Method and apparatus for filtering a manchester encoded signal

Abstract

The application provides a filtering method and device of a Manchester coding signal, the method comprising: obtaining sampling data of the Manchester coding signal, the time length of the sampling data being not less than one bit width; judging whether a dense jump cluster exists in the sampling data; if the dense jump cluster exists, determining a target waveform corresponding to the dense jump cluster as a filtering result based on a Manchester coding jump rule. The application establishes a global observation window with time domain continuity by obtaining long-time sampling data and combining a reference stationary section, uses a logic background to more accurately identify real logic jumps in a dense interference area, improves jump edge identification accuracy, effectively solves the problem that a traditional local filtering is prone to misjudgment near a jump edge, and can be applied to the fields of Manchester signal coding and decoding.

Description

Technical Field

[0001] This application relates to the field of digital signal processing technology, and in particular to a filtering method and apparatus for Manchester encoded signals. Background Technology

[0002] Manchester encoding, due to its excellent self-synchronization characteristics and resistance to DC offset, is widely used in various communication systems, such as automotive Ethernet, vehicle bus, and various automotive sensor communication protocols. With the continuous improvement of automotive electronics and intelligence, in-vehicle communication networks place extremely high demands on the accuracy and real-time performance of data transmission.

[0003] However, automobiles integrate numerous electronic control units, drive motors, inverters, and high-voltage wiring harnesses, creating a complex electromagnetic environment within the vehicle. During vehicle operation, high-frequency transient interference can easily occur on the communication link due to high-frequency switching of power devices, electrostatic discharge, or electromagnetic coupling between wiring harnesses. This interference can generate numerous spikes in the signal, exhibiting strong randomness and suddenness.

[0004] Common filtering methods, such as simple pulse width filters, typically make decisions based solely on the width of a single transition edge or a single pulse. For example... Figure 1 As shown, when a glitch signal appears close to the real transition edge, traditional filtering modules often directly filter out the first transition edge. This can easily lead to the wrong deletion of the real transition edge, while retaining the glitch transition or incorrectly merging the glitch with the real transition edge, resulting in deviations in subsequent decoding.

[0005] Such incorrect filtering can cause severe phase jitter in the signal, leading to clock recovery failure or data extraction errors at the receiver. In vehicle safety-critical systems (such as intelligent driving assistance or chassis control), incorrect filtering results can cause communication link interruptions, loss of control commands, or even system malfunctions, resulting in serious driving safety risks. Summary of the Invention

[0006] The purpose of this application is to provide a filtering method and apparatus for Manchester encoded signals. This filtering method can distinguish between normal transitions and abnormal interference from a global perspective, improve the accuracy of transition edge recognition, and thus ensure the stability of real-time communication systems.

[0007] To achieve the above objectives, one embodiment of this application provides a filtering method for Manchester-coded signals, comprising the following steps:

[0008] Acquire sampled data of the Manchester encoded signal, wherein the time length of the sampled data is not less than one bit width; Determine whether there is a dense transition cluster in the sampled data, wherein the dense transition cluster is located between two level-stable reference stable segments, the spacing between adjacent transition edges in the dense transition cluster is less than a preset threshold, the duration of the reference stable segment is greater than the preset threshold, and the preset threshold is determined according to the bit width; Wherein, if dense transition clusters exist in the sampled data, the target waveform corresponding to the dense transition clusters is determined based on Manchester coding transition rules; and The target waveform is output as the filtering result.

[0009] As a further improvement to this application, determining the target waveform corresponding to the dense transition cluster includes: A variety of different candidate waveforms are generated, wherein each candidate waveform is obtained by retaining or removing different transition edges in the dense transition cluster; For each candidate waveform, a plurality of candidate spacings are determined by sequentially dividing adjacent transition edges in the candidate waveform, and the plurality of candidate spacings form a candidate spacing sequence; and The target waveform is determined from the candidate waveforms by comparing the candidate spacing sequence of each candidate waveform with the Manchester encoding transition rule.

