General high-precision signal arrival time estimation method and device, medium and product

Abstract

The invention discloses a universal high-precision signal arrival time estimation method and device, a medium and a product, and relates to the field of wireless measurement. Timing curve sampling points are obtained by means of direct sampling or sliding correlation and the like, a timing curve analysis mathematical expression is obtained, optimal matching of a timing curve analysis model and timing curve actual sampling is completed by means of a nonlinear least square method, and timing point positions are extracted and mapped and converted into signal arrival time. According to the method, TOA estimation is completed by fully utilizing all information, the arrival time estimation precision is greatly improved, and the requirements for the signal-to-noise ratio and the sampling rate are reduced; the method does not depend on specific timing curve shapes, has adaptability of various signal waveforms and timing curves, and can be widely applied to the technical fields of communication, navigation, radar, electronic reconnaissance and the like.

Description

Technical Field

[0001] The present invention relates to the field of wireless measurement, and in particular to a universal high-precision signal arrival time estimation method, device, medium and product. Background Art

[0002] The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.

[0003] Multi-platform collaborative operations are mission effectiveness multipliers. In the military field, in addition to using communication interaction sensors and command and control data, it is also necessary to build a unified time and space reference in collaborative reconnaissance, collaborative detection, collaborative positioning, collaborative tracking, and collaborative strike missions. Signal arrival time (TOA) estimation is a key link in building a unified time and space reference between platforms based on radio technology. It measures the time required for the signal to travel from the transmitting point to the receiving point, and can achieve precise time synchronization, ranging, and relative positioning between nodes. It has a wide range of applications in mobile communication terminal positioning systems.

[0004] In wireless communications, radar, and positioning systems, pulse compression technology based on phase-coded waveforms is usually used to achieve accurate TOA measurements. Phase coding is achieved by modulating the carrier phase with a pseudo-random sequence. Common modulation methods include binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), and minimum shift keying (MSK). Pulse compression uses the statistical characteristics of the correlation of pseudo-random sequences and the uncorrelated nature of noise to obtain a correlation peak by correlating the received signal with a locally generated signal replica, and estimates TOA based on the peak position. This type of method is also called a correlation TOA estimation method based on matched filtering.

[0005] According to different processing methods, the TOA estimation method based on matched filtering can be divided into closed-loop processing and open-loop processing. The typical closed-loop processing method is to use a delay locked loop (DLL) to estimate the delay difference between the local signal and the received signal, and to gradually align the local signal with the received signal by continuously adjusting the delay. However, in order to maintain loop lock, it is necessary to receive pulse signals at a high frequency or receive continuous wave signals for a long time. The loop convergence time is long and the dynamic adaptability is weak, which is not suitable for short burst pulse communication systems. The open-loop processing method is to find the maximum value of the correlator output based on single-point sliding near the capture peak after completing the capture of the signal carrier and delay, and use it as the correlation peak value for TOA estimation. This method is simple to implement, but the performance is greatly affected by the sampling rate and noise, and the estimation accuracy is limited. Summary of the invention

[0006] The purpose of the present invention is to provide a universal high-precision signal arrival time estimation method, device, medium and product to address the problem that the accuracy of the current traditional open-loop TOA estimation method is greatly affected by the sampling rate and noise, and the estimation accuracy is limited. The method optimally matches the output of a correlator based on single-point sliding with a theoretical timing curve, fully utilizes all available information to suppress the influence of noise, accurately extracts the timing point position, and realizes TOA estimation at the sub-sampling point level of the arrival signal, with high estimation accuracy.

[0007] The technical solution of the present invention is as follows:

[0008] A universal high-precision signal arrival time estimation method includes: obtaining timing curve sampling points, obtaining timing curve analytical mathematical expressions; using nonlinear least squares method to complete the best match between the timing curve analytical model and the actual sampling of the timing curve, extracting the timing point position, and mapping and converting it into the signal arrival time.

[0009] Furthermore, the timing curve waveform is not limited to an ideal triangle, but can be any shape, and its mathematical analytical expression is known. If the form is too complex or there is no analytical expression, its time domain expression and partial differential form can also be provided by constructing a lookup table and linear interpolation to reduce the computational complexity.

