A method and apparatus for estimating the total time of origin (TOA) of a signal based on frequency domain weighted inverse Fourier transform.

Abstract

This invention discloses a signal TOA estimation method and apparatus based on frequency-domain weighted inverse Fourier transform, belonging to the field of radar signal processing. The method includes the following steps: frequency-domain weighted parameter analysis and real-time parameter estimation processing. In the frequency-domain weighted parameter analysis, a frequency-domain weighting method is determined. In the real-time parameter estimation processing, the frequency-domain weighting method determined in the frequency-domain weighted parameter analysis is used to perform frequency-domain weighting calculation on the spectral data frame containing the pulse leading edge, and then the TOA estimation value is calculated using the TOA detection threshold determined in the frequency-domain weighted parameter analysis. This invention can meet the bandwidth adaptive filtering requirements of signals with different pulse widths, thereby improving the signal-to-noise ratio of TOA estimation, especially improving the TOA estimation accuracy under low signal-to-noise ratio conditions.

Description

Technical Field

[0001] This invention relates to the field of radar signal processing, and more specifically, to a method and apparatus for estimating the TOA of a signal based on frequency domain weighted inverse Fourier transform. Background Technology

[0002] Time of arrival (TOA) estimation is a crucial topic in passive radar signal processing, forming the basis for measuring pulse width (PW) and pulse repetition interval (PRI) parameters. TOA estimation, as part of the parameter measurement function of a passive radar signal receiver, is inseparable from the signal detection process. Correct detection of the radiation source signal is a prerequisite for TOA estimation. However, under low signal-to-noise ratio (SNR) conditions, if only the signal is detected but the TOA cannot be accurately measured, the radiation source pulse signal lacks a correct description of the TOA, PRI, and PW parameters, rendering signal detection meaningless. The FFT processing receiver detects and estimates TOA frame-by-frame for the wideband, high-speed sampled signal: first, a long FFT spectrum is generated for high-sensitivity signal detection; then, based on the pilot signal frequency, the FFT spectrum outside a certain bandwidth is zeroed (i.e., narrowband frequency domain filtering); then, an IFFT is performed, transforming back to the time domain to extract the envelope and perform TOA estimation. The bandwidth reserved in the FFT spectrum needs to be matched with the minimum measurable pulse width and the TOA time-domain resolution requirement. Generally, it is taken as several times the frequency domain resolution. This results in the time domain signal-to-noise ratio after IFFT being lower than the frequency domain detection signal-to-noise ratio of FFT in most pulse width cases. However, existing literature shows that the signal-to-noise ratio required for accurate TOA estimation should be higher than the receiver sensitivity. Therefore, at low signal-to-noise ratios, the problem of detectable signals in the frequency domain but incorrect TOA measurement often occurs.

[0003] In traditional TOA estimation algorithms, the adaptive threshold method proposed by TORRIERID J is a commonly used method in engineering. It is simple and computationally efficient, but it does not meet the requirements for TOA estimation under low signal-to-noise ratio (SNR) conditions. The piecewise FFT method proposed by Chan YT et al. and the reverse correlation accumulation method proposed by Hu Guobing et al. directly process the original wideband sampled data superimposed with Gaussian white noise. These methods are not suitable for out-of-band noise filtering in FFT-processed receivers, and their computational complexity limits their engineering applications. Even after filtering to zero out-of-band spectra, the original wideband sampled data processed by FFT still retains some narrowband colored noise. Under low SNR conditions, this can cause significant fluctuations in the signal envelope, easily leading to TOA estimation errors. Therefore, under low SNR conditions, it is still necessary to consider improving filtering methods to further reduce the impact of noise on TOA estimation and improve its accuracy. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a signal TOA estimation method and apparatus based on frequency domain weighted inverse Fourier transform. Specifically, it addresses the principle of TOA estimation in FFT processing receivers based on narrowband filtering and inverse Fourier transform, and proposes a frequency domain weighted filtering and TOA correction estimation method to meet the bandwidth adaptive filtering requirements of signals with different pulse widths, thereby improving the signal-to-noise ratio of TOA estimation, especially improving the accuracy of TOA estimation under low signal-to-noise ratio conditions.

