Adaptive threshold detection method and device based on DMF peak-to-average ratio tracking and medium
The adaptive threshold detection method based on DMF peak-to-average power ratio tracking dynamically adjusts the threshold value, solving the resource and power consumption limitations in IoT ASICs. It achieves efficient synchronous capture and low false alarm rate, making it suitable for IoT terminals.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-01-27
- Publication Date
- 2026-04-07
AI Technical Summary
In IoT ASIC applications, existing adaptive threshold decision algorithms do not meet the requirements of ultra-low power and ultra-long-range wireless communication in terms of resources and power consumption, and fixed thresholds are easily affected by noise interference, resulting in high rates of missed detections and false alarms.
An adaptive threshold detection method based on DMF peak-to-average ratio tracking is adopted. By dynamically updating the absolute and relative thresholds, adaptive threshold adjustment is performed using a digital matched filter (DMF). Combined with peak-to-average ratio tracking and continuous decision-making, synchronous acquisition is achieved.
It maintains a high detection probability and a controllable false alarm probability when the noise environment changes, reduces resource consumption, and is suitable for low-cost and low-power design of IoT terminals.
Smart Images
Figure CN121814282A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of wireless communication, in particular to a self-adaptive threshold detection method and device based on DMF peak-to-average ratio tracking and a medium. BACKGROUND
[0002] With the increasing maturity of wireless communication technology, the application scenarios of Internet of Things such as smart city and smart home have also developed rapidly. These application scenarios often face the dual difficulties of how to achieve ultra-low power consumption and how to achieve ultra-long distance communication, which requires balancing the performance of wireless transmission under the constraints of limited area and power consumption. With the development of integrated circuit technology, Internet of Things wireless ASIC has emerged.
[0003] In software radio, an important part of realizing wireless communication is to rely on the cooperative work of the transmitter and the receiver, that is, the receiver needs to quickly locate the transmitted signal in the signal containing noise, which requires synchronous acquisition processing of the received signal. In synchronous acquisition, the transmitter sends a data frame containing a pseudo-code synchronization sequence, and the receiver uses a digital matched filter (DMF) to capture the synchronization sequence, thereby realizing synchronization of the received signal and subsequent data processing.
[0004] When using a digital matched filter to capture the synchronization sequence, the setting of the decision threshold is particularly important. If a fixed threshold is used for decision, the noise, interference and other factors of wireless communication will affect the decision result, and false negatives and false positives are likely to occur. If the threshold is set too high, the signal strength is not enough, resulting in false negatives and reducing the detection probability. If the threshold is set too low, the detection probability increases, but noise interference and other factors will also cause false positives, and the false detection probability also increases. False negatives and false positives will seriously affect the efficiency of wireless transmission, so adaptive threshold decision algorithms such as constant false alarm rate detection (CFAR), preset multi-level threshold, and FFT-based fast correlation algorithm are proposed.
[0005] In actual Internet of Things applications, compared with low-orbit satellite, communication terminal and other wireless transmission devices or systems, Internet of Things wireless ASIC needs to integrate radio frequency transceiver systems and digital signal processing systems, and at the same time, considering the application scenario of ultra-low power consumption, the resource area required by the above adaptive threshold decision algorithm is obviously not in line with the actual needs. SUMMARY
[0006] The purpose of the present application is to provide a self-adaptive threshold detection method and device based on DMF peak-to-average ratio tracking, to solve the problem of still being able to adaptively adjust the threshold value when the resource power consumption is limited in Internet of Things ASIC applications, to complete synchronous acquisition, and to ensure high-performance ultra-long distance wireless communication.
[0007] The present application is implemented by the following technical solutions: An adaptive threshold detection method based on DMF peak-to-average ratio tracking, the method comprising the following steps: Step 1, the transmitter sends a data frame containing two identical synchronization sequences, each sequence has a length of ; Step 2, the digital matched filter DMF in the receiver performs correlation operation on the received signal and the local signal to obtain a correlation value sequence, and simultaneously starts the synchronization capture; Step 3, an absolute threshold that can be dynamically updated is set , and correlation values exceeding the absolute threshold are continuously stored to form a detection window, wherein is the window length for adaptive threshold detection; Step 4, based on correlation values, the peak-to-average ratio at the current time is calculated; Step 5, a relative threshold is set , if consecutive peak-to-average ratios are greater than the relative threshold, it is considered that the position of the maximum correlation peak value has been obtained at this time, and the absolute threshold , is the decision length of the correlation peak and the sidelobe; Step 6, if the condition that consecutive peak-to-average ratios are greater than the relative threshold is not met, return to step 3 for continuous detection; if the temporary peak value has been captured but the synchronization has not been completed, steps 3 to 6 are continuously executed for peak tracking and threshold updating; Step 7, when the correlation peaks corresponding to the two synchronization sequences are detected in the received signal in sequence, and the position difference between the two peaks meets the preset sequence length relationship, it is determined that the synchronization capture is completed, and the adaptive threshold detection is ended.