[0010] Furthermore, the timing point is not limited to the peak position of the timing curve, and may also be located at the half-width point or other positions, mainly considering the position where the slope changes greatly.

[0011] Furthermore, the method specifically includes the following steps:

[0012] Step S1: Based on the reference timing curve h=[h0,h1,…,h N-1 ] and the received and processed baseband data i=[i0,i1,…,iM-1 ], and a correlation operation r = l * h is performed in the correlator; if the synchronization head signal is judged to exist through the correlation threshold search, the sampling time corresponding to the autocorrelation peak is recorded, that is, the time t of the TOA rough estimation n ; At the same time, extract the sampling points within one chip period near the autocorrelation peak in r, denoted as y = [y0, y1, ..., y L-1 ], complete data preparation;

[0013] Step S2: Solve the analytical expression of the reference autocorrelation function h(x, t)=f(a0, a1, ..., a k-1 , t), thereby determining the curve fitting parameters x = {a0, a1, ..., a L-1}, and initialize the parameters x0 = [a 00 , a 10 , ..., a (L-1)0 ] and the partial differentials of each parameter Calculate, set the number of iterations to 1, and complete the model preparation;

[0014] Step S3: Perform curve fitting TOA estimation and calculate the partial differential matrix And the loss function vector v = yh(x j , t), using the calculation equation Δx=(H T H) -1 H T v performs matrix solving and passes Update the fitting parameters and judge the convergence of the least squares by detecting the change of Δx. If it tends to 0 and basically does not change, exit the least squares calculation process and give the final calculation result. Otherwise, continue the iterative calculation and increase the number of iterations by 1. At the same time, set the maximum number of iterations to N. After reaching the maximum number of iterations, exit the iterative process regardless of whether it converges or not.

[0015] Step S4: From the fitting parameters The time-shift parameter in The deviation of the accurate estimate of the time position of the autocorrelation peak is Δt s , and thus calculate the high-precision TOA estimate value as t_arrival = t n -Δt s .

[0016] The present invention also provides a computing device, comprising:

[0017] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor so that the at least one processor can execute the generalized high-precision signal arrival time estimation method as described above.

[0018] The present invention also provides a computer terminal storage medium storing computer terminal executable instructions, wherein the computer terminal executable instructions are used to execute the above-mentioned generalized high-precision signal arrival time estimation method.

[0019] The present invention also provides a computer program product, which implements the above-mentioned universal high-precision signal arrival time estimation method when the computer program is executed by a processor.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] 1. Compared with the traditional method, the present invention makes full use of all information to complete TOA estimation, greatly improves the accuracy of arrival time estimation, and reduces the requirements for signal-to-noise ratio and sampling rate.

[0022] 2. The present invention does not rely on the specific shape of the timing curve, has the adaptability to various signal waveforms and timing curves, and can be generalized and applied to technical fields such as communication, navigation, radar, and electronic reconnaissance. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A flow chart of a generalized high-precision signal arrival time estimation method;

[0024] Figure 2 The main application of TOA measurement;

[0025] Figure 3 This is the schematic diagram of TOA measurement technology. DETAILED DESCRIPTION

[0026] It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0027] The features and performance of the present invention are further described in detail below in conjunction with the embodiments.

[0028] Embodiment 1

[0029] TOA estimation is widely used in the fields of wireless navigation, communication, radar and electronic reconnaissance. Figure 2 Several main applications are demonstrated.

[0030] In the field of navigation technology, the platform needs to obtain a unified expression of its own spatiotemporal attributes, that is, to obtain position and time coordinates in the same spatiotemporal coordinate system. The core means to construct unified spatiotemporal coordinates and complete position and time solution between platforms that are dispersed in geographic space and relatively mobile is radio measurement. The basic wireless measurement information is distance measurement and time difference measurement value. On the one hand, the relative distance or clock deviation measurement can be completed by the transmitter actively asking-receiver answering-transmitter processing the answering signal, and then the spatiotemporal information solution is completed respectively. On the other hand, the receiver can also passively accept the method to complete the pseudo-range measurement, and combine multiple pseudo-range measurements for joint spatiotemporal information solution. Whether it is active inquiry-answering or passive reception, the underlying basic measurement is the measurement of signal arrival time (TOA). The TOA measurement accuracy determines the accuracy of the distance, time difference, pseudo-range and other measurement values, and indirectly affects the accuracy of positioning and time information solution.

