A Real-time Inversion Method for Soil Moisture Based on FPGA+DSP Architecture in GNSS-R

Abstract

The present invention provides a method for real-time inversion of soil moisture based on the FPGA+DSP architecture, belonging to the field of inversion of typical surface parameters; the method includes: receiving GNSS direct and reflected signals, filtering and amplifying the signals and then mixing them with the local oscillator signal for down-conversion; the down-converted signals are filtered by a low-pass filter to remove high-frequency components and then sampled by an ADC; the sampled signals are subjected to complex phase rotation down-conversion and correlation processing to obtain five-way correlation outputs; the real-time result of soil moisture is obtained by combining the power ratio obtained from the correlation values with the Topp model, and compared with the true value collected on-site by a soil moisture meter to verify the reliability of the data results. Using this technology, soil moisture monitoring can be carried out in real time. This method has low power consumption and high operating efficiency, providing a real-time and feasible monitoring means for agricultural development and food production.

Description

Technical Field

[0001] The present invention relates to the technical field of inversion of typical surface parameters, and particularly to a real-time inversion method for GNSS-R soil moisture based on an FPGA+DSP architecture. Background Art

[0002] The Global Navigation Satellite System (GNSS) was initially only used in the fields of positioning, timing and navigation. With the continuous progress of technologies in various countries, today's GNSS signals have the characteristics of all-weather, multi-frequency and multi-mode, high spatio-temporal resolution and high coverage. These characteristics have given rise to the Global Navigation Satellite System Reflectometry (GNSS-R) technology, which uses the direct and reflected signals of GNSS to invert and detect typical surface environmental parameters, and is a new remote sensing means that has developed rapidly in recent years. Among them, soil moisture monitoring is one of the important application fields of GNSS-R technology, and soil moisture is closely related to major issues related to human life such as food production and soil drought.

[0003] In the current GNSS-R soil moisture inversion, there are obvious bottlenecks. Traditional methods mainly rely on software receivers and test baseband signal processing algorithms. However, this traditional method has serious problems of poor real-time performance. In agricultural production, obtaining accurate soil moisture data in real time is crucial for crop planting, irrigation and other links. However, due to the insufficient real-time performance of traditional methods, it is impossible to provide effective data support for agricultural production in time, and it is difficult to meet the urgent needs of agricultural production for the real-time performance and accuracy of soil moisture data, which greatly restricts the wide application of GNSS-R technology in food production, agricultural monitoring and other aspects. New technological breakthroughs are urgently needed to solve these problems and promote the further development of this technology in related fields. Summary of the Invention

[0004] In view of the deficiencies in the prior art, the present invention provides a real-time inversion method for GNSS-R soil moisture based on an FPGA+DSP architecture, aiming to provide a real-time soil moisture inversion technical means through the high-speed operation and multi-unit parallel processing capabilities of FPGA.

[0005] The present invention adopts the following scheme:

[0006] A real-time inversion method for GNSS-R soil moisture based on an FPGA+DSP architecture includes the following steps:

[0007] S1. Build a baseband processing platform for a navigation satellite receiver based on an FPGA+DSP architecture: The platform includes:

[0008] Right-handed GNSS antenna, slanting upward, receiving direct signals from navigation satellites; left-handed GNSS antenna, slanting downward, receiving reflected signals from navigation satellites;

[0009] The GNSS antenna is connected to the FPGA+DSP real-time processing device through a radio frequency cable for processing;

[0010] The soil moisture meter collects soil moisture values at multiple locations on-site and takes the average as the true value for comparison;

[0011] S2. Fast acquisition of navigation satellite signals based on FPGA+DSP;

[0012] The fast acquisition module performs signal search based on the segmented correlation accumulation in the time domain and the DFT transform in the frequency domain running on the master clock. The module is divided into 22 channels for parallel code phase search, and each channel shares the data cached in real time;

[0013] S3. Channel baseband signal processing;

[0014] By performing complex phase rotation down-conversion and narrow correlation operation on the intermediate frequency signal, five-way correlation value outputs are obtained, and then the real-time soil moisture value is obtained through the Topp model and compared with the true value collected on-site by the soil moisture meter to verify the reliability of the data results.

