A blind signal time domain detection method based on self-correlation three-window decision

Abstract

The application discloses a blind signal time domain detection method based on self-correlation three-window decision, belongs to the communication technical field, and aims at providing a blind signal time domain detection method based on self-correlation three-window decision which is suitable for various modulation type communication signals and low duty cycle radar signals under the condition of low signal noise ratio. The technical points of the application are as follows: the detection decision threshold of the disclosed method can be self-adaptive; for signals with different amplitudes and different durations, the relative threshold and the absolute threshold can be combined according to the size of the calculated correlation value, the self-adaptive threshold can be set according to the signal type and characteristics in the window, and the problems of signal detection segmentation caused by the fluctuation of the correlation value of the amplitude modulation signal, signal missing detection caused by the amplitude difference of the received data of the mixed signal, and signal detection false alarm caused by the high pulse correlation peak value of the radar signal are improved, so that the detection accuracy of the algorithm for various signals is improved, and the data set detection result shows that the detection accuracy probability is above 95% when the signal noise ratio is greater than 8dB.

Description

Technical Field

[0001] This invention belongs to the field of communication technology, and specifically relates to a blind signal time-domain detection method based on autocorrelation three-window decision applicable in blind signal reconnaissance receivers. Background Technology

[0002] With the rapid development of modern communication technology, various wireless communication methods and devices are constantly increasing. Each frequency-using wireless device occupies a certain amount of spectrum, leading to a concentration of many types of wireless devices in certain frequency bands. Blind signal detection technology refers to the detection of signals when prior information is uncertain. Research on this technology is of great significance for improving systems such as non-cooperative communication and cognitive radio. Blind signal detection methods can generally be divided into two categories: time-domain detection and frequency-domain detection. Based on the advantages of time-domain detection, such as simple computation and high real-time performance, this invention focuses on the research of time-domain detection algorithms for blind signals.

[0003] Traditional time-domain signal detection methods include energy detection, dual-window sliding detection, and correlation detection. Energy detection is the most basic and classic method, relying on the energy or power of the signal to detect its presence. In blind signal detection, energy detection requires no prior information, has low algorithm complexity, and is relatively easy to implement. It determines the decision by comparing the energy calculated from the received signal with a threshold. However, the decision threshold is closely related to the noise power. When the noise variance is affected by the external environment, the energy detection algorithm's performance degrades due to the difficulty in determining the decision threshold. Dual-window sliding detection is an improvement on energy detection. It calculates the energy ratio of two adjacent windows and sums them as a decision function. The energy difference between the two windows is maximized when one window contains noise and the other a signal, resulting in a peak in the decision function. The signal location is determined based on the peak position of the decision function. However, this algorithm relies on the energy difference between adjacent windows. Even without noise, the energy difference between single and aliased signals in adjacent windows can lead to misjudgments of signal location. Correlation detection algorithms utilize the fundamental difference that noise is not correlated while signals are correlated. They detect the location of a signal based on whether the correlation value of the received data exceeds a set threshold. They have good noise immunity and real-time performance, but determining the optimal detection threshold is a key research topic. Summary of the Invention

[0004] The technical problem to be solved by this invention is to provide a blind signal time-domain detection method based on autocorrelation three-window decision, which is applicable to communication signals of various modulation types and radar signals with low duty cycle under low signal-to-noise ratio conditions, and has the advantages of good real-time performance and strong adaptability.

[0005] The present invention adopts the following technical solution:

[0006] An improved method for time-domain detection of blind signals based on autocorrelation three-window decision-making includes the following steps:

[0007] Step 1: Perform correlation calculation on the received discrete data x(n) to obtain the autocorrelation function R of the data. n (n), where n represents the number of sampling points:

[0008] The expression for discrete data x(n) is:

[0009]

[0010] In the above formula, A represents the actual signal; A represents the signal amplitude. The initial phase; T s f is the sampling interval; n Let w(n) be the carrier frequency, and w(n) be the zero-mean additive white Gaussian noise after sampling.

