CN112946580A - Multiprocessor cooperative radiation source frequency parameter estimation device and method - Google Patents
Multiprocessor cooperative radiation source frequency parameter estimation device and method Download PDFInfo
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Abstract
The invention discloses a multiprocessor cooperative radiation source frequency parameter estimation device and method, wherein the device forms an intra-pulse parameter estimation hardware platform based on an FPGA and a DSP processor, and comprises the following steps: the device comprises an EMIF interface communication module, an SRIO interface communication module and a parameter estimation module. When facing signals with complex intra-pulse modulation mode and low signal-to-noise ratio, the method can overcome the problems of difficult algorithm implementation and low efficiency caused by the realization of intra-pulse modulation characteristic analysis based on FPGA, and can also overcome the problem of long time consumption of the method for realizing intra-pulse modulation characteristic analysis based on DSP, thereby realizing complex signal processing algorithm while improving software processing time, and simultaneously ensuring the processing precision and the processing speed.
Description
Technical Field
The invention relates to the technical field of radar pulse signal interception, in particular to a multiprocessor cooperative radiation source frequency parameter estimation device and method after passive radar pulse signal interception.
Background
With the rapid development of radar technology, the functional requirements of the industry on radar are richer and the performance requirements are stricter, so that a more complex radar signal processing algorithm is brought. The implementation of these algorithms requires the radar signal processing system to increase the operation speed and increase the data processing capability, which is a challenge for the current signal processing technology.
The traditional radar signal processing system mostly adopts a single processor, and generally has two schemes of being based on an FPGA chip and a DSP chip. The DSP is used as a special microprocessor, has the advantages of very flexible software and high updating speed, is particularly suitable for complex multi-algorithm tasks, greatly improves the universality and the flexibility of the system, and has the defects of low system sampling rate and low processing speed. The FPGA has the characteristics of large scale, high integration level, high processing speed, flexible and convenient programming and strong real-time performance, and has the defects of incapability of processing multiple events, unsuitability for conditional operation and limited processing precision.
In the prior art, generally, an intermediate frequency signal is injected into a frequency measurement circuit and frequency points of the intermediate frequency signal are recorded, primary information of each correction circuit is converted into a measured frequency value, then a frequency error curve is generated according to the measured frequency value and the injected frequency value, and an analytic expression is used for fitting so as to perform secondary correction. Although the technology improves the precision of the frequency measurement to a certain extent, a plurality of groups of frequency data need to be recorded, the processing is complex, and the real-time performance is poor.
Therefore, in order to overcome the drawbacks in the prior art, an efficient device and method for estimating frequency parameters of a multi-processor cooperative radiation source are urgently needed to improve the precision of estimating the chirp rate of a chirp signal, so as to ensure the indirect measurement precision of the chirp signal modulation bandwidth, especially the precision in the aspect of real-time analysis of intra-pulse modulation characteristics in a dense signal environment.
Disclosure of Invention
In order to solve the above problems, an object of the present invention is to provide a device and a method for estimating frequency parameters of a multi-processor cooperative radiation source, which can overcome the problems of difficulty in implementing an algorithm and low efficiency caused by implementing intra-pulse modulation characteristic analysis based on an FPGA and long time consumption of a method for implementing intra-pulse modulation characteristic analysis based on a DSP when facing signals with a complex intra-pulse modulation mode and a low signal-to-noise ratio, thereby implementing a complex signal processing algorithm while increasing software processing time, and simultaneously ensuring processing accuracy and processing speed.