[0010] As a further improvement of this application, the dense transition cluster contains at least three transition edges; The generation of various candidate waveforms includes: The target transition direction is determined based on the signal level state preceding the dense transition cluster; Multiple candidate edges consistent with the target transition direction were identified within the dense transition cluster; and Each candidate edge is set as a real transition edge, and the logic value corresponding to the remaining edges is corrected to be consistent with its adjacent level, thereby generating multiple candidate waveforms corresponding to the number of candidate edges.

[0011] As a further improvement to this application, the Manchester encoding transition rule includes half a bit width and / or one bit width determined based on the bit width; The step of comparing the candidate spacing sequence of each candidate waveform with the preset standard transition spacing to determine the target waveform from the multiple candidate waveforms includes: For each candidate waveform, calculate the deviation score between its corresponding candidate spacing sequence and the half-bit width and / or the one-bit width, and select the candidate waveform with the smallest deviation score as the target waveform.

[0012] As a further improvement to this application, the step of calculating the deviation score between the corresponding candidate spacing sequence and the half-bit width and / or the one-bit width for each candidate waveform includes: Calculate the first deviation of each candidate spacing in the candidate spacing sequence from the half bit width, and the second deviation from the one bit width; The smaller value between the first deviation and the second deviation is selected as the individual deviation score for the candidate spacing; The individual deviation scores of all candidate spacings in the candidate spacing sequence are summed to obtain the deviation score of the candidate waveform.

[0013] As a further improvement of this application, the first deviation is the absolute value of the difference between the candidate spacing and the half-bit width; The second deviation is the absolute value of the difference between the candidate spacing and the one-bit width.

[0014] As a further improvement to this application, the step of comparing the proximity of the candidate spacing sequence of each candidate waveform to a preset standard transition spacing, and determining the target waveform from the plurality of candidate waveforms, includes: The candidate spacing sequence of each candidate waveform is used as a query index to retrieve data in a preset spacing mapping table. The preset spacing mapping table stores preset spacing combinations, which are determined based on the Manchester encoding transition rules. Based on the search results, from the multiple candidate waveforms, the candidate waveform whose candidate spacing sequence matches the preset spacing combination is selected as the target waveform.

[0015] As a further improvement of this application, each of the candidate waveforms consists of two candidate spacings; The step of comparing the candidate spacing sequence of each candidate waveform with the preset standard transition spacing to determine the target waveform from the multiple candidate waveforms includes: Determine the numerical ratio between two candidate spacings in the candidate spacing sequence; The numerical ratio is compared with the standard ratio defined by the Manchester encoding transition rule, wherein the standard ratio includes 1:1, 1:2 and 2:1; Based on the comparison results, the candidate waveform whose numerical ratio is closest to the standard ratio is selected from the multiple candidate waveforms as the target waveform.

[0016] As a further improvement to this application, the step of comparing the proximity of the candidate spacing sequence of each candidate waveform to a preset standard transition spacing, and determining the target waveform from the plurality of candidate waveforms, includes: Based on the statistical characteristics of historical sampling data, assign corresponding weight values ​​and / or probability distribution values ​​to different candidate intervals; The candidate spacing sequence of each candidate waveform is comprehensively scored by combining the weight value and / or the probability distribution value. The candidate waveform with the best overall score is selected as the target waveform.

[0017] As a further improvement to this application, the step of comparing the proximity of the candidate spacing sequence of each candidate waveform to a preset standard transition spacing, and determining the target waveform from the plurality of candidate waveforms, includes: Each candidate waveform is correlated with an ideal feature template, wherein the ideal feature template is determined based on the Manchester coding transition rule. Based on the correlation coefficient generated by the calculation, the candidate waveform with the highest correlation to the ideal feature template is selected as the target waveform.