[0011] Data autocorrelation function R n The formula for calculating (n) is:

[0012]

[0013] In the above formula, N is the length of the relevant window, * indicates taking the conjugate of the data, and i sam Represents discrete sampling points;

[0014] Step 2: Based on the length L of x(n), set three windows (start, middle, and end) for data type judgment within each window. The window numbers m are 1, 2, and 3, and the window length is defined as N. Det ,and Calculate the average power within each of the three windows. And the maximum autocorrelation R m And obtain the proportionality coefficient k between the two:

[0015]

[0016] Step 3, set the proportional coefficient judgment index k ind =1.8, if k≥k ind If amplitude-modulated data exists within the window, the absolute threshold value th of the received data can be calculated. abs_1 ;

[0017] Step 4, if k < k ind Calculate the relative threshold th within the decision window m respectively. rel Based on the specific circumstances of the autocorrelation value within the window exceeding the relative threshold, the absolute threshold value th of the received data is calculated. abs_2or th abs_3 ;

[0018] Step 5: Compare the absolute threshold values ​​calculated in Steps 3 and 4 with the data autocorrelation function R. n (n) Compare, setting the sliding window length and sliding interval to both L. win If there is an autocorrelation value greater than the absolute threshold within the sliding window, it is determined that the entire sliding window contains a signal. The sliding window then iterates through all received data and outputs the signal position.

[0019] Furthermore, step 3 includes:

[0020] Step 31: Determine the window number j, j∈m, where amplitude-modulated data exists, and calculate the maximum autocorrelation value R of the data within the window. j max ;

[0021] Step 32, take R j max Minimum value R j ' max Multiply by the threshold coefficient α below the signal level to calculate the absolute threshold value th of the received data. abs_1 :

[0022] th abs_1 =R j ' max *α.

[0023] Furthermore, step 4 includes:

[0024] Step 41: Calculate the maximum autocorrelation R within the decision window m. m , will R m Multiplying the relative threshold decision coefficient ε by the relative threshold decision coefficient, the relative threshold th of the received data is calculated. rel :

[0025] th rel =R m *ε

[0026] Step 42: Determine whether the data type in the window is noise or signal;

[0027] Step 43: If the data types of the decisions in all three decision windows are noise, then calculate the maximum autocorrelation R within each decision window m. i max , i∈m, take R i max The maximum value R i ' max Multiplying the threshold value th by a threshold coefficient λ higher than the noise level, the absolute threshold value of the received data is calculated. abs_3 :

[0028] th abs_3 =R i ' max *λ

[0029] Step 44: If the data types decided by the three decision windows are not all noise, then calculate the absolute threshold value th according to the different data types. abs_2 :

[0030] If all three windows are signals, then calculate the maximum autocorrelation R within the decision window m. k max , k∈m, take R k max Minimum value R k ' max Multiply by the threshold coefficient α below the signal level to calculate the absolute threshold value th of the received data. abs_2 :

[0031] th abs_2 =R k ' max *α

[0032] If not all three windows contain signals, then the minimum value among the maximum autocorrelation values ​​of all windows containing signals is selected and defined as Rmini1. Then, the maximum autocorrelation value in the received data is defined as RmaxAll. If the ratio of RmaxAll to Rmini1 is greater than the decision coefficient μ, then the absolute threshold value th is set. abs_2 for:

[0033] th abs_2 =R p ' max *λ

[0034] In the above formula, p is the window index where noise exists, p∈m, and the maximum autocorrelation R of the data within window p is calculated respectively. p max Take R p max Minimum value R p ' max λ is a threshold coefficient higher than the noise level;

[0035] If the ratio of RmaxAll to Rmini1 is less than the decision coefficient μ, then the absolute threshold value th abs_2 for:

[0036] th abs_2 =R q ' max *α

[0037] In the above formula, q is the window number where the signal exists, q∈m, and the maximum autocorrelation R of the data within the q-window is calculated respectively.q max Take R q max Minimum value R q ' max α is the threshold coefficient below the signal level.

[0038] Furthermore, α is set to 0.5, ε to 0.2-0.5, λ to 1.5, and μ to 1.8.

[0039] A blind signal time-domain detection method system based on autocorrelation three-window decision is provided. The system has program modules corresponding to the steps of the above-mentioned technical solution, and executes the steps in the blind signal time-domain detection method based on autocorrelation three-window decision when running.

[0040] A computer-readable storage medium storing a computer program configured to, when invoked by a processor, implement the steps of the blind signal time-domain detection method based on autocorrelation three-window decision.