In order to achieve the above technical objects and achieve the above technical effects, an aspect of the present invention provides a multiprocessor cooperative radiation source frequency parameter estimation apparatus, where the apparatus forms an intra-pulse parameter estimation hardware platform based on an FPGA and a DSP processor, and the apparatus includes: the system comprises an EMIF interface communication module, an SRIO interface communication module and a parameter estimation module;
the EMIF interface communication module is used for realizing the communication of instructions and data between the DSP and the FPGA, and comprises the steps of sending control flow instructions, sending linear frequency modulation signal parameters and sending slopes and frequencies in the parameter estimation process;
the SRIO interface communication module is used for realizing high-speed data transmission between the DSP and the FPGA;
the parameter estimation module is used for calculating and iteratively analyzing the frequency spectrum centroid and the eccentricity of the frequency measurement deskew data transmitted by the FPGA to obtain the accurate slope and the initial frequency of the deskew local oscillator;
further, the parameter estimation module comprises a rough slope estimation direction unit, an iteration unit and an extraction frequency point unit;
the rough slope direction estimating unit is used for estimating the parameter values of five iterative slopes and estimating the slope correction direction; the iteration unit is used for controlling FPGA iteration to carry out frequency measurement and deskew operation according to the correction strategy to obtain an accurate slope and an initial frequency; and the frequency point extracting unit is used for extracting and fitting the frequency value with the maximum amplitude and the most isolated frequency value in the frequency spectrum.
Another aspect of the present invention provides a frequency parameter estimation method based on the above mentioned multiprocessor cooperative radiation source frequency parameter estimation apparatus, including the following steps:
step 1: after the DSP is initialized, the FPGA is informed to start the front edge detection through the EMIF interface, and meanwhile, the detection threshold is sent to the FPGA;
step 2: the FPGA detects the leading edge according to a leading edge detection threshold, caches data when an effective leading edge is detected, and transmits the leading edge data to the DSP through the SRIO interface;
and step 3: after the DSP receives the SRIO interface interrupt, calculating an extraction factor nexctrct corresponding to the current pulse signal, and then sending the extraction factor nexctrct, the coarse frequency fest0 and the coarse slope kest0 to the FPGA through an EMIF interface;
and 4, step 4: the FPGA extracts the cached data according to the received parameters, on one hand, the extracted data is subjected to n multiplied by m-point fast Fourier transform, on the other hand, the extracted data is subjected to n times of m-point fast Fourier transform according to a time sequence, and amplitude data is sent to the DSP through an SRIO interface;
and 5: after the DSP receives the data, the rough slope direction estimating unit carries out frequency spectrum centroid calculation according to the amplitude data of n times of m-point fast Fourier transform, judges the slope correction direction, calculates the iterative slope, and the iterative unit calculates the frequency spectrum centroid and the eccentricity according to the amplitude data of n multiplied by m-point fast Fourier transform;
step 6: an iteration unit of the DSP controls the FPGA to iterate according to a correction strategy to carry out frequency measurement and deskew operation, then calculates the mass center and the eccentricity of a frequency spectrum according to the amplitude data of n multiplied by m point fast Fourier transform returned by the FPGA, estimates and calculates the optimal residual slope and the optimal residual initial frequency according to the eccentricity data and transmits the optimal residual slope and the optimal residual initial frequency to the FPGA through an EMIF interface, and the iteration process is finished;
and 7: after the iteration process is finished, the frequency point extracting unit of the DSP extracts the frequencies of the first k most isolated extreme points with the maximum amplitude from the amplitude data of the nxm point fast Fourier transform returned by the SRIO interface, then fits and corrects the frequency values of the extreme points according to the adjacent amplitude data of the mass center, and sends the frequency values back to the FPGA through the SRIO interface to finish the subsequent related processing; the DSP is then ready for processing of the next frame data.