[0018] As a further improvement to this application, before determining whether there are dense transition clusters in the sampled data, the method further includes: The transition patterns in the waveform of the preset duration are statistically analyzed to calibrate or obtain the bit width of the current Manchester encoded signal in real time.

[0019] As a further improvement to this application, determining the target waveform corresponding to the dense transition cluster includes: The sampled data containing the dense jump clusters is used as the input sequence, wherein the input sequence is obtained through a shift register; The input sequence is used to retrieve a preset waveform mapping table, which is determined based on the Manchester encoding transition rule and records the correspondence between various abnormal sampling sequence features and standard protocol waveform sequences. When the input sequence matches a specific abnormal sampling sequence feature in the waveform mapping table, the corresponding standard protocol waveform sequence is obtained as the target waveform.

[0020] As a further improvement to this application, if the result of determining whether there are dense abrupt change clusters in the sampled data is negative, then at least one of the following steps is performed: Return to the process of acquiring the sampled data of the Manchester encoded signal; Perform normal pulse width filtering, wherein if there are pulses in the sampled data whose duration is shorter than a preset pulse width threshold, they are filtered out; The signal deadlock monitoring is performed. If the sampled data remains at a fixed level without any jump within a preset deadlock duration, it is determined to be a communication abnormality and the corresponding error handling logic is triggered.

[0021] To achieve one of the above objectives, one embodiment of this application provides a filtering device for Manchester-coded signals, comprising: The data acquisition module is used to acquire sampled data of the Manchester encoded signal, wherein the time length of the sampled data is not less than one bit width; A transition cluster determination module is used to determine whether there are dense transition clusters in the sampled data. The dense transition cluster is located between two level-stable reference stable segments. The spacing between adjacent transition edges in the dense transition cluster is less than a preset threshold. The duration of the reference stable segment is greater than the preset threshold. The preset threshold is determined according to the bit width. The target waveform determination module is used to determine the target waveform corresponding to the dense transition cluster based on the Manchester encoding transition rule when the judgment result of the transition cluster judgment module is yes. The filtering output module is used to output the target waveform as the filtering result.

[0022] Compared with commonly used techniques, this application has the following advantages: The filtering method for Manchester encoded signals acquires sampling data with a width of not less than one bit, places dense transition clusters between two relatively stable reference segments for consideration, utilizes the logical background before and after the Manchester encoded signal as a reference benchmark, and establishes a global observation window with temporal continuity, thereby more accurately identifying the real logical transitions in dense interference regions, improving the accuracy of transition edge identification, and solving the problem that traditional local filtering is prone to misjudgment near transition edges. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of error filtering in a commonly used technique according to an embodiment of this application.

[0024] Figure 2 This is a flowchart of a filtering method for Manchester encoded signals according to an embodiment of this application.

[0025] Figure 3 Based on Figure 2 The flowchart of one specific implementation of step S30 in the process.

[0026] Figure 4 This is a schematic diagram of generating a candidate waveform based on a sampled data according to an embodiment of this application.

[0027] Figure 5 This is a schematic diagram of generating a candidate waveform based on another sampled data according to an embodiment of this application.

[0028] Figure 6 This is a schematic diagram of generating candidate waveforms based on resampled data according to an embodiment of this application.

[0029] Figure 7 This is a flowchart of the method for calculating the deviation score of each candidate waveform in Example 1.

[0030] Figure 8 This is a flowchart of determining the target waveform based on numerical proportional relationships in Example 2.

[0031] Figure 9 This is a flowchart of determining the target waveform based on historical statistical features in Example 3.

[0032] Figure 10 This is a flowchart of determining the target waveform based on the correlation coefficient in Example 4.

[0033] Figure 11 This is a flowchart of determining the target waveform using a preset spacing mapping table in Example 5.

[0034] Figure 12 It is based on Example 6 Figure 2 The flowchart of another specific embodiment of step S30 in the process.