[0041] The beneficial effects of this invention are:

[0042] The technical solution proposed in this invention is a blind signal time-domain detection method based on autocorrelation three-window decision, applicable to various modulation types of communication signals and low duty cycle radar signals under low signal-to-noise ratio conditions. Key technical points of this invention: The detection decision threshold disclosed in this invention is adaptive. For signals of different amplitudes and durations, an adaptive threshold is set based on the magnitude of the calculated correlation value, employing a combination of relative and absolute thresholds. This improves upon the problems of signal detection segmentation caused by correlation value fluctuations in amplitude-modulated signals, signal missed detection caused by differences in received data amplitude in aliased signals, and false alarms caused by high pulse correlation peaks in radar signals, ultimately improving the algorithm's detection accuracy for multiple signal types.

[0043] The method disclosed in this invention has strong noise resistance. Due to the fundamental difference between noise and signals, which are not correlated, autocorrelation calculations on the received data can distinguish the location of signals and noise based on the difference in correlation amplitude, and the signal location can be detected even under low signal-to-noise conditions, such as 0 dB.

[0044] The method disclosed in this invention has low computational complexity and can efficiently process received data. When making data type determinations, three windows—the beginning, middle, and end of the received data—are selected for detection. Too few windows may affect the accuracy of the overall data type determination, while too many windows increase computational complexity. Therefore, to balance these two factors, a three-window decision is chosen, avoiding threshold decision calculations for all data, thus improving computational efficiency and real-time performance.

[0045] The method disclosed in this invention has no signal type restriction for input data. It is applicable to communication signals with various modulation types and radar signals with different duty cycles because the algorithm sets different detection procedures based on the data type judgment results. Since the correlation value of amplitude-modulated signals changes with the signal amplitude, it cannot be uniformly judged with non-amplitude-modulated signals such as frequency-modulated and phase-modulated signals. Furthermore, due to the amplitude difference between the pulse part and the rest period of the radar signal, the pulse part is equivalent to amplitude-modulated data, and the rest period part is equivalent to noise data, thereby performing the corresponding detection procedure according to the data type.

[0046] The method disclosed in this invention features an adaptive detection decision threshold. For signals of different amplitudes and durations, it employs a combination of relative and absolute thresholds based on the magnitude of the calculated correlation value. This adaptive threshold is set according to the signal type and characteristics within the window, improving upon issues such as signal detection segmentation caused by correlation value fluctuations in amplitude-modulated signals, missed detections caused by differences in received data amplitude in aliased signals, and false alarms caused by high pulse correlation peaks in radar signals. Ultimately, this enhances the algorithm's detection accuracy for multiple signal types. Verification results on datasets show that the detection accuracy is above 95% when the signal-to-noise ratio is greater than 8dB. Attached Figure Description

[0047] Figure 1 This is a flowchart illustrating the method of the present invention;

[0048] Figure 2 This is a schematic diagram of the non-amplitude data detection process in the method of the present invention;

[0049] Figure 3 This is a schematic diagram of GMSK signal acquisition and detection when the signal-to-noise ratio is 0dB;

[0050] Figure 4 This is a waveform diagram of the radar signal when the signal-to-noise ratio is 2dB;

[0051] Figure 5 This is a schematic diagram of radar signal detection with a signal-to-noise ratio of 2dB;

[0052] Figure 6 These are single-signal waveforms of various modulation types in the simulation dataset;

[0053] Figure 7 This is a waveform diagram of the aliased signal in the simulation dataset;

[0054] Figure 8 This is a schematic diagram of low duty cycle radar signal detection. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of this invention clearer, the following description is provided in conjunction with the appendix. Figure 1-8 The present invention will be further described in detail with reference to the embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0056] Example 1 discloses a blind signal time-domain detection method based on autocorrelation three-window decision. It employs a three-window threshold setting approach, combining relative and absolute thresholds. The detection threshold is jointly determined based on the data type of the three detection windows. Finally, the signal position is determined by sliding detection of the autocorrelation value of the received data. Figure 1 As shown, it includes the following steps:

[0057] Step 1: Perform correlation calculation on the received discrete data x(n) to obtain the autocorrelation function R of the data. n (n), where n represents the number of sampling points:

[0058] The expression for discrete data x(n) is:

[0059]

[0060] In the above formula, A represents the actual signal; A represents the signal amplitude. The initial phase; T s f is the sampling interval; n Let w(n) be the carrier frequency, and w(n) be the zero-mean additive white Gaussian noise after sampling.

