GNSS high-precision phase frequency detection method and phase frequency detector

Abstract

The invention relates to the field of phase frequency detection, and discloses a GNSS high-precision phase frequency detection method and a phase frequency detector, and the method comprises the following steps: dividing N pieces of 1ms dispreading data into K = N / L groups with the length of L, and each group contains L pieces of 1ms data; accumulating L pieces of 1ms data to obtain a complex result, wherein K complex results: C1 to CK are provided; carrying out DFT (Discrete Fourier Transform) on the C1-CK to obtain coarse frequency offset estimation # imgabs0 #, and carrying out reverse rotation on the C1-CK according to the frequency offset estimation # imgabs1 # to obtain new complex numbers C1 '-Ck'; calculating a modulus value and a phase of each complex number, and recording the modulus value and the phase as Ak and theta k; and performing least square estimation according to the Ak and the theta k to obtain fine frequency discrimination and phase discrimination results FreqOffset and Phase, wherein the final frequency discrimination result is # imgabs2, and the phase discrimination result is Phase. The invention designs a phase frequency detector based on carrier phase least square, the anti-noise performance of the phase frequency detector is close to theoretical optimal estimation, and meanwhile, the calculation complexity can be controlled within a relatively low range.

Description

Technical Field

[0001] The present invention relates to the field of frequency and phase discrimination, and in particular to a GNSS high-precision frequency and phase discrimination method and system. Background Art

[0002] GNSS is a general term for a variety of satellite navigation systems, including GPS / Beidou / GLONASS / Galileo, etc. The navigation signals sent by different satellite systems are slightly different, but in general, the receiver is required to continuously track the dynamic frequency and phase changes caused by the Doppler effect to ensure that the main signal energy can be continuously projected on the I channel of the receiver's IQ two-channel receiver.

[0003] Since the time corresponding to the pseudo-random code is about 1ms, the receiver obtains the baseband signal of N consecutive 1ms intervals after despreading and performs frequency and phase discrimination. For the sake of simplicity, the problem of data bit symbol flipping that may occur when N is too long is not considered for the time being (such as for the GPS system, assuming N <= 20ms). For the despread data of N 1ms intervals, there are two traditional methods.

[0004] 1. Divide N milliseconds of data into N / L groups of length L. Each group contains L 1ms of data. Generally, in order to save calculation, the phase change of the data in the group is ignored. The L 1ms of data are directly accumulated to obtain a complex result. There are a total of K = N / L complex results: C1~C K， Perform DFT transformation on these K data. Assuming that DFT transformation of M points is performed, M complex results C1~C M Then select the point with the largest modulus value. The position of the point with the largest modulus value corresponds to the frequency discrimination result, and the phase of the maximum value corresponds to the phase discrimination result. The size of L generally needs to be configured based on the comprehensive signal-to-noise ratio and the total data length.

[0005] 2. Divide the N milliseconds of data into N / L groups of length L. Each group contains L 1ms of data. Generally, in order to save calculation, the phase change of the data in the group is ignored. The L 1ms of data are directly accumulated to obtain a complex result. There are a total of K = N / L complex results: C1~C K， Then the complex accumulation results of adjacent groups are conjugate multiplied and accumulated. The phase of the complex result D obtained corresponds to the frequency discrimination result, and then all data are reversely rotated and accumulated according to the frequency discrimination result, and the obtained complex phase is the phase discrimination result. The size of L generally needs to be configured based on the comprehensive signal-to-noise ratio and the total data length.

[0006] For the traditional method 1, i.e. using a DFT-based frequency detector, its anti-noise performance can reach the theoretical optimum, but its frequency detector performance depends on the number of bins in the DFT operation. In order to achieve the theoretical optimum performance, the number of bins required is positively correlated with the signal-to-noise ratio, i.e. the higher the signal-to-noise ratio, the more bins are required. However, an excessively high number of bins results in a high computational complexity of the DFT, which is not conducive to implementation.

