Time difference positioning method and device based on signal interpolation

Abstract

The invention provides a time difference positioning method and device based on signal interpolation, and particularly relates to the technical field of space electromagnetic target localization, and the method achieves the precise determination of a target position through receiving and processing signals from a plurality of signal sources, and comprises the steps: firstly, collecting the discrete sample data of each signal source; the method comprises the following steps of: acquiring a sampling interval, intelligently identifying a relationship between the sampling interval and an estimated time difference true value, if a non-integer ratio exists, automatically starting a signal interpolation technology, generating dense sampling data to improve time resolution, then accurately calculating a time difference between signal sources based on the dense data, and quickly and accurately positioning a target position by combining position information and applying a time difference positioning algorithm. The technical problem that an existing time difference positioning technology is low in positioning precision is solved, so that the positioning precision of various modulation signals can be effectively improved, and the requirement that the positioning result is smaller than 5% R (typical value) is met.

Description

Technical Field

[0001] The present invention relates to the technical field of space electromagnetic target positioning, and in particular to a time difference positioning method and device based on signal interpolation. Background Art

[0002] With the rapid development of modern information technology and the growing demand for national defense and security, time difference positioning technology (TDOA), as an efficient and accurate passive positioning method, has shown great application potential and value in the fields of radar detection, wireless communication networks, drone monitoring, electronic warfare and counter-terrorism. In recent years, with the continuous emergence of new threat targets such as low-altitude drones and stealth aircraft, higher requirements have been placed on the real-time, accuracy and anti-interference capabilities of positioning systems. However, in practical applications, time difference positioning technology also faces many challenges, such as signal interference in complex electromagnetic environments, positioning errors caused by multipath effects, signal detection difficulties under low signal-to-noise ratios, and data processing and real-time requirements under large-scale node deployment. In addition, the diversity of different modulated signals also increases the complexity of the design of time difference positioning algorithms, making it difficult for traditional methods to maintain high positioning accuracy in all scenarios.

[0003] In view of the above problems encountered by time difference positioning technology in practical applications, traditional technologies mainly solve them through the following ways: First, at the signal processing level, advanced signal processing technologies such as filtering, denoising, and feature extraction are used to improve signal quality and reduce the impact of interference and multipath effects on positioning accuracy. Secondly, in terms of algorithm design, the time difference measurement algorithm is optimized, and the correlation and statistical characteristics of the signal are used to accurately estimate the time difference of arrival (TDOA). At the same time, multi-station data fusion technology is combined to improve the robustness and accuracy of positioning. In addition, in response to the data processing challenges brought by large-scale node deployment, traditional technologies also use advanced architectures such as distributed computing and cloud computing to achieve rapid data processing and real-time positioning. For the positioning problem of different modulated signals, traditional methods build general models suitable for multiple signal types or use adaptive algorithms to adapt to the characteristics of different modulated signals, thereby improving the flexibility and accuracy of positioning. However, although traditional technologies have solved the above problems to a certain extent, when facing more complex and changeable application scenarios, they are affected by factors such as time measurement errors, the relative geometric positions of signal sources and various monitoring devices (monitoring node layout methods), resulting in low positioning accuracy.

[0004] In summary, how to overcome the technical problem of low positioning accuracy of the existing time difference positioning technology is a difficult problem that needs to be solved urgently in this field. Summary of the invention

[0005] The main purpose of the present invention is to provide a time difference positioning method and device based on signal interpolation to overcome the technical problem of low positioning accuracy of existing time difference positioning technology, thereby effectively improving the positioning accuracy of multiple modulated signals and meeting the requirement that the positioning result is less than 5%R (typical value).

[0006] In order to achieve the above object, the present invention provides a time difference positioning method and device based on signal interpolation, which can effectively improve the positioning accuracy of various modulated signals and meet the requirement that the positioning result is less than 5%R (typical value).