[0005] The objective of this invention is achieved through the following solution:

[0006] A signal TOA estimation method based on frequency domain weighted inverse Fourier transform includes the following steps:

[0007] The frequency domain weighted parameter analysis and parameter estimation are processed in real time. In the frequency domain weighted parameter analysis, the frequency domain weighting method is determined. In the real time parameter estimation, the frequency domain weighting method determined in the frequency domain weighted parameter analysis is used to perform frequency domain weighting calculation on the spectral data frame where the pulse leading edge is located. Then, the TOA estimation value is calculated using the TOA detection threshold determined in the frequency domain weighted parameter analysis.

[0008] Furthermore, the frequency domain weighted parameter analysis also includes the following sub-steps:

[0009] Step 1: Determine the frequency domain weighting method;

[0010] Step 2: Time-domain waveform analysis of signals with different duty cycles based on frequency domain weighting;

[0011] Step 3, TOA detection threshold fitting.

[0012] Furthermore, the real-time parameter estimation processing also includes the following sub-steps:

[0013] Step S1, FFT frequency domain detection: Generate an FFT spectrum for the sampled broadband digital signal, perform signal detection processing, and then output the signal frequency and determine the frame number where the pulse leading edge is located;

[0014] Step S2, spectrum zeroing and weighted calculation: Based on the frequency domain weighting method determined in step 2 of the spectrum weighting parameter analysis process, frequency domain weighted calculation is performed on the spectrum data frame where the pulse leading edge of step S1 is located;

[0015] Step S3, IFFT Transformation and Envelope Extraction: Perform IFFT operation based on the weighted FFT spectrum from Step S2 to convert it into time-domain waveform data, and calculate the amplitude envelope;

[0016] Step S4, TOA time point search: Based on the amplitude envelope data from step S3 and the TOA detection threshold fitting formula determined in step 3 during the frequency domain weighted parameter analysis, calculate the TOA amplitude detection threshold that matches the current time domain envelope waveform, and then search for the TOA time point in the amplitude envelope data to give the TOA estimate.

[0017] Further, in step 1, the method for determining frequency domain weighting specifically includes the following sub-steps: frequency domain weighting specifically includes two parts: out-of-band filtering and in-band weighting. A certain filtering bandwidth is set, and with the signal frequency as the center, the spectral weighting coefficients outside the bandwidth are set to zero. The spectral weighting coefficients within the bandwidth should be positively correlated with the magnitude of the spectral amplitude, so as to achieve adaptive matching with the actual signal bandwidth to filter out noise.

[0018] Furthermore, in step 2, the time-domain waveform analysis of the signals with different duty cycles based on frequency-domain weighting specifically includes the following sub-steps:

[0019] The FFT spectrum is processed using the frequency domain weighting determined in step 1, and then the time domain waveform envelope is extracted by inverse Fourier transform. The TOA amplitude threshold is adjusted, and the influence on the optimal TOA amplitude threshold is comprehensively analyzed by analyzing the time domain waveforms of signals with different duty cycles based on frequency domain weighting.

[0020] Furthermore, in step 3, the TOA detection threshold fitting specifically includes the following sub-steps: under the case of frequency domain weighted inverse Fourier transform processing, a new TOA amplitude threshold formula is generated by fitting the filtered time-domain waveform data corresponding to signals with different duty cycles.

[0021] Further, in step S1, generating the FFT spectrum specifically includes generating the FFT spectrum frame by frame.

[0022] Furthermore, in step S3, the search for TOA time point specifically includes searching for TOA time point starting from the maximum value.

[0023] Furthermore, the step of searching for TOA time points starting from the maximum value specifically includes searching for TOA time points in reverse order starting from the maximum value.

[0024] A signal TOA estimation device based on frequency domain weighted inverse Fourier transform includes a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is loaded by the processor, it executes the method described in any of the preceding methods.

[0025] The beneficial effects of this invention include:

[0026] The frequency domain weighting method of this invention, compared with the unweighted spectral IFFT processing method, achieves adaptive matched filtering processing with the actual signal bandwidth, improves the TOA estimation signal-to-noise ratio, and especially improves the TOA estimation accuracy of the original broadband data under low signal-to-noise ratio conditions.