[0031] In the field of radar technology, it is necessary to measure the distance information relative to the target. The basic principle is that the radar transmits a detection signal, the signal propagates through space to reach the target, and after being reflected by the target, it returns to the radar array through the same path. The radar calculates the distance information relative to the target by measuring the time difference between signal transmission and reception. Among them, the arrival time detection of the target echo is the core link and determines the accuracy of the relative distance measurement.

[0032] In the field of electronic reconnaissance technology, there is an application demand for locating radiation sources. A commonly used method is multi-station measurement, which measures the time difference between the arrival of signals emitted by the same radiation source at multiple measuring stations and uses hyperbola intersection positioning to locate the radiation source. The core link is the high-precision arrival time measurement of the radiation source signal.

[0033] In the field of communication technology, the timing synchronization link needs to determine the starting position of the bit, which is essentially a TOA measurement. Time division multiple access channel access requires time synchronization between platforms. A commonly used method is the round-trip timing RTT technology, and its basic measurement value is also TOA measurement. Unlike other fields, the requirements for signal arrival time measurement in the traditional communication field are usually not high, while there are higher requirements for signal arrival time measurement in fields such as navigation, radar detection and electronic reconnaissance. In recent years, there has been a trend of integrated development of communication and navigation technologies, that is, adding high-precision measurement functions to communication systems and using collaborative positioning technology to support high-precision relative positioning and time synchronization between nodes. Therefore, the core is still the high-precision TOA measurement method.

[0034] Figure 3 The technical principle of TOA measurement is demonstrated. Figure 3 (a) shows the correlation operation process, performing sliding correlation between the received signal and the local signal, and performing peak detection on the correlation result. This method is widely used in the field of signal detection and reception synchronization technology. Figure 3 The solid line in (b) is an ideal autocorrelation curve in the absence of noise, and the circles are autocorrelation values ​​at different sampling times in the presence of noise. To illustrate the basic principle, this embodiment assumes that the timing curve is a triangle, but in fact the timing curve can be of any shape.

[0035] In the signal receiving and processing stage, the arrival time 201 of the signal is estimated by sliding correlation or direct sampling of the received signal 203. The receiver time is usually represented by sampling points. As shown in the figure, the adjacent sampling moments are t n-1 ,t n ,t n+1 , the interval between adjacent sampling points is Δt s , the actual signal arrival time is at t n-1 and t n between.

[0036] During the sliding correlation process, the first local signal is 204, which is sampled at intervals of the sampling period to obtain the sampled local signal. The first sampling point is at time t 204_1 =t n -Δt1, the initial phase of the sampling point is t sample , and perform correlation operation on it and the received signal to obtain a correlation value 211.

[0037] The second local signal is 205, which is sampled at intervals of a sampling period to obtain a sampled local signal. The first sampling point is at time t 205_1 =t n -Δt2, the initial phase of the sampling point is t sample, and perform correlation operation on it and the received signal to obtain a correlation value 210.

[0038] The third local signal is 206, which is sampled at intervals of a sampling period to obtain a sampled local signal. The first sampling point is at time t 206_1 =t n +Δt3, the initial phase of the sampling point is t sample , and perform correlation operation on it and the received signal to obtain a correlation value 209.

[0039] The fourth local signal is 207, which is sampled at intervals of a sampling period to obtain the sampled local signal. The first sampling point is at time t 207_1 =t n +Δt4, the initial phase of the sampling point is t sample , and perform correlation operation on it and the received signal to obtain a correlation value 208.

[0040] As shown in the figure, the correlation peak should appear at the local signal 206, and the peak point is located at 210. If no other processing method is used, the TOA estimate value should be located at t n 202, and the actual signal arrival time t_arrival201 is:

[0041] t_arrival = t n-1 +Δt arrival

[0042] The number of local signal copies can be adjusted according to actual conditions and is not limited to 4. Increasing local signals with different phases can improve the accuracy of signal arrival time estimation.