[0061] Data autocorrelation function R n The formula for calculating (n) is:

[0062]

[0063] In the above formula, N is the length of the relevant window, * indicates taking the conjugate of the data, and i sam Represents discrete sampling points;

[0064] In this embodiment, as the signal carrier frequency increases, the relative positions of the sampling points become uncertain in different periods, causing a significant deterioration in the correlation value. To optimize this problem, the absolute value of the signal can be taken when performing autocorrelation calculations, and the autocorrelation function of the signal can be updated through sliding recursion, which is a fast algorithm that is easy to implement in hardware. The expression is:

[0065] R n (n+1)=R n (n)+|x(n+N)x*(n+N+1)|

[0066] -|x(n)x*(n+1)|

[0067] Step 2: Based on the length L of x(n), set three windows (start, middle, and end) for data type determination within each window. The data types in each window can be categorized into three types: noise, constant-modulus signal, and amplitude-varying signal. The average power P of noise and constant-modulus signal... m And the maximum autocorrelation R m Numerically similar, the amplitude-varying signals differ significantly. The pulse portion and pauses of the radar signal exhibit obvious amplitude differences, which can be considered amplitude-varying signals during window decision-making. If the window consists entirely of the radar signal's pauses, it is considered noise. The possible data forms of amplitude-varying signals within the window include: noise-signal, single-signal-aliased signal, amplitude-modulated signal, and radar pulse signal. Since a signal always exists within the window and the data amplitude changes, for simplicity, they are collectively referred to as amplitude-modulated data. The decision window number m takes values ​​of 1, 2, and 3, and the decision window length is defined as N. Det ,and Calculate the average power within each of the three windows. And the maximum autocorrelation R m And obtain the proportionality coefficient k between the two:

[0068]

[0069] Step 3, set the proportional coefficient judgment index k ind =1.8 (value determined based on amplitude modulation data characteristics), if k≥k ind If a signal exists within the window and its amplitude changes, indicating the presence of amplitude-modulated data, then the absolute threshold value th of the received data can be calculated. abs_1 ;

[0070] Step 3 includes:

[0071] Step 31: Determine the window number j, j∈m, where amplitude-modulated data exists, and calculate the maximum autocorrelation R of the data within each window. j max ;

[0072] Step 32, take R j max Minimum value R j ' max Multiply by the threshold coefficient α below the signal level to calculate the absolute threshold value th of the received data. abs_1 :

[0073] th abs_1 =R j ' max *α.

[0074] Since a signal must exist, the threshold should be less than the autocorrelation value of the signal, i.e., α < 1. In order for the threshold to distinguish the correlation values ​​of the signal and noise, in this embodiment, α is set to 0.5. Too large a threshold may cause the signal to be missed, while too small a threshold may cause noise to be judged as a signal, resulting in a false alarm.

[0075] Step 4, if k < k ind If the signal does not exist within the window, a threshold determination is required for the non-amplitude modulated data. The relative threshold th within the decision window m is calculated separately. rel Based on the specific circumstances of the autocorrelation value within the window exceeding the relative threshold, the absolute threshold value th of the received data is calculated. abs_2 or th abs_3 ;

[0076] like Figure 2 As shown, step 4 includes:

[0077] Step 41: When calculating the absolute threshold for non-amplitude modulated data, calculate the maximum autocorrelation value R within the decision window m. m , will R m Multiplying this by the relative threshold decision coefficient ε (where ε ranges from 0.2 to 0.5), the relative threshold th of the received data is calculated. rel :

[0078] th rel =R m *ε

[0079] Step 42, the correlation value of the noise within the window is random, and it is within the relative threshold th. rel The correlation value of the constant modulus signal remains stable despite fluctuations. Based on this difference between noise and signal, we can determine whether the data type within the window is noise or signal.

