Joint Parameter Estimation Method for Time-Frequency Overlapping Multi-Signals in Non-Gaussian Noise Fading Channels

Abstract

The embodiments of the present application relate to the field of parameter estimation technology and disclose a method for joint estimation of parameters of time-frequency overlapping multi-signals in a non-Gaussian noise fading channel, comprising: using a tanh function to perform nonlinear suppression on the time-frequency overlapping multi-signals to obtain a tanh-fractional low-order autocorrelation function; calculating the cyclic frequency and tanh-fractional low-order cyclic spectrum of the tanh-fractional low-order autocorrelation function to obtain a first section where the cyclic frequency is equal to 0 and a second section where the spectrum frequency is equal to the carrier frequency; using a flock optimization algorithm to perform spectral peak search on the first section and the second section respectively to obtain the maximum spectral peak position of the first section and the second maximum spectral peak position of the second section; based on the two spectral peak positions, performing joint estimation of the carrier frequency, symbol rate, and delay of the time-frequency overlapping multi-signals in a non-Gaussian noise fading channel. This method has stable estimation performance and has important theoretical and practical significance for the innovation and development of parameter estimation technology in complex electromagnetic environments.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of parameter estimation technology, and in particular to a method for joint estimation of parameters of time-frequency overlapping multi-signals in a non-Gaussian noise fading channel. Background Art

[0002] In digital communication systems, the effective extraction of characteristic parameters of digitally modulated signals is a crucial prerequisite for subsequent signal capture, spectrum detection, modulation identification, signal demodulation, and blind signal separation and extraction. Accurate estimation of key modulation parameters, such as carrier frequency, symbol rate, and delay, is essential for this extraction. Furthermore, due to the influence of non-Gaussian noise fading channels and the time-frequency overlap of multiple signal components, joint estimation of signal parameters becomes increasingly challenging. Therefore, joint estimation of parameters of multiple signals with time-frequency overlap in non-Gaussian noise fading channels is of vital theoretical and practical significance for the innovation and development of signal processing technologies in complex environments.

[0003] Currently, research teams at home and abroad have proposed numerous parameter estimation methods. Classic parameter estimation methods include maximum likelihood estimation, time-frequency analysis, correlation estimation, and wavelet transform. Newer parameter estimation methods include those based on spectral coherence, machine learning, and cyclostationary properties. Most of these methods estimate a single parameter of a single signal, ignoring the time-frequency overlap of multiple signals and the mutual influence of multiple parameters. Therefore, these methods cannot meet practical needs.

[0004] Research teams at home and abroad have conducted research on the joint estimation of parameters for time-frequency overlapping multi-signals and proposed several solutions. Bolcskei et al. proposed a joint parameter estimation method based on cyclic statistics, Guo Lili et al. proposed a parameter estimation method for time-frequency overlapping dual-signals based on fourth-order cyclic cumulants, Sisi et al. proposed a joint carrier frequency-symbol rate estimation method based on cyclic spectra, Liu Sheng et al. proposed a parameter estimation method for time-frequency overlapping multi-signals based on improved phase difference correction, and Neves et al. proposed a joint multi-parameter estimation method based on symbol rate optimization. Most of these parameter estimation methods assume that the ambient noise is Gaussian white noise. However, actual communication environments are very complex and contain non-Gaussian noise interference, which can cause signal corruption and degrade the parameter estimation performance under the Gaussian assumption. Most methods ignore the time-frequency overlap of multiple signals and the mutual influence of multiple parameters, resulting in a significant degradation in the performance of these parameter estimation methods for time-frequency overlapping multi-signals in non-Gaussian noise fading channels.

[0005] Through the above analysis, the current parameter estimation method has the following defects.

[0006] First, current parameter estimation methods mostly focus on estimating a single parameter of a single signal. However, in actual situations, there are time-frequency overlapping signals, and the various parameters may affect each other, so they cannot meet actual needs.

[0007] Second, most current parameter estimation methods consider the influence of Gaussian noise, and there is no complete research on parameter estimation of time-frequency overlapping multiple signals under non-Gaussian fading channels. Summary of the Invention

[0008] To solve the above technical problems, an embodiment of the present application proposes a method for joint estimation of time-frequency overlapping multi-signal parameters in a non-Gaussian noise fading channel, which effectively realizes the joint estimation of carrier frequency-symbol rate-delay in a non-Gaussian noise fading channel, and has good estimation performance under low signal-to-noise ratio conditions.

[0009] To achieve the above-mentioned purpose, an embodiment of the present application proposes a method for joint estimation of parameters of time-frequency overlapping multiple signals under a non-Gaussian noise fading channel, the method comprising the following steps: using a tanh function to perform nonlinear suppression on the acquired time-frequency overlapping multiple signals to obtain a tanh-fractional low-order autocorrelation function; calculating the cyclic frequency and tanh-fractional low-order cyclic spectrum of the tanh-fractional low-order autocorrelation function, obtaining a first section of the tanh-fractional low-order cyclic spectrum at a cyclic frequency equal to 0, and a second section at a spectral frequency equal to the carrier frequency; using a chicken swarm optimization algorithm to perform spectral peak search on the first section and the second section respectively, to obtain the maximum spectral peak position of the first section and the second largest spectral peak position of the second section; based on the maximum spectral peak position of the first section and the second largest spectral peak position of the second section, performing a joint estimation of the carrier frequency-symbol rate-delay of the time-frequency overlapping multiple signals under a non-Gaussian noise fading channel.