[0008] Specifically, the transmitter in step 1 is the transmitting part of the wireless radio frequency transceiver, which includes a wireless radio frequency transmitting circuit and a corresponding digital signal processing unit.
[0009] The synchronization sequence in step 1 is a pseudo-random sequence with good autocorrelation characteristics, which is generated locally and constitutes a data frame with other data sequences.
[0010] Specifically, the receiver in step 2 is the receiving part of the wireless radio frequency transceiver, which includes a wireless radio frequency receiving circuit and a corresponding digital signal processing unit.
[0011] The received signal in step 2 is a data sequence after the data frame is transmitted by the wireless radio frequency transmitter and received by the receiver after signal processing.
[0012] Local signal in step 2 It is the same synchronization sequence stored in the receiver as that in the transmitter, and its length is also [missing information]. .
[0013] The correlation operation of the digital matched filter (DMF) in step 2 results in the convolution of the received sequence and the local synchronization sequence. The correlation result is... The calculation process is expressed as follows: Its output result is represented by a window length of at a certain time. The sum of the products of two sequences is the correlation value of the two sequences at a certain moment.
[0014] Specifically, the absolute threshold in step 3 It is dynamically updatable. The update sources are: firstly, the average correlation peak value calculated after the start of synchronous capture, initialized with the channel noise intensity; and secondly, the correlation peak value detected in subsequent steps of the method.
[0015] The number of stored related values in step 3 In practice, this manifests as the window length required for peak-to-average power ratio (PAPR) tracking, which is limited, but not limited, by the hardware implementation resources of the RF transceiver and the length of the associated sequence.
[0016] Step 4 includes the following sub-steps: Step 401, in Find the maximum value among the relevant values. And record the position of the maximum value. ; Step 402: Calculate the maximum correlation value to discard. and adjacent Besides the relevant values, the remaining The average amplitude of each related value .in This represents the length of the side lobes on both sides of the relevant peak, used to exclude interference values near the peak when calculating the average value. The calculation process is expressed as follows: Step 403: Calculate the peak-to-average ratio (PAR) corresponding to the maximum peak value at this time. The calculation process is expressed as follows: The relative threshold in step 5 This is the reference ratio between the correlation peak and the noise energy within the current detection window.
[0017] In step 5, in order to filter out peak fluctuations caused by noise, interference, and other factors that could affect threshold decision, a continuous [function / set] is configured. The threshold comparison condition is actually manifested as a filtering process for determining the relevant peaks, while... and Satisfying Relationships .
[0018] In step 6, the absolute threshold is continuously updated by tracking the peak-to-average ratio of relevant peaks, thereby forming an adaptive capability and no longer being limited to a fixed threshold value.
[0019] The termination condition in step 7 is specifically manifested as the discovery of relevant peaks in two synchronization sequences during the synchronization acquisition process, and the relationship between the positions of the relevant peaks. If the length requirement of the synchronization sequence is met, the adaptive threshold detection ends.
[0020] It is worth noting that the above parameters Satisfying Relationships Considering the difficulty of engineering implementation and resource constraints, using a digital matched filter to obtain correlation values offers advantages such as shorter sequence length requirements and extremely high data throughput. Therefore, the values of each parameter are particularly important, starting with the length of the synchronization sequence. It is generally an integer power of 2, which requires specific algorithms. One shift register, A parallel complex multiplier and For adder trees of level 1024, a length less than 1024 is typically chosen. And for parameters... In practice, this refers to the detection window length of the adaptive algorithm. As it is a key computational parameter of the adaptive algorithm, it also determines the cache size for preprocessing. The resource area is positively correlated with the selected parameter value. If the value is too small, the algorithm's detection success rate is easily affected by noise or sidelobes, impacting the final detection probability and false alarm probability. Therefore, a smaller value is recommended. For parameters as well as The selection of the correlation peak is actually a filtering process for determining the correlation peak. It mainly filters out higher sidelobe correlation values, reduces the false alarm probability and increases the detection probability. The selection is based on the autocorrelation results of the synchronization sequence obtained from the simulation.
