Low earth orbit satellite uplink signal blind detection method and system

Abstract

The invention particularly relates to a blind detection method and system for an uplink signal of a low-orbit satellite, and the method comprises the steps: solving a power spectrum of a received signal, and equally dividing the power spectrum into M continuous sub-power spectrum segments; solving a cepstrum zero point for each sub-power spectrum section, constructing normalized discrete probability distribution according to the solved M cepstrum zero points, and further calculating Shannon entropy as a detection statistic; estimating the noise power in the signal from the received signal, calculating the theoretical mean value and variance of the cepstrum zero entropy by using the noise power and the number of power spectrum segments, and calculating the threshold according to the mean value, the variance and the set false alarm probability; and the blind detection of the existence of the signal is realized by comparing the obtained information entropy with a threshold. According to the signal existence detection method provided based on the cepstrum energy non-uniform characteristic of the received signal power spectrum, noise and signals can be effectively distinguished without knowing any prior information in advance, and meanwhile, the calculation complexity is low.

Description

Technical Field

[0001] This invention relates to the field of satellite communication and signal processing technology, specifically to a blind presence detection method for uplink signals of ground terminals in low-Earth orbit satellite communication systems, and more particularly to a blind presence detection method for uplink signals of low-Earth orbit satellites based on segmented power spectrum cepstral zero-point entropy characteristics combined with noise estimation adaptive detection threshold. Background Technology

[0002] In low Earth orbit (LEO) satellite communication systems, ground terminals transmit signals to high-speed moving satellites via uplink. For purposes such as spectrum monitoring, electromagnetic environment detection, or interference identification, non-cooperative receivers (such as radio monitoring stations and electronic reconnaissance platforms) often need to perform blind presence detection on these uplink signals without any cooperative information. Due to the low orbital altitude and rapid movement of LEO satellites, the uplink is characterized by strong bursts, large Doppler frequency deviations, and uncertain signal arrival times, posing a significant challenge to traditional signal detection methods.

[0003] Currently, signal detection methods in non-cooperative scenarios mainly include energy detection, detection methods based on cyclostationary features, and detection methods based on higher-order statistical properties. However, these methods have significant limitations in actual LEO uplink signal detection. First, while traditional energy detection methods do not require prior information and have low computational complexity, their performance heavily relies on accurate noise power estimation. In LEO ground receiving scenarios, the combined effects of multipath effects, Doppler frequency offset, atmospheric noise, and receiver thermal noise make it difficult to control the false alarm probability at a fixed threshold, especially when the signal-to-noise ratio is below 0 dB, the detection probability drops sharply. Second, some studies have attempted to use the structural features of modern waveforms for detection, such as using the autocorrelation periodicity caused by the cyclic prefix of OFDM signals or complex cepstral impulses for signal detection. However, in multipath Rayleigh fading channels, this periodicity is significantly weakened, resulting in poor robustness of such structure-dependent methods. Furthermore, while detection methods based on statistical characteristics, such as higher-order cumulants, can theoretically suppress Gaussian noise, they are essentially based on the assumption of higher-order stationarity of the signal. However, the uplink signal is susceptible to multipath effects and Doppler frequency shift, which destroys the original statistical stationarity of the signal. In addition, such methods have high computational complexity and are difficult to deploy in real time in resource-constrained non-cooperative detection equipment.

[0004] In recent years, information entropy theory has been introduced into non-cooperative detection, distinguishing signals from noise by quantifying the non-uniformity of energy distribution. However, existing power spectral entropy detection methods construct power spectral entropy using each spectral component, failing to effectively capture the local energy accumulation characteristics of LEO uplink signals in the frequency domain. More importantly, these methods generally employ empirical thresholds to establish a mapping relationship between noise power and entropy statistics, making it impossible to achieve constant false alarm probability detection in dynamic noise environments and thus failing to meet the reliability requirements of practical surveillance or reconnaissance tasks.