[0035] Figure 13 This is a schematic diagram of a filter device for a Manchester encoded signal according to an embodiment of this application. Detailed Implementation

[0036] The present application will now be described in detail with reference to the specific embodiments shown in the accompanying drawings. However, these embodiments do not limit the present application, and any structural, methodological, or functional modifications made by those skilled in the art based on these embodiments are included within the scope of protection of this application.

[0037] One embodiment of this application provides a filtering method and apparatus for Manchester encoded signals. This filtering method can distinguish between normal transitions and abnormal interference from a global perspective, improve the accuracy of transition edge recognition, and thus ensure the stability of the real-time communication system.

[0038] In digital communication systems, Manchester coding possesses excellent self-synchronization and DC offset resistance due to the transition at the center of each bit cycle. However, in actual signal transmission environments, transient interference, i.e., glitches, often gets mixed into the raw signal acquired by the receiver due to electromagnetic interference, channel noise, or internal circuit competition hazards.

[0039] Commonly used digital filtering techniques primarily rely on pulse width filters or debouncing circuits. These techniques determine interference based on the width of the current pulse; if a pulse's duration is shorter than a preset hard threshold, it is considered interference and filtered out. However, in complex interference scenarios, especially when glitches appear near the logic transition edges of Manchester encoding, this localized and isolated filtering logic can lead to poor filtering results. Figure 1 As shown, when a glitch occurs immediately after a true transition edge, traditional width determination may mistakenly identify the true transition as a glitch and erase it, or mistakenly identify the glitch as a true transition and trigger it prematurely, leading to phase shift. Especially in automotive environments, such misjudgments can pose significant safety risks.

[0040] This embodiment provides a filtering scheme based on logic verification. Instead of simply observing the width of a single pulse, this scheme acquires sampling data over a longer band, introducing a holistic perspective and combining it with Manchester coding protocol features to identify and correct interference.

[0041] The following is combined with Figures 2-12 This application describes a method for filtering Manchester encoded signals according to an embodiment. Although this application provides method operation steps as shown in the following embodiments or flowcharts, the execution order of these steps is not limited to the execution order provided in the embodiments of this application, based on conventional or non-creative labor, where there is no necessary causal relationship in the logical process.

[0042] Specifically, such as Figure 2 As shown, the filtering method for Manchester encoded signals in this embodiment includes steps S10-S40.

[0043] Step S10: Obtain sampled data of the Manchester encoded signal, wherein the time length of the sampled data is not less than one bit width.

[0044] Step S20: Determine whether there is a dense transition cluster in the sampled data, wherein the dense transition cluster is located between two stable reference segments, the spacing between adjacent transition edges in the dense transition cluster is less than a preset threshold, the duration of the reference stable segment is greater than the preset threshold, and the preset threshold is determined according to the bit width.

[0045] Step S30: If there are dense transition clusters in the sampled data, the target waveform corresponding to the dense transition clusters is determined based on the Manchester encoding transition rules.

[0046] Step S40: Output the target waveform as the filtering result.

[0047] In the Manchester coding rules, the characteristic points of a signal are the transition rhythms of half a bit width (0.5 bit) and one bit width (1.0 bit). By maintaining sampled data of at least one full bit width in step S10, the filter can capture enough transition features to establish a reference benchmark for the phase and standard timing characteristics of the current signal.

[0048] In step S10, the sampled data can be stored in a buffer, and a sliding window storage operation can be performed using the buffer. First, the system determines whether enough sampling points have been stored in the buffer (i.e., the preset length is not less than one bit width). If there are not enough sampling points, sampling continues until the window length requirement is met.

[0049] Once the buffer is full, step S10 updates the sampled data points using a sliding window. This means that as new sampled points enter, the oldest data points in the window are removed, ensuring that the buffer always maintains a segment of the latest "long band" data containing the current transition edge and its preceding and following logical context. This enables real-time, continuous monitoring of the Manchester coded signal, ensuring that each dense transition cluster entering the window can be verified within a complete context.