[0007] In a first aspect, the present invention provides a time difference positioning method based on signal interpolation, the method comprising:

[0008] Receive signals from multiple signal sources, and perform sampling processing on each signal to obtain discrete sample data of each signal;

[0009] Determine the sampling interval of the discrete sample data and the true value of the estimated time difference between the signal sources;

[0010] When the ratio of the sampling interval to the true value of the estimated time difference is a non-integer, starting a signal interpolation process to process the discrete sample data to obtain densely sampled sequence data;

[0011] Based on the densely sampled sequence data, obtaining the time difference between the signal sources;

[0012] The position information of the signal source is obtained, and the target position is determined by using a time difference positioning algorithm according to the time difference value and the position information of the signal source.

[0013] Optionally, the start signal interpolation process processes the discrete sample data to obtain densely sampled sequence data, including:

[0014] Signal interpolation technology is used to insert additional data points between the discrete sample data to form a denser sampling sequence, thereby obtaining dense sampling sequence data.

[0015] Optionally, the signal interpolation technology includes but is not limited to linear interpolation, spline interpolation, interpolation based on a sinc function, and parabola interpolation.

[0016] Optionally, the selection criteria of the signal interpolation technology include but are not limited to interpolation accuracy, computational complexity and real-time requirements.

[0017] Optionally, after the start signal interpolation process processes the discrete sample data to obtain dense sampling sequence data, it also includes: filtering the dense sampling sequence data, wherein the time difference between the signal sources is obtained based on the dense sampling sequence data, and the time difference between the signal sources is obtained based on the dense sampling sequence data after filtering.

[0018] In a second aspect, the present invention provides a time difference positioning device based on signal interpolation, and the device is applied to various possible implementations of the first aspect, including:

[0019] A signal receiving module, used for receiving signals from multiple signal sources;

[0020] A sampling processing module is used to perform sampling processing on the received signal to generate discrete sample data;

[0021] An interpolation start module, used for starting and executing a signal interpolation process to process the discrete sample data;

[0022] A time difference calculation module calculates the time difference between signal sources based on the interpolated data;

[0023] The acquisition and positioning calculation module acquires the position information of the signal source, and determines the target position by using a time difference positioning algorithm according to the time difference value and the position information of the signal source.

[0024] Optionally, the interpolation starting module includes:

[0025] A signal interpolation module, the signal interpolation module is used to insert additional data points between the discrete sample data, wherein the signal interpolation module includes an interpolation method selection unit, the interpolation method selection unit is used to select the most appropriate signal interpolation technology according to a preset criterion.

[0026] Optionally, the device further includes: a filtering processing module, configured to perform filtering processing on the interpolated data.

[0027] Optionally, the device further includes a display module for displaying the positioning result of the target position.

[0028] The present application provides a time difference positioning method and device based on signal interpolation, which realizes the precise positioning of the target position by accurately processing signals from multiple signal sources. The technology first collects discrete sample data of each signal source and identifies the relationship between the sampling interval and the estimated true value of the time difference. When a non-integer ratio is found, the signal interpolation technology is automatically used to convert the discrete sample data into a dense sampling sequence to improve the accuracy of time measurement. Subsequently, the time difference between the signal sources is accurately calculated based on the dense sampling data, and the target position is quickly and accurately inferred by combining the known position information of the signal source with the advanced time difference positioning algorithm. The technical problem of low positioning accuracy of the existing time difference positioning technology is overcome, so that the positioning accuracy of a variety of modulated signals can be effectively improved to meet the requirement that the positioning result is less than 5%R (typical value). BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The drawings constituting a part of the present application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0030] Figure 1 A schematic diagram of the flow of the time difference positioning method based on signal interpolation provided by this application;

[0031] Figure 2 This is a schematic diagram of the time difference positioning device based on signal interpolation provided in this application.

[0032] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below in conjunction with the drawings in this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0034] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in sequences other than those illustrated or described herein.

[0035] In the present invention, 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 this 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.

[0036] With the rapid development of modern information technology and the growing demand for national defense and security, time difference positioning technology (TDOA), as an efficient and accurate passive positioning method, has shown great application potential and value in the fields of radar detection, wireless communication networks, drone monitoring, electronic warfare and counter-terrorism. In recent years, with the continuous emergence of new threat targets such as low-altitude drones and stealth aircraft, higher requirements have been placed on the real-time, accuracy and anti-interference capabilities of positioning systems. However, in practical applications, time difference positioning technology also faces many challenges, such as signal interference in complex electromagnetic environments, positioning errors caused by multipath effects, signal detection difficulties under low signal-to-noise ratios, and data processing and real-time requirements under large-scale node deployment. In addition, the diversity of different modulated signals also increases the complexity of the design of time difference positioning algorithms, making it difficult for traditional methods to maintain high positioning accuracy in all scenarios.

