High-precision time delay estimation method for ranging communication composite signal based on ultra-narrow band modulation
By improving the linear frequency modulated Z-transform and the correlation peak precise interpolation algorithm, the problem of insufficient accuracy of traditional time delay estimation algorithm is solved, and high-precision time delay estimation and coherent demodulation effect with low bit error rate are achieved.
Patent Information
- Application Number
- CN202511499615.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-20
- Publication Date
- 2026-01-13
AI Technical Summary
Existing generalized cross-correlation delay estimation algorithms are difficult to improve accuracy further under hardware sampling rate limitations, and traditional FFT algorithms suffer from the picket fence effect, resulting in insufficient delay estimation accuracy.
An improved linear frequency modulated Z-transform (MCZT) algorithm is used to replace the traditional FFT algorithm. Combined with the correlation peak precise interpolation algorithm (FICP), the spectral resolution and the resolution of the correlation function peak are improved by calculating the refined spectrum of the signal and zero-padding in the frequency domain.
High-precision time delay estimation under low signal-to-noise ratio conditions is achieved, with the time delay estimation RMSE reaching the subsampling rate level, and the bit error rate of coherent demodulation is significantly reduced.
Smart Images
Figure CN121333452A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of signal processing technology, specifically relating to a high-precision time delay estimation method for ranging communication composite signals based on ultra-narrowband modulation. Background Technology
[0002] With the rapid development of wireless communication technology, spectrum resources are becoming increasingly scarce, and the demand for high spectrum utilization and high-precision positioning in ranging-communication composite systems is becoming more and more urgent. Against this backdrop, ranging-communication composite signals based on ultra-narrowband modulation exhibit significant advantages: these signals have high spectrum utilization and good anti-interference performance, enabling them to achieve the dual functions of long-distance high-precision ranging and reliable communication; furthermore, these signals employ constant envelope modulation, allowing them to be embedded in existing, publicly known frequency-modulated and amplitude-modulated signals without affecting the host signal, thus achieving both communication and ranging transmission.
[0003] Ultra-narrowband modulation-based composite signals for ranging and communication can be used to achieve long-distance ranging and communication functions. The accuracy of time delay estimation affects the subsequent ranging and positioning accuracy; therefore, high-precision time delay estimation of composite signals is crucial. Currently, commonly used time delay estimation algorithms for narrowband signals mainly include those based on wavelet transform, higher-order statistics, subspace decomposition (e.g., MUSIC, ESPRIT), and adaptive filtering (e.g., LMS, NLMS, RLS). These algorithms offer high resolution but are complex in principle and computationally intensive. Generalized Cross-Correlation (GCC) time delay estimation algorithms have become a research hotspot in the field of time delay estimation due to their simple principle, low computational cost, strong stability, and wide applicability.
[0004] Generalized cross-correlation time delay estimation algorithms estimate time delay by calculating the cross-correlation function between signals and detecting peak values. Depending on the order of the correlation operation, they can be categorized into several forms, including generalized cross-correlation, generalized second cross-correlation (GSCC), and generalized third cross-correlation (GTCC). However, generalized cross-correlation algorithms typically use Fast Fourier Transform (FFT) for time delay estimation. While FFT is a fast implementation of Discrete Fourier Transform (DFT), it suffers from the "pick-and-fence effect," limiting its estimation to integer multiples of sampling intervals. In practical applications, the accuracy of generalized cross-correlation time delay estimation algorithms is difficult to further improve due to limitations in hardware sampling rates. Summary of the Invention
[0005] To overcome the shortcomings of existing technologies, this invention provides a high-precision time delay estimation method for composite signals in ranging communication based on ultra-narrowband modulation. It utilizes an improved linear frequency modulated Z-transform (MCZT) algorithm to replace the traditional FFT algorithm. By calculating the refined spectrum of the signal, the picket fence effect of the traditional FFT can be overcome, improving spectral resolution. After obtaining the refined spectra of the two signals, the cross-spectrum is calculated. The Fine Interpolation of Correlation Peak (FICP) algorithm zero-padding the cross-spectrum in the frequency domain yields a waveform that is an "amplified" form of the correlation peak obtained by the FFT method. By improving the resolution near the peak of the correlation function, the accuracy of signal time delay estimation is further improved.