[0010] To achieve the above-mentioned purpose, an embodiment of the present application also proposes a joint estimation system for parameters of time-frequency overlapping multiple signals under a non-Gaussian noise fading channel, the system comprising: a nonlinear transformation module, used to use a tanh function to perform nonlinear suppression on the acquired time-frequency overlapping multiple signals to obtain a tanh-fractional low-order autocorrelation function; a calculation module, used to calculate the cyclic frequency and tanh-fractional low-order cyclic spectrum of the tanh-fractional low-order autocorrelation function, and obtain the first section of the tanh-fractional low-order cyclic spectrum at the cyclic frequency equal to 0, and the second section at the cyclic frequency equal to the carrier frequency; a spectrum peak search module, used to use a chicken swarm optimization algorithm to perform spectrum peak search on the first section and the second section respectively, to obtain the maximum spectrum peak position of the first section and the second largest spectrum peak position of the second section; a parameter estimation module, used to perform joint estimation of carrier frequency-symbol rate-delay of time-frequency overlapping multiple signals under a non-Gaussian noise fading channel based on the maximum spectrum peak position of the first section and the second largest spectrum peak position of the second section.

[0011] To achieve the above-mentioned purpose, an embodiment of the present application also proposes an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a method for joint estimation of time-frequency overlapping multi-signal parameters in a non-Gaussian noise fading channel as described above.

[0012] To achieve the above-mentioned purpose, an embodiment of the present application also proposes a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it can implement a method for joint estimation of time-frequency overlapping multi-signal parameters in a non-Gaussian noise fading channel as described above.

[0013] The embodiment of the present application proposes a method for joint estimation of parameters of time-frequency overlapping multiple signals under non-Gaussian noise fading channels, which uses the tanh function to perform nonlinear suppression on the acquired time-frequency overlapping multiple signals. While suppressing non-Gaussian noise, it reduces the influence of invalid spectral lines on parameter estimation performance, realizes parameter estimation under non-Gaussian noise fading channels, and has good estimation performance under low signal-to-noise ratio conditions. Through the chicken flock optimization algorithm, the spectrum peak search method of the first section and the second section is improved, and the speed and accuracy of the spectrum peak search are improved by optimizing the iteration method, thereby further improving the performance of parameter estimation. By constructing a tanh-fractional low-order cyclic spectrum, the carrier frequency-symbol rate-delay joint estimation is realized according to the spectrum peak characteristics searched in different sections. This method is not affected by the prior knowledge of noise and has relatively stable estimation performance. It has vital theoretical and practical significance for the innovation and development of parameter estimation technology in complex electromagnetic environments.

[0014] Optionally, before performing nonlinear suppression on the acquired time-frequency overlapping multiple signals using a tanh function, the method further includes:

[0015] Obtain time-frequency overlapping multi-signals, and establish a mathematical model of time-frequency overlapping multi-signals under non-Gaussian noise fading channels. The mathematical model of time-frequency overlapping multi-signals under non-Gaussian noise fading channels is expressed by the formula:

[0016]

[0017] Among them, A i 、f ci , τ i 、T bi are the amplitude, carrier frequency, initial delay and symbol period of the i-th signal component, respectively, a i (k) is an independent and identically distributed random data symbol sequence, K iis the length of the data sequence of the transmitted signal, θ is the initial phase, q(t) is the shaping function, s i (t) represents the i-th signal component, h(t) is the amplitude attenuation factor of the fading channel, ω(t) is the non-Gaussian noise that is uncorrelated with all components of the transmitted signal, N is the total number of signal components, and x(t) represents the mathematical model of time-frequency overlapping multiple signals under non-Gaussian noise fading channels.

[0018] Optionally, the tanh function is used to perform nonlinear suppression on the acquired time-frequency overlapping multiple signals to obtain a tanh-fractional low-order autocorrelation function, which is implemented by the following formula:

[0019] R x (t,τ) p =E{tanh[x(t)·x*(t+τ) (p-1) ]};

[0020] Among them, p is the order, the value range of p is [0,2], tanh(·) is the hyperbolic tangent function, that is, the tanh function, E(·) represents the expectation, R x (t,τ) p represents the resulting tanh-fractional low-order autocorrelation function.