[0050] Step S20 scans the sampled data in the buffer to determine if there are dense transition clusters. The bit width and preset threshold can be either fixed or both can be non-fixed values. For example, the bit width can be adjusted in real time based on statistics, and the preset threshold can be dynamically determined based on the bit width of the Manchester encoded signal. For instance, the preset threshold can be set to 1% to 30% of the bit width.

[0051] The two reference stable segments before and after a cluster of dense transitions represent the normal effective signal level. When a chaotic transition (cluster of dense transitions) is located between two stable reference segments, it indicates that this region should have been a regular logical transition, but due to interference, it has become multiple dense false transitions. By identifying this structure of "reference stable segment - dense transition cluster - reference stable segment", the location of the glitch can be accurately identified (or located).

[0052] In step S30, the Manchester encoding transition rule refers to the inherent physical constraints and logical characteristics followed by the Manchester encoding protocol in terms of temporal distribution, logical transformation, and structural proportions. In practical applications, this rule varies depending on the processing logic, as illustrated in the following six embodiments: The Manchester coding transition rule in Example 1 corresponds to the time-domain spacing feature, specifically manifested as the physical distance constraint between transition edges on the time axis. Since Manchester coding requires a transition at the center of each bit, theoretically, under normal signal timing, the spacing between two adjacent transition edges can only have two valid values: half a bit width (0.5 bit) or one bit width (1.0 bit). Step S30 compares the bit width, which will be elaborated below.

[0053] The Manchester encoding transition rule in Example 2 corresponds to a structural proportion feature, specifically manifested as a numerical proportion constraint between two adjacent transition intervals. Since Manchester encoding only has two effective pulse widths: half a bit width and one bit width, the numerical proportion between two consecutive intervals is logically locked within three legal proportions: 1:1 (0.5 bit to 0.5 bit, or 1.0 bit to 1.0 bit), 1:2 (0.5 bit to 1.0 bit), or 2:1 (1.0 bit to 0.5 bit). This feature allows the filter to perform logical waveform verification through the relative proportion between intervals without relying on absolute bit width values.

[0054] The Manchester encoding transition rules in Examples 5 and 6 correspond to logical mapping features, specifically manifested as level transition sequences conforming to protocol requirements and their corresponding standard waveform patterns. Under a specific sampling length, Manchester encoding exhibits a finite and exhaustive set of characteristics regarding the order of level transitions, phase changes, and combinations of transition edges. These legitimate waveform features or spacing combinations can be pre-extracted and stored in a mapping table. By matching the actually captured abnormal sequences or spacing sequences with the preset legitimate patterns in the mapping table, direct repair of damaged signals can be achieved.

[0055] Manchester coding transition rules can also be an extension of the aforementioned temporal spacing features, structural proportion features, and logical mapping features at the level of advanced mathematical algorithms. For example, combining statistical and signal processing methods, examples 3 and 4 are derived. For instance, example 3 incorporates a probability distribution dimension on the basis of temporal spacing features; example 4 includes both logical mapping features and temporal spacing features, focusing on the continuous signal representation of Manchester coding rules in the time domain, as well as the overall similarity between the actual signal and the protocol standard waveform.

[0056] Step S30 uses the stable segments before and after the region as the start or end point of the timing sequence, and combines them with the Manchester coding transition rules mentioned above to reconstruct a standard target waveform that conforms to Manchester coding logic.

[0057] Finally, step S40 outputs the determined target waveform, replacing the original dense abrupt change clusters in the sampled data, thus completing the filtering process.

[0058] Furthermore, if the judgment result corresponding to step S20 is negative, then there is no need to perform filtering in step S30. Instead, at least one of the following steps is executed, and then the corresponding steps are repeated. For example, only at least one of embodiments 1-3 is executed; or a second one is executed; for example, embodiment 1 is executed after embodiment 2 or 3; or all three are executed, for example, embodiment 1 is executed after embodiment 2 and 3, or 3 and 2.