[0080] Step 43: If the data types of the decisions in all three decision windows are noise, then calculate the maximum autocorrelation R within each decision window m. i max , i∈m, take R i max The maximum value R i ' max Multiplying the threshold value th by a threshold coefficient λ higher than the noise level, the absolute threshold value of the received data is calculated. abs_3 :

[0081] th abs_3 =R i ' max *λ

[0082] Since the decision window is entirely noise, the threshold should be higher than the autocorrelation value of the noise, i.e., λ > 1. In order for the threshold to distinguish the correlation values ​​of the signal and the noise, in this embodiment, λ is set to 1.5. Too large a threshold may cause the signal to be missed, while too small a threshold may cause the noise to be judged as a signal, thus leading to a false alarm.

[0083] Step 44: If the data types decided by the three decision windows are not all noise, i.e., there is non-amplitude data, then three absolute threshold values ​​th are calculated according to different data types. abs_2 :

[0084] The first approach is to calculate the maximum autocorrelation R within each decision window m if all three windows contain signals. k max , k∈m, take R k max Minimum value R k ' max Multiply by the threshold coefficient α below the signal level to calculate the absolute threshold value th of the received data. abs_2 :

[0085] th abs_2 =R k ' max *α

[0086] If not all three windows contain signals, the minimum value among the maximum autocorrelation values ​​of all windows containing signals is selected and defined as Rmini1. Then, the maximum autocorrelation value in the received data is defined as RmaxAll. Alternatively, if the ratio of RmaxAll to Rmini1 is greater than the decision coefficient μ, in this embodiment, μ is taken as 1.8 based on the difference between the radar signal pulse correlation peak and the rest period correlation value. Then the absolute threshold value th... abs_2 for:

[0087] th abs_2 =R p ' max *λ

[0088] In the above formula, p is the window index where noise exists, p∈m, and the maximum autocorrelation R of the data within window p is calculated respectively. p max Take R p max Minimum value R p ' max λ is a threshold coefficient higher than the noise level;

[0089] The third type is if the ratio of RmaxAll to Rmini1 is less than the decision coefficient μ, then the absolute threshold value th abs_2 for:

[0090] th abs_2=R q ' max *α

[0091] In the above formula, q is the window number where the signal exists, q∈m, and the maximum autocorrelation R of the data within the q-window is calculated respectively. q max Take R q max Minimum value R q ' max α is the threshold coefficient below the signal level.

[0092] Step 5: Compare the absolute threshold values ​​calculated in Steps 3 and 4 with the data autocorrelation function R. n (n) Compare, setting the sliding window length and sliding interval to both L. win If there is an autocorrelation value greater than the absolute threshold within the sliding window, it is determined that the entire sliding window contains a signal. The sliding window then iterates through all received data and outputs the signal position.

[0093] In summary, due to the fluctuations in the signal amplitude of amplitude-modulated data, the autocorrelation function fluctuates. Some locations where a signal exists may have a low autocorrelation function value. Simply judging the presence of a signal based on the autocorrelation function being greater than a threshold can easily lead to missed signals and segmented detection. The method of this invention effectively improves the signal detection accuracy by using a sliding window for decision-making.

[0094] The performance of the method of this invention is illustrated through simulation experiments. A software-defined radio platform was used to acquire a continuous GMSK signal with a signal-to-noise ratio of 0 dB, a signal symbol rate of 9600 sps, a carrier frequency of 70 MHz, and a sampling rate of 245.76 MHz. The data length for time-domain detection was 2,457,600 sampling points, or 10 milliseconds. Figure 3 This is a schematic diagram of GMSK signal acquisition and detection when the signal-to-noise ratio is 0dB. Figure 3 It can be seen that the autocorrelation function of the vast majority of the data is above the decision threshold, with a few sampling points below the threshold. Since the sliding window performs a sliding decision on the signal, the detected signal position covers the entire data interval, indicating correct detection. Simulations verified the time-domain detection of radar signals with a signal-to-noise ratio of 2dB and a duty cycle of 1%. Figure 4 and Figure 5 These are, respectively, the radar signal waveform and a schematic diagram of radar signal detection. From Figure 4 It can be seen that due to the low signal-to-noise ratio and the short pulse duration of the radar, the location of the pulse cannot be determined from the waveform diagram. Figure 5As can be seen, after autocorrelation of the data, the amplitude of the pulse signal is significant and exceeds the decision threshold, indicating that the signal can be correctly detected. The above detection results for communication and radar signals under low signal-to-noise ratios demonstrate that signals can be correctly detected at signal-to-noise ratios of 0dB and 2dB, indicating that the method of this invention has strong noise resistance.