[0021] Optionally, calculating the cyclic frequency and the tanh-fractional low-order cyclic spectrum of the tanh-fractional low-order autocorrelation function, and obtaining a first section of the tanh-fractional low-order cyclic spectrum at a cyclic frequency equal to 0 and a second section at a spectrum frequency equal to the carrier frequency, includes:

[0022] Expanding the tanh-fractional low-order autocorrelation function into a Fourier series form, we get the tanh-fractional low-order cyclic autocorrelation function, which is expressed by the formula:

[0023]

[0024] Where ε is the cycle frequency, ε=m / T0, T0 is the signal period, is the tanh-fractional low-order cyclic autocorrelation function, that is, the Fourier coefficient of the tanh-fractional low-order autocorrelation function expanded into the Fourier series form;

[0025] Performing Fourier transform on the tanh-fractional low-order cyclic autocorrelation function, we get the tanh-fractional low-order cyclic spectrum, which is expressed as follows:

[0026]

[0027] At ε = 0 and f = f cAt the tanh-fractional low-order cyclic spectrum cross section, the first cross section is obtained and the second section f c is the carrier frequency.

[0028] Optionally, a swarm optimization algorithm is used to search for spectral peaks on the first section and the second section respectively to obtain the maximum spectral peak position of the first section and the second maximum spectral peak position of the second section, including: initializing relevant parameters of the swarm optimization algorithm, setting the size of the swarm, the dimension of the search space, the update frequency of the population relationship, and the maximum number of iterations; using the swarm optimization algorithm to search for spectral peaks on the first section, calculating the fitness value of the updated individual, if the fitness of the current position is better than the optimal value of the current individual, then updating the optimal value of the current individual, if the optimal value of the current individual is better than the global optimal value of the swarm, then updating the global optimal value of the swarm; judging the fitness of the current iteration; Whether the number of generations is equal to the maximum number of iterations. If the current number of iterations is equal to the maximum number of iterations, the global optimal value of the chicken group is output to obtain the maximum spectral peak position of the first section; the chicken group optimization algorithm is used to search the spectral peak of the second section, and the fitness value of the updated individual is calculated. If the fitness of the current position is better than the optimal value of the current individual, the optimal value of the current individual is updated. If the optimal value of the current individual is better than the global optimal value of the chicken group, the global optimal value of the chicken group is updated; determine whether the current number of iterations is equal to the maximum number of iterations. If the current number of iterations is equal to the maximum number of iterations, the global suboptimal value of the chicken group is output to obtain the second largest spectral peak position of the first section.

[0029] Optionally, based on the maximum spectrum peak position of the first section and the second maximum spectrum peak position of the second section, a joint estimation of carrier frequency-symbol rate-delay of time-frequency overlapping multi-signals under a non-Gaussian noise fading channel is performed, including: a As an estimate of the carrier frequency The second largest peak f b As an estimate of the symbol rate The second largest peak f based on the second cross section b The time-frequency overlapping multi-signal phase θ corresponding to the location determines the estimated value of the delay Among them, the second largest spectrum peak f b The phase θ of the time-frequency overlapping multi-signal corresponding to the location is always equal to 2πτ0f b ,therefore,

[0030] Optionally, after obtaining an estimate of the carrier frequency Estimated symbol rate and the estimated value of the delay Then, the method includes: calculating the estimated value of the carrier frequency obtained based on the normalized root mean square error Estimated symbol rate and the estimated value of the delay Evaluation is performed and the initial values ​​of relevant parameters of the flock optimization algorithm are adjusted based on the evaluation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the related technologies, the following is a brief introduction to the drawings required for use in the embodiments of the present application or the description of the related technologies. Obviously, the following drawings are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. The drawings described here are only used to explain the present application and are not used to limit the present application.

[0032] Figure 1 This is a flow chart of a method for joint estimation of time-frequency overlapping multi-signal parameters in a non-Gaussian noise fading channel provided in one embodiment of the present application;

[0033] Figure 2 This is a curve diagram comparing the estimation performance of the tanh-fractional low-order cyclic spectrum method and the traditional fractional low-order cyclic spectrum method provided in one embodiment of the present application;

[0034] Figure 3 This is a comparison curve of the estimation performance of the tanh-fractional low-order cyclic spectral method provided in one embodiment of the present application and the Cramer-Rao bound under different generalized signal-to-noise ratios;

[0035] Figure 4 1 is a structural diagram of a system for joint estimation of time-frequency overlapping multi-signal parameters in a non-Gaussian noise fading channel provided in another embodiment of the present application;

[0036] Figure 5 It is a structural diagram of an electronic device provided in another embodiment of the present application. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the embodiments of the present application will be described in detail below with reference to the accompanying drawings. In the various embodiments of the present application, many technical details are proposed to enable the reader to better understand the present application. However, even without these technical details and various changes and modifications based on the following embodiments, the technical solutions claimed in the present application can be implemented. The division of the following embodiments is only for the convenience of description and should not constitute any limitation on the specific implementation of the present application. The various embodiments can be combined with each other and referenced to each other under the premise of no contradiction.