[0037] In view of the above problems encountered by time difference positioning technology in practical applications, traditional technologies mainly solve them through the following ways: First, at the signal processing level, advanced signal processing technologies such as filtering, denoising, and feature extraction are used to improve signal quality and reduce the impact of interference and multipath effects on positioning accuracy. Secondly, in terms of algorithm design, the time difference measurement algorithm is optimized, and the correlation and statistical characteristics of the signal are used to accurately estimate the time difference of arrival (TDOA). At the same time, multi-station data fusion technology is combined to improve the robustness and accuracy of positioning. In addition, in response to the data processing challenges brought by large-scale node deployment, traditional technologies also use advanced architectures such as distributed computing and cloud computing to achieve rapid data processing and real-time positioning. For the positioning problem of different modulated signals, traditional methods build general models suitable for multiple signal types or use adaptive algorithms to adapt to the characteristics of different modulated signals, thereby improving the flexibility and accuracy of positioning. However, although traditional technologies have solved the above problems to a certain extent, when facing more complex and changeable application scenarios, they are affected by factors such as time measurement errors, the relative geometric positions of signal sources and various monitoring devices (monitoring node layout methods), resulting in low positioning accuracy.

[0038] In summary, how to overcome the technical problem of low positioning accuracy of the existing time difference positioning technology is a difficult problem that needs to be solved urgently in this field.

[0039] The time difference positioning method and device based on signal interpolation provided by the present application, the method receives signals from multiple signal sources, samples and obtains discrete data, identifies the relationship between the sampling interval and the estimated time difference, and uses interpolation technology to enhance data density when the ratio is non-integer, accurately calculates the time difference between signals, combines the position information of the signal source, and uses the time difference positioning algorithm to achieve accurate determination of the target position. The method overcomes the technical problem of low positioning accuracy of the existing time difference positioning technology, thereby effectively improving the positioning accuracy of multiple modulated signals and meeting the requirement that the positioning result is less than 5%R (typical value).

[0040] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0041] Figure 1 The flowchart of the time difference positioning method based on signal interpolation provided by the present application is provided, and the time difference positioning method based on signal interpolation is described in detail, such as Figure 1 As shown, the time difference positioning method based on signal interpolation provided in this embodiment includes:

[0042] S101: receiving signals from multiple signal sources, and performing sampling processing on each signal to obtain discrete sample data of each signal.

[0043] Among them, when implementing this step specifically, it is necessary to build a receiving and sampling processing system for multiple signal source signals. The receiving and sampling processing system for multiple signal source signals described in this embodiment mainly includes the following parts:

[0044] Multi-channel signal receiving module: responsible for receiving analog signals from multiple signal sources at the same time. This module uses a high-performance multiplexer or independent signal receiver to ensure that the signals from each signal source can be received without interfering with each other.

[0045] High-precision analog-to-digital converter (ADC) array: Each signal channel corresponds to one or more high-precision ADCs, which are used to convert the received analog signals into digital signals. The selection of ADCs should be based on the requirements of signal bandwidth, resolution, and sampling rate to ensure the accuracy and efficiency of signal conversion.

[0046] Signal processing unit (DSP / FPGA): responsible for the preliminary processing of the converted digital signal, including but not limited to filtering, gain adjustment, denoising, etc., to ensure the accuracy and reliability of subsequent sampling processing.

[0047] Sampling control module: controls the ADC sampling process according to preset sampling parameters (such as sampling rate, sampling time length, etc.) to ensure that the synchronous or asynchronous sampling requirements of all signal source signals are met.

[0048] Data storage and transmission module: Store the processed discrete sample data in local storage media (such as SD card, hard disk, etc.), or transmit it to external devices or cloud servers in real time through communication interfaces (such as Ethernet, USB, Wi-Fi, etc.).