[0007] The advantage of the traditional method 2 is that the computational complexity is very low. However, due to the use of conjugate multiplication of adjacent signals, there is a square noise term. Its anti-noise performance will deteriorate sharply under low signal-to-noise ratio conditions, making it difficult to obtain satisfactory performance. In addition, when the length L of the group is configured to be large, its frequency discrimination range will be greatly reduced, which cannot meet high dynamic requirements. Summary of the invention

[0008] The main purpose of the present invention is to solve the technical problem that the frequency discrimination range in the prior art is greatly reduced and cannot meet the high dynamic requirements. A GNSS high-precision frequency and phase discrimination method comprises the following steps:

[0009] A GNSS high-precision frequency and phase discrimination method, characterized in that it comprises the following steps:

[0010] Obtain N 1ms despread data;

[0011] Divide N 1ms despread data into K = N / L groups of length L, each group contains L 1ms data;

[0012] Accumulate L 1ms data to get a complex result. There are K complex results in total: C1~C K

[0013] For the C1~C K Perform DFT transformation to obtain a rough frequency offset estimate

[0014] The C1~C K According to the frequency deviation estimation Perform reverse rotation, for the kth data, that is

[0015] Calculate each C k ''s modulus and phase, denoted as A k and θ k ;

[0016] remember

[0017] Then the estimation of frequency and phase discrimination is:

[0018]

[0019] Among them, pinv represents pseudo-inverse, diag represents diagonal matrix;

[0020] The final frequency discrimination result is: The phase detection result is Phase.

[0021] The N despread data are accumulated to obtain a complex result: C1~C K ;include:

[0022] Divide N despread data into N / L groups of length L, each group contains L 1ms data;

[0023] Accumulate L 1ms data to get a complex result. There are K = N / L complex results in total: C1~C K .

[0024] The number of Bins of the DFT transformation is 4-16.

[0025] The number of Bins of the DFT transformation is 8.

[0026] A second aspect of the present invention provides a GNSS high-precision frequency and phase detector, comprising:

[0027] A data acquisition unit, used for acquiring N despread data;

[0028] The data accumulation unit is used to accumulate N despread data to obtain a complex result: C1~C K ; Where, K = N / L;

[0029] A frequency deviation estimation unit is used to estimate the frequency deviation of the C1 to C K Perform DFT transformation to obtain a rough frequency offset estimate

[0030] The frequency and phase discrimination unit is used to K According to the frequency deviation estimation Perform reverse rotation, for the kth data, that is

[0031] Calculate each C k ''s modulus and phase, denoted as A k and θ k ;

[0032] remember

[0033] Then the estimation of frequency and phase discrimination is:

[0034]

[0035] Among them, pinv represents pseudo-inverse, diag represents diagonal matrix;

[0036] The final frequency discrimination result is: The phase detection result is Phase.

[0037] A third aspect of the present invention provides an electronic device, comprising: a memory and at least one processor, wherein instructions are stored in the memory, and the memory and the at least one processor are interconnected via lines; the at least one processor calls the instructions in the memory so that the electronic device executes the above-mentioned GNSS high-precision frequency and phase discrimination method.

[0038] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the above-mentioned GNSS high-precision frequency and phase discrimination method.

[0039] The present invention has the following beneficial effects:

[0040] The present invention designs a frequency and phase detector based on the least squares of carrier phase, the anti-noise performance of which approaches the theoretical optimal estimation, and the calculation complexity can be controlled at a low level.

[0041] Compared with the standard DFT frequency and phase detector, taking 20ms data as an example, if each group is 5ms, then 4 complex data are formed after accumulation. In order to achieve high-performance frequency and phase detection under high signal-to-noise ratio, it is generally necessary to perform DFT operations of more than 128 Bins to approach the performance of the ideal DFT. The method proposed in the present invention only needs to perform DFT operations of 8 Bins, as well as several matrix multiplications and inversion operations of size 4, and its performance is close to the frequency and phase detection performance of the ideal DFT. Its operation amount is controllable and fixed.