[0038] An embodiment of the present application proposes a method for joint estimation of time-frequency overlapping multi-signal parameters in a non-Gaussian noise fading channel, which is applied to a server. The implementation details of the method for joint estimation of time-frequency overlapping multi-signal parameters in a non-Gaussian noise fading channel proposed in this embodiment are described in detail below. The following content is only the implementation details provided for the convenience of understanding and is not necessary for the implementation of this solution.

[0039] The specific process of the method for joint estimation of parameters of time-frequency overlapping multi-signals in a non-Gaussian noise fading channel proposed in this embodiment can be as follows: Figure 1 As shown, including:

[0040] S1, use the tanh function to perform nonlinear suppression on the acquired time-frequency overlapping multi-signals to obtain the tanh-fractional low-order autocorrelation function.

[0041] In the specific implementation, the server must first obtain time-frequency overlapping multiple signals, and then use the tanh function to perform nonlinear suppression on the obtained time-frequency overlapping multiple signals to obtain the tanh-fractional low-order autocorrelation function. It is worth noting that the nonlinear suppression process suppresses non-Gaussian noise while reducing the impact of invalid spectral lines on parameter estimation performance.

[0042] In one example, after the server obtains the time-frequency overlapping multi-signal, it can establish a mathematical model of the time-frequency overlapping multi-signal in a non-Gaussian noise fading channel. The mathematical model of the time-frequency overlapping multi-signal in a non-Gaussian noise fading channel is expressed by the formula:

[0043]

[0044] Among them, A i 、f ci , τ i 、T bi are the amplitude, carrier frequency, initial delay and symbol period of the i-th signal component, respectively, a i (k) is an independent and identically distributed random data symbol sequence, K i is the length of the data sequence of the transmitted signal, θ is the initial phase, q(t) is the shaping function, s i (t) represents the i-th signal component, h(t) is the amplitude attenuation factor of the fading channel, ω(t) is the non-Gaussian noise that is uncorrelated with all components of the transmitted signal, N is the total number of signal components, and x(t) represents the mathematical model of time-frequency overlapping multiple signals under non-Gaussian noise fading channels.

[0045] In one example, the tanh function is used to perform nonlinear suppression on the acquired time-frequency overlapping multiple signals to obtain the tanh-fractional low-order autocorrelation function, which is implemented by the following formula:

[0046] R x (t,τ) p =E{tanh[x(t)·x*(t+τ) (p-1) ]};

[0047] Among them, p is the order, the value range of p is [0,2], tanh(·) is the hyperbolic tangent function, that is, the tanh function, E(·) represents the expectation, R x (t,τ) p Represents the obtained tanh-fractional low-order autocorrelation function. The tanh function can be expressed by the formula:

[0048] tanh[x(t)]=[e x(t) -e -x(t) ] / [e x(t) +e -x(t) ].

[0049] S2, calculate the cyclic frequency and tanh-fractional low-order cyclic spectrum of the tanh-fractional low-order autocorrelation function, obtain the first section of the tanh-fractional low-order cyclic spectrum when the cyclic frequency is equal to 0, and the second section when the spectrum frequency is equal to the carrier frequency.

[0050] In the specific implementation, after the server completes the establishment of the tanh-fractional low-order autocorrelation function, it needs to calculate the cyclic frequency and tanh-fractional low-order cyclic spectrum of the tanh-fractional low-order autocorrelation function, and obtain the first section of the tanh-fractional low-order cyclic spectrum at the cyclic frequency equal to 0, and the second section at the spectrum frequency equal to the carrier frequency.

[0051] In one example, when the server calculates the cyclic frequency and tanh-fractional low-order cyclic spectrum of the tanh-fractional low-order autocorrelation function, it first needs to expand the tanh-fractional low-order autocorrelation function into a Fourier series form, that is, to obtain the tanh-fractional low-order cyclic autocorrelation function. The tanh-fractional low-order cyclic autocorrelation function can be expressed by the formula:

[0052]

[0053] Where ε is the cycle frequency, ε=m / T0, T0 is the signal period, is the tanh-fractional low-order cyclic autocorrelation function, that is, the Fourier coefficient of the tanh-fractional low-order autocorrelation function expanded into the Fourier series form.

[0054] The tanh-fractional low-order cyclic autocorrelation function and the tanh-fractional low-order cyclic spectrum are a set of Fourier transform pairs. Therefore, the server needs to perform Fourier transform on the tanh-fractional low-order cyclic autocorrelation function to obtain the tanh-fractional low-order cyclic spectrum. The tanh-fractional low-order cyclic spectrum is expressed by the formula:

[0055]

[0056] in, represents the tanh-fractional low-order cyclic spectrum.

[0057] Finally, the server is at ε = 0 (i.e., the cycle frequency is equal to 0) and f = f c At (i.e., the spectrum frequency is equal to the carrier frequency, f c (carrier frequency) for the tanh-fractional low-order cyclic spectrum section, and the first section is obtained and the second section

[0058] S3, using a flock optimization algorithm to perform spectrum peak search on the first section and the second section respectively, to obtain the maximum spectrum peak position of the first section and the second maximum spectrum peak position of the second section.