Further, the unit for roughly estimating the slope direction in step 5 performs noise floor estimation and spectrum centroid calculation according to the amplitude data of the n-time m-point fast fourier transform, determines the slope correction direction, and calculates the iterative slope, and the specific steps include:
step 5-1, estimating a noise bottom according to the amplitude data of n-time m-point fast Fourier transform;
step 5-2, respectively calculating the mass centers of the n m-point fast Fourier transform frequency spectrum amplitude values, and screening all frequency point amplitude values larger than the noise bottom to calculate the mass centers;
step 5-3, estimating a residual slope kdir: obtaining a centroid curve according to the n centroids obtained in the step 5-2, and calculating by using the two most gentle points on the curve to obtain a residual slope kdir;
step 5-4, calculating an iteration slope: if the residual slope kdir is smaller than a certain range compared with the rough slope kest0, selecting a plurality of slope values around a reference value as iteration slopes according to certain steps;
if the residual slope kdir is larger than a certain range and has the same sign as the rough slope kest0, selecting a plurality of slope values on the right side of the reference value as iteration slopes according to certain steps;
if the residual slope kdir is greater than a certain range and opposite in sign compared to the coarse slope kest0, several slope values to the left of the reference value are selected in steps as iteration slopes.
Further, the iteration unit in step 6 controls the FPGA to iterate according to a correction strategy to perform frequency measurement and deskew, then calculates the centroid and the eccentricity of the frequency spectrum according to the amplitude data of the n × m point fast fourier transform returned by the FPGA, estimates and calculates the optimal residual slope and the optimal start frequency according to the eccentricity data, and transmits the optimal residual slope and the optimal start frequency to the FPGA through the EMIF interface, and the specific steps include:
step 6-1, respectively calculating a noise bottom and a spectrum centroid for the quintic n multiplied by m point fast Fourier transform data;
step 6-2, calculating the eccentricity of the quintic nxm point fast Fourier transform data, wherein the eccentricity calculation method comprises the following steps of: after the centroid is calculated, the n multiplied by m point fast Fourier transform data is divided into a left section and a right section by taking the centroid frequency as a segmentation point, a left half spectrum center mbc1 and a right half spectrum center mbc2 are calculated, and the eccentricity eccntrcdst is the difference between the left spectrum center and the right spectrum center;
and 6-3, calculating the optimal slope according to the five eccentricity estimations: first find the minimum eccentricity eccntrcdst (l); secondly, when the minimum eccentricity is very close to the eccentricity on the left side of the minimum eccentricity, taking the slope value corresponding to the minimum eccentricity and the center of the slope value on the left side of the minimum eccentricity as the optimal slope kopt; when the minimum eccentricity is very close to the eccentricity on the right side of the minimum eccentricity, taking the center of the slope value corresponding to the minimum eccentricity and the slope value on the right side of the minimum eccentricity as the optimal slope kopt; otherwise, the optimal slope kopt is the fitting value of the three nearby points.
And 6-4, sending the optimal slope to the FPGA through the EMIF interface, performing frequency measurement and deskew once again, solving the centroid mbcopt of the returned n x m-point fast Fourier transform data, estimating the optimal initial frequency, and sending the optimal slope and the optimal initial frequency to the FPGA through the EMIF interface, and performing frequency measurement and deskew once again.
Further, in the step 7, the frequency point extracting unit extracts frequencies of the first k most isolated extreme points with the largest amplitude from the amplitude data of the nxm point fast fourier transform returned by the SRIO interface, and then fits and corrects the frequency values of the extreme points according to the approximate amplitude data of the centroid, and the specific steps include:
7-1, performing forward difference and backward difference on the nxm point fast Fourier transform data, wherein a spectrum point with the forward difference and the backward difference both larger than or equal to zero is a maximum value point, and a spectrum point with the forward difference and the backward difference both smaller than or equal to zero is a minimum value point;
7-2, searching and extracting maximum value points of the amplitude values near the frequency point with the maximum frequency of the frequency spectrum amplitude, if the number is not met, sequencing the extracted maximum value points, then extracting the maximum value points from large to small according to the priority in the remaining effective range for complement, and if the number exceeds the required number, extracting the first k maximum value points from large to small according to the priority;
and 7-3, performing correction calculation on the k extreme points in the step 7-2 to obtain a more optimal frequency of the separation spectrum point, wherein the specific method comprises the following steps: and searching spectral points with the similar amplitude values to the original spectral points in the spectral points at the two sides of the original peak spectral point as correction sample data, and estimating the frequency corresponding to the peak point by using a frequency spectrum centroid algorithm to serve as correction frequency.