[0042] Compared with the frequency detector that conjugates and multiplies the data groups, the method proposed by the present invention has a slightly higher amount of calculation, but can obtain near-ideal performance in weak signals. In order to further reduce the amount of calculation, the calculation strategy can be arranged according to the size of the carrier-to-noise ratio. If the carrier-to-noise ratio is very good, then the frequency detector that conjugates and multiplies the data groups is selected. If the carrier ratio is poor, then the frequency detector proposed by the present invention is selected. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 Schematic diagram of GNSS high-precision frequency and phase discrimination method.

[0044] Figure 2 This is a schematic diagram of the GNSS high-precision frequency and phase detection device. DETAILED DESCRIPTION

[0045] 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 data used in this way can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0046] Despread data

[0047] In spread spectrum technology, removing the spreading code before data recovery in the receiving link is called despreading. Despreading is the process of reconstructing information on the original bandwidth of the signal. The characteristic of spread spectrum communication is that the bandwidth used to transmit information is much larger than the bandwidth of the information itself. The transmitter performs spread spectrum modulation with a spreading code (usually a pseudo-random code) to spread the information into a wider frequency band. At the receiving end, in order to recover the data, the signal must be despread using the same spreading code as the transmitting end. The despreading process is usually performed before demodulation. Through correlated synchronous reception, the broadband signal is restored to narrowband information, and the power spectrum density increases, thereby realizing data recovery.

[0048] DFT Transform

[0049] DFT (Discrete Fourier Transform) is a discrete form of Fourier transform in both time domain and frequency domain. It transforms the sampling of time domain signals into sampling in the discrete time Fourier transform (DTFT) frequency domain.

[0050] DFT transforms the signal from the time domain to the frequency domain by calculating the conversion between the time domain and the frequency domain, and then studies the spectral structure and change law of the signal. Specifically, DFT represents a finite length sequence of length N as N complex numbers in the frequency domain, and each complex number represents the component of the signal at different frequencies.

[0051] The calculation formula of DFT is as follows:

[0052] Forward transformation: X(k) = Σ[n = 0 to N-1]x(n)*e^(-j2πk*n / N)

[0053] Inverse transformation: x(n)=(1 / N)*Σ[k=0 to N-1]X(k)*e^(j2πk*n / N)

[0054] Among them, x(n) is the time domain signal, X(k) is the frequency domain signal, n and k are the indexes of the time domain and frequency domain respectively, N is the number of sampling points of the signal, e is the base of the natural logarithm, and j is the imaginary unit.

[0055] The physical meaning of DFT is that it samples the spectrum X(ejω) of the time domain signal at N points in the interval [0,2π] at equal intervals, thus discretizing the signal spectrum. This discretization allows us to further analyze and process the signal in the frequency domain.

[0056] Bin Quantity

[0057] In the DFT spectrum diagram, the frequency axis is divided into several equally spaced intervals, each of which is called a "Bin". The number of Bins directly determines the resolution of the spectrum.

[0058] The number of DFT bins is usually related to the number of sampling points (N) and sampling frequency (fs) of the signal. Specifically, after performing DFT transformation on a signal of length N, spectrum information of N points will be obtained in the frequency domain, and these N points correspond to N bins. Therefore, it can be said that the number of DFT bins is equal to the number of sampling points of the signal. The more bins there are, the higher the resolution in the frequency domain, which can more finely describe the spectrum characteristics of the signal. Conversely, the fewer bins there are, the lower the frequency domain resolution is, and the detailed characteristics of the signal may not be accurately revealed.

[0059] GNSS receivers need to continuously track the satellite's carrier phase and frequency changes. This function is generally achieved by using a phase-locked loop and a frequency-locked loop structure. The phase detector and frequency detector are important components of the phase-locked loop and the frequency-locked loop, and their performance directly determines the tracking performance of the entire loop.

[0060] There are many ways to implement phase detectors and frequency detectors. Generally speaking, their design needs to comprehensively consider the algorithm's noise resistance and implementation complexity, and find a compromise between them.

[0061] The invention designs a frequency and phase detector based on the least square of carrier phase, the anti-noise performance of which approaches the theoretical optimal estimation, and the calculation complexity can be controlled in a relatively low range.