[0095] To further verify the detection performance of the method of the present invention, a dataset was generated using MATLAB simulation to simulate a real-world scenario where signals and noise alternate and occur randomly. The dataset has different signal-to-noise ratios, data types, and modulation methods, as shown in Table 1 below.

[0096] Table 1: MATLAB Simulation Datasets

[0097]

[0098] Figure 6 This simulation dataset contains single-signal waveforms with various modulation types. The carrier frequency is set to 70MHz, and the sampling frequency to 160MHz. Eight single signals are simulated in the following modulation order: BPSK, QPSK, 8PSK, 2FSK, 4FSK, 2ASK, 16QAM, and GMSK. Then, communication signal combinations with random amplitude and position are generated. The eight single signals are then paired and aliased, resulting in 64 possible combinations, as shown in Table 2 below.

[0099] Table 2: Simulation Combinations of Aliased Signals

[0100] BPSK √ √ √ √ √ √ √ √ QPSK √ √ √ √ √ √ √ √ 8PSK √ √ √ √ √ √ √ √ 2FSK √ √ √ √ √ √ √ √ 4FSK √ √ √ √ √ √ √ √ 2ASK √ √ √ √ √ √ √ √ 16QAM √ √ √ √ √ √ √ √ GMSK √ √ √ √ √ √ √ √

[0101] We will use one type of aliasing signal for illustration – BPSK signal aliasing. Signal 1 has a carrier frequency of 70MHz, signal 2 has a carrier frequency of 50MHz, and the sampling frequency is 160MHz. Figure 7 This is a simulation dataset of aliased signal waveforms. Two random natural numbers are used to change the baseband signal amplitude, with values ​​ranging from 1 to 5, to achieve a random signal amplitude effect. During data fusion, the signal position is variable, and noise is added before and after the signal, presenting a noise-signal-noise format. The length of the noise added before and after the signal is determined by randomly generated positive integers. Each signal-to-noise ratio dataset contains 740 random signals, and the specific detection results are shown in Table 3 below.

[0102] Table 3: Detection results of datasets under different signal-to-noise ratio conditions

[0103]

[0104] For the software simulation dataset, all generated signals (single signal, aliased signal, communication signal, and radar signal) were integrated and divided into four packets according to their signal-to-noise ratio (SNR), with SNRs of 8, 10, 15, and 20 dB, respectively. Table 3 shows that the detection accuracy for each packet was above 95%. Although some signals were missed, specifically, the detected intervals did not completely encompass the actual signal intervals; no signals were completely missed. This demonstrates the universality of the method, which does not require differentiation between communication and radar signals, is applicable to various modulation schemes of communication signals, and can correctly detect pulse signals even with low radar duty cycles, such as 0.06%. Figure 8 This is a schematic diagram of low duty cycle radar signal detection, by Figure 8 It can be seen that the location of the radar pulse signal is correctly detected if the amplitude of the autocorrelation function is higher than the adaptive decision threshold. In summary, the method of the present invention has high efficiency, noise resistance, universality, and adaptability.

[0105] Verification has shown that the method proposed in this invention solves the technical problem identified in this invention. Simulation experiments and practical applications have both verified the technical effects described in this specification.

[0106] The algorithm (method) proposed in this invention is the underlying technical core of this invention, and various products can be derived based on the algorithm.

[0107] Based on the algorithm (method) proposed in this invention, a blind signal time-domain detection system based on autocorrelation three-window decision is developed using a programming language. This system has program modules corresponding to the steps of the above technical solution, and executes the steps in the above-mentioned blind signal time-domain detection method based on autocorrelation three-window decision when running.

[0108] The developed system (software) computer program is stored on a computer-readable storage medium. This computer program is configured to implement the steps of the aforementioned blind signal time-domain detection method based on autocorrelation three-window decision when called by a processor. In other words, the invention is materialized on a carrier, becoming a computer program product.

[0109] A blind signal time-domain detection device based on autocorrelation three-window decision is disclosed. The device includes at least one processor and a memory communicatively connected to the at least one processor. The memory stores instructions executable by the at least one processor. The instructions are executed by the at least one processor to enable the at least one processor to execute the aforementioned blind signal time-domain detection method based on autocorrelation three-window decision, thereby realizing blind signal time-domain detection.