[0005] The method proposed in this invention is a solution to the aforementioned problems. This method does not rely on any prior information about the signal and is more robust to adverse channel conditions such as multipath fading, low signal-to-noise ratio, and large frequency offset. This method can estimate the noise variance from the received data and dynamically calculate the detection threshold based on a probabilistic statistical model of power spectrum cepstral zero-point entropy under the pure noise assumption, combined with a preset false alarm probability. Furthermore, the computational complexity of this method is moderate, making it more suitable for real-time operation on general-purpose monitoring equipment.

[0006] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of the present invention, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0007] To address the problems of traditional signal detection methods in non-cooperative low-Earth orbit (LEO) satellite reception scenarios, which rely on prior information, are sensitive to Doppler frequency offset and low signal-to-noise ratio, and cannot maintain a constant false alarm rate in dynamic noise environments, this invention provides a blind detection method and system for LEO satellite uplink signals. This method can achieve robust and adaptive detection of uplink signals without requiring any prior knowledge of the signal.

[0008] Other features and advantages of the invention will become apparent from the following detailed description, or may be learned in part by practice of the invention.

[0009] According to a first aspect of the present invention, a method for blind detection of uplink signals of low-Earth orbit satellites is provided, the method comprising: Step 1: Segment the received signal to obtain multiple signal segments of equal length; Step 2: Calculate the power spectrum for each signal segment and divide the power spectrum into M consecutive sub-power spectrum segments; Step 3: Calculate the cepstral zeros for each sub-power spectrum band, construct a normalized discrete probability distribution based on the obtained M cepstral zeros, and calculate its Shannon entropy as the detection statistic; Step 4: Estimate the noise power from the received signal; Step 5: Based on the estimated noise power and the number of power spectrum segments M, calculate the theoretical mean and variance of the cepstral zero-point entropy under the pure noise assumption, and calculate the detection threshold according to the preset false alarm probability. Step 6: Compare the Shannon entropy calculated in Step 3 with the threshold calculated in Step 5. If the Shannon entropy is greater than the threshold, the current signal segment is determined to be noise; otherwise, it is determined to be a valid communication signal.

[0010] In some exemplary embodiments, in step one, the received signal x[n] is divided into Segment, each segment has a signal length of N bits, the first segment... l The segment signal is represented as: .

[0011] In some exemplary embodiments, in step two, the power spectrum is calculated for each sub-signal segment. l The power spectrum of each sub-signal is represented as:

[0012] in The number of points in the Fast Fourier Transform is used to divide the power spectrum into M equal segments, resulting in M ​​sub-power spectrum segments. If it is divisible by M, then each sub-power spectrum contains The frequency point; the first l The m-th sub-power spectrum segment of a sub-signal segment is represented as: .

[0013] In some exemplary embodiments, in step three, the first... l Taking the logarithm of the power spectrum of each sub-signal segment yields the logarithmic spectrum. , No. l The cepstral zero of the m-th sub-power spectrum segment of the sub-signal segment is:

[0014] Normalizing the calculated cepstral zero values ​​of the M sub-power spectra yields a discrete probability function, then the... l The probability of the m-th cepstral zero of a sub-signal segment is:

[0015] Calculate the normalized result Information entropy: .

[0016] In some exemplary embodiments, in step four, the noise variance is estimated by selecting several time periods with the lowest energy from the received signal x(n). .

[0017] In some exemplary embodiments, step five calculates the theoretical mean and variance of the cepstral zero-point entropy under the pure noise assumption using the following formula:

[0018]

[0019] in It is Euler's constant; The probability distribution of the cepstral zero-point entropy is modeled as follows:

[0020] make ,in If the distribution follows a standard normal distribution with a mean of 0 and a variance of 1, then the normalized probability of each cepstral zero is expressed as:

[0021] Then its Shannon entropy is expressed as:

[0022] make:

[0023] Shannon entropy can then be written as:

[0024] The mean value of Shannon entropy is obtained as follows:

[0025] Taking the variance of Shannon entropy yields:

[0026] The theoretical mean and variance of the cepstral zero entropy are calculated using the mean and variance of the cepstral zeros, combined with the number of power spectrum segments M. Then, the false alarm probability is considered. The noise threshold is calculated, and the specific expression is as follows:

[0027] in It is the upper quantile function of the standard normal distribution.