The invention provides a multiprocessor cooperative radiation source frequency parameter estimation device and a method, which have the beneficial effects that: aiming at the problem of poor processing effect of a single processor in the radar pulse signal parameter estimation process, the invention adopts the high-performance FPGA and the multi-core DSP processor to carry out parameter estimation, and simultaneously ensures the processing precision and the processing speed. By adopting a DSP and FPGA cooperative processing mode, the problem of low estimation precision of the signal parameters of the linear frequency modulation signal radar is effectively solved, the complex signal processing algorithm is realized while the software processing time is prolonged, and the method has good real-time performance and wide adaptability.
Drawings
In order that the present disclosure may be more readily and clearly understood, the following detailed description of the present disclosure is provided in connection with specific embodiments thereof and the accompanying drawings, in which,
fig. 1 is a block diagram of a frequency parameter estimation device of a multi-processor cooperative radiation source according to the present invention.
FIG. 2 is a flow chart of a method for estimating frequency parameters of a multi-processor cooperative radiation source according to the present invention.
Detailed Description
In order to make the implementation objects, technical solutions and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be described in more detail below with reference to the accompanying drawings in the embodiments of the present invention. The described embodiments are only some, but not all embodiments of the invention. Other embodiments obtained by similar modifications and adjustments made by those skilled in the art without inventive efforts shall be considered as the protection scope of the present invention.
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
Example 1:
as shown in fig. 1, a multiprocessor cooperative radiation source frequency parameter estimation apparatus, where the apparatus forms an intra-pulse parameter estimation hardware platform based on an FPGA and a DSP processor, includes: the system comprises an EMIF interface communication module, an SRIO interface communication module and a parameter estimation module;
the EMIF interface communication module is used for realizing the communication of instructions and data between the DSP and the FPGA, and comprises the steps of sending control flow instructions, sending linear frequency modulation signal parameters and sending slopes and frequencies in the parameter estimation process;
the SRIO interface communication module is used for realizing high-speed data transmission between the DSP and the FPGA;
the parameter estimation module is used for calculating and iteratively analyzing the frequency spectrum centroid and the eccentricity of the frequency measurement deskew data transmitted by the FPGA to obtain the accurate slope and the initial frequency of the deskew local oscillator;
the parameter estimation module comprises a rough slope estimation direction unit, an iteration unit and an extraction frequency point unit; the rough slope direction estimating unit is used for estimating the parameter values of five iterative slopes and estimating the slope correction direction; the iteration unit is used for controlling FPGA iteration to carry out frequency measurement and deskew operation according to the correction strategy to obtain an accurate slope and an initial frequency; and the frequency point extracting unit is used for extracting and fitting the frequency value with the maximum amplitude and the most isolated frequency value in the frequency spectrum.