[0043] This embodiment proposes a universal high-precision signal arrival time estimation method that fully utilizes the shape of the ideal timing curve and the timing curve sampling information to estimate Δt arrival , and then estimate t_arrival.

[0044] In this embodiment, please refer to Figure 1 , including the following steps:

[0045] Step S1: Based on the reference timing curve 302 generated by the design 301 of the synchronization header h=[h0,h1,…,h N-1 ] and the received and processed baseband data 303i=[i0,i1,…,i M-1 ], a correlation operation r = i * h is performed in the correlator 304; if the synchronization header signal is judged to exist through the correlation threshold search 305, the sampling time corresponding to the autocorrelation peak value is recorded, that is, the time t of the TOA rough estimation 306 n; At the same time, the sampling point 307 within one chip period near the autocorrelation peak is extracted from r, which is recorded as y = [y0, y1, ..., y L-1 ], complete data preparation;

[0046] It should be noted that if the signal energy is sufficient, the timing curve sampling can also be obtained by directly sampling the time domain waveform, and the other processing steps are the same.

[0047] Step S2: Solve the analytical expression of the reference autocorrelation function 308 h(x, t)=f(a0, a1, ..., a k-1 , t), thereby determining the curve fitting parameters 309x = {a0, a1, ..., a L-1}, and initialize the parameters 310x0=[a 00 , a 10 , ..., a (L-1)0 ] and the partial differentials of each parameter The number of iterations 312 is set to 1, and the model preparation is completed.

[0048] Step S3: Perform curve fitting TOA estimation and calculate the partial differential matrix And the loss function vector v = yh(x j , t), using the calculation equation Δx=(H T H) -1 H T v performs matrix solving and passes Implement the fitting parameter update 317, and judge the convergence of the least squares by detecting the change of Δx. If it tends to 0 and basically does not change, then exit the least squares calculation process and give the final calculation result. Otherwise, continue the iterative calculation, and increase the number of iterations 312 by 1. At the same time, set the maximum number of iterations to 10. After reaching the maximum number of iterations, exit the iterative process regardless of whether it converges or not.

[0049] It should be noted that if the timing curve waveform and the related partial differential form are too complex, the calculation can be completed by establishing a lookup table and linear interpolation to reduce the amount of calculation.

[0050] Step S4: From the fitting parameters The time-shift parameter in The deviation of the accurate estimate of the time position of the autocorrelation peak is Δt s , and thus calculate the high-precision TOA estimate value as t_arrival = t n -Δt s ; That is, after completing the state update, the relationship between the local sampling clock and the timing point position is integrated, and the signal arrival time is calculated by linear mapping;

[0051] It should be noted that the method of using Gauss-Newton iteration to perform curve fitting to obtain the autocorrelation peak can be described as Algorithm 1, where y = [y0, y1, ..., y L-1 ] is a chip period near the autocorrelation peak Sampling Point, f s is the sampling rate, f c is the chip rate; h(x, t) is the reference autocorrelation function, x = {a0, a1, ..., a L-1} is the fitting parameter, assuming a0 is the time-shift parameter of the reference autocorrelation function;

[0052] The least squares loss function is defined as the sum of the squared values ​​of the measurement errors, i.e., J LS =v T v. In the process of continuous iteration, the loss function J LS Gradually converged, the fitting parameters tended to constant values, and through statistical analysis, the total number of iterations required to converge to the estimated index was 313 N.

[0053] When the curve fitting meets the requirements after N iterations, the fitting parameters The time-shift parameter in The accurate estimated deviation 318 about the time position of the autocorrelation peak is obtained as Δt s Then the high-precision TOA estimate 319 can be calculated as t_arrival = t n -Δt s .

[0054] In addition, in some embodiments, a computing device is provided, comprising:

[0055] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the generalized high-precision signal arrival time estimation method as described above; examples of computing devices include a PC, a tablet computer, a smart phone or a PDA, etc.

[0056] In addition, in some embodiments, a computer terminal storage medium is also proposed, which stores computer terminal executable instructions, and the computer terminal executable instructions are used to execute the generalized high-precision signal arrival time estimation method as described above; examples of computer storage media include magnetic storage media (e.g., floppy disks, hard disks, etc.), optical recording media (e.g., CD-ROMs, DVDs, etc.) or memories, such as memory cards, ROMs or RAMs, etc. Computer storage media can also be distributed on network-connected computer systems, such as application stores.

