylor algorithms to achieve multi-algorithm optimization and flexible switching to adapt to different scenario requirements. The iterative optimization of the Taylor algorithm further improves the accuracy. Even if there is a deviation in the initial estimate, the error can still be effectively corrected. At the same time, the dynamic elimination strategy of TDOA data reduces NLOS errors and gradually refines the positioning result, ensuring that the system can still maintain high accuracy and stability in complex environments.

[0131] An embodiment of the present invention also provides a storage medium for storing a computer program, which when executed, at least executes the method described above.

[0132] An embodiment of the present invention also provides a control device, including a processor and a storage medium for storing a computer program; wherein, the processor is used to execute the computer program to at least execute the method described above.

[0133] An embodiment of the present invention also provides a processor, which executes a computer program to at least execute the method described above.

[0134] The storage medium can be implemented by any type of non-volatile storage device, or a combination thereof. Among them, the non-volatile memory can be a read-only memory (ROM, Read Only Memory), a programmable read-only memory (PROM, Programmable Read-Only Memory), an erasable programmable read-only memory (EPROM, Erasable Programmable Read-Only Memory), an electrically erasable programmable read-only memory (EEPROM, Electrically Erasable Programmable Read-Only Memory), a ferromagnetic random access memory (FRAM, Ferromagnetic Random Access Memory), a flash memory (Flash Memory), a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM, Compact Disc Read-Only Memory); the magnetic surface memory can be a disk memory or a tape memory. The storage medium described in the embodiments of the present invention is intended to include, but not limited to, these and any other suitable types of memories.

[0135] In several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the couplings between the various components shown or discussed, either direct couplings or communication connections, can be through some interfaces. The indirect couplings or communication connections of devices or units can be electrical, mechanical, or in other forms.

[0136] The units described above as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. One can select some or all of the units according to actual needs to achieve the purpose of the solution of this embodiment.

[0137] In addition, in each embodiment of the present invention, the various functional units can all be integrated in one processing unit, or each unit can be separately used as a unit, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware, or in the form of a combination of hardware and software functional units.

[0138] Those of ordinary skill in the art can understand that all or part of the steps for implementing the above method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps including the above method embodiments. The aforementioned storage medium includes various media that can store program codes, such as removable storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0139] Alternatively, if the above integrated units of the present invention are implemented in the form of software functional modules and sold or used as independent products, they may also be stored in a computer-readable storage medium. Based on such an understanding, the technical solutions of the embodiments of the present invention, in essence or the part that contributes to the prior art, may be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as removable storage devices, ROM, RAM, magnetic disks, or optical discs.

[0140] The methods disclosed in the several method embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method embodiments.

[0141] The features disclosed in the several product embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new product embodiments.

[0142] The features disclosed in the several method or device embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method embodiments or device embodiments.

[0143] The above content is a further detailed description of the present invention in combination with specific preferred implementation manners. It cannot be determined that the specific implementation of the present invention is only limited to these descriptions. For those skilled in the art of the present invention, without departing from the concept of the present invention, several equivalent substitutions or obvious variations can be made, and as long as the performance or use is the same, they should all be regarded as belonging to the protection scope of the present invention.

Claims

1. A low-cost UWB downlink TDOA indoor positioning optimization method applicable to non-line-of-sight environments, characterized in that The following steps are involved: S1. Data collection and preprocessing: Collect TDOA data and RSSI data of UWB signals, convert TDOA data into distance difference information and perform geometric condition verification, and eliminate data that does not meet physical constraints; S2. Hierarchical calculation and dynamic data screening: First, select the TDOA values ​​without abnormalities that meet the preset conditions from the available TDOA data, and remove the values ​​with the largest RSSI changes in the remaining TDOA data, and then execute the Chan algorithm for preliminary positioning calculation; if the Chan algorithm cannot be resolved, further remove the values ​​with the largest RSSI changes from the currently available TDOA data, and use the least squares (LS) iterative algorithm for optimization calculation; if the number of currently available TDOA data meets the preset conditions, remove the TDOA values ​​with the largest RSSI changes in the currently available data again, and use the Taylor series iterative algorithm for high-precision positioning; S3. Dynamic data verification and exception elimination: After each calculation stage of step S2 is completed, the Euclidean distance between the current positioning result and the last valid positioning coordinate is calculated. If it exceeds the set first threshold, it is marked as abnormal; at the same time, the Euclidean distance between the current positioning result and the preliminary positioning result of the Chan algorithm is calculated. If it exceeds the set second threshold, it is determined to be unreliable data; if the data is marked as abnormal, return to the corresponding calculation stage in step S2 to re-screen the data and recalculate; S4. Output the final positioning result: If the positioning result after iterative calculation passes the verification of step S3, the valid coordinates are output.