[0015] Furthermore, in step S1, the FPGA+DSP real-time processing method is as follows:

[0016] The navigation satellite antenna receives the GNSS antenna signal, which first passes through a band-pass filter to filter out signals in other frequency bands outside the navigation signal frequency band and eliminate signal interference;

[0017] The filtered signal is power-amplified by a low-noise amplifier for the next step of processing;

[0018] The frequency synthesizer generates a local oscillation signal based on the device's local oscillator and amplifier, and performs mixing down-conversion with the GNSS signal through a mixer;

[0019] The down-converted signal passes through an amplifier and a low-pass filter to filter out the high-frequency components in the signal, and then passes through automatic gain control and an analog-to-digital converter for sampling and is input into the FPGA+DSP for processing.

[0020] Furthermore, the specific method for the fast acquisition of navigation satellite signals based on FPGA+DSP in step S2 is as follows:

[0021] (1) Call the code NCO generation module to generate a pseudo-code base frequency rate clock enable signal, that is, a 2.046 MHz clock enable signal;

[0022] (2) Cumulatively reduce the speed of the input zero-IF baseband signal with a data rate of 60M through the enable signal to generate a down-sampled baseband signal with a data rate of 2.046M;

[0023] (3) Real-time store the down-sampled baseband signal into the dual-port RAM, with a storage depth of 6138 points, that is, 3ms time;

[0024] (4) Generate 22-channel local pseudo-code sequences and output them at the main clock rate;

[0025] (5) Read 4096 points of data from the dual-port RAM, align them with the local pseudo-code sequence, and perform correlation accumulation. One point is accumulated for every 64 points, and a total of 64 points of correlation accumulation values are obtained;

[0026] (6) Under the drive of the main clock, perform a 64-point DFT operation on each obtained accumulation value and store the result in the 64-point dual-port RAM. When the second accumulation value is obtained, perform the second 64-point DFT operation on it and accumulate the result into the first result. After completing the 64th DFT operation, obtain the complete 64-point DFT result;

[0027] (7) Real-time calculate the peak energy and average energy of the DFT and buffer them into the register;

[0028] (8) Re-read the data in the dual-port RAM, align it with the local pseudo-code sequence delayed by one data point, and perform correlation accumulation to obtain the 64-point DFT result, calculate the peak energy and average energy of the DFT, repeat 2046 times, and record the time with the maximum peak energy in the 64-point DFT to obtain the pseudo-code initial phase, the frequency point at the maximum peak energy of the DFT, and the peak energy and average energy information;

[0029] (9) Generate a capture completion flag. When the DSP queries that this flag is valid, read the capture result and perform judgment and processing.

[0030] Further, the steps of channel baseband signal processing are as follows:

[0031] (1) Driven by the input of the 60MHz system main clock, call the carrier NCO to generate a local 1561.098MHz oscillation signal, and complete digital down-conversion by mixing with the input quadrature signal;

[0032] (2) When performing narrow correlation, select a 1 / 4 chip time interval for correlation operation, that is, locally generate five pseudo-code sequences: early, early-instant, instant, instant-late, and late. After correlation operation, five outputs are output;

[0033] (3)When processing the reflected signals of navigation satellites, select the 2 channels with the highest signal-to-noise ratio (SNR) in the direct path channel for open-loop tracking processing. To prevent frequent satellite switching, it is stipulated that satellite switching occurs only when the third satellite is 3 dB greater than either of the first two satellites. The Doppler frequency offset has 15 channels, which are non-interval distributed, namely 0 Hz, ±25 Hz, ±50 Hz, ±100 Hz, ±200 Hz, ±300 Hz, ±400 Hz, and ±500 Hz. The 60 MHz main clock is used to drive the speed reduction to obtain a 4 MHz rate clock enable signal. Then, the selector is used to sequentially select 15 data streams for the operation of the accumulator and cache the accumulated results accordingly. After the next polling clock arrives, the previously cached results are retrieved and participate in the accumulation operation of the corresponding channels;

[0034] (4)Use the Topp model to invert soil moisture from the relevant results. Based on the reflectivity obtained from the relevant output, the soil dielectric constant can be obtained as follows:

[0035] ;

[0036] wherein, is the soil dielectric constant, is the satellite elevation angle, is the power ratio. Further, the real-time soil moisture ( ) data can be obtained through the Topp model:

[0037] .