[0006] The technical solution adopted by this invention to solve its technical problem is as follows: Step 1: The transmitting end sends a ranging communication composite signal. The composite signal is modulated using full-hopping MPPSK modulation. The expression for full-hopping MPPSK modulation is:
[0007] in, The carrier period; For carrier frequency; This represents the number of carrier flips in a single transition. The ratio of the symbol period to the carrier period; the transmitted data is... number system; Represents a time variable; This indicates the selected modulation symbol for the current modulation, and different ones... Different phase transition positions correspond to different digital information; Step 2: The signal received by the receiver is The received signal is preprocessed to obtain the signal. ;
[0008] in, This is the signal transmission attenuation factor; For signal transmission noise; For signal transmission delay; Represents the discrete-time index, representing the first... n One sampling point; It is a function The discrete-time form is a discrete sequence obtained after sampling; Step 3: Process the preprocessed signals separately. and local sample signals Perform MCZT transformation, and the local sample signal is the locally generated, known frame synchronization header part;
[0009] Signal and The sequence length is Spectrum and The interval is determined by Decide; Step 4: Based on the spectrum obtained in Step 3 and Calculate the cross-power spectral density function of the signal;
[0010] in, Indicates signal and The cross-power spectral density function; express The conjugate function; Step 5: Based on the conjugate symmetry properties of the spectrum, from Two signals were obtained The quadratic cross spectrum of points :
[0011] in, express The conjugate function; Step 6: Convert the quadratic cross-spectral function Zero-padding in the frequency domain, the length is... Expanded to , A new spectrum was obtained. ;beg The inverse transform yields the correlation function of the two signals. :
[0012] Among them, the quadratic correlation function The resolution is ; , The value of satisfies ,and , It is an integer; Step 7: forward Points are recorded as , back Points are recorded as ,but:
[0013]
[0014] in, express forward point, express back point, This indicates that the inverse MCZT transform is being performed; Obtain the required cross-correlation function:
[0015] Step 8: Apply the cross-correlation function obtained in Step 7 Peak detection is performed to obtain delay estimation. :
[0016] Step 9: Based on the time delay estimate obtained in Step 8 Locate the start position of the communication data and send the signal. After multiplying with the carrier extracted using a phase-locked loop, bandpass filtering is performed, and finally detection and decision are made to obtain the communication data.
[0017] Preferably, the preprocessing of the received signal includes receiver filtering and coherent accumulation.
[0018] An electronic device includes: a processor and a memory; the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory to enable the electronic device to perform the above-described high-precision time delay estimation method.
[0019] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described high-precision delay estimation method.
[0020] A chip includes a processor for calling and running a computer program from a memory, causing a device equipped with the chip to perform the high-precision time delay estimation method described above.
[0021] A computer program product includes a computer storage medium storing a computer program, the computer program including instructions executable by at least one processor, which, when executed by the at least one processor, implement the above-described high-precision delay estimation method.
[0022] The beneficial effects of this invention are as follows: This invention proposes a high-precision time delay estimation method for composite signals in ranging communication based on ultra-narrowband modulation. First, the signal-to-noise ratio (SNR) is improved by receiver filtering and coherent accumulation. Then, the preprocessed signal and the local sample signal are subjected to MCZT transform to obtain refined spectra. Next, the cross-spectrum of the two signals is calculated, and the FICP algorithm further improves the resolution of the time-domain cross-correlation function by zero-padding the cross-spectrum, ultimately yielding a high-precision time delay estimation result. Simulation results show that, under low SNR conditions, the algorithm used in this invention has significant advantages over traditional GCC, GSCC, and GTCC methods, achieving sub-sampling rate level time delay estimation with a high RMSE. The magnitude of the error rate can also be achieved with coherent demodulation. Magnitude. Attached Figure Description
[0023] Figure 1 This is a diagram of the composite signal frame structure for ranging communication; Figure 2 This is a flowchart of the ranging communication composite signal receiving and processing process; Figure 3 This is the time-domain waveform of the received signal after preprocessing (SNR=0 dB). Figure 4 These are comparison charts of the refined spectrum of MZCT: (a) signal spectrum calculated by FFT, and (b) signal spectrum calculated by MCZT. Figure 5 These are peak plots of the FICP exact interpolation cross-correlation function: (a) peak of the generalized first-order cross-correlation function, and (b) peak of the FICP cross-correlation function. Figure 6 These are the RMSE (Reset Time Estimate) graphs for different algorithms.
[0024] Figure 7 It is the bit error rate curve of coherent demodulation under low signal-to-noise ratio. Detailed Implementation
[0025] The present invention will be further described below with reference to the accompanying drawings and embodiments.