[0028] According to a second aspect of the present invention, a blind detection system for uplink signals of low-Earth orbit satellites is provided, comprising: The signal segmentation module is used to segment the received signal into multiple signal segments of equal length; The power spectrum analysis and segmentation module is used to calculate the power spectrum for each signal segment and divide the power spectrum into M sub-power spectrum segments. The entropy calculation module is used to calculate the Shannon entropy of the cepstral zero point of the sub-power spectrum corresponding to each signal segment; the noise estimation module is used to estimate the noise power from the received signal. The threshold calculation module is used to calculate the theoretical statistics of the cepstral zero-point entropy based on the noise power and the number of segments M, and to generate the detection threshold according to the false alarm probability. The decision module compares the Shannon entropy output by the entropy calculation module with the threshold output by the threshold calculation module to achieve blind detection of signal presence.

[0029] According to a third aspect of the present invention, a storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the blind detection method for low-Earth orbit satellite uplink signals described in the first aspect.

[0030] According to a fourth aspect of the present invention, a computer program product is provided, on which a computer program is stored, wherein when the computer program is executed by a processor, the method for blind detection of low-Earth orbit satellite uplink signals described in the first aspect is implemented.

[0031] According to a fifth aspect of the present invention, an electronic device is provided, comprising: Processor; and Memory for storing the executable instructions of the processor; The processor is configured to implement the low-Earth orbit satellite uplink signal blind detection method described in the first aspect by executing the executable instructions.

[0032] The embodiments of this invention provide a method and system for blind detection of uplink signals in low-Earth orbit (LEO) satellites. The method characterizes the local energy of the signal in the frequency domain by calculating segmented power spectrum cepstral zeros, and utilizes Shannon entropy to reflect the non-uniformity of this local energy, i.e., the communication signal exhibits low entropy characteristics due to energy concentration. To avoid the cumbersome process of relying on empirical data for traditional fixed thresholds, an adaptive threshold setting method based on a probabilistic statistical model is proposed. By estimating the noise variance in the received signal and combining it with a theoretical probabilistic statistical model of cepstral zero-point entropy under pure noise, a detection threshold is dynamically generated based on a preset probability, thereby ensuring the theoretical optimality of the decision and achieving a constant false alarm rate. Crucially, this detection scheme requires no prior information regarding modulation scheme, frame structure, or carrier frequency, making it suitable for non-cooperative reception scenarios. Simultaneously, the algorithm exhibits good robustness to conditions such as large frequency offset, multipath fading, and low signal-to-noise ratio in LEO satellite communication. This method achieves efficient and reliable blind signal presence determination while maintaining moderate computational complexity.

[0033] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0034] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention. It is obvious that the drawings described below are merely some embodiments of the invention, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0035] Figure 1 This is a flowchart of a method for detecting the blind presence of uplink signals in low-orbit satellites based on segmented power spectrum cepstral zero-point entropy, provided in an embodiment of the present invention.

[0036] Figure 2 is a graph showing the variation of different signal detection statistics with signal-to-noise ratio under a Gaussian channel for the blind presence detection method of low-orbit satellite uplink signal based on segmented power spectrum cepstral zero-point entropy provided in the embodiment of the present invention.

[0037] In Figure 2: Figure (a) is a global curve including the signal-to-noise ratio range from -20dB to 10dB; Figure (b) is a magnified curve of the low signal-to-noise ratio region of Figure (a), with a signal-to-noise ratio range of -20dB to -6dB.

[0038] Figure 3 is a graph showing the variation of different signal detection statistics with signal-to-noise ratio under Rayleigh channel for the blind presence detection method of low-orbit satellite uplink signal based on segmented power spectrum cepstral zero-point entropy provided in the embodiment of the present invention.