[0038] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0039] (1)The soil moisture monitoring method designed in the present invention is based on the FPGA+DSP architecture and uses GNSS satellites as signal sources, which can realize real-time soil moisture monitoring, and the obtained results have good correlation with the real results;

[0040] (2)The FPGA+DSP architecture receiver in the present invention only serves as a signal receiving source rather than a signal transmitting source, and has the advantages of low power consumption and low cost; due to the programmability of the FPGA, while achieving high operation speed, the present invention retains the flexibility of the device, making this technology more widely applicable to various different soil moisture monitoring scenarios;

[0041] (3)The present invention makes up for the shortcoming of the traditional use of ground-based GNSS-R to invert soil moisture with time lag, can promote the application of GNSS-R technology, and has an important role in fields such as agricultural monitoring, food production, and soil drought monitoring; the present invention has high operation efficiency and low power consumption, and due to the flexibility of the FPGA, it can be further modified according to different application scenarios. Description of the Drawings

[0042] Figure 1 is the real-time system of the present invention based on the FPGA+DSP architecture;

[0043] Figure 2 is the baseband signal processing flow in the present invention;

[0044] Figure 3 is the soil moisture result measured with Beidou (BDS) as the signal source in an embodiment of the present invention;

[0045] Figure 4 is the soil moisture result measured with GPS as the signal source in an embodiment of the present invention. Detailed implementation manners

[0046] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0047] The following further describes the present invention in detail with reference to the accompanying drawings:

[0048] A GNSS-R soil moisture real-time inversion method based on the FPGA+DSP architecture, the implementation example time is November 15, 2024, the location is the experimental farm of the Agricultural Academy of Sciences in Weihai City, Shandong Province, and the signal sources are BDS B1I signal and GPS L1C / A signal. According to the specific situation of the embodiment, the specific method for real-time inversion of soil moisture includes:

[0049] S1. Construction of the baseband processing platform of the navigation satellite receiver based on the FPGA+DSP architecture:

[0050] 1) Use a tripod to build an up-and-down view antenna. The right-handed GNSS antenna is inclined upward to receive the direct signal of the navigation satellite, and the left-handed GNSS antenna is inclined downward to receive the reflected signal of the navigation satellite;

[0051] 2) The GNSS antenna is connected to the FPGA+DSP real-time processing device through a radio frequency cable, and the device processes and saves the data results in real time;

[0052] 3) Use a soil moisture meter to collect the soil moisture values at multiple locations on-site and take the average as the true value for comparison.

[0053] S2. Composition, parameter setting and specific processing flow of the real-time system based on the FPGA+DSP architecture:

[0054] Among them, the system composition and parameter settings are as follows:

[0055] 1) The FPGA selected is the XC6VLX240T of Xilinx, with 241,152 logic cells, which is a high-performance mainstream FPGA chip specifically for signal processing development and programming;

[0056] 2) The DSP selected is the TMS320C6748 floating-point processor of TI, with a main frequency of 456 MHz;

[0057] 3) The analog-to-digital converter ADC uses the AD9233 chip of Analog Device. It is a single-chip, 12-bit, 125 MSPS analog-to-digital converter. The highest sampling rate can reach 125 MHz, with a 12-bit resolution, and the analog bandwidth can reach up to 650 MHz, which can be used for direct radio frequency band-pass sampling;

[0058] 4) The on-board reference clock: 10 MHz, an oven-controlled crystal oscillator, up to 0.005 ppm;

[0059] 5) The Doppler range is -16 kHz to +16 kHz;

[0060] 6) The mapping method of the delay-Doppler image (DDM) is 1 ms coherent integration and 1000 ms non-coherent integration;

[0061] 7) The intermediate frequency data sampling rate is 60 MHz.

[0062] For the specific processing flow, refer to Figure 1 as shown:

[0063] The navigation satellite antenna receives the GNSS antenna signal. First, it passes through a band-pass filter to filter out signals in other frequency bands except the navigation signal frequency band and eliminate signal interference;

[0064] Since the GNSS signal has the characteristic of low power, the filtered signal is amplified in power by a low-noise amplifier for the next step of processing;

[0065] The frequency synthesizer generates a local oscillation signal based on the device's local oscillator and amplifier, and mixes it with the GNSS signal through a mixer for down-conversion;

[0066] The down-converted signal passes through an amplifier and a low-pass filter to filter out the high-frequency components in the signal, and is sampled by an automatic gain control (VGA) and an analog-to-digital converter (ADC) and input into the FPGA+DSP for processing.