[0059] Implementation method 1: Return to the sampling data of the Manchester encoded signal obtained by performing step S10.

[0060] Implementation Method 2: Perform normal pulse width filtering, wherein if there are pulses in the sampled data whose duration is shorter than a preset pulse width threshold, they are filtered out. That is, the judgment is based on the width of the current pulse; if the duration of a pulse is shorter than a preset hard threshold, it is judged as interference and filtered out.

[0061] Implementation Method 3: Perform signal deadlock monitoring. If the sampled data remains at a fixed level without any transitions within a preset deadlock duration, it is determined to be a communication anomaly and the corresponding error handling logic is triggered. This involves checking whether the current signal is in a state where it remains at a fixed level (high or low) for an extended period without any transitions. If the signal remains unchanged for a long time, it may indicate a physical link disconnection or a serious fault at the transmitting end. If signal deadlock is detected (i.e., the preset deadlock duration threshold is met), Implementation Method 3 will determine it to be a communication anomaly and terminate the current filtering process, triggering the corresponding error handling logic. If no signal deadlock is detected, it means that although the current waveform does not have dense interference, it is still in a normal communication state. The system then loops back to the sliding window storage in step S10 to process the next set of sampling points.

[0062] The filtering method for Manchester-coded signals, by introducing long-band sampling and preceding and following reference stationary segments, can calibrate the position of the true transition edge from a global perspective. Even if the time interval between the interference signal and the true transition edge is extremely short, it can be filtered out based on protocol rules, ensuring the accuracy of the transition position. The filtering process is deeply coupled with the inherent logic of the Manchester-coded transition rules, reducing the bit error rate of the subsequent decoding module. Furthermore, since the determined sampled data is not less than at least one bit wide, this method has strong adaptability to the frequency offset of the signal. Even if the actual number of sampling points changes due to the clock difference between the transmitting and receiving ends, as long as the signal still maintains its basic proportional characteristics, this scheme can still accurately lock the true edge and suppress interference, solving the problem that traditional filters may misfilter valid signals in scenarios with many glitches or high-speed communication.

[0063] In this embodiment, the bit width can be a one-bit time width, or an oversampling rate corresponding to a one-bit value, or the average one-bit time of the steady state of the transmitter measured by the receiver.

[0064] Taking the bit width as the average time of 1 bit of the steady state of the transmitter as measured by the receiver as an example, before step S20 determines whether there are dense transition clusters in the sampled data, it further includes: The transition patterns in the waveform of the preset duration are statistically analyzed to calibrate or obtain the bit width of the current Manchester encoded signal in real time.

[0065] Because communication systems may experience clock frequency discrepancies between the transmitting and receiving ends during actual operation, or frequency offsets caused by physical factors such as ambient temperature during signal transmission, the theoretical bit width may be scaled during actual reception. For example, it may change from 8 sampling points to 7 or 9 sampling points.

[0066] This embodiment utilizes long-wavelength data stored in a buffer to statistically analyze normally occurring transition edges (i.e., transition edges whose spacing conforms to Manchester coding transition rules and does not belong to dense transition clusters). By calculating the average spacing or the spacing value with the highest frequency among these normal transition edges, this embodiment can calibrate and obtain the true bit width of the current Manchester coded signal in real time. This provides a dynamic reference benchmark for subsequent filtering decisions, enabling the bit width to have dynamic adaptive capabilities. This ensures that the filter can accurately distinguish between standard timing characteristics and abnormal dense interference even in environments with frequency offsets, avoiding false filtering or missed filtering caused by improper hard threshold settings, and enhancing system compatibility.