[0049] The specific implementation steps include:

[0050] Step 1: Initialize the system

[0051] Start the system and initialize the multi-channel signal receiving module, ADC array, signal processing unit, sampling control module and data storage and transmission module.

[0052] Configure the parameters of each module, including the resolution and sampling rate of the ADC, the processing algorithm of the signal processing unit, and the target address and format of data storage and transmission.

[0053] Step 2: Receive the signal

[0054] The multi-channel signal receiving module receives analog signals from multiple signal sources simultaneously, and each signal source is received through an independent channel.

[0055] The built-in signal integrity detection mechanism ensures that the quality of the received signal meets the preset standards.

[0056] Step 3: Signal sampling

[0057] The sampling control module controls the ADC array to perform synchronous or asynchronous sampling on the signal of each channel according to preset sampling parameters.

[0058] Each ADC converts the analog signal of its corresponding channel into a digital signal and outputs it to the signal processing unit.

[0059] Step 4: Signal processing

[0060] The signal processing unit receives the digital signal from the ADC and performs pre-processing operations such as filtering, gain adjustment, and denoising to improve signal quality.

[0061] If necessary, signal synchronization processing can be performed to ensure the temporal consistency of data from multiple signal sources.

[0062] Step 5: Data storage and transmission

[0063] The processed discrete sample data is stored in a local storage medium according to a predetermined format.

[0064] If real-time transmission is required, the data is sent to an external device or cloud server through the data storage and transmission module.

[0065] S102: Determine the sampling interval of the discrete sample data and the true value of the estimated time difference between the signal sources.

[0066] The sampling interval is closely related to the sampling rate. The higher the sampling rate, the smaller the sampling interval, the higher the resolution of the signal, and the higher the accuracy of the processing result. On the contrary, if the sampling rate is too low or the sampling interval is too large, it will cause signal distortion and affect the reliability of the processing result. The estimated time difference true value refers to the best estimate of the time difference between the signals sent by two or more signal sources and the arrival of a certain receiving point or receiving device under certain measurement and calculation conditions.

[0067] The specific implementation includes the following steps:

[0068] Step 1: Analyze signal characteristics and requirements

[0069] Collect signal source information: First, collect basic information of all signal sources, including signal type (such as analog signal, digital signal), frequency range, bandwidth, etc.

[0070] Clarify application requirements: According to the actual application scenario, clarify the accuracy requirements for the sampling interval and the true value of the time difference. For example, in a communication system that requires high-precision synchronization, the accuracy requirements for the true value of the time difference may be very high.

[0071] Step 2: Determine the sampling interval

[0072] Based on Nyquist theorem: Based on the highest frequency component of the signal, the minimum sampling rate is determined using Nyquist sampling theorem. The sampling interval is the inverse of the sampling rate.

[0073] Consider system resources: On the basis of satisfying the Nyquist theorem, combined with factors such as system processing capability and storage resources, the actual sampling interval to be used should be determined comprehensively.

[0074] Experimental verification: Through actual sampling and observing the signal reconstruction effect, the sampling interval is fine-tuned to ensure that both the accuracy requirements are met and the system resources are effectively utilized.

[0075] Step 3: Estimate the true value of the time difference between signal sources

[0076] Synchronous signal sources: If possible, use synchronized signal sources or synchronized trigger mechanisms to ensure that the signal sources are time-coherent. This helps to simplify the estimation of the true value of the time difference.

[0077] Acquire reference signal: Under known or assumed synchronization conditions, simultaneously acquire a reference signal from each signal source.

[0078] Feature extraction and matching: Extract features from the collected reference signal, such as waveform features, frequency components, phase difference, etc. Use these features to match between signal sources to identify relative time offsets.

[0079] Statistical analysis and optimization: Statistical analysis is performed on the time offsets obtained from multiple measurements, outliers are removed, and the average or weighted average is calculated as the estimated true time difference value. At the same time, optimization algorithms (such as least squares method, Kalman filtering, etc.) can be used to further improve the estimation accuracy.

[0080] Verification and calibration: Verify the estimated time difference true value through test data in actual application scenarios. If necessary, calibrate the estimation process based on the verification results.

[0081] S103: When the ratio of the sampling interval to the true value of the estimated time difference is a non-integer, a signal interpolation process is started to process the discrete sample data to obtain densely sampled sequence data.