[0067] S3. Fast acquisition of navigation satellite signals based on FPGA+DSP:

[0068] The fast capture module performs signal search by running segmented correlation accumulation in the time domain and DFT transformation in the frequency domain based on the master clock. The module is divided into 22 channels for parallel code phase search, and each channel shares the data stored in the real-time cache. Specifically as follows:

[0069] 1) Call the code NCO generation module to generate the pseudo-code base frequency rate clock enable signal, that is, the clock enable signal of 2.046 MHz;

[0070] 2) Through the enable signal, perform cumulative deceleration processing on the input zero-IF baseband signal with a data rate of 60 M to generate a decelerated baseband signal with a data rate of 2.046 M;

[0071] 3) Store the decelerated baseband signal in the dual-port RAM in real time, with a storage depth of 6138 points, that is, 3 ms (2 ms is the data length time, and the other 1 ms is used to traverse the 2046 chip search);

[0072] 4) Generate 22-channel local pseudo-code sequences and output them at the master clock rate;

[0073] 5) Read 4096 points of data from the dual-port RAM, align them with the local pseudo-code sequence, and perform correlation accumulation. One point is accumulated for every 64 points, and a total of 64 points of correlation accumulation values are obtained;

[0074] 6) Under the drive of the master clock, perform a 64-point DFT operation on each obtained accumulation value and store the result in the 64-point dual-port RAM. When the second accumulation value is obtained, perform the second 64-point DFT operation on it and add the result to the first result. When the 64th DFT operation is completed, the complete 64-point DFT result is obtained;

[0075] 7) Calculate the DFT peak energy and average energy in real time and buffer them into the register;

[0076] 8) Read the data from the dual-port RAM again, align it with the local pseudo-code sequence delayed by one data point, and perform correlation accumulation to obtain the 64-point DFT result. Calculate the DFT peak energy and average energy, and repeat this 2046 times. Record the time with the maximum peak energy in the 64-point DFT, and the information such as the pseudo-code initial phase, the frequency point with the maximum DFT peak energy, and the peak energy and average energy can be obtained;

[0077] 9) Generate a capture completion flag. When the DSP queries that this flag is valid, read the capture result and perform judgment and processing.

[0078] S4. Channel baseband signal processing flow:

[0079] By performing complex phase rotation down-conversion and narrow correlation operations on the intermediate frequency signal, five-way correlation value outputs can be obtained, and then real-time soil moisture values can be obtained through the Topp model, as follows:

[0080] 1) Driven by the input of a 60 MHz system main clock, call the carrier NCO to generate a local 1561.098 MHz oscillation signal, and mix it with the input quadrature signal to complete digital down-conversion;

[0081] The in-phase and quadrature signal components obtained by the phase rotation method digital down-conversion in the present invention do not have other frequency components. Compared with the traditional quadrature down-conversion method, the low-pass filter can be omitted, reducing the delay and attenuation of the signal. The in-phase and quadrature signals are respectively:

[0082] ;

[0083] ;

[0084] It can be expressed in complex numbers as:

[0085] ;

[0086] The output of the carrier NCO is expressed in complex numbers as:

[0087] ;

[0088] So:

[0089] ;

[0090] Then the I and Q signals after phase rotation are:

[0091] ;

[0092] ;

[0093] In the above method, d(t) is the data modulation signal, p(t) is the pseudo-code signal, ω c represents the carrier angular frequency I in , Q in are the in-phase and quadrature components before quadrature down-conversion respectively, and are the decomposition of the original signal based on trigonometric functions; S(t) is the signal representing I in and Q in in complex form. In the output of the carrier NCO, ω n is the oscillation angular frequency generated by the NCO, and its output signal is used for mixing operation with the input signal S(t) to achieve digital down-conversion. When mixing, the signal frequency becomes ω c +ω n, through trigonometric function operations and complex number operations, the result is represented by a new in-phase component and a quadrature component . I out and Q out are the in-phase and quadrature signals after digital down-conversion by the phase rotation method.