[0059] In a specific implementation, the server obtains the first section and the second section After that, the first section needs to be optimized using the swarm optimization algorithm. and the second section Perform a spectrum peak search to obtain the maximum spectrum peak position of the first section and the second maximum spectrum peak position of the second section.

[0060] In one example, the server needs to initialize the relevant parameters of the swarm optimization algorithm, setting the size of the swarm, the dimension of the search space, the update frequency of the population relationship, and the maximum number of iterations.

[0061] For the first section The first section This is input into the swarm optimization algorithm, which searches for the peak of the first section using the swarm optimization algorithm. The fitness value of the updated individual is calculated. If the fitness of the current position is better than the current individual's optimal value, the current individual's optimal value is updated; otherwise, no update is performed. If the current individual's optimal value is better than the global optimal value of the swarm, the global optimal value of the swarm is updated; otherwise, no update is performed. After the update is completed, it is determined whether the current number of iterations is equal to the maximum number of iterations. If the current number of iterations is equal to the maximum number of iterations, the global optimal value of the swarm is output to obtain the maximum peak position of the first section. Otherwise, the iteration continues.

[0062] For the second section The second section This is input into the swarm optimization algorithm, which uses the swarm optimization algorithm to search for the spectral peak of the second section and calculate the fitness value of the updated individual. If the fitness of the current position is better than the current individual's optimal value, the current individual's optimal value is updated; otherwise, no update is performed. If the current individual's optimal value is better than the global optimal value of the swarm, the global optimal value of the swarm is updated; otherwise, no update is performed. After the update is completed, it is determined whether the current number of iterations is equal to the maximum number of iterations. If the current number of iterations is equal to the maximum number of iterations, the global suboptimal value of the swarm is output to obtain the position of the second largest spectral peak of the second section. Otherwise, the iteration will continue.

[0063] S4, performing a joint estimation of carrier frequency-symbol rate-delay of time-frequency overlapping multi-signals in a non-Gaussian noise fading channel based on the maximum spectrum peak position of the first section and the second maximum spectrum peak position of the second section.

[0064] In a specific implementation, the server obtains the first section The maximum peak position and the second cross section After the second largest spectrum peak position of the first section is obtained, the carrier frequency-symbol rate-delay joint estimation of the time-frequency overlapping multi-signal in the non-Gaussian noise fading channel can be performed based on the maximum spectrum peak position of the first section and the second largest spectrum peak position of the second section.

[0065] In one example, the server will first The maximum spectral peak f a As an estimate of the carrier frequency The second section The second largest peak f b As an estimate of the symbol rate Based on the second section The second largest peak f b The time-frequency overlapping multi-signal phase θ corresponding to the location determines the estimated value of the delay Among them, the second section The second largest peak f b The phase θ of the time-frequency overlapping multi-signal corresponding to the location is always equal to 2πτ0f b ,therefore,

[0066] In one example, the server obtains an estimate of the carrier frequency Estimated symbol rate and the estimated value of the delay After that, it is necessary to estimate the carrier frequency based on the normalized root mean square error Estimated symbol rate and the estimated value of the delay Evaluation is performed and the initial values ​​of relevant parameters of the swarm optimization algorithm are adjusted based on the evaluation results to further improve the estimation performance.

[0067] In this embodiment, the tanh function is used to perform nonlinear suppression on the acquired time-frequency overlapping multiple signals. While suppressing non-Gaussian noise, the influence of invalid spectral lines on parameter estimation performance is reduced, and parameter estimation under non-Gaussian noise fading channels is realized, with good estimation performance under low signal-to-noise ratio conditions. Through the chicken swarm optimization algorithm, the spectrum peak search method of the first section and the second section is improved, and the speed and accuracy of the spectrum peak search are improved by optimizing the iteration method, thereby further improving the performance of parameter estimation. By constructing a tanh-fractional low-order cyclic spectrum, the carrier frequency-symbol rate-delay joint estimation is realized according to the spectrum peak characteristics searched in different sections. This method is not affected by prior knowledge of noise and has relatively stable estimation performance. It has vital theoretical and practical significance for the innovation and development of parameter estimation technology in complex electromagnetic environments.

[0068] The steps of the various methods above are divided only for clarity of description. They can be combined into one step or some steps can be decomposed into multiple steps during implementation. As long as they include the same logical relationship, they are all within the scope of protection of this application. Adding insignificant modifications or introducing insignificant designs to the algorithm or process without changing the core design of the algorithm and process are all within the scope of protection of this application.

[0069] In one embodiment, in order to evaluate the performance superiority of a method for joint estimation of time-frequency overlapping multi-signal parameters in a non-Gaussian noise fading channel proposed in this application (hereinafter referred to as tanh-fractional low-order cyclic spectrum method), we conducted a simulation experiment.