[0082] The densely sampled sequence data is used to indicate data generated after the discrete sample data is processed through an interpolation process.

[0083] The specific implementation includes the following steps:

[0084] Step 1: Detect ratio status

[0085] Calculate the ratio: First, calculate the ratio between the preset sampling interval and the estimated true value of the time difference.

[0086] Determine non-integer conditions: Determine whether the signal interpolation process needs to be started by comparing whether the calculation result is an integer. If the ratio is a non-integer, execute the subsequent steps.

[0087] Step 2: Select the interpolation method

[0088] Analyze signal characteristics: Select an appropriate interpolation method based on the signal type, frequency content, noise level and other characteristics. Common interpolation methods include linear interpolation, polynomial interpolation, Lagrange interpolation, spline interpolation (such as cubic spline interpolation), etc.

[0089] Consider accuracy and computational complexity: When choosing an interpolation method, you need to weigh the interpolation accuracy and computational complexity. High-precision interpolation methods may require more computing resources, while interpolation methods with less computational complexity may sacrifice a certain degree of accuracy in some cases.

[0090] Step 3: Perform signal interpolation

[0091] Determine interpolation points: Based on the non-integer ratio and the original discrete sample data, determine where new sample points need to be inserted. These locations are usually located between the original sample points to generate a denser sampling sequence.

[0092] Apply interpolation algorithm: Use the selected interpolation method to calculate and generate sample values ​​at the interpolation points. These interpolated sample values ​​should be continuous and smoothly transitioned with the original sample values ​​in time.

[0093] Construct a dense sampling sequence: combine the interpolated sample values ​​with the original sample values ​​to construct a denser sampling sequence data. This sequence data should be able to more accurately reflect the actual changes in the signal.

[0094] Step 4: Verification and Adjustment

[0095] Verify interpolation effect: Verify whether the interpolation effect meets the expected requirements by comparing the signal waveform and spectrum characteristics before and after interpolation.

[0096] Adjust interpolation parameters: If the interpolation effect is not ideal, you can adjust the interpolation method, interpolation point location or interpolation algorithm parameters to obtain better interpolation results.

[0097] Preferably, the start signal interpolation process processes the discrete sample data to obtain densely sampled sequence data, comprising:

[0098] Signal interpolation technology is used to insert additional data points between the discrete sample data to form a denser sampling sequence, thereby obtaining dense sampling sequence data.

[0099] Optionally, the signal interpolation technology includes but is not limited to linear interpolation, spline interpolation, interpolation based on a sinc function, and parabola interpolation.

[0100] Optionally, the selection criteria of the signal interpolation technology include but are not limited to interpolation accuracy, computational complexity and real-time requirements.

[0101] Optionally, after the start signal interpolation process processes the discrete sample data to obtain dense sampling sequence data, it also includes: filtering the dense sampling sequence data, wherein the time difference between the signal sources is obtained based on the dense sampling sequence data, and the time difference between the signal sources is obtained based on the dense sampling sequence data after filtering.

[0102] It can be understood that this step provides a method for processing discrete sample data and obtaining dense sampling sequence data by using a signal interpolation process based on non-integer ratios. By detecting the ratio state of the sampling interval and the true value of the estimated time difference, selecting a suitable interpolation method, performing signal interpolation, and verifying and adjusting the interpolation effect, this method can ensure that more accurate and denser sampling sequence data are generated under non-integer ratio conditions, providing strong support for subsequent signal processing and analysis.

[0103] S104: Based on the dense sampling sequence data, obtain the time difference between the signal sources.

[0104] The specific implementation process of obtaining the time difference between the signal sources includes:

[0105] Step 1: Prepare densely sampled sequence data

[0106] Data acquisition: First, based on the densely sampled sequence data generated in the previous steps, ensure that the data has been interpolated to more accurately reflect the actual changes in the signal.

[0107] Data preprocessing: Perform necessary preprocessing on densely sampled sequence data, including denoising, filtering, and outlier removal, to improve the accuracy of subsequent time difference calculations.