[0094] The phase rotation method not only simplifies the down-conversion to simple four arithmetic operations, but also does not introduce other frequency components. When is selected, the Doppler information can be completely eliminated, and the remaining pseudo-code and data modulation information;

[0095] 2) When performing narrow correlation, a 1 / 4 chip time interval is selected for correlation operation, that is, five pseudo-code sequences of early, early-instantaneous, instantaneous, instantaneous-late, and late are locally generated, and five outputs are output after correlation operation;

[0096] 3) When processing the reflected signal of the navigation satellite, select the 2 channels with the highest signal-to-noise ratio (SNR) in the direct path channel for open-loop tracking processing. To prevent frequent satellite switching, it is specified that when the third satellite is 3 dB greater than any one of the first two satellites, satellite switching is performed. The Doppler frequency offset is 15 channels, using non-interval distribution, which are 0 Hz, ±25 Hz, ±50 Hz, ±100 Hz, ±200 Hz, ±300 Hz, ±400 Hz, and ±500 Hz respectively. The 60 MHz main clock is used to drive the speed reduction to obtain a 4 MHz rate clock enable signal, and then the 15-channel data stream is sequentially selected through the selector to perform the operation of the accumulator and cache the accumulated result accordingly. After the next polling clock arrives, the previously cached result is retrieved and participates in the accumulation operation of the corresponding channel;

[0097] 4) Use the Topp model to perform soil moisture inversion on the correlation result. Based on the reflectivity obtained from the correlation output, the soil dielectric constant can be obtained as:

[0098] ;

[0099] Among them, is the soil dielectric constant, is the satellite elevation angle, is the power ratio, and further through the Topp model, the real-time soil moisture data can be obtained:

[0100] .

[0101] S5. Results of the embodiment:

[0102] In this embodiment, the soil moisture results measured through the above steps are compared with the values collected on-site using a soil moisture meter to verify the reliability of the data. After multi-point sampling with the soil moisture meter, measurements are taken on the bare soil where sweet potatoes have just been harvested. The results with Beidou and GPS as signal sources are respectively as Figure 3 , Figure 4 shown. The true value is 0.15. The average soil moisture values obtained with BDS and GPS as signal sources are 0.10 and 0.19 respectively. The root mean square errors (RMSE) can be obtained as 0.06 and 0.06 respectively, and the mean differences are 0.05 and 0.04 respectively, showing an obvious correlation with the true value obtained by the soil moisture meter.

[0103] In summary, the soil moisture monitoring method designed by the present invention is based on the FPGA+DSP architecture and uses GNSS satellites as signal sources, enabling real-time soil moisture monitoring. The obtained results have a good correlation with the true results. The present invention makes up for the shortcoming of the traditional use of ground-based GNSS-R for soil moisture inversion, which has a time lag, and can promote the application of GNSS-R technology, playing an important role in fields such as agricultural monitoring, food production, and soil drought monitoring. The present invention has high operating efficiency and low power consumption, and due to the flexibility of FPGA, it can be further modified according to different application scenarios.