[0067] The filtering method provided in this embodiment has modular features and can be executed independently of the decoder. It can not only be used as a preprocessing unit in the decoding front end, but also be applied to relay, forwarding or protocol conversion links in the Manchester encoded signal transmission link. By performing waveform reconstruction based on protocol logic, the damaged sampled data is converted into a logical sequence that meets the requirements of the standard protocol, thereby suppressing the accumulation of interference noise along the transmission link and improving the robustness of the communication system.

[0068] The filtering method for the Manchester encoded signal is described in detail below through six examples.

[0069] Example 1 In determining the target waveform corresponding to the dense transition cluster, this embodiment employs a screening mechanism based on multi-candidate evaluation. This process does not directly physically modify the waveform, but rather constructs multiple possible logical paths and selects the optimal one, thereby ensuring that the filtered result best matches the protocol characteristics.

[0070] Specifically, in this embodiment, such as Figure 3 As shown, step S30, which determines the target waveform corresponding to the dense transition cluster, includes: Step S31: Generate a variety of different candidate waveforms, wherein each candidate waveform is obtained by retaining or removing different transition edges in the dense transition cluster.

[0071] Step S32: For each candidate waveform, determine multiple candidate spacings that are sequentially divided by adjacent transition edges in the candidate waveform, and form a candidate spacing sequence from the multiple candidate spacings.

[0072] Step S33: Compare the candidate spacing sequence of each candidate waveform with the Manchester coding transition rule to determine the target waveform from the multiple candidate waveforms.

[0073] Due to the presence of glitches and interference, these transition edges contain both genuine signal edges and spurious edges generated by interference. After identifying dense transition clusters, step S31 treats each transition edge as a potential logical turning point. By performing different combinations of retention and rejection on these transition edges, multiple candidate waveform sequences are constructed. For example, if there are three transition edges within a dense transition cluster, the first edge, the second edge, or the third edge can be assumed to be true, thereby deriving multiple logically possible candidate waveforms.

[0074] After removing the transition edges identified as interference, new time intervals are formed between the remaining transition edges. Step S32 records the length values ​​between these adjacent edges in chronological order. These values ​​reflect the rhythmic distribution of the candidate waveform in the time domain. Since Manchester coding has strict requirements on pulse width ratios, these interval values ​​will serve as a basis for evaluating the reasonableness of the candidate waveform. Figure 4-6 As shown, the candidate spacing sequence for each candidate waveform is illustrated. The candidate spacing sequence for candidate waveform 1 is A1 and B1, the candidate spacing sequence for candidate waveform 2 is A2 and B2, and the candidate spacing sequence for candidate waveform 3 is A3 and B3. The A and B of each candidate waveform are determined sequentially by adjacent transition edges.

[0075] Step S33 treats the spacing sequence generated by each candidate waveform as a whole and compares it with the Manchester coding transition rule. By measuring the rhythmic fit of the entire sequence, the waveform that is closest to the ideal state is selected from multiple candidate schemes and determined as the target waveform.

[0076] Example 1 effectively extracts the true transition edges from dense interference transitions by constructing candidate waveforms and performing sequence comparison. This method can handle dense interference scenarios; even if the true transition edge is located between multiple glitches, as long as there is one transition edge that meets the protocol specifications, Example 1 can recover it, significantly improving the signal recovery rate.

[0077] In generating various candidate waveforms, this embodiment further introduces a filtering mechanism based on signal transition direction. When a dense transition cluster contains at least three transition edges, the continuity of signal levels is utilized to significantly reduce the logic search space and improve processing efficiency.

[0078] Specifically, step S31 generates a variety of different candidate waveforms, including: Step S311: Determine the target transition direction based on the signal level state before the dense transition cluster.

[0079] Step S312: Identify multiple candidate edges in the dense transition cluster that are consistent with the target transition direction.

[0080] Step S313: Set each of the candidate edges as a real transition edge, and correct the logic value corresponding to the remaining edges to be consistent with its adjacent level, thereby generating multiple candidate waveforms corresponding to the number of candidate edges.