[0108] Step 2: Feature extraction and matching

[0109] Feature extraction: Extract key features from the densely sampled sequence data of each signal source, such as waveform characteristics, frequency components, phase information, etc. These features should be able to uniquely identify the signal source and reflect the relative time relationship between signals.

[0110] Feature matching: Using the extracted features, matching is performed between densely sampled sequence data from different signal sources. The matching process should ensure that the corresponding signal segments or feature points are found so that the time difference can be calculated later.

[0111] Step 3: Calculate the time difference

[0112] Determine reference points: Select one or more reference points from the successfully matched signal segments or feature points as reference points for time difference calculation. These reference points should have significant waveform features or phase information to facilitate accurate measurement of time difference.

[0113] Calculate time difference: Calculate the time difference between different signal sources based on the selected reference point. The calculation process should take into account the influence of factors such as signal propagation speed and propagation path on the time difference, and make corresponding corrections.

[0114] Statistical analysis and optimization: Statistical analysis is performed on the time difference values ​​obtained from multiple measurements, outliers are eliminated, and the average or weighted average is calculated as the final true time difference value. At the same time, optimization algorithms (such as least squares method, Kalman filtering, etc.) can be used to further improve the accuracy and stability of time difference calculation.

[0115] Step 4: Verification and Calibration

[0116] Experimental verification: Verify the accuracy and reliability of the time difference calculation through simulation experiments or actual tests. The verification process should cover conditions such as different signal sources, different propagation environments, and different noise levels.

[0117] Calibration and adjustment: Based on the verification results, necessary calibration and adjustment are performed on the time difference calculation process. The calibration content may include adjusting the feature extraction method, optimizing the matching algorithm, correcting the propagation model, etc.

[0118] It can be understood that this method can ensure accurate calculation of the time difference between signal sources in a complex environment by preparing densely sampled sequence data, feature extraction and matching, time difference calculation, and verification and calibration.

[0119] S105: Acquire the location information of the signal source, and determine the target location using a time difference positioning algorithm according to the time difference value and the location information of the signal source.

[0120] The specific implementation steps of determining the target position by using the time difference positioning algorithm according to the time difference value and the position information of the signal source include:

[0121] Step 1: Get the location information of the signal source

[0122] Data collection: First, collect the location information of all signal sources involved in positioning. This information can be longitude and latitude coordinates, three-dimensional space coordinates, or other forms of positioning identification, depending on the application scenario and the requirements of the positioning system.

[0123] Data verification: Verify the location information of the collected signal source to ensure its accuracy and reliability. The verification process may include checking the data format, comparing historical records, using auxiliary positioning equipment, etc.

[0124] Step 2: Determine the time difference

[0125] Time difference acquisition: According to the above steps, the time difference between signal sources is calculated using densely sampled sequence data. These time differences should accurately reflect the relative time difference of the target signal reaching different signal sources.

[0126] Data preprocessing: Perform necessary preprocessing on the time difference, such as denoising, filtering, and outlier removal, to improve the accuracy of subsequent positioning calculations.

[0127] Step 3: Apply the time difference positioning algorithm

[0128] Algorithm selection: Select the appropriate time difference positioning algorithm according to the application scenario and positioning accuracy requirements. Common time difference positioning algorithms include hyperbolic positioning method, least squares method, Kalman filter method, etc.

[0129] Geometric relationship construction: The geometric relationship between the target position and the signal source is constructed using the position information and time difference of the signal source. In two-dimensional space, this is usually expressed as a series of hyperbolas; in three-dimensional space, it may involve more complex geometric structures.

[0130] Position calculation: The target's position coordinates are calculated by solving the constructed geometric relationship. The calculation process may involve complex mathematical operations and iterative optimization to ensure the accuracy and stability of the results.

[0131] Step 4: Result verification and optimization

[0132] Result verification: Verify the calculated target position to ensure that it meets the actual situation and expected requirements. The verification process may include comparison with known target positions, cross-validation using other positioning methods, etc.

[0133] Optimization and adjustment: According to the verification results, make necessary optimization and adjustment to the time difference positioning algorithm and parameters. The optimization may include improving the algorithm model, adjusting the calculation parameters, introducing new auxiliary information, etc.