[0062] The present invention realizes that the DFT-based frequency and phase discrimination algorithm is the optimal estimate for the mathematical problem of short-time carrier tracking. For the data D1 to D N， Its optimal frequency estimate is:

[0063]

[0064] After obtaining the frequency estimate, all data points can be phase compensated and accumulated to obtain the phase estimate. Therefore, the core of the problem is how to quickly and efficiently find the solution to equation 1. In order to find the optimal frequency offset estimate w, the DFT transform actually discretizes w1,…w according to the number of DFT bins in all possible frequency ranges. L , and then for each possible frequency deviation, calculate In fact, it is an exhaustive algorithm. As the number of Bins increases, its complexity increases linearly.

[0065] But in fact, the effect of frequency offset on carrier phase is linear, except that the size of carrier phase has a 2PI inversion. If a rough frequency offset estimate can be obtained at the beginning, the problem of phase flip can be effectively avoided, thus converting frequency and phase detection into a least squares problem, and the least squares problem has an analytical solution, and the optimal estimate can be obtained with a fixed amount of calculation.

[0066] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 , the embodiment of the GNSS high-precision frequency and phase discrimination method in the embodiment of the present invention includes:

[0067] Divide N milliseconds of data into N / L groups of length L. Each group contains L 1ms of data. Generally, in order to save calculation, the phase change of the data in the group is ignored. The L 1ms of data are directly accumulated to obtain a complex result. There are a total of K = N / L complex results: C1~C K

[0068] For C1~C K Perform DFT transformation. The number of DFT bins can be controlled to 8 to ensure a rough frequency offset estimate with a low amount of calculation.

[0069] C1~C K These K data are estimated according to the frequency deviation Perform reverse rotation, for the kth data, that is Then calculate each C k ''s modulus and phase, denoted as A k and θ k .

[0070] 1. Remember Then the estimation of frequency and phase discrimination is:

[0071]

[0072] Among them, pinv represents pseudo-inverse and diag represents diagonal matrix.

[0073] The final frequency discrimination result is: The phase detection result is Phase

[0074] The present invention designs a frequency and phase detector based on the least squares of carrier phase, the anti-noise performance of which approaches the theoretical optimal estimation, and the calculation complexity can be controlled at a low level.

[0075] Compared with the standard DFT frequency and phase detector, taking 20ms data as an example, if each group is 5ms, then 4 complex data are formed after accumulation. In order to achieve high-performance frequency and phase detection under high signal-to-noise ratio, it is generally necessary to perform DFT operations of more than 128 Bins to approach the performance of the ideal DFT. The method proposed in the present invention only needs to perform DFT operations of 8 Bins, as well as several matrix multiplications and inversion operations of size 4, and its performance is close to the frequency and phase detection performance of the ideal DFT. Its operation amount is controllable and fixed.

[0076] Compared with the frequency detector that conjugates and multiplies the data groups, the method proposed by the present invention has a slightly higher amount of calculation, but can obtain near-ideal performance in weak signals. In order to further reduce the amount of calculation, the calculation strategy can be arranged according to the size of the carrier-to-noise ratio. If the carrier-to-noise ratio is very good, then the frequency detector that conjugates and multiplies the data groups is selected. If the carrier ratio is poor, then the frequency detector proposed by the present invention is selected.

[0077] The above describes the GNSS high-precision frequency and phase discrimination method in the embodiment of the present invention. The following describes the GNSS high-precision frequency and phase discrimination device in the embodiment of the present invention. like Figure 2 , The first embodiment of the GNSS high-precision frequency and phase discrimination device in the embodiment of the present invention includes:

[0078] A data acquisition unit, used for acquiring N despread data;

[0079] The data accumulation unit is used to accumulate N despread data to obtain a complex result: C1~C K ; Where, K = N / L;

[0080] A frequency deviation estimation unit is used to estimate the frequency deviation of the C1 to C K Perform DFT transformation to obtain a rough frequency offset estimate

[0081] The frequency and phase discrimination unit is used to K According to the frequency deviation estimation Perform reverse rotation, for the kth data, that is

[0082] Calculate each C k ''s modulus and phase, denoted as A k and θ k ;

[0083] remember

[0084] Then the estimation of frequency and phase discrimination is:

[0085]

[0086] Among them, pinv represents pseudo-inverse, diag represents diagonal matrix;

[0087] The final frequency discrimination result is: The phase detection result is Phase.