2. The low-cost UWB downlink TDOA indoor positioning optimization method applicable to non-line-of-sight environments according to claim 1, wherein In step S1, the geometric condition verification is specifically as follows: based on the triangle principle, determine whether the distance difference between the two sides is greater than the length of the third side. If so, mark the corresponding TDOA data as invalid and discard it.

3. The low-cost UWB downlink TDOA indoor positioning optimization method applicable to non-line-of-sight environments according to claim 1, characterized in that In step S2, the selecting of the TDOA values ​​without abnormality that meet the preset conditions is selecting the first two TDOA values ​​without abnormality.

4. The low-cost UWB downlink TDOA indoor positioning optimization method applicable to non-line-of-sight environments according to claim 1, wherein, In step S2, the number of available TDOA data meets a preset condition that the number of available TDOA data reaches or exceeds three.

5. The low-cost UWB downlink TDOA indoor positioning optimization method applicable to non-line-of-sight environments according to claim 1, wherein In step S2, the elimination operation based on RSSI screening is performed step by step, that is, after each calculation stage is completed, the value with the largest RSSI change in the currently available TDOA data is eliminated to gradually reduce the impact of NLOS errors.

6. The low-cost UWB downlink TDOA indoor positioning optimization method applicable to non-line-of-sight environments according to any one of claims 1 to 5, characterized in that, In step S2, the RSSI-based elimination operation specifically includes: If the Chan algorithm analysis is valid, the value with the largest RSSI change is removed from the remaining TDOA data; If the Chan algorithm analysis is invalid, the value with the largest RSSI change is removed from the first two TDOA data; The value with the largest RSSI change is determined by traversing the currently available TDOA data and comparing the RSSI differences thereof, so as to preferentially suppress signal errors caused by non-line-of-sight propagation.

7. The low-cost UWB downlink TDOA indoor positioning optimization method applicable to non-line-of-sight environments according to any one of claims 1 to 6, characterized in that In step S2, the hierarchical calculation and dynamic data screening further include: If the verification results of the Chan algorithm and the LS algorithm are both valid, the calculation results of the LS algorithm are used as the initial value, and the Taylor series iteration algorithm is executed for positioning optimization; If any of the verification results of the Chan algorithm or the LS algorithm is invalid, a data rejection operation is triggered to screen and reject the TDOA values that do not meet the signal quality requirements from the current available TDOA data, and the hierarchical calculation process is restarted. The termination conditions of the hierarchical calculation process include: the Taylor series iteration algorithm is verified to be effective, the preset maximum number of loops is reached, or the number of available TDOA data is insufficient.

8. The low-cost UWB downlink TDOA indoor positioning optimization method applicable to non-line-of-sight environments according to any one of claims 1 to 7, characterized in that, In step S4, if the verification still cannot pass after the set number of iterations, the data acquisition and processing process is restarted.

9. A computer-readable storage medium storing a computer program, characterized in that, When executed by a processor, the computer program implements the low-cost UWB downlink TDOA indoor positioning optimization method applicable to non-line-of-sight environments according to any one of claims 1 to 8.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the low-cost UWB downlink TDOA indoor positioning optimization method applicable to non-line-of-sight environments according to any one of claims 1 to 8.

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