[0026] To address the problem of high-precision time delay estimation for composite signals in ranging communication based on ultra-narrowband modulation, this invention proposes a high-precision time delay estimation method based on MCZT-FICP. The improved linear frequency modulated Z-transform (MCZT) algorithm replaces the traditional FFT algorithm. By calculating the refined spectrum of the signal, the picket fence effect of the traditional FFT can be overcome, improving the spectral resolution. After obtaining the refined spectra of the two signals, the cross-spectrum is calculated. The Fine Interpolation of Correlation Peak (FICP) algorithm zero-padding the cross-spectrum in the frequency domain yields a waveform that is an "amplified" form of the correlation peak obtained by the FFT method. By improving the resolution near the peak of the correlation function, the accuracy of signal time delay estimation is further improved.
[0027] A high-precision time delay estimation method for ranging communication composite signals based on ultra-narrowband modulation includes the following steps: Step 1: The transmitting end sends a ranging communication composite signal. The composite signal is modulated using full-hopping MPPSK modulation. The expression for full-hopping MPPSK modulation is:
[0028] in, The carrier period; For carrier frequency; This represents the number of carrier flips in a single transition. The ratio of the symbol period to the carrier period; the transmitted data is... Number system.
[0029] Step 2: The signal received by the receiver is The received signal is obtained by preprocessing it through receiver filtering and coherent accumulation. Receiver filtering can remove some noise and out-of-band signals, while coherent accumulation is used to improve the signal-to-noise ratio of the received signal.
[0030]
[0031] in, For signal transmission attenuation factor, For signal transmission noise, This refers to the signal transmission delay.
[0032] Step 3: Process the preprocessed signals separately. and local sample signals Perform MCZT transformation, and the sample signal is the locally generated, known frame synchronization header part.
[0033]
[0034] Signal and The sequence length is Spectrum and The interval is determined by The decision is made. MCZT has the same form as FFT and can be computed quickly using the FFT algorithm.
[0035] Step 4: Refine the spectra of the two signals obtained in Step 3. and Calculate the cross-power spectral density function of the signal.
[0036]
[0037] Step 5: The result calculated in Step 4 Only the first part of the cross spectrum, based on the conjugate symmetry property of the spectrum, is... Two signals can be obtained. The quadratic cross spectrum of points :
[0038] Step 6: Convert the quadratic cross-spectral function Zero-padding in the frequency domain, the length is... Expanded to ( (Points) to obtain a new spectrum This can improve the resolution of related functions. Find... The inverse transform yields the correlation function of the two signals. :
[0039] in quadratic correlation function The resolution is . , The value of should satisfy ,and ( (integer), increase This can improve the resolution of related functions.
[0040] Step 7: For Pointed Only the front part needs to be taken. Dot and after Using points can greatly reduce the amount of computation. forward Points are recorded as , back Points are recorded as ,but:
[0041]
[0042] in: ; The required related functions can be obtained from the above formula:
[0043] Step 8: Apply the cross-correlation function obtained in Step 7 Peak detection is performed to obtain delay estimation. The obtained time delay estimate can provide accurate time synchronization for coherent demodulation of the signal.
[0044]
[0045] Step 9: Based on the transmission delay obtained in Step 8 Locate the start position of the communication data and send the signal. After multiplying with the carrier extracted using a phase-locked loop, bandpass filtering is performed, and finally, detection and decision are made to obtain the communication data.
[0046] Example: Step 1: Set the full-step MPPSK modulation parameters to M =64, K =3, N=M×K The center frequency of the signal 1MHz, sampling rate At 100MHz, the signal transmission delay is 1234.5 seconds. The signal coherent accumulation count is 15 times, signal Length is N , , The composite signal frame structure of ranging communication is as follows: Figure 1 As shown, the receiving and processing flow is as follows: Figure 2 As shown.
[0047] Step 2: Perform receive-end filtering and coherent accumulation on the received signal. Receive-end filtering removes some noise and out-of-band signals, while coherent accumulation improves the signal-to-noise ratio. The preprocessed signal is then obtained. Taking SNR=0 dB as an example, the preprocessed signal time-domain waveform is as follows: Figure 3 As shown. According to Figure 6 and Figure 7 After 15 coherent accumulations, the signal-to-noise ratio can be effectively improved, thereby further enhancing the performance of subsequent coherent demodulation and delay estimation.