[0039] In Figure 3: Figure (a) is a global curve including the signal-to-noise ratio range from -20dB to 10dB; Figure (b) is a magnified curve of the low signal-to-noise ratio region of Figure (a), with a signal-to-noise ratio range of -20dB to -6dB. Detailed Implementation

[0040] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the invention will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0041] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0042] To address the technical challenges in non-cooperative reception scenarios, this invention provides a blind detection method for uplink signals from low-Earth orbit (LEO) satellites. This method involves segmenting the received signal; calculating the power spectrum of each segment; further segmenting the power spectrum of sub-segments; extracting cepstral zeros from each sub-power spectrum segment and constructing a normalized probability distribution; calculating the Shannon entropy as a detection statistic; and simultaneously using adaptive estimation of noise variance from low-energy signal segments, combined with a theoretical probabilistic statistical model of cepstral zero entropy under pure noise conditions, and dynamically generating a detection threshold based on a preset false alarm probability. This enables blind presence detection of uplink signals from LEO satellite ground terminals.

[0043] refer to Figure 1 As shown, the blind detection method for uplink signals of low-Earth orbit satellites may specifically include the following steps: Step 1: Divide the received signal x[n] into Segment, each segment has a signal length of N bits, the first segment... l A segment signal can be represented as:

[0044] Step two, calculate the power spectrum for each sub-signal segment. l The power spectrum of each sub-signal can be expressed as:

[0045] in The number of points in the Fast Fourier Transform is used to divide the power spectrum into M equal segments, resulting in M ​​sub-power spectrum segments. If it is divisible by M, then each sub-power spectrum contains One frequency point. l The m-th sub-power spectrum segment of a sub-signal segment can be represented as:

[0046] Step 3, for the first l Taking the logarithm of each sub-power spectrum segment of the sub-signal segment yields the logarithmic spectrum. Since this algorithm only focuses on the values ​​at index zero in the cepstral domain, i.e., the cepstral zeros, therefore, the first... lThe cepstral zero of the m-th sub-power spectrum segment of the sub-signal segment is:

[0047] Normalizing the calculated cepstral zero values ​​of the M sub-power spectra yields a discrete probability function, then the... l The probability of the m-th cepstral zero of a sub-signal segment is:

[0048] Calculate the normalized result Information entropy:

[0049] Step 4: Select several time periods with the lowest energy from the received signal x(n) to estimate the noise variance. .

[0050] Step 5: Estimate the variance of the noise. Assuming the number of frequency points in the sub-power spectrum band If the value is large enough, the cepstral zeros of the noise power spectrum can be assumed to follow a Gaussian distribution. The mean and variance of the cepstral zeros can be calculated using the following two formulas:

[0051]

[0052] in It is Euler's constant. In the analysis of cepstral zero-entropy in segmented power spectrum, the cepstral zero values ​​originate from the cepstral analysis of each sub-band of the received signal power spectrum, and the cepstral zero values ​​under the assumption of pure Gaussian white noise follow mutually independent and identically distributed Gaussian distributions. Assuming that the estimated cepstral zero-entropy follows a theoretically valuable Gaussian distribution, the probability distribution of the cepstral zero-entropy can be modeled as:

[0053] make ,in It follows a standard normal distribution with a mean of 0 and a variance of 1. The normalized probability of each cepstral zero can then be expressed as:

[0054] Then its Shannon entropy can be expressed as:

[0055] make:

[0056] Then Shannon entropy can be written as:

[0057] Taking the mean of Shannon entropy yields:

[0058] Taking the variance of the Shannon entropy yields:

[0059] The theoretical mean and variance of the cepstral zero entropy are calculated using the mean and variance of the cepstral zeros, combined with the number of power spectrum segments M. Then, the false alarm probability is considered. The noise threshold is calculated, and the specific expression is as follows:

[0060] in It is the upper quantile function of the standard normal distribution.

[0061] Step 6: Calculate the cepstral zero-point entropy of this signal segment. With threshold If the signal is compared to the threshold, the signal segment is determined to be noise; otherwise, the signal segment is determined to be a valid communication signal.