[0027] This invention addresses the problem that the signal-to-noise ratio required for effective TOA estimation in traditional FFT receivers is lower than that required for frequency domain detection. It reduces the signal-to-noise ratio requirement for effective TOA estimation, making it closer to the frequency domain detection capability, thereby improving the overall performance of the receiver. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 This is a schematic diagram of the components of an FFT processing receiver;

[0030] Figure 2 This is a schematic diagram of the process of the present invention;

[0031] Figure 3 This is an analysis of the TOA detection threshold fitting of signals with different duty cycles after frequency domain weighting and IFFT processing in a specific embodiment;

[0032] Figure 4 This document presents a specific example of the Monte Carlo algorithm's effectiveness under different SNR conditions for two different TOAs.

[0033] Figure 5 This document presents a specific embodiment for analyzing the Monte Carlo algorithm verification performance under different SNR conditions for two different TOA values. Detailed Implementation

[0034] All features disclosed in all embodiments of this specification, or steps in all methods or processes implied in the disclosure, may be combined and / or extended or replaced in any way, except for mutually exclusive features and / or steps.

[0035] In a preferred embodiment, particularly relating to the field of passive radar signal processing, a method for estimating the signal arrival time of an FFT-processing receiver is provided, such as... Figure 1 and Figure 2 As shown, the processing is divided into two parts: frequency domain weighted parameter analysis and real-time parameter estimation processing.

[0036] The steps for frequency domain weighted parameter analysis are as follows:

[0037] Step 1, determine the frequency domain weighting method: Frequency domain weighting includes two parts: out-of-band filtering and in-band weighting. Set a certain filtering bandwidth, take the signal frequency as the center, set the spectral weighting coefficients outside the bandwidth to zero, and the spectral weighting coefficients inside the bandwidth should be positively related to the magnitude of the spectral amplitude, so as to achieve adaptive matching with the actual signal bandwidth to filter out noise.

[0038] Step 2, Time-domain waveform analysis of signals with different duty cycles based on frequency domain weighting: The FFT spectrum is processed using the frequency domain weighting determined in Step 1, and then IFFT (Inverse Fourier Transform) is performed to extract the time-domain waveform envelope. Compared with unweighted IFFT processing in the frequency domain, this will broaden the pulse waveform and change the magnitude of the amplitude value corresponding to the true TOA. Therefore, it is necessary to adjust the TOA amplitude threshold. By analyzing the time-domain waveform of signals with different duty cycles based on frequency domain weighting, the impact on the optimal TOA amplitude threshold is comprehensively analyzed.

[0039] Step 3, TOA detection threshold fitting: Under the case of frequency domain weighted IFFT processing, a new TOA amplitude threshold formula is generated by fitting the filtered time domain waveform data corresponding to signals with different duty cycles.

[0040] The real-time parameter estimation processing steps are as follows:

[0041] Step S1, FFT frequency domain detection: Generate a long FFT spectrum frame by frame for the sampled broadband digital signal, perform signal detection processing, and then output the signal frequency and determine the frame number where the pulse leading edge is located;

[0042] Step S2, spectrum zeroing and weighted calculation: Based on the frequency domain weighting method determined in step 2 of the spectrum weighting parameter analysis process, frequency domain weighting calculation is performed on the spectrum data frame where the pulse leading edge of step S1 is located;

[0043] Step S3, IFFT Transformation and Envelope Extraction: Perform IFFT operation based on the weighted FFT spectrum from Step S2 to convert it into time-domain waveform data, and calculate the amplitude envelope;

[0044] Step S4, TOA time point search: Based on the amplitude envelope data and frequency domain weighted parameter analysis process in step S3, the TOA detection threshold fitting formula in step 3 is used to calculate the TOA amplitude detection threshold that matches the current time domain envelope waveform. Then, in the amplitude envelope data, the TOA time points are searched in reverse order starting from the maximum value to give the estimated TOA value.

[0045] Example 1

[0046] The FFT processing receiver in this embodiment uses 1GHz wideband digital sampling. The original wideband sampled data is processed frame by frame according to 1024-point FFT to generate spectrum and perform frequency domain signal detection. TOA estimation is completed in the time domain after FFT spectrum weighting and IFFT transformation.