[0088] The structure of an electronic device provided by an embodiment of the present invention may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) (for example, one or more processors) and memories, and one or more storage media for storing applications or data (for example, one or more mass storage devices). Among them, the memories and storage media may be short-term storage or permanent storage. The program stored in the storage medium may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the electronic device. Furthermore, the processor may be configured to communicate with the storage medium and execute a series of instruction operations in the storage medium on the electronic device.

[0089] The electronic device may also include one or more power supplies, one or more wired or wireless network interfaces, one or more input and output interfaces, and / or one or more operating systems, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art will appreciate that the electronic device structure does not constitute a limitation on the electronic device, and may include more or less components than the aforementioned, or combine certain components, or arrange the components differently.

[0090] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of the GNSS high-precision frequency and phase discrimination method.

[0091] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, device, or unit can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0092] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0093] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A GNSS high-precision frequency and phase discrimination method, characterized in that: The following steps are involved: Obtain N 1ms despread data; Divide N 1ms despread data into K = N / L groups of length L, each group contains L 1ms data; Accumulate L 1ms data to get a complex result. There are K complex results in total: C1~C K For the C1~C K Perform DFT transformation to obtain a rough frequency offset estimate The C1~C K According to the frequency deviation estimation Perform reverse rotation, for the kth data, that is Calculate each C k ′ The modulus and phase of k and θ k ; remember Then the estimation of frequency and phase discrimination is: Among them, pinv represents pseudo-inverse, diag represents diagonal matrix; The frequency and phase discrimination results are obtained, and the frequency discrimination results are: The phase detection result is Phase.

2. A GNSS high-precision frequency and phase discrimination method according to claim 1, characterized in that: The N despread data are accumulated to obtain a complex result: C1~C K ; include: Divide N despread data into N / L groups of length L, each group contains L 1ms data; Accumulate L 1ms data to get a complex result. There are K = N / L complex results in total: C1~C K .

3. A GNSS high-precision frequency and phase discrimination method according to claim 1, characterized in that: The number of Bins of the DFT transformation is 4-16.

4. A GNSS high-precision frequency and phase discrimination method according to claim 1, characterized in that: The number of Bins of the DFT transformation is 8.

5. A GNSS high-precision frequency and phase detector, characterized in that: The phase frequency detector comprises: A data acquisition unit, used for acquiring N despread data; The data accumulation unit is used to accumulate N despread data to obtain a complex result: C1~C K ; Where, K = N / L; A frequency deviation estimation unit is used to estimate the frequency deviation of the C1 to C K Perform DFT transformation to obtain a rough frequency offset estimate The frequency and phase discrimination unit is used to K According to the frequency deviation estimation Perform reverse rotation, for the kth data, that is Calculate each C k ′ The modulus and phase of k and θ k ; remember Then the estimation of frequency and phase discrimination is: Among them, pinv represents pseudo-inverse, diag represents diagonal matrix; The final frequency discrimination result is: The phase detection result is Phase.

6. A GNSS high-precision frequency and phase detector according to claim 5, characterized in that: The N despread data are accumulated to obtain a complex result: C1~C K ; include: Divide N despread data into N / L groups of length L, each group contains L 1ms data; Accumulate L 1ms data to get a complex result. There are K = N / L complex results in total: C1~C K .

7. A GNSS high-precision frequency and phase detector according to claim 5, characterized in that: The number of Bins of the DFT transformation is 4-16.

8. A GNSS high-precision frequency and phase detector according to claim 5, characterized in that: The number of Bins of the DFT transformation is 8.

9. An electronic device, comprising a memory and at least one processor, wherein instructions are stored in the memory; The at least one processor calls the instructions in the memory so that the electronic device executes each step of the GNSS high-precision frequency and phase discrimination method as described in any one of claims 1-7.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the various steps of the GNSS high-precision frequency and phase discrimination method as described in any one of claims 1 to 7 are implemented.

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