[0134] Step 5: Output positioning results

[0135] The finalized target location information is output in an appropriate format for subsequent application or analysis. The output format may include text, charts, electronic maps, etc., depending on user needs and system configuration.

[0136] Figure 2 A schematic diagram of a time difference positioning device based on signal interpolation provided by the present application; Figure 2 As shown, the time difference positioning device 200 based on signal interpolation provided in this embodiment includes:

[0137] A signal receiving module 201, used for receiving signals from multiple signal sources;

[0138] The sampling processing module 202 is used to perform sampling processing on the received signal to generate discrete sample data;

[0139] An interpolation start module 203, used to start and execute a signal interpolation process to process the discrete sample data;

[0140] A time difference calculation module 204 calculates the time difference between signal sources based on the interpolated data;

[0141] The acquisition and positioning calculation module 205 acquires the position information of the signal source, and determines the target position by using a time difference positioning algorithm according to the time difference value and the position information of the signal source.

[0142] Optionally, the interpolation starting module 203 includes:

[0143] A signal interpolation module, the signal interpolation module is used to insert additional data points between the discrete sample data, wherein the signal interpolation module includes an interpolation method selection unit, the interpolation method selection unit is used to select the most appropriate signal interpolation technology according to a preset criterion.

[0144] Optionally, the device further includes: a filtering processing module, configured to perform filtering processing on the interpolated data.

[0145] Optionally, the device further includes a display module for displaying the positioning result of the target position.

[0146] A more specific embodiment of the present invention is as follows:

[0147] 1. Hardware Platform

[0148] The test system of the present invention is composed of 4 monitoring nodes and a signal transmitting source. Each monitoring node includes a computer, a monitoring receiving device (including GPS), an antenna, a feeder and other devices or cables. The signal transmitting source includes an antenna, a feeder, a power amplifier and a signal source.

[0149] 2. Experimental Data

[0150] In the experiment of the present invention, in order to reduce the influence of multipath effect, an open field was selected to build the test system and conduct the experiment. An open field in Sanyuan County, Shaanxi Province was selected for the experiment. The coordinates of the four monitoring nodes (301, 402, 601, 701) are shown in Table 1, where the longitude and latitude are measured using the "Longitude and Latitude Query and Conversion" APP, which is the WGS84 coordinate. The altitude of the monitoring node antenna was adjusted by selecting points and tripods to ensure data collection in the same plane.

[0151] Table 1 Location of monitoring nodes

[0152]

[0153] During the experiment, the position of the signal source was adjusted four times and four rounds of data collection were carried out. The position of the signal source is shown in Table 2, where the longitude and latitude were determined using the “Longitude and Latitude Query and Conversion” APP and are WGS84 coordinates.

[0154] Table 2 Signal source location

[0155]

[0156] Data collection is carried out in four rounds. In each round of experiments, the signal source transmits signals of different modulation types, such as ASK, 8PSK, QPSK, and 16QAM. The four monitoring nodes collect IQ data simultaneously at different sampling rates. Finally, the collected data are summarized for algorithm error analysis.

[0157] The data IQ1, IQ2, IQ3, and IQ4 collected by the four monitoring nodes represent the first, second, third, and fourth rounds of collection respectively. Folders 1 to 24 represent different modulation types, sampling rates, and collection bandwidths respectively. The *bin file is the collected IQ data.

[0158] 3. Experimental results and evaluation

[0159] In the experiment, node 601 is taken as the main node, and the positioning error of the algorithm is calculated under the conditions of no interpolation, 3 times interpolation, 5 times interpolation, 10 times interpolation and 15 times interpolation.

[0160] The data analysis results of four rounds of collection show that under 10 times interpolation, the positioning error basically meets the requirement of positioning accuracy less than or equal to 5%R (typical value), which can effectively improve the positioning accuracy of the non-interpolation case. The positioning error is within a circular area (with a diameter of 46m and the center is the signal source).

[0161] The positioning method mainly includes modules such as interpolation, data alignment, and correlation positioning. The running time of each module is shown in Table 3.

[0162] Table 3. Running time of each algorithm module

[0163]

[0164] Through analysis, it is found that the interpolation module is time-consuming. After 10 times interpolation, the running time of the algorithm basically meets the requirement of positioning time ≤ 5s.