[0047] The frequency domain weighted parameter analysis process is as follows:

[0048] Step 1, determine the frequency domain weighting method: with the signal detection frequency as the center, set the weighting coefficients of the out-of-band spectrum beyond 10MHz to 0, and set the weighting coefficients of the in-band frequency points within 10MHz to the normalized amplitude coefficients of the corresponding frequency points (i.e., the frequency point weighting coefficients are completely proportional to the amplitude).

[0049] Step 2, Frequency-domain weighted TOA amplitude detection threshold fitting: For typical frequency pulse signals with different duty cycles, random samples are generated. After frequency-domain weighting and IFFT, the time-domain amplitude envelope is extracted. Then, the amplitude envelope data is normalized relative to the maximum amplitude value. The relationship between the normalized envelope data and the known TOA corresponding amplitude detection threshold is observed. Figure 3 As shown, the minimum value of the normalized amplitude envelope is selected as the feature, and a piecewise linear approximation is used to fit the TOA amplitude detection threshold.

[0050] th(x)=max{(x-0.35)·0.8+0.45,0.45}

[0051] Where x represents the minimum value of the normalized magnitude envelope, and th(x) is the normalized TOA magnitude detection threshold calculated by fitting.

[0052] The parameter estimation real-time processing is as follows:

[0053] Step S1, FFT frequency domain detection: For the input wideband sampling data, perform 1024-point FFT spectrum calculation frame by frame, and perform signal detection according to a certain frequency domain detection threshold or based on the CFAR threshold, output the frequency point of the detected signal, and determine the frame number where the pulse leading edge is located. Subsequently, perform TOA estimation on the FFT spectrum data of that frame.

[0054]

[0055] Where X(k) is the FFT calculation result of the frame where the pulse leading edge is located, and let X(k0) be the FFT calculation result of the frequency point of the detection signal.

[0056] Step S2, Frequency Domain Zeroing and Weighted Calculation: Based on the spectral weighting method determined by the frequency domain weighting parameter analysis process, the FFT spectrum is corrected;

[0057]

[0058] Among them, X M (k), k = 0, 1, 2, ..., 511 are the weighted spectrum data.

[0059] Step S3, IFFT transformation and envelope extraction: Perform IFFT operation based on the weighted FFT spectrum from step 2;

[0060]

[0061] Then extract the envelope amplitude data, i.e., |x M (n)|, n=0,1,2,…,511, It should be noted that the sampling time interval here has become twice the original AD sampling interval.

[0062] Step S4: Based on the amplitude envelope data, generate the TOA detection threshold according to the fitting formula in step 2 of the frequency domain weighted parameter analysis process.

[0063]

[0064] In the amplitude envelope data, the TOA time point is searched in reverse order: based on the amplitude detection threshold, starting from the maximum point of the amplitude envelope data, the TOA time points are searched in reverse order, and the time points that meet the conditions are selected. Make a TOA estimate;

[0065]

[0066] To illustrate the effectiveness of the algorithm of this invention, a single frame of 1GHz sampled data (1024 points) containing the pulse leading edge was simulated. Two TOA parameters (0.4µs ​​and 0.8µs) were selected for simulation, and Gaussian white noise with different signal-to-noise ratios was superimposed. The effectiveness of the method of this invention and three other algorithms (the adaptive threshold method proposed by TORRIERID J, the piecewise FFT method proposed by Chan YT et al., and the reverse correlation accumulation method proposed by Hu Guobing et al.) were verified. Figure 4 The root mean square error statistics (TOA = 0.4 μs) for four algorithms under different SNRs are presented. Figure 5 The root mean square error (TOA) statistics for four algorithms under different SNRs are presented (TOA = 0.8 μs). From Figure 4 and Figure 5 As can be seen, in the low signal-to-noise ratio range (typically -12dB to -5dB), the method of this invention outperforms the other three methods, and the TOA effectively estimates the required signal-to-noise ratio more closely to the frequency domain detection capability. In the high signal-to-noise ratio range (above -5dB), although the estimation accuracy of the method of this invention is slightly worse than the other methods, the root mean square error of 30ns is sufficient for most scenarios.