Example 2:
a frequency parameter estimation method based on the multiprocessor cooperative radiation source frequency parameter estimation device described in embodiment 1 includes the following steps,
step 1: after the DSP is initialized, the FPGA is informed to start the front edge detection through the EMIF interface, and meanwhile, the detection threshold is sent to the FPGA;
step 2: the FPGA detects the leading edge according to a leading edge detection threshold, caches data when an effective leading edge is detected, and transmits the leading edge data to the DSP through the SRIO interface;
and step 3: after the DSP receives the SRIO interface interrupt, calculating an extraction factor nexctrct corresponding to the current pulse signal, and then sending the extraction factor nexctrct, the coarse frequency fest0 and the coarse slope kest0 to the FPGA through an EMIF interface;
and 4, step 4: the FPGA extracts the cached data according to the received parameters, on one hand, the extracted data is subjected to n multiplied by m-point fast Fourier transform, on the other hand, the extracted data is subjected to n times of m-point fast Fourier transform according to a time sequence, and amplitude data is sent to the DSP through an SRIO interface;
and 5: after the DSP receives the data, the rough slope direction estimating unit carries out frequency spectrum centroid calculation according to the amplitude data of n times of m-point fast Fourier transform, judges the slope correction direction, calculates the iterative slope, and the iterative unit calculates the frequency spectrum centroid and the eccentricity according to the amplitude data of n multiplied by m-point fast Fourier transform;
step 6: an iteration unit of the DSP controls the FPGA to iterate according to a correction strategy to carry out frequency measurement and deskew operation, then calculates the mass center and the eccentricity of a frequency spectrum according to the amplitude data of n multiplied by m point fast Fourier transform returned by the FPGA, estimates and calculates the optimal residual slope and the optimal residual initial frequency according to the eccentricity data and transmits the optimal residual slope and the optimal residual initial frequency to the FPGA through an EMIF interface, and the iteration process is finished;
and 7: after the iteration process is finished, the frequency point extracting unit of the DSP extracts the frequencies of the first k most isolated extreme points with the maximum amplitude from the amplitude data of the nxm point fast Fourier transform returned by the SRIO interface, then fits and corrects the frequency values of the extreme points according to the adjacent amplitude data of the mass center, and sends the frequency values back to the FPGA through the SRIO interface to finish the subsequent related processing; the DSP is then ready for processing of the next frame data.
Example 3:
a method for estimating frequency parameters of a multiprocessor cooperative radiation source according to embodiment 2, wherein the unit for roughly estimating a slope direction performs noise floor estimation and spectral centroid calculation according to the amplitude data of m-point fast fourier transform for n times, determines a slope correction direction, and calculates an iterative slope, and the method specifically includes:
step 5-1, estimating a noise bottom according to the amplitude data of n-time m-point fast Fourier transform;
step 5-2, respectively calculating the mass centers of the n m-point fast Fourier transform frequency spectrum amplitude values, and screening all frequency point amplitude values larger than the noise bottom to calculate the mass centers;
step 5-3, estimating a residual slope kdir: obtaining a centroid curve according to the n centroids obtained in the step 5-2, and calculating by using the two most gentle points on the curve to obtain a residual slope kdir;
step 5-4, calculating an iteration slope: if the residual slope kdir is smaller than a certain range compared with the rough slope kest0, selecting a plurality of slope values around a reference value as iteration slopes according to certain steps;
if the residual slope kdir is larger than a certain range and has the same sign as the rough slope kest0, selecting a plurality of slope values on the right side of the reference value as iteration slopes according to certain steps;
if the residual slope kdir is greater than a certain range and opposite in sign compared to the coarse slope kest0, several slope values to the left of the reference value are selected in steps as iteration slopes.
Example 4:
a method for estimating frequency parameters of a multi-processor cooperative radiation source according to embodiment 2, in which, in step 6, an iteration unit controls an FPGA to iterate frequency measurement and deskew according to a correction strategy, then calculates a spectrum centroid and an eccentricity according to amplitude data of n × m-point fast fourier transform returned by the FPGA, calculates an optimal residual slope and an optimal start frequency according to the eccentricity data, and transmits the optimal residual slope and the optimal start frequency to the FPGA through an EMIF interface, and the method specifically includes:
step 6-1, respectively calculating a noise bottom and a spectrum centroid for the quintic n multiplied by m point fast Fourier transform data;
step 6-2, calculating the eccentricity of the quintic nxm point fast Fourier transform data, wherein the eccentricity calculation method comprises the following steps of: after the centroid is calculated, the n multiplied by m point fast Fourier transform data is divided into a left section and a right section by taking the centroid frequency as a segmentation point, a left half spectrum center mbc1 and a right half spectrum center mbc2 are calculated, and the eccentricity eccntrcdst is the difference between the left spectrum center and the right spectrum center;
and 6-3, calculating the optimal slope according to the five eccentricity estimations: first find the minimum eccentricity eccntrcdst (l); secondly, when the minimum eccentricity is very close to the eccentricity on the left side of the minimum eccentricity, taking the slope value corresponding to the minimum eccentricity and the center of the slope value on the left side of the minimum eccentricity as the optimal slope kopt; when the minimum eccentricity is very close to the eccentricity on the right side of the minimum eccentricity, taking the center of the slope value corresponding to the minimum eccentricity and the slope value on the right side of the minimum eccentricity as the optimal slope kopt; otherwise, the optimal slope kopt is the fitting value of the three nearby points.