[0110] Various implementations of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, application-specific integrated circuits (ASICs), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include: implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transferring data and instructions to the storage system, the at least one input device, and the at least one output device.

[0111] The computational programs (also referred to as programs, software, software applications, or code) of this invention include machine instructions of a programmable processor and can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, device, and / or apparatus (e.g., disk, optical disk, memory, programmable logic device PLD) for providing machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0112] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A time-domain detection method for blind signals based on autocorrelation three-window decision, characterized in that, Includes the following steps: Step 1, process the received discrete data Perform correlation calculations to obtain the autocorrelation function of the data. , Indicates the sampling point: Discrete data The expression is: In the above formula, For actual signals; The signal amplitude; This is the initial phase; The sampling interval; For carrier frequency, The sampled zero-mean additive white Gaussian noise; Data autocorrelation function The calculation formula is: In the above formula, For the relevant window length, This indicates finding the conjugate of the data. Represents discrete sampling points; Step 2, according to length Set up three windows—the beginning, middle, and end of the data—to determine the data type within each window, and label the decision windows. The value of is 1, 2, or 3, and the decision window length is defined as . ,and Calculate the average power within each of the three windows. and maximum autocorrelation And obtain the ratio coefficient between the two. : Step 3, set the proportional coefficient judgment index ,if If amplitude-modulated data exists within the window, the absolute threshold value of the received data can be calculated. ; Step 4, if Calculate the decision window separately Relative threshold within Based on the specific circumstances of the autocorrelation value within the window exceeding the relative threshold, the absolute threshold value of the received data is calculated. or ; Step 5: Compare the absolute threshold values ​​calculated in Steps 3 and 4 with the data autocorrelation function. In comparison, the sliding window length and sliding interval are both set to... If there is a signal greater than the absolute threshold within the sliding window, it is determined that the entire sliding window contains a signal. The sliding window then iterates through all received data and outputs the signal position. Step 3 includes: Step 31: Determine the window number where amplitude modulation data exists. , Calculate the maximum autocorrelation value of the data within the window. ; Step 32, take minimum value and below the threshold coefficient of the signal Multiply by each other to calculate the absolute threshold value of the received data. : ； Step 4 includes: Step 41, calculate the decision window respectively. Maximum value of intrinsic autocorrelation ,Will and relative threshold decision coefficient Multiply to calculate the relative threshold of the received data. : Step 42: Determine whether the data type in the window is noise or signal; Step 43: If the data types of the decisions made by the three decision windows are all noise, then calculate the decision window for each decision window. Maximum value of intrinsic autocorrelation , ,Pick maximum value and threshold coefficient higher than noise Multiply by each other to calculate the absolute threshold value of the received data. : Step 44: If the data types decided by the three decision windows are not all noise, then calculate the absolute threshold value according to the different data types. : If all three windows are signals, then calculate the decision window separately. Maximum value of intrinsic autocorrelation , ,Pick minimum value and below the threshold coefficient of the signal Multiply by each other to calculate the absolute threshold value of the received data. : If not all three windows contain signals, then the minimum value is selected from the maximum autocorrelation values ​​of all windows containing signals and defined as follows: Then find the maximum autocorrelation value in the received data, defined as ,if and The ratio is greater than the decision coefficient. Then the absolute threshold value for: In the above formula, The window number where noise exists. Calculate separately Maximum autocorrelation value of data within the window ,Pick minimum value , A threshold factor higher than the noise level; if and The ratio is less than the decision coefficient. Then the absolute threshold value for: In the above formula, The window number where the signal exists. Calculate separately Maximum autocorrelation value of data within the window ,Pick minimum value , This is a threshold coefficient lower than the signal threshold.

2. The blind signal time-domain detection method based on autocorrelation three-window decision according to claim 1, characterized in that: Take 0.5, The value range is 0.2-0.

5. Take 1.5, Take 1.

8.

3. A blind signal time-domain detection method system based on autocorrelation three-window decision, characterized in that: The system has a program module corresponding to the steps of any one of the claims 1-2 above, and executes the steps in the blind signal time-domain detection method based on autocorrelation three-window decision as described in any one of the claims 1-2 when it is run.

4. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program configured to, when invoked by a processor, implement the steps of the blind signal time-domain detection method based on autocorrelation three-window decision as described in any one of claims 1-2.

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