[0021] Secondly, this invention provides an adaptive threshold detection device based on DMF peak-to-average power ratio tracking, mainly comprising: Transmitting unit: It assembles the data to be transmitted and the synchronization sequence into a data frame in sequence, and transmits the data frame through signal modulation and radio frequency transmission link.
[0022] The receiving unit converts the received signal into a baseband-processable digital signal through the RF receiving link and ADC circuit. Then, it completes the synchronous acquisition of the received signal and locates the data frame position using an adaptive threshold detection method based on DMF peak-to-average ratio tracking. Finally, it demodulates the data sequence to recover the original information.
[0023] Control unit: Writes the data to be transmitted to the transmitting unit, configures the transmitting unit mode and status to complete data transmission; configures the receiving unit mode and status to receive the transmitted signal, and reads the recovered original data from the receiving unit.
[0024] Finally, the present invention also provides a computer storage medium storing a computer program that, when executed by at least one processor, implements the adaptive threshold detection method based on DMF peak-to-average ratio tracking as described above.
[0025] By building a simulation model of the system and comparing it with a fixed threshold, the detection probability and false alarm probability under different signal-to-noise ratio conditions are verified. The system uses a ZC (Zadoff-Chu) sequence as the synchronization sequence, with its root set to 1, and the ZC sequence length is... The algorithm's detection window length is 256. The value is 32, and the correlation peak determination length is... The threshold is set to 9, and the method described in this invention is used to adaptively adjust the threshold. For comparison, under a fixed threshold simulation condition, a noise power value three times that of the original threshold is selected as the threshold value. Simulations were performed with a signal-to-noise ratio (SNR) range of -30 to 0 dB. The results clearly show that when the SNR is good, the detection probability and false alarm probability of both methods are roughly the same. When the SNR is poor, the detection probability of the adaptive threshold detection method proposed in this invention is about 30% higher than that of the fixed threshold. Regarding the false alarm probability, due to noise, the peak-to-average power ratio (PAPR) decreases as the SNR decreases, so the overall false alarm probability is about 10% higher than that of the fixed threshold. However, this false alarm probability is controllable, unlike the large fluctuations that occur with environmental changes under a fixed threshold.
[0026] Furthermore, in terms of engineering implementation, compared to FFT-based frequency domain adaptive detection methods, such as the PMF-FFT algorithm, the latter consumes more RAM resources, has more complex control logic, and suffers from higher latency. Considering both algorithm complexity and acquisition timeliness, the adaptive threshold detection method based on DMF peak-to-average ratio tracking described in this invention is more suitable for IoT ASIC applications.
[0027] In summary, based on the comparative description of the present invention, the present invention has the following beneficial effects: 1) This invention achieves the first step of filtering for correlation peak detection by configuring an absolute threshold and initializing the threshold value to the channel noise intensity, while also increasing the adaptability of the threshold detection.
[0028] 2) This invention utilizes the peak-to-average power ratio of the relative threshold to track the relevant peak, thereby achieving the second-step filtering process for signal detection, and continuously adjusting the decision threshold through continuous detection.
[0029] 3) This invention achieves the purpose of adaptive threshold detection by using a DMF peak-to-average ratio tracking method. It utilizes relatively small resources to enable the system to maintain a good detection probability and a controllable false alarm probability when the environment changes. It is suitable for IoT application scenarios with unknown or time-varying noise characteristics. Attached Figure Description
[0030] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0031] Figure 1 This is a flowchart of the adaptive threshold detection method based on DMF peak-to-average power ratio tracking according to the present invention; Figure 2 This is a schematic diagram of a wireless transmission system applicable to the present invention; Figure 3 This is a structure diagram of the data frames used for synchronous capture; Figure 4 This is a schematic diagram of the synchronous capture module of the adaptive threshold detection method based on DMF peak-to-average ratio tracking. Detailed Implementation
[0032] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0033] like Figure 1 As shown in the figure, this embodiment provides a flowchart of an adaptive threshold detection method based on DMF peak-to-average power ratio tracking. The specific implementation steps are as follows: Step 1: The transmitter sends a data frame containing two identical synchronization sequences, each sequence having a length of [length missing]. .