And 6-4, sending the optimal slope to the FPGA through the EMIF interface, performing frequency measurement and deskew once again, solving the centroid mbcopt of the returned n x m-point fast Fourier transform data, estimating the optimal initial frequency, and sending the optimal slope and the optimal initial frequency to the FPGA through the EMIF interface, and performing frequency measurement and deskew once again.
Example 5:
a method for estimating frequency parameters of a multi-processor cooperative radiation source according to embodiment 2, in step 7, the extracting frequency point unit extracts frequencies of the first k extreme points with the largest amplitude and the most isolated from amplitude data of n × m-point fast fourier transform returned by an SRIO interface, and then fits and corrects the frequency values of the extreme points according to the approximate amplitude data of the centroid, which includes the specific steps of:
7-1, performing forward difference and backward difference on the nxm point fast Fourier transform data, wherein a spectrum point with the forward difference and the backward difference both larger than or equal to zero is a maximum value point, and a spectrum point with the forward difference and the backward difference both smaller than or equal to zero is a minimum value point;
7-2, searching and extracting maximum value points of the amplitude values near the frequency point with the maximum frequency of the frequency spectrum amplitude, if the number is not met, sequencing the extracted maximum value points, then extracting the maximum value points from large to small according to the priority in the remaining effective range for complement, and if the number exceeds the required number, extracting the first k maximum value points from large to small according to the priority;
and 7-3, performing correction calculation on the k extreme point points in the step 7-2 to obtain a more optimal frequency of the separation spectrum point, wherein the specific method comprises the following steps: and searching spectral points with the similar amplitude values to the original spectral points in the spectral points at the two sides of the original peak spectral point as correction sample data, and estimating the frequency corresponding to the peak point by using a frequency spectrum centroid algorithm to serve as correction frequency.
In summary, the invention provides a multiprocessor cooperative radiation source frequency parameter estimation device and method, which can overcome the problems of difficult algorithm implementation and low efficiency caused by the implementation of intra-pulse modulation characteristic analysis based on an FPGA, and can overcome the problem of long time consumption of the implementation of intra-pulse modulation characteristic analysis based on a DSP, and the device and method adopt a high-performance FPGA and a multi-core DSP processor to perform parameter estimation, ensure the processing precision and the processing speed, and implement a complex signal processing algorithm while improving the software processing time, and have good real-time performance and wide adaptability.
Claims (9)
1. A multiprocessor cooperative radiation source frequency parameter estimation device is characterized in that the device forms an intra-pulse parameter estimation hardware platform based on an FPGA and a DSP processor, and comprises: the system comprises an EMIF interface communication module, an SRIO interface communication module and a parameter estimation module;
the EMIF interface communication module is used for realizing the communication of instructions and data between the DSP and the FPGA, and comprises the steps of sending control flow instructions, sending linear frequency modulation signal parameters and sending slopes and frequencies in the parameter estimation process;
the SRIO interface communication module is used for realizing high-speed data transmission between the DSP and the FPGA;
and the parameter estimation module is used for calculating and iteratively analyzing the frequency spectrum centroid and the eccentricity of the frequency measurement deskew data transmitted by the FPGA to obtain the accurate slope and the initial frequency of the deskew local oscillator.