[0067] The units described in the embodiments of the present invention can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0068] According to one aspect of the present invention, a computer program product or computer program is provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and executes the computer instructions, causing the computer device to perform the methods provided in the various optional implementations described above.

[0069] In another aspect, embodiments of the present invention also provide a computer-readable medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the methods described in the above embodiments.

Claims

1. A signal TOA estimation method based on frequency domain weighted inverse Fourier transform, characterized in that, Includes the following steps: The frequency domain weighted parameter analysis and parameter estimation are processed in real time. In the frequency domain weighted parameter analysis, the frequency domain weighting method is determined. In the real time parameter estimation, the frequency domain weighting method determined in the frequency domain weighted parameter analysis is used to perform frequency domain weighting calculation on the spectrum data frame where the pulse leading edge is located. Then, the TOA detection threshold determined in the frequency domain weighted parameter analysis is used to calculate the TOA estimate. The frequency domain weighted parameter analysis also includes the following sub-steps: Step 1: Determine the frequency domain weighting method. Frequency domain weighting specifically includes two parts: out-of-band filtering and in-band weighting. Set a certain filtering bandwidth, center on the signal frequency, set the spectral weighting coefficients outside the bandwidth to zero, and the spectral weighting coefficients inside the bandwidth should be positively correlated with the magnitude of the spectral amplitude, so as to achieve adaptive matching with the actual signal bandwidth to filter out noise. Step 2: Time-domain waveform analysis of signals with different duty cycles based on frequency domain weighting; The FFT spectrum is processed using the frequency domain weighting determined in Step 1, and then the inverse Fourier transform is performed to extract the time-domain waveform envelope. The TOA amplitude threshold is adjusted, and the impact on the optimal TOA amplitude threshold is comprehensively analyzed through time-domain waveform analysis of signals with different duty cycles based on frequency domain weighting. Step 3, TOA detection threshold fitting; Under the case of frequency domain weighted inverse Fourier transform processing, a new TOA amplitude threshold formula is generated by fitting the filtered time domain waveform data corresponding to signals with different duty cycles. The real-time parameter estimation processing also includes the following sub-steps: Step S1, FFT frequency domain detection: Generate an FFT spectrum for the sampled broadband digital signal, perform signal detection processing, and then output the signal frequency and determine the frame number where the pulse leading edge is located. Subsequently, perform TOA estimation on the FFT spectrum data of this frame. ； in, Let the FFT calculation result of the frame containing the pulse leading edge be given. The FFT calculation results are used to detect the frequency points of the signal; Step S2, Spectrum Zeroing and Weighted Calculation: Based on the frequency domain weighting method determined in Step 2 of the spectrum weighting parameter analysis process, frequency domain weighting calculation is performed on the spectrum data frame where the pulse leading edge of Step S1 is located; the FFT spectrum is then corrected. in, The data is the weighted spectrum. Step S3, IFFT Transformation and Envelope Extraction: Perform IFFT operation based on the weighted FFT spectrum from Step S2 to convert it into time-domain waveform data, and calculate the amplitude envelope; Step S4, TOA time point search: Based on the amplitude envelope data from step S3 and the TOA detection threshold fitting formula determined in step 3 during the frequency domain weighted parameter analysis, calculate the TOA amplitude detection threshold that matches the current time domain envelope waveform, and then search for the TOA time point in the amplitude envelope data to give the TOA estimate.

2. The signal TOA estimation method based on frequency domain weighted inverse Fourier transform according to claim 1, characterized in that, In step S1, generating the FFT spectrum specifically includes generating the FFT spectrum frame by frame.

3. The signal TOA estimation method based on frequency domain weighted inverse Fourier transform according to claim 1, characterized in that, In step S4, the search for TOA time point specifically includes searching for TOA time point starting from the maximum value.

4. The signal TOA estimation method based on frequency domain weighted inverse Fourier transform according to claim 3, characterized in that, The search for TOA time points starting from the maximum value specifically includes searching for TOA time points in reverse order starting from the maximum value.

5. A signal TOA estimation device based on frequency domain weighted inverse Fourier transform, characterized in that, It includes a processor and a memory, in which a computer program is stored, which, when loaded by the processor, executes the method of any one of claims 1 to 4.

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