[0062] This invention also provides a low-Earth orbit satellite uplink signal blind detection system, comprising: The signal segmentation module is used to segment the received signal into multiple signal segments of equal length; The power spectrum analysis and segmentation module is used to calculate the power spectrum for each signal segment and divide the power spectrum into M sub-power spectrum segments. The entropy calculation module is used to calculate the Shannon entropy of the cepstral zero point of the sub-power spectrum corresponding to each signal segment; the noise estimation module is used to estimate the noise power from the received signal. The threshold calculation module is used to calculate the theoretical statistics of the cepstral zero-point entropy based on the noise power and the number of segments M, and to generate the detection threshold according to the false alarm probability. The decision module compares the Shannon entropy output by the entropy calculation module with the threshold output by the threshold calculation module to achieve blind detection of signal presence.

[0063] The technical effects of this invention will be described in detail below in conjunction with simulation delay.

[0064] To evaluate the performance of this invention, simulation verification was conducted. The specific parameters for the simulation experiment are as follows: OFDM signals, pure Gaussian white noise, and single-carrier signals (including QPSK, 16QAM, and 64QAM) were used for testing. The OFDM signal used was the uplink signal from a low-Earth orbit satellite, and its specific parameters are shown in Table 1. The center frequency of the single-carrier signal was the same as the center frequency of the OFDM signal. The Monte Carlo simulation was run 1000 times, considering both Gaussian channel and multipath Rayleigh channel models. The multipath Rayleigh channel model used four delay paths with path delays of 0ms, 5ms, 10ms, and 20ms, and path powers of 0dB, -3dB, -6dB, and -12dB, respectively.

[0065] Figure 2 shows the curves of the detection statistics of the Gaussian channel as a function of the signal-to-noise ratio (SNR). Each figure includes two sub-figures: Figure (a) is the full-range curve, and Figure (b) is a magnified view of the low SNR region to clearly observe the performance of the algorithm in the key low SNR region. Figure 2(a) shows the curves of the cepstral zero-point entropy of each signal as a function of the SNR under the Gaussian channel. As can be seen from the figure, the cepstral zero-point entropy of the proposed method remains basically unchanged as the SNR increases. The cepstral zero-point entropy of single-carrier or OFDM signals decreases as the SNR increases, but the cepstral zero-point entropy of OFDM signals decreases faster. This is due to the multi-carrier nature of OFDM signals: their energy is mainly concentrated in the entropy of multiple orthogonal subcarriers, resulting in the cepstral zero-point values ​​of multiple sub-power spectrum bands being much larger than those of other sub-power spectra, thus leading to a greater reduction in Shannon entropy. For single-carrier signals, their energy is mainly concentrated in a certain sub-power spectrum band, and the non-uniformity of their power spectrum is weaker than that of OFDM signals, but stronger than that of completely uniform Gaussian white noise. As can be seen from the figure, the single-carrier modulation order has little impact on its entropy value. Figure (b) shows the low signal-to-noise ratio region, where it can be seen that the entropy values ​​of the three types of signals can still maintain good separation between -11dB and -6dB, especially the OFDM signal. This also proves that the algorithm of this invention can still effectively distinguish signal noise under low signal-to-noise ratio conditions.

[0066] Figure 3 shows the curves of detection statistics versus signal-to-noise ratio (SNR) under the Rayleigh channel. Each figure includes two sub-figures: Figure (a) is the full-range curve, and Figure (b) is a magnified view of the low SNR region to clearly observe the algorithm's performance in the key low SNR region. Compared with the Gaussian channel, the entropy curves of all signals show some fluctuations. This is because the multipath effect introduces frequency-selective fading, which partially smooths the non-uniformity of the power spectrum, resulting in an increase in the entropy value of the cepstrum zeros. Despite the influence of channel fading, the entropy values ​​of the three types of signals still maintain the same relationship as under the Gaussian channel, i.e., OFDM signal < single-carrier signal < noise. More importantly, in the low SNR magnified view, the separation between the OFDM signal and noise is still very large, supporting reliable decision-making. This indicates that the proposed algorithm has strong robustness to multipath fading, and the essence of the proposed algorithm is the non-uniformity of local energy distribution in the frequency domain.

[0067] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0068] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention herein. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the claims.

[0069] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is defined only by the appended claims.