[0070] In the simulation experiment, a series of operations were performed using the MATLAB software platform, and the results were analyzed. The types of time-frequency overlapping signal components used in the simulation were two BPSK modulated signals with different symbol rates and no integer multiple relationship. The experiment was carried out 500 times of Monte Carlo simulation. The performance evaluation standard of the parameter estimation method was the normalized root mean square error, and the background noise was Alpha stable distribution noise. The symbol rates of the two signal components in the time-frequency overlapping signal were set to f and f respectively. b1 =800Baud and f b2 =1000Baud, the carrier frequencies are f c1 =3580Hz and f c2 =4000Hz, sampling frequency is f s =16000Hz, the generalized signal-to-noise ratio varies from -10dB to 15dB, with a step size of 5dB.

[0071] The comparison curve of the estimation performance between tanh-fractional low-order cyclic spectrum method and traditional fractional low-order cyclic spectrum method is as follows: Figure 2 As shown, from Figure 2 As can be seen from the figure, with the increase of generalized signal-to-noise ratio, the estimation performance of the two parameter estimation methods gradually improves, but the estimation performance of the tanh-fractional low-order cyclic spectrum method is significantly better than the traditional fractional low-order cyclic spectrum method. The comparison curve of the estimation performance of the tanh-fractional low-order cyclic spectrum method under different generalized signal-to-noise ratios and the Cramer-Rao bound is shown in the figure. Figure 3 As shown, from Figure 3 It can be seen that the estimation performance of carrier frequency, symbol rate and time delay improves with the increase of generalized signal-to-noise ratio. When the generalized signal-to-noise ratio is greater than or equal to 5dB, the performance of carrier frequency, symbol rate and time delay estimation is close to the Cramer-Rao bound, which proves the effectiveness of the tanh-fractional low-order cyclic spectral method.

[0072] Another embodiment of the present application proposes a joint estimation system for time-frequency overlapping multi-signal parameters in a non-Gaussian noise fading channel. The implementation details of the joint estimation system for time-frequency overlapping multi-signal parameters in a non-Gaussian noise fading channel proposed in this embodiment are described in detail below. The following content is only the implementation details provided for the convenience of understanding and is not necessary for the implementation of this example.

[0073] Figure 4 This is a structural diagram of a time-frequency overlapping multi-signal parameter joint estimation system under a non-Gaussian noise fading channel proposed in this embodiment, including: a nonlinear transformation module M1, a calculation module M2, a spectrum peak search module M3 and a parameter estimation module M4.

[0074] The nonlinear transformation module M1 is used to perform nonlinear suppression on the acquired time-frequency overlapping multi-signals using the tanh function to obtain a tanh-fractional low-order autocorrelation function.

[0075] The calculation module M2 is used to calculate the cyclic frequency and tanh-fractional low-order cyclic spectrum of the tanh-fractional low-order autocorrelation function, and obtain a first section of the tanh-fractional low-order cyclic spectrum when the cyclic frequency is equal to 0, and a second section when the cyclic frequency is equal to the carrier frequency.

[0076] The spectrum peak search module M3 is used to perform spectrum peak search on the first section and the second section respectively using a flock optimization algorithm to obtain the maximum spectrum peak position of the first section and the second maximum spectrum peak position of the second section.

[0077] The parameter estimation module M4 is used to perform joint estimation of carrier frequency, symbol rate and delay of time-frequency overlapping multi-signals in a non-Gaussian noise fading channel based on the maximum spectrum peak position of the first section and the second largest spectrum peak position of the second section.

[0078] It is worth mentioning that all modules involved in this embodiment are logical modules. In actual applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. In addition, to highlight the innovation of this application, this embodiment does not include units that are not closely related to solving the technical problem proposed by this application. However, this does not mean that other units do not exist in this embodiment.

[0079] It is not difficult to find that this embodiment is a system embodiment corresponding to the above-mentioned method embodiments, and this embodiment can be implemented in conjunction with the above-mentioned method embodiments. The relevant technical details and technical effects mentioned in the above-mentioned method embodiments are still valid in this embodiment, and to reduce repetition, they are not repeated here. Accordingly, the relevant technical details mentioned in this embodiment can also be applied to the above-mentioned method embodiments.

[0080] Another embodiment of the present application provides an electronic device, the specific structure of which is as follows: Figure 5 As shown, it includes: at least one processor C1; and a memory C2 communicatively connected to the at least one processor C1; wherein the memory C2 stores instructions that can be executed by the at least one processor C1, and the instructions are executed by the at least one processor C1 so that the at least one processor C1 can execute a method for joint estimation of time-frequency overlapping multi-signal parameters in a non-Gaussian noise fading channel as described in the above-mentioned method embodiments.

[0081] The memory and processor can be connected using a bus. The bus can include any number of interconnected buses and bridges, connecting various circuits within one or more processors and the memory. The bus can also connect various other circuits, such as peripherals, voltage regulators, and power management circuits. These are well known in the art and will not be described further herein. The bus interface is responsible for providing an interface between the bus and the transceiver. The transceiver can be a single component or multiple components, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium.