[0034] The transmitter is the transmitting part of a wireless radio frequency transceiver, which includes wireless radio frequency transmitting circuits and corresponding digital signal processing units.
[0035] Synchronization sequences are sequences with good autocorrelation properties. They are generated locally and form data frames with other data sequences.
[0036] Step 2: The receiver begins synchronous acquisition, and the digital matched filter (DMF) simultaneously adjusts the received signal. With local signals Perform the relevant calculations.
[0037] The receiver is the receiving part of a wireless radio frequency transceiver, which includes a wireless radio frequency receiving circuit and a corresponding digital signal processing unit.
[0038] Received signal It is a data sequence that has been transmitted by a wireless radio frequency transmitter, received by a receiver, and then processed as a signal. Local signal. It is a synchronization sequence stored in the wireless radio frequency receiver that is identical to that of the transmitter, and its length is also [missing information]. .
[0039] In addition, the correlation operation result of the digital matched filter (DMF) is the convolution of the received sequence and the local synchronization sequence, and the correlation result is... The calculation process is expressed as follows: Its output result is represented by a window length of at a certain time. The sum of the products of two sequences is the correlation value of the two sequences at a certain moment.
[0040] Step 3: Set absolute threshold Continuous storage A correlation value exceeding the absolute threshold, The window length for adaptive threshold detection.
[0041] absolute threshold It is dynamically updatable. The update sources are: firstly, the average correlation peak value calculated after the start of synchronous capture, initialized with the channel noise intensity; and secondly, the correlation peak value detected in subsequent steps of the method.
[0042] Number of related values stored In practice, this manifests as the window length required for peak-to-average power ratio (PAPR) tracking, which is limited, but not limited, to the hardware implementation resources of the RF transceiver or the length of the relevant sequence.
[0043] Step 4: Obtain a specific time point Peak-to-peak ratio of each correlation value .
[0044] The above steps include the following sub-steps: Step 401, in Find the maximum value among the relevant values. And record the position of the maximum value. ; Step 402: Calculate the maximum correlation value to discard. and adjacent Besides the relevant values, the remaining The average amplitude of each related value .in This represents the lengths of the side lobes on both sides of the relevant peak. The calculation process is expressed as follows: Step 403: Calculate the peak-to-average ratio (PAR) corresponding to the maximum peak value at this time. The calculation process is expressed as follows: Step 5: Set relative threshold If continuous If the peak-to-average ratio is greater than the relative threshold, it is considered that the position of the maximum correlated peak has been obtained, and the absolute threshold is updated. , The decision length for the relevant peak and sidelobes. Relative threshold. This is the reference ratio between the correlation peak and the noise energy within the current detection window.
[0045] In addition, to filter out peak jitter caused by noise, interference, and other factors, which could affect threshold decision, a continuous [function / set] is set. The threshold comparison condition is actually manifested as a filtering process for determining the relevant peaks, while... and Satisfying Relationships .
[0046] Step 6: If the conditions are met, continue to track and detect the relevant peaks; if the conditions are not met, return to step 3 to continue detection.
[0047] The specific manifestation of tracking related peak detection is that, after obtaining the temporary maximum peak position, but before the synchronous capture process has ended, steps 3 to 6 are repeated.
[0048] By tracking the peak-to-average ratio of relevant peaks and continuously updating the absolute threshold, an adaptive capability is formed, no longer limited to a fixed threshold value.
[0049] Step 7: Find the relevant peaks that satisfy the positional relationship, end the adaptive threshold detection, and complete the synchronous capture.
[0050] The termination condition is specifically manifested in the finding of correlation peaks in two synchronization sequences during the synchronization acquisition process, and the relationship between the positions of the correlation peaks. If the length requirement of the synchronization sequence is met, the adaptive threshold detection ends.
[0051] like Figure 2 As shown, this embodiment provides a wireless transmission system architecture suitable for an adaptive threshold detection method based on DMF peak-to-average power ratio tracking. The method described in this invention is mainly implemented in the synchronization acquisition module. At the transmitter end, the transmitted data and the synchronization sequence form a data frame, and the data frame format is as follows: Figure 3 As shown, the signal is modulated by the modulation module and then transmitted via the RF DAC, LPF, MIXER, and PA modules. At the receiver, the signal is first processed by the RF LNA, MIXER, BPF, PGA, and ADC modules to convert it into a digital signal. Then, a synchronization sequence is used to capture the signal synchronously. After successful capture, the final data demodulation is completed.