Claims

1. A method for blind detection of uplink signals in low-Earth orbit satellites, characterized in that, The method includes: Step 1: Segment the received signal to obtain multiple signal segments of equal length; Step 2: Calculate the power spectrum for each signal segment and divide the power spectrum into M consecutive sub-power spectrum segments; Step 3: Calculate the cepstral zeros for each sub-power spectrum band, construct a normalized discrete probability distribution based on the obtained M cepstral zeros, and calculate its Shannon entropy as the detection statistic; Step 4: Estimate the noise power from the received signal; Step 5: Based on the estimated noise power and the number of power spectrum segments M, calculate the theoretical mean and variance of the cepstral zero-point entropy under the pure noise assumption, and calculate the detection threshold according to the preset false alarm probability. Step 6: Compare the Shannon entropy calculated in Step 3 with the threshold calculated in Step 5. If the Shannon entropy is greater than the threshold, the current signal segment is determined to be noise; otherwise, it is determined to be a valid communication signal.

2. The method according to claim 1, characterized in that, In step one, the received signal x[n] is divided into Segment, each segment has a signal length of N bits, the first segment... l The segment signal is represented as: 。 3. The method according to claim 2, characterized in that, In step two, the power spectrum is calculated for each sub-signal segment. l The power spectrum of each sub-signal is represented as: in The number of points in the Fast Fourier Transform is used to divide the power spectrum into M equal segments, resulting in M ​​sub-power spectrum segments. If it is divisible by M, then each sub-power spectrum contains The frequency point; the first l The m-th sub-power spectrum segment of a sub-signal segment is represented as: 。 4. The method according to claim 3, characterized in that, In step three, the first... l Taking the logarithm of the power spectrum of each sub-signal segment yields the logarithmic spectrum. , No. l The cepstral zero of the m-th sub-power spectrum segment of the sub-signal segment is: Normalizing the calculated cepstral zero values ​​of the M sub-power spectra yields a discrete probability function, then the... l The probability of the m-th cepstral zero of a sub-signal segment is: Calculate the normalized result Information entropy: 。 5. The method according to claim 4, characterized in that, In step four, the noise variance is estimated by selecting several time periods with the lowest energy from the received signal x(n). .

6. The method according to claim 5, characterized in that, In step five, the theoretical mean and variance of the cepstral zero-point entropy under the pure noise assumption are calculated using the following formula: in It is Euler's constant; The probability distribution of the cepstral zero-point entropy is modeled as follows: make ,in If the distribution follows a standard normal distribution with a mean of 0 and a variance of 1, then the normalized probability of each cepstral zero is expressed as: Then its Shannon entropy is expressed as: make: Shannon entropy can then be written as: The mean value of Shannon entropy is obtained as follows: Taking the variance of Shannon entropy yields: The theoretical mean and variance of the cepstral zero entropy are calculated using the mean and variance of the cepstral zeros, combined with the number of power spectrum segments M. Then, the false alarm probability is considered. The noise threshold is calculated, and the specific expression is as follows: in It is the upper quantile function of the standard normal distribution.

7. A blind detection system for uplink signals of low-Earth orbit satellites, characterized in that, include: The signal segmentation module is used to segment the received signal into multiple signal segments of equal length; The power spectrum analysis and segmentation module is used to calculate the power spectrum for each signal segment and divide the power spectrum into M sub-power spectrum segments. The entropy calculation module is used to calculate the Shannon entropy of the cepstral zero point of the sub-power spectrum corresponding to each signal segment; A noise estimation module is used to estimate the noise power from the received signal; The threshold calculation module is used to calculate the theoretical statistics of the cepstral zero-point entropy based on the noise power and the number of segments M, and to generate the detection threshold according to the false alarm probability. The decision module compares the Shannon entropy output by the entropy calculation module with the threshold output by the threshold calculation module to achieve blind detection of signal presence.

8. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the blind detection method for low-orbit satellite uplink signals as described in any one of claims 1 to 6.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the blind detection method for low-orbit satellite uplink signals as described in any one of claims 1 to 6.

10. An electronic device, characterized in that, include: processor; as well as Memory for storing the executable instructions of the processor; The processor is configured to execute the blind detection method for low-Earth orbit satellite uplink signals according to any one of claims 1 to 6 by executing the executable instructions.

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