[0165] Through theoretical analysis and practical verification, after a certain multiple of interpolation processing of the original data, the positioning error and positioning time can basically meet the requirements. For the original data with a sampling rate of 5M, the positioning accuracy meets the requirements when no interpolation is performed, and the positioning time is about 1s. For the original data with a sampling rate of 2.5M, the positioning accuracy of some data does not meet the requirements when no interpolation is performed, and most of the positioning accuracy can meet the requirements when 3 times interpolation is performed, and the positioning time is about 2s. For the original data with a sampling rate of 1.25M, the positioning accuracy of most data does not meet the requirements when no interpolation is performed, and most of the positioning accuracy can meet the requirements when 10 times interpolation is performed, and the positioning time is about 2s. For the original data with a sampling rate of 0.625M, all the requirements are not met when no interpolation is performed, and some of the positioning accuracy can meet the requirements when 3 times and 5 times interpolation is performed, and most of the positioning accuracy can meet the requirements when 10 times and 15 times interpolation is performed, and the positioning time is about 1s when 10 times interpolation is performed. Therefore, the results of time difference positioning of the collected digital signals in the air show that the time difference positioning method improved by dynamic interpolation of the collected data can effectively improve the positioning accuracy.

[0166] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In a hardware implementation, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or may be implemented as hardware, or may be implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or a non-transitory medium) and a communication medium (or a temporary medium).

[0167] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0168] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A time difference positioning method based on signal interpolation, characterized in that: The method comprises the following steps: Receive signals from multiple signal sources, and perform sampling processing on each signal to obtain discrete sample data of each signal; Determine the sampling interval of the discrete sample data and the true value of the estimated time difference between the signal sources; When the ratio of the sampling interval to the true value of the estimated time difference is a non-integer, starting a signal interpolation process to process the discrete sample data to obtain densely sampled sequence data; Based on the densely sampled sequence data, obtaining the time difference between the signal sources; The position information of the signal source is obtained, and the target position is determined by using a time difference positioning algorithm according to the time difference value and the position information of the signal source.

2. The method according to claim 1, characterized in that: The start signal interpolation process processes the discrete sample data to obtain densely sampled sequence data, including: Signal interpolation technology is used to insert additional data points between the discrete sample data to form a denser sampling sequence, thereby obtaining dense sampling sequence data.

3. The method according to claim 2, characterized in that The signal interpolation techniques include, but are not limited to, linear interpolation, spline interpolation, interpolation based on a sinc function, and parabolic interpolation.

4. The method according to claim 3, characterized in that The selection criteria of the signal interpolation technology include but are not limited to interpolation accuracy, computational complexity and real-time requirements.

5. The method according to claim 1, characterized in that After the start signal interpolation process processes the discrete sample data to obtain dense sampling sequence data, the method further includes: filtering the dense sampling sequence data; The step of obtaining the time difference between the signal sources based on the densely sampled sequence data is obtaining the time difference between the signal sources based on the densely sampled sequence data after filtering.

6. A time difference positioning device based on signal interpolation, characterized in that: The device is applied to the time difference positioning method based on signal interpolation according to any one of claims 1 to 5, comprising: A signal receiving module, used for receiving signals from multiple signal sources; A sampling processing module is used to perform sampling processing on the received signal to generate discrete sample data; An interpolation start module, used for starting and executing a signal interpolation process to process the discrete sample data; A time difference calculation module calculates the time difference between signal sources based on the interpolated data; The acquisition and positioning calculation module acquires the position information of the signal source, and determines the target position by using a time difference positioning algorithm according to the time difference value and the position information of the signal source.

7. The device according to claim 6, characterized in that The interpolation starting module comprises: A signal interpolation module, the signal interpolation module is used to insert additional data points between the discrete sample data, wherein the signal interpolation module includes an interpolation method selection unit, the interpolation method selection unit is used to select the most appropriate signal interpolation technology according to a preset criterion.

8. The device according to claim 6, characterized in that The device also includes: a filtering processing module, which is used to perform filtering processing on the interpolated data.

9. The device according to claim 6, characterized in that The device also includes a display module for displaying the positioning result of the target position.

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