[0052] like Figure 4 As shown in one embodiment of the present invention, the specific implementation steps of the synchronization acquisition process include: The Digital Matched Filter (DMF) module performs multiply-accumulate calculations on the received sequence and the local sequence, i.e., correlation operations, to obtain the correlation value at a certain moment. The storage unit stores the relevant values according to the current threshold value, and the storage length is [missing information]. ; The maximum value positioning module is for this The stored data is used to search for and locate the maximum value, and then the maximum value and adjacent peak values are filtered to facilitate the subsequent peak-to-average ratio calculation. The peak-to-average ratio (PAPR) calculation module first obtains the average value excluding the maximum peak value and adjacent peak values, and then calculates the ratio of the maximum correlated peak value to the average value. The counting and comparison module mainly performs secondary filtering on the acquired peak-to-average ratio (relative threshold) to prevent peak fluctuations from affecting the results.
[0053] The threshold decision module mainly makes a decision on the current correlation value based on the new threshold value obtained in real time. The decision results in the temporary maximum correlation peak and the location of the synchronization sequence, which are not used as the final synchronization result.
[0054] The synchronization sequence capture and positioning module mainly determines whether the length condition of the synchronization sequence is met based on the relative position of the two captured synchronization sequences. If the condition is met, the synchronization capture of the synchronization sequence is completed; otherwise, the peak-to-average ratio detection will continue until the synchronization capture is completed.
[0055] Specifically, to verify the effectiveness of the method in this embodiment, simulations were performed under a typical urban IoT channel model (with path loss, shadowing fading, and frequency-selective fading), and compared with the fixed threshold detection method.
[0056] Fixed threshold method: The threshold is set to 3.0 * P noise (P) noise (mean noise power) The method of this invention: the parameter configuration is as described above.
[0057] Simulation results show that under high signal-to-noise ratio (SNR>-10dB) conditions, both methods can achieve a detection probability close to 100%. However, in harsh environments with low signal-to-noise ratio (SNR between -20dB and -10dB): The detection probability of the method of this invention is significantly higher than that of the fixed threshold method, with an improvement of 25% to 40%. This is because of the adaptive absolute threshold (…). This is improved after initial acquisition, enhancing the ability to identify weak signals in strong noise backgrounds; simultaneously, based on peak-to-average power ratio (PAPR)... ) and consecutive judgments ( The dual filtering mechanism effectively reduces missed detections caused by noise fluctuations.
[0058] The false alarm probability remains at a controllable low level (<5%), and the fluctuation is much smaller than that of the fixed threshold method. With the fixed threshold, the false alarm rate may increase or decrease sharply when environmental noise changes, while this invention tracks... And compare with relative threshold This makes false alarm decisions more dependent on the relative strength of the signal and noise, rather than the absolute level, thus making them more stable.
[0059] Resource consumption: Compared to traditional adaptive algorithms such as CFAR, this method only requires a length of... With its simple cache, comparison logic, and arithmetic operation units, it eliminates the need for complex statistical models or a large number of training samples. The hardware resource overhead (number of logic gates, memory size) implemented on ASIC can be reduced by more than 60%, which is very much in line with the design requirements of IoT terminals for low cost and low power consumption.
[0060] This invention also provides a computer storage medium, including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps described in this invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.
[0061] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims
1. An adaptive threshold detection method based on DMF peak-to-average power ratio tracking, characterized in that, Includes the following steps: Step 1: The transmitter sends a data frame containing two identical synchronization sequences, each with a length of K. Step 2: The digital matched filter (DMF) in the receiver performs correlation calculations on the received signal and the local signal to obtain a correlation value sequence, and then initiates the synchronization acquisition process; Step 3: Set a dynamically updatable absolute threshold for continuous storage. A detection window is formed by identifying correlation values exceeding the absolute threshold. The window length for adaptive threshold detection; Step 4, based on Calculate the peak-to-average ratio (PAR) at the current time using several relevant values; Step 5: Set a relative threshold; if continuous... If the peak-to-average ratio is greater than the relative threshold, it is considered that the position of the maximum correlated peak has been obtained, and the absolute threshold value is updated. The decision length for the relevant peak and sidelobes; Step 6: If the continuity is not satisfied If the peak-to-average ratio is greater than the relative threshold, return to step 3 to continue detection; if a temporary peak has been captured but synchronization has not been completed, continue to steps 3 to 6 to perform peak tracking and threshold update. Step 7: When two corresponding peaks of the synchronization sequence are detected in the received signal, and the position difference between the two peaks meets the preset sequence length relationship, the synchronization acquisition is determined to be completed and the adaptive threshold detection ends.