[0082] The processor is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory can be used to store data used by the processor when performing operations.

[0083] Another embodiment of the present application proposes a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it can implement a method for joint estimation of time-frequency overlapping multi-signal parameters in a non-Gaussian noise fading channel as described in the above method embodiments.

[0084] That is, those skilled in the art will appreciate that all or part of the steps in the above-described method embodiments can be implemented by instructing related hardware through a program, wherein the program is stored in a storage medium and includes a number of instructions for causing a device (such as a single-chip microcomputer, chip, etc.) or a processor to execute all or part of the steps in the above-described method embodiments. The storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0085] Those skilled in the art will appreciate that the above embodiments are specific embodiments for implementing the present application, and that in actual applications, various changes may be made thereto in form and detail without departing from the spirit and scope of the present application.

Claims

1. A method for joint estimation of parameters of time-frequency overlapping multi-signals in non-Gaussian noise fading channels, characterized in that: The method comprises: The tanh function is used to perform nonlinear suppression on the acquired time-frequency overlapping multi-signals to obtain the tanh-fractional low-order autocorrelation function; Calculating the cyclic frequency and the tanh-fractional low-order cyclic spectrum of the tanh-fractional low-order autocorrelation function, obtaining a first section of the tanh-fractional low-order cyclic spectrum at a cyclic frequency equal to 0 and a second section at a spectral frequency equal to the carrier frequency; The swarm optimization algorithm is used to search the spectrum peaks of the first section and the second section respectively, and the maximum spectrum peak position of the first section and the second maximum spectrum peak position of the second section are obtained; Based on the maximum spectrum peak position of the first section and the second maximum spectrum peak position of the second section, a joint estimation of carrier frequency, symbol rate and delay of time-frequency overlapping multi-signals under non-Gaussian noise fading channel is performed; Calculating the cyclic frequency and the tanh-fractional low-order cyclic spectrum of the tanh-fractional low-order autocorrelation function, obtaining a first section of the tanh-fractional low-order cyclic spectrum at a cyclic frequency equal to 0 and a second section at a spectral frequency equal to a carrier frequency, including: Expanding the tanh-fractional low-order autocorrelation function into a Fourier series form, we get the tanh-fractional low-order cyclic autocorrelation function, which is expressed by the formula: ； ； in, is the cycle frequency, , is the signal period, is the tanh-fractional low-order cyclic autocorrelation function, that is, the Fourier coefficient of the tanh-fractional low-order autocorrelation function expanded into the Fourier series form, The mathematical model of time-frequency overlapping multiple signals in non-Gaussian noise fading channels, is the order, The value range is , is the hyperbolic tangent function, that is, the tanh function, represents the tanh-fractional low-order autocorrelation function; Performing Fourier transform on the tanh-fractional low-order cyclic autocorrelation function, we get the tanh-fractional low-order cyclic spectrum, which is expressed as follows: ； exist Chuhe At the tanh-fractional low-order cyclic spectrum cross section, the first cross section is obtained and the second section , is the carrier frequency.

2. The method for joint estimation of parameters of time-frequency overlapping multi-signals in a non-Gaussian noise fading channel according to claim 1, characterized in that: Before using the tanh function to perform nonlinear suppression on the acquired time-frequency overlapping multiple signals, the method further includes: Obtain time-frequency overlapping multi-signals, and establish a mathematical model of time-frequency overlapping multi-signals under non-Gaussian noise fading channels. The mathematical model of time-frequency overlapping multi-signals under non-Gaussian noise fading channels is expressed by the formula: ； ； in, 、 、 、 Respectively The amplitude, carrier frequency, initial delay and symbol period of each signal component, is an independent and identically distributed random data symbol sequence, is the length of the data sequence of the transmitted signal, is the initial phase, is the shaping function, Indicates the signal components, is the amplitude attenuation factor of the fading channel, is a non-Gaussian noise that is uncorrelated with each component of the transmitted signal. is the total number of signal components, A mathematical model representing time-frequency overlapping multiple signals in non-Gaussian noise fading channels.

3. The method for joint estimation of parameters of time-frequency overlapping multi-signals in a non-Gaussian noise fading channel according to claim 2, characterized in that: The tanh function is used to perform nonlinear suppression on the acquired time-frequency overlapping multi-signals to obtain the tanh-fractional low-order autocorrelation function, which is implemented by the following formula: ； in, is the order, The value range is , is the hyperbolic tangent function, that is, the tanh function, Express expectations, represents the resulting tanh-fractional low-order autocorrelation function.