2. The device for estimating the frequency parameters of the multi-processor cooperative radiation source according to claim 1, wherein the parameter estimation module comprises a unit for roughly estimating a slope direction, an iteration unit and a unit for extracting frequency points.
3. The apparatus as claimed in claim 2, wherein the slope direction unit is configured to estimate the values of the five iteration slopes and estimate the slope correction direction.
4. The device for estimating the frequency parameters of the multi-processor cooperative radiation source according to claim 2, wherein the iteration unit is configured to control the FPGA to iterate frequency measurement and deskew according to a modification strategy, so as to obtain an accurate slope and an initial frequency.
5. The device for estimating frequency parameters of a multi-processor cooperative radiation source according to claim 2, wherein the extracting frequency point unit is configured to extract and fit a frequency value with a maximum amplitude and a most isolated frequency in a frequency spectrum.
6. A frequency parameter estimation method based on the multiprocessor cooperative radiation source frequency parameter estimation device defined in any one of claims 1 to 5, the method comprising the steps of:
step 1: after the DSP is initialized, the FPGA is informed to start the front edge detection through the EMIF interface, and meanwhile, the detection threshold is sent to the FPGA;
step 2: the FPGA detects the leading edge according to a leading edge detection threshold, caches data when an effective leading edge is detected, and transmits the leading edge data to the DSP through the SRIO interface;
and step 3: after the DSP receives the SRIO interface interrupt, calculating an extraction factor nexctrct corresponding to the current pulse signal, and then sending the extraction factor nexctrct, the coarse frequency fest0 and the coarse slope kest0 to the FPGA through an EMIF interface;
and 4, step 4: the FPGA extracts the cached data according to the received parameters, on one hand, the extracted data is subjected to n multiplied by m-point fast Fourier transform, on the other hand, the extracted data is subjected to n times of m-point fast Fourier transform according to a time sequence, and amplitude data is sent to the DSP through an SRIO interface;
and 5: after the DSP receives the data, the rough slope direction estimating unit carries out frequency spectrum centroid calculation according to the amplitude data of n times of m-point fast Fourier transform, judges the slope correction direction, calculates the iterative slope, and the iterative unit calculates the frequency spectrum centroid and the eccentricity according to the amplitude data of n multiplied by m-point fast Fourier transform;
step 6: an iteration unit of the DSP controls the FPGA to iterate according to a correction strategy to carry out frequency measurement and deskew operation, then calculates the mass center and the eccentricity of a frequency spectrum according to the amplitude data of n multiplied by m point fast Fourier transform returned by the FPGA, estimates and calculates the optimal residual slope and the optimal residual initial frequency according to the eccentricity data and transmits the optimal residual slope and the optimal residual initial frequency to the FPGA through an EMIF interface, and the iteration process is finished;
and 7: after the iteration process is finished, the frequency point extracting unit of the DSP extracts the frequencies of the first k most isolated extreme points with the maximum amplitude from the amplitude data of the nxm point fast Fourier transform returned by the SRIO interface, then fits and corrects the frequency values of the extreme points according to the adjacent amplitude data of the mass center, and sends the frequency values back to the FPGA through the SRIO interface to finish the subsequent related processing; the DSP is then ready for processing of the next frame data.
7. The method as claimed in claim 6, wherein the unit for roughly estimating the slope direction in step 5 performs noise floor estimation and spectral centroid calculation according to the magnitude data of n-th m-point fast fourier transform, determines the slope correction direction, and calculates the iterative slope, and the specific steps include:
step 5-1, estimating a noise bottom according to the amplitude data of n-time m-point fast Fourier transform;
step 5-2, respectively calculating the mass centers of the n m-point fast Fourier transform frequency spectrum amplitude values, and screening all frequency point amplitude values larger than the noise bottom to calculate the mass centers;
step 5-3, estimating a residual slope kdir: obtaining a centroid curve according to the n centroids obtained in the step 5-2, and calculating by using the two most gentle points on the curve to obtain a residual slope kdir;
step 5-4, calculating an iteration slope: if the residual slope kdir is smaller than a certain range compared with the rough slope kest0, selecting a plurality of slope values around a reference value as iteration slopes according to certain steps;
if the residual slope kdir is larger than a certain range and has the same sign as the rough slope kest0, selecting a plurality of slope values on the right side of the reference value as iteration slopes according to certain steps;
if the residual slope kdir is greater than a certain range and opposite in sign compared to the coarse slope kest0, several slope values to the left of the reference value are selected in steps as iteration slopes.