[0104] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A GNSS-R soil moisture real-time inversion method based on FPGA+DSP architecture, characterized in that: The following steps are involved: S1. Build a navigation satellite receiver baseband processing platform based on FPGA+DSP architecture: The platform includes: The right-handed GNSS antenna is tilted upward to receive the direct signal from the navigation satellite; the left-handed GNSS antenna is tilted downward to receive the reflected signal from the navigation satellite; The GNSS antenna is connected to the FPGA+DSP real-time processing device via a radio frequency line for processing; The soil moisture meter collects soil moisture values ​​at multiple locations on site and takes the average value as the true value for comparison; S2, fast capture of navigation satellite signals based on FPGA+DSP; The fast capture module searches for signals based on the segmented correlation accumulation in the domain when the master clock is running and the DFT transform in the frequency domain. The module is divided into 22 channels for parallel code phase search, and each channel shares the real-time cached data. S3, channel baseband signal processing; By performing complex phase rotation down-conversion and narrow correlation operation on the intermediate frequency signal, five-way correlation value output is obtained, and then the real-time soil moisture value is obtained through the Topp model, and compared with the true value collected on-site by the soil moisture meter to verify the reliability of the data results; Among them, the steps of channel baseband signal processing are as follows: (1) Driven by the 60MHz system master clock input, the carrier NCO is called to generate a local 1561.098MHz oscillation signal, which is mixed with the input orthogonal signal to complete digital down-conversion; (2) When performing narrow correlation, a 1 / 4 chip time interval is selected for correlation operation, that is, five pseudo-code sequences of advance, advance instant, instant, instant lag, and lag are generated locally, and five outputs are output after correlation operation; (3) When processing the reflected signal of the navigation satellite, the two channels with the highest signal-to-noise ratio (SNR) in the direct channel are selected for open-loop tracking processing. In order to prevent frequent satellite changes, it is stipulated that the satellite will be changed only when the third satellite is 3 dB greater than any one of the first two satellites. The Doppler frequency shift is 15 channels, which are non-intervally distributed, namely 0 Hz, ±25 Hz, ±50 Hz, ±100 Hz, ±200 Hz, ±300 Hz, ±400 Hz and ±500 Hz. The 60 MHz master clock is driven to reduce the speed to obtain a 4 MHz rate clock enable signal, and then the selector selects 15 data streams in turn to pass through the accumulator operation and cache the accumulated results accordingly. After the next polling clock arrives, the previously cached results are called out and participate in the accumulation operation of the corresponding channel; (4) The Topp model is used to invert the soil moisture of the relevant results. Based on the reflectivity obtained from the relevant output, the soil dielectric constant can be obtained as: ; in, is the soil dielectric constant, is the satellite altitude angle, The power ratio is further used to obtain the real-time soil moisture through the Topp model. data: .

2. The GNSS-R soil moisture real-time inversion method based on FPGA+DSP architecture according to claim 1 is characterized in that: In step S1, the FPGA+DSP real-time processing method is: The navigation satellite antenna receives the GNSS antenna signal, which first passes through a bandpass filter to filter out signals in other frequency bands except the navigation signal band to eliminate signal interference; The filtered signal is amplified by a low noise amplifier for further processing; The frequency synthesizer generates a local oscillation signal based on the local oscillator and amplifier of the device, and mixes and down-converts the signal with the GNSS signal through the mixer; The down-converted signal is passed through an amplifier and a low-pass filter to remove the high-frequency components in the signal, and is sampled by an automatic gain control and an analog-to-digital converter and input into the FPGA+DSP for processing.

3. The GNSS-R soil moisture real-time inversion method based on FPGA+DSP architecture according to claim 1 is characterized in that: The specific method for fast capture of navigation satellite signals based on FPGA+DSP in step S2 is as follows: (1) Call the code NCO generation module to generate a pseudo code base frequency rate clock enable signal, that is, a 2.046 MHz clock enable signal; (2) The input 60M data rate zero intermediate frequency baseband signal is accumulated and down-converted through the enable signal to generate a down-converted baseband signal with a data rate of 2.046M; (3) Store the decelerated baseband signal in real time in a dual-port RAM with a storage depth of 6138 points, or 3 ms; (4) Generate 22-channel local pseudo-code sequence and output it at the master clock rate; (5) Read 4096 points of data from the dual-port RAM and align them with the local pseudo-code sequence and perform correlation accumulation. One point is accumulated every 64 points, and a total of 64 points of correlation accumulation values ​​are obtained; (6) Driven by the master clock, a 64-point DFT operation is performed on each accumulated value, and the result is stored in a 64-point dual-port RAM. When the second accumulated value is obtained, a second 64-point DFT operation is performed on it, and the result is added to the first result. After the 64th DFT operation is completed, a complete 64-point DFT result is obtained. (7) Calculate DFT peak energy and average energy in real time and buffer them into registers; (8) Re-read the dual-port RAM data and align it with the local pseudo code sequence with a delay of one data point and perform correlation accumulation to obtain a 64-point DFT result. Calculate the DFT peak energy and average energy. Repeat 2046 times and record the one with the largest peak energy among the 64-point DFTs. You can then obtain the pseudo code initial phase, the frequency point with the maximum DFT peak energy, and the peak energy and average energy information. (9) Generate a capture completion flag. When the DSP finds that this flag is valid, it reads the capture result and makes judgments and processes it.

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