2. The adaptive threshold detection method based on DMF peak-to-average power ratio tracking according to claim 1, characterized in that, The transmitter in step 1 is the transmitting part of a wireless radio frequency transceiver, which includes a wireless radio frequency transmitting circuit and a corresponding digital signal processing unit. The synchronization sequence in step 1 is a pseudo-random sequence with good autocorrelation characteristics. It is generated locally and forms a data frame with other data sequences.
3. The adaptive threshold detection method based on DMF peak-to-average power ratio tracking according to claim 1, characterized in that, The receiver in step 2 is the receiving part of the wireless radio frequency transceiver, which includes a wireless radio frequency receiving circuit and a corresponding digital signal processing unit. The received signal in step 2 is a data sequence that is transmitted by a wireless radio frequency transmitter, received by a receiver, and then processed. The local signal in step 2 is the same synchronization sequence as that of the transmitter stored in the receiver; The correlation operation result of the digital matched filter (DMF) in step 2 is the convolution of the received sequence and the local synchronization sequence.
4. The adaptive threshold detection method based on DMF peak-to-average power ratio tracking according to claim 1, characterized in that, The absolute threshold in step 3 is dynamically updatable; one of the update sources is the average correlation peak value calculated after the start of synchronous capture, which is initialized to the channel noise intensity. Secondly, the relevant peak values obtained are detected in subsequent steps of the method. The number of stored related values in step 3 In practice, this manifests as the window length required for peak-to-average power ratio (PAPR) tracking. This window length is limited, but not limited, to the hardware implementation resources of the RF transceiver and the length of the relevant sequence. The value of is in the range of 1 / 8 to 1 / 4 of the synchronization sequence length K, and Greater than .
5. The adaptive threshold detection method based on DMF peak-to-average power ratio tracking according to claim 1, characterized in that, Step 4 includes the following sub-steps: Step 401, in Find the maximum value among the relevant values and record its position; Step 402: Calculate and discard the maximum correlation value and adjacent values. Besides the relevant values, the remaining The average of the relevant values, This is the length of the side lobes on both sides of the relevant peak, used to exclude interference values near the peak when calculating the average value; Step 403: Calculate the peak-to-average ratio corresponding to the maximum peak value at this time.
6. The adaptive threshold detection method based on DMF peak-to-average power ratio tracking according to claim 1, characterized in that, The relative threshold in step 5 is the reference ratio of the correlation peak to the noise energy in the current detection window; In step 5, in order to filter out peak fluctuations caused by noise, interference, and other factors that could affect threshold decision, a continuous [function / set] is configured. The threshold comparison condition is actually manifested as a filtering process for determining the relevant peaks, while... and Satisfying the relation .
7. The adaptive threshold detection method based on DMF peak-to-average power ratio tracking according to claim 1, characterized in that, In step 6, the absolute threshold is continuously updated by tracking the peak-to-average ratio of relevant peaks, thereby forming an adaptive capability and no longer being limited to a fixed threshold value.
8. An adaptive threshold detection device based on DMF peak-to-average power ratio tracking, characterized in that, include: Transmission unit: It assembles the data to be transmitted and the synchronization sequence into a data frame in sequence, and transmits the data frame through signal modulation and radio frequency transmission link; The receiving unit converts the received signal into a baseband-processable digital signal through the radio frequency receiving link and ADC circuit. Then, it completes the synchronous acquisition of the received signal, locates the data frame position, and finally demodulates the data sequence to recover the original information. Control unit: Writes the data to be transmitted to the transmitting unit, configures the transmitting unit mode and status to complete data transmission; configures the receiving unit mode and status to receive the transmitted signal, and reads the recovered original data from the receiving unit.
9. A computer storage medium, characterized in that, The computer storage medium stores a computer program, which, when executed by at least one processor, implements the adaptive threshold detection method based on DMF peak-to-average ratio tracking as described in any one of claims 1-7.