8. The method as claimed in claim 6, wherein the iterative unit in step 6 controls the FPGA to iterate frequency measurement and deskew according to a modification strategy, then calculates the centroid and eccentricity of the frequency spectrum according to the amplitude data of the n × m point fast fourier transform returned by the FPGA, calculates the optimal residual slope and the optimal start frequency according to the eccentricity data estimation, and transmits the optimal residual slope and the optimal start frequency to the FPGA through the EMIF interface, and the specific steps include:
step 6-1, respectively calculating a noise bottom and a spectrum centroid for the quintic n multiplied by m point fast Fourier transform data;
step 6-2, calculating the eccentricity of the quintic nxm point fast Fourier transform data, wherein the eccentricity calculation method comprises the following steps of: after the centroid is calculated, the n multiplied by m point fast Fourier transform data is divided into a left section and a right section by taking the centroid frequency as a segmentation point, a left half spectrum center mbc1 and a right half spectrum center mbc2 are calculated, and the eccentricity eccntrcdst is the difference between the left spectrum center and the right spectrum center;
and 6-3, calculating the optimal slope according to the five eccentricity estimations: first find the minimum eccentricity eccntrcdst (l); secondly, when the minimum eccentricity is very close to the eccentricity on the left side of the minimum eccentricity, taking the slope value corresponding to the minimum eccentricity and the center of the slope value on the left side of the minimum eccentricity as the optimal slope kopt; when the minimum eccentricity is very close to the eccentricity on the right side of the minimum eccentricity, taking the center of the slope value corresponding to the minimum eccentricity and the slope value on the right side of the minimum eccentricity as the optimal slope kopt; otherwise, the optimal slope kopt is the fitting value of the three nearby points.
And 6-4, sending the optimal slope to the FPGA through the EMIF interface, performing frequency measurement and deskew once again, solving the centroid mbcopt of the returned n x m-point fast Fourier transform data, estimating the optimal initial frequency, and sending the optimal slope and the optimal initial frequency to the FPGA through the EMIF interface, and performing frequency measurement and deskew once again.
9. The method according to claim 6, wherein the step 7 of extracting the frequency point unit extracts frequencies of the first k most isolated extreme points with the largest amplitude from the amplitude data of the nxm point fast fourier transform returned by the SRIO interface, and then fits and corrects the frequency values of the extreme points according to the approximate amplitude data of the centroid, and the specific steps include:
7-1, performing forward difference and backward difference on the nxm point fast Fourier transform data, wherein a spectrum point with the forward difference and the backward difference both larger than or equal to zero is a maximum value point, and a spectrum point with the forward difference and the backward difference both smaller than or equal to zero is a minimum value point;
7-2, searching and extracting maximum value points of the amplitude values near the frequency point with the maximum frequency of the frequency spectrum amplitude, if the number is not met, sequencing the extracted maximum value points, then extracting the maximum value points from large to small according to the priority in the remaining effective range for complement, and if the number exceeds the required number, extracting the first k maximum value points from large to small according to the priority;
and 7-3, performing correction calculation on the k extreme points in the step 7-2 to obtain a more optimal frequency of the separation spectrum point, wherein the specific method comprises the following steps: and searching spectral points with the similar amplitude values to the original spectral points in the spectral points at the two sides of the original peak spectral point as correction sample data, and estimating the frequency corresponding to the peak point by using a frequency spectrum centroid algorithm to serve as correction frequency.
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