4. The method for joint estimation of parameters of time-frequency overlapping multi-signals in a non-Gaussian noise fading channel according to claim 1, characterized in that: The swarm optimization algorithm is used to search the spectrum peaks of the first section and the second section respectively, and the maximum spectrum peak position of the first section and the second maximum spectrum peak position of the second section are obtained, including: Initialize the relevant parameters of the swarm optimization algorithm, set the swarm size, the dimension of the search space, the update frequency of the population relationship, and the maximum number of iterations; The swarm optimization algorithm is used to search the spectrum peak of the first section and calculate the fitness value of the updated individual. If the fitness of the current position is better than the optimal value of the current individual, the optimal value of the current individual is updated. If the optimal value of the current individual is better than the global optimal value of the swarm, the global optimal value of the swarm is updated. Determine whether the current number of iterations is equal to the maximum number of iterations. If the current number of iterations is equal to the maximum number of iterations, output the global optimal value of the flock and obtain the maximum spectrum peak position of the first section. The swarm optimization algorithm is used to search the spectrum peak of the second section and calculate the fitness value of the updated individual. If the fitness of the current position is better than the optimal value of the current individual, the optimal value of the current individual is updated. If the optimal value of the current individual is better than the global optimal value of the swarm, the global optimal value of the swarm is updated. Determine whether the current number of iterations is equal to the maximum number of iterations. If the current number of iterations is equal to the maximum number of iterations, output the global suboptimal value of the flock and obtain the position of the second largest spectral peak of the first section.

5. The method for joint estimation of parameters of time-frequency overlapping multi-signals in a non-Gaussian noise fading channel according to claim 4, characterized in that: Based on the maximum spectrum peak position of the first section and the second maximum spectrum peak position of the second section, a joint estimation of carrier frequency, symbol rate, and delay of time-frequency overlapping multi-signals in a non-Gaussian noise fading channel is performed, including: The maximum peak of the first section As an estimate of the carrier frequency ; The second largest peak of the second section As an estimate of the symbol rate ; The second largest peak based on the second cross section The phase of the time-frequency overlapping multi-signal corresponding to the location , determine the estimated value of the delay Among them, the second largest peak of the second section The phase of the time-frequency overlapping multi-signal corresponding to the location Identical to ,therefore, .

6. The method for joint estimation of parameters of time-frequency overlapping multiple signals in a non-Gaussian noise fading channel according to claim 5, characterized in that: After obtaining the estimated value of the carrier frequency , an estimate of the symbol rate and the estimated value of the delay Thereafter, the method comprises: Based on the normalized root mean square error, the estimated value of the carrier frequency is obtained , an estimate of the symbol rate and the estimated value of the delay Evaluation is performed and the initial values ​​of relevant parameters of the flock optimization algorithm are adjusted based on the evaluation results.

7. A joint estimation system for time-frequency overlapping multi-signal parameters in non-Gaussian noise fading channels, characterized by: The system comprises: A nonlinear transformation module is used to perform nonlinear suppression on the acquired time-frequency overlapping multi-signals using a tanh function to obtain a tanh-fractional low-order autocorrelation function; a calculation module, configured to calculate the cyclic frequency and the tanh-fractional low-order cyclic spectrum of the tanh-fractional low-order cyclic function, and obtain a first section of the tanh-fractional low-order cyclic spectrum at a cyclic frequency equal to 0 and a second section at a cyclic frequency equal to a carrier frequency; A spectrum peak search module is used to perform spectrum peak search on the first section and the second section respectively using a flock optimization algorithm to obtain the maximum spectrum peak position of the first section and the second maximum spectrum peak position of the second section; A parameter estimation module is used to perform a joint estimation of carrier frequency, symbol rate, and delay of a time-frequency overlapping multi-signal in a non-Gaussian noise fading channel based on the maximum spectrum peak position of the first section and the second largest spectrum peak position of the second section; Calculating the cyclic frequency and the tanh-fractional low-order cyclic spectrum of the tanh-fractional low-order autocorrelation function, obtaining a first section of the tanh-fractional low-order cyclic spectrum at a cyclic frequency equal to 0 and a second section at a spectral frequency equal to a carrier frequency, including: Expanding the tanh-fractional low-order autocorrelation function into a Fourier series form, we get the tanh-fractional low-order cyclic autocorrelation function, which is expressed by the formula: ； ； in, is the cycle frequency, , is the signal period, is the tanh-fractional low-order cyclic autocorrelation function, that is, the Fourier coefficient of the tanh-fractional low-order autocorrelation function expanded into the Fourier series form, The mathematical model of time-frequency overlapping multiple signals in non-Gaussian noise fading channels, is the order, The value range is , is the hyperbolic tangent function, that is, the tanh function, represents the tanh-fractional low-order autocorrelation function; Performing Fourier transform on the tanh-fractional low-order cyclic autocorrelation function, we get the tanh-fractional low-order cyclic spectrum, which is expressed as follows: ； exist Chuhe At the tanh-fractional low-order cyclic spectrum cross section, the first cross section is obtained and the second section , is the carrier frequency.

8. An electronic device, characterized in that: include: at least one processor; and, a memory communicatively coupled to the at least one processor; In which, the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a method for joint estimation of time-frequency overlapping multi-signal parameters in a non-Gaussian noise fading channel as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, it can implement a method for joint estimation of time-frequency overlapping multi-signal parameters in a non-Gaussian noise fading channel according to any one of claims 1 to 6.

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