[0057] In addition, in some embodiments, a computer program product is also proposed. When the computer program is executed by a processor, the computer program implements the above-mentioned generalized high-precision signal arrival time estimation method.

[0058] The above-mentioned embodiments only express the specific implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the protection scope of the present application. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the technical solution concept of the present application, and these all belong to the protection scope of the present application.

[0059] This background section is provided to generally present the context of the invention, and the work of the presently named inventors, the work to the extent described in this background section, and aspects of this section that did not constitute prior art at the time of application are neither explicitly nor implicitly admitted to be prior art to the present invention.

Claims

1. A generalized high-precision signal arrival time estimation method, characterized in that: include: Acquire the sampling points of the timing curve and the mathematical expression of the timing curve analysis; use the nonlinear least square method to complete the best match between the timing curve analysis model and the actual sampling of the timing curve, extract the timing point position, and map it into the signal arrival time.

2. The generalized high-precision signal arrival time estimation method according to claim 1, characterized in that: The acquisition method of the timing curve sampling points includes: direct sampling or sliding correlation.

3. The generalized high-precision signal arrival time estimation method according to claim 1, characterized in that: include: The timing curve waveform is of arbitrary shape, and its mathematical analytical expression is known. If the form is too complex or there is no analytical expression, its time domain expression and partial differential form can be provided by constructing a lookup table and linear interpolation.

4. The generalized high-precision signal arrival time estimation method according to claim 1, characterized in that: The timing point position is the position where the slope changes greatly.

5. The generalized high-precision signal arrival time estimation method according to claim 4, characterized in that: The timing point position is the peak position, half-width point or other position of the timing curve.

6. The universal high-precision signal arrival time estimation method according to claim 1, characterized in that: The specific steps include: Step S1: Based on the reference timing curve h=[h0,h1,…,h N-1 ] and the received and processed baseband data i=[i0,i1,…,i M-1 ], and a correlation operation r = i * h is performed in the correlator; if the synchronization head signal is judged to exist through the correlation threshold search, the sampling time corresponding to the autocorrelation peak is recorded, that is, the time t of the rough TOA estimation n ; At the same time, extract the sampling points within one chip period near the autocorrelation peak in r, denoted as y = [y0, y1, ..., y L-1 ], complete data preparation; Step S2: Solve the analytical expression of the reference autocorrelation function h(x, t) = f(a0, a1, ..., a k-1 , t), thereby determining the curve fitting parameters x = {a0, a1, ..., a L-1 }, and initialize the parameters x0 = [a 00 , a 10 , ..., a (L-1)0 ] and the partial differentials of each parameter Calculate, set the number of iterations to 1, and complete the model preparation; Step S3: Perform curve fitting TOA estimation and calculate the partial differential matrix And the loss function vector v = yh(x j , t), using the calculation equation Δx=(H T H) -1 H T v performs matrix solving and passes Update the fitting parameters and judge the convergence of the least squares by detecting the change of Δx. If it tends to 0 and basically does not change, exit the least squares calculation process and give the final calculation result. Otherwise, continue the iterative calculation and increase the number of iterations by 1. At the same time, set the maximum number of iterations to N. After reaching the maximum number of iterations, exit the iterative process regardless of whether it converges or not. Step S4: From the fitting parameters The time-shift parameter in The deviation of the accurate estimate of the time position of the autocorrelation peak is Δt s , and thus calculate the high-precision TOA estimate value as t_arrival = t n -Δt s .

7. A computing device, characterized in that: include: at least one processor; And a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the generalized high-precision signal arrival time estimation method as described in any one of claims 1-6.

8. A computer terminal storage medium storing computer terminal executable instructions, characterized in that: The computer terminal executable instructions are used to execute the generalized high-precision signal arrival time estimation method as described in any one of claims 1-6.

9. A computer program product, characterized in that When the computer program is executed by a processor, the generalized high-precision signal arrival time estimation method according to any one of claims 1 to 6 is implemented.

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