[0048] Step 3: Process the preprocessed received signal obtained in Step 2 and local sample signals Performing MZCT transform, the signal spectrum is refined by a factor of 1. .like Figure 4 As shown, the signal spectrum is calculated using FFT and MCZT transform respectively. MCZT calculates a richer spectrum component, which can achieve the purpose of refining the spectrum.
[0049] Step 4: Use the refined spectrum obtained in Step 3 and After obtaining the cross spectrum, precise interpolation (FICP) of the correlation peaks is performed. Because... , Then the resolution of the cross-correlation function is improved. Times. For example... Figure 5 As shown, comparing the peak values of the cross-correlation function of the MCZT-FICP algorithm and the generalized first-order cross-correlation algorithm, the peak value of the MCZT-FICP algorithm is smoother and the resolution near the peak value is higher.
[0050] Step 5: Perform peak detection on the cross-correlation function obtained in Step 4, and conduct 1000 Monte Carlo simulations using the MCZT-FICP algorithm, GCC, GSCC, and GTCC algorithm respectively. The simulation results are as follows: Figure 6 As shown, compared with traditional generalized cross-correlation time delay estimation algorithms, the algorithm proposed in this invention has better performance and higher time delay estimation accuracy.
[0051] Step 6: The time delay estimate obtained in Step 5 can provide accurate time synchronization information for coherent demodulation of the signal. After finding the start position of the communication data, the carrier signal is extracted through a phase-locked loop, and then compared with the preprocessed signal. After multiplication, bandpass filtering and detection are performed, and finally, communication data is output.
Claims
1. A high-precision time delay estimation method for ranging communication composite signals based on ultra-narrowband modulation, characterized in that, Includes the following steps: Step 1: The transmitting end sends a ranging communication composite signal. The composite signal is modulated using full-hopping MPPSK modulation. The expression for full-hopping MPPSK modulation is: in, The carrier period; For carrier frequency; This represents the number of carrier flips in a single transition. The ratio of the symbol period to the carrier period; the transmitted data is... number system; Represents a time variable; This indicates the selected modulation symbol for the current modulation, and different ones... Different phase transition positions correspond to different digital information; Step 2: The signal received by the receiver is The received signal is preprocessed to obtain the signal. ; in, This is the signal transmission attenuation factor; For signal transmission noise; For signal transmission delay; Represents the discrete-time index, representing the first... n One sampling point; It is a function The discrete-time form is a discrete sequence obtained after sampling; Step 3: Process the preprocessed signals separately. and local sample signals Perform MCZT transformation, and the local sample signal is the locally generated, known frame synchronization header part; Signal and The sequence length is Spectrum and The interval is determined by Decide; Step 4: Based on the spectrum obtained in Step 3 and Calculate the cross-power spectral density function of the signal; in, Indicates signal and The cross-power spectral density function; express The conjugate function; Step 5: Based on the conjugate symmetry properties of the spectrum, from Two signals were obtained The quadratic cross spectrum of points : in, express The conjugate function; Step 6: Convert the quadratic cross-spectral function Zero-padding in the frequency domain, the length is... Expanded to , A new spectrum was obtained. ;beg The inverse transform yields the correlation function of the two signals. : Among them, the quadratic correlation function The resolution is ; , The value of satisfies ,and , It is an integer; Step 7: forward Points are recorded as , back Points are recorded as ,but: in, express forward point, express back point, This indicates that the inverse MCZT transform is being performed; Obtain the required cross-correlation function: Step 8: Apply the cross-correlation function obtained in Step 7 Peak detection is performed to obtain delay estimation. : Step 9: Based on the time delay estimate obtained in Step 8 Locate the start position of the communication data and send the signal. After multiplying with the carrier extracted using a phase-locked loop, bandpass filtering is performed, and finally detection and decision are made to obtain the communication data.
2. The high-precision time delay estimation method for ranging communication composite signals based on ultra-narrowband modulation according to claim 1, characterized in that, The preprocessing of the received signal includes receiver filtering and coherent accumulation.
3. An electronic device, characterized in that, include: Processor and memory; The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory to cause the electronic device to perform the method as described in any one of claims 1 to 2.
4. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 2.
5. A chip, characterized in that, include: A processor for retrieving and running a computer program from memory, causing a device on which the chip is mounted to perform the method as described in any one of claims 1 to 2.
6. A computer program product, characterized in that, The computer program product includes a computer storage medium storing a computer program, the computer program including instructions executable by at least one processor, which, when executed by the at least one processor, implement the method as described in any one of claims 1 to 2.