[0081] In step S311, since the transition direction of Manchester encoding at a specific timing point is restricted and predictable, if the level before the dense transition cluster is high, then the valid logic transition at that position must be a falling edge; conversely, if the level before is low, then the valid transition must be a rising edge.

[0082] In step S312, for example, at the position predicted as a falling edge transition, all rising edges within the dense transition cluster can be ignored, and only all falling edges can be extracted as candidates. These ignored reverse edges physically correspond to the "level bounce" or "oscillation" caused by glitches, and logically cannot be considered as real transition edges.

[0083] In step S313, the remaining edges include other candidate edges in the same direction and interference edges in the opposite direction. Assuming that a certain falling edge is the only true transition edge, then other transition actions within this dense transition cluster should be regarded as invalid interference and logically smoothed out to keep the level stable before and after the true edge, and then generate multiple candidate waveforms consistent with the number of candidate edges.

[0084] like Figure 4-6 As shown, since the signal level before the dense transition cluster is high, the candidate edge must be a falling edge. Figure 4 The waveform shown has a glitch following the actual transition edge. Figure 5 The waveform shown has a glitch preceding the actual transition edge. Figure 6 The waveforms with glitches before and after the actual transition edge are shown, and Figure 4 and Figure 5 A glitch appeared in the middle, and this glitch corresponds to two candidate edges, which can generate two candidate waveforms; Figure 6 Two spikes appeared, and the two spikes correspond to three candidate edges, which can generate three candidate waveforms.

[0085] Steps S311-S313 described above perform preliminary screening of interference, thereby filtering out a large number of logical paths that are physically impossible. This not only reduces the computational load of subsequent comparison steps, but more importantly, it eliminates interference from interference edge-to-true edge identification caused by signal overshoot or ringing effects, improving identification accuracy and processing speed.

[0086] Example 1 further introduces a deviation score as a quantitative indicator to convert the proximity of the waveform to the protocol rules into a calculable numerical value. Specifically, the Manchester encoding transition rule includes half a bit width and / or one bit width determined based on the bit width.

[0087] Step S33 compares the candidate spacing sequence of each candidate waveform with the proximity of the preset standard transition spacing to determine the target waveform from the multiple candidate waveforms, including: For each candidate waveform, calculate the deviation score between its corresponding candidate spacing sequence and the half-bit width and / or the one-bit width, and select the candidate waveform with the smallest deviation score as the target waveform.

[0088] Since the duration of each valid level in Manchester encoding can theoretically be only half a bit or one bit, half a bit and one bit constitute the standard reference for determining the validity of the waveform.

[0089] Example 1 retrieves the candidate spacing sequence corresponding to the candidate waveform and calculates the deviation of each spacing value in the sequence from the preset standard parameters (half a bit width and / or one bit width). Through mathematical calculations, a deviation score reflecting its overall logical deviation is generated for each candidate waveform. Then, a global comparison is performed on the deviation scores of all candidate waveforms. The smaller the deviation score, the closer the candidate waveform is to the physical characteristics of the Manchester protocol, that is, the higher the probability that the waveform is a real signal. Finally, the candidate waveform with the smallest deviation score is selected as the final target waveform.

[0090] This embodiment 1 provides a clear decision-making basis for complex logical judgments through the quantitative calculation of deviation scores, effectively solving the problem of slight shifts in transition positions caused by sampling errors or frequency offsets when interference signals are present. By selecting the scheme with the minimum global deviation, the timing characteristics of the original signal can be efficiently restored, ensuring the reliability of the filtering results.

[0091] In one embodiment, such as Figure 7 As shown, for each candidate waveform, calculating the deviation score between its corresponding candidate spacing sequence and the half-bit width and / or the one-bit width includes: Step S3311: Calculate the first deviation between each candidate spacing in the candidate spacing sequence and the half bit width, and the second deviation between each candidate spacing and the one bit width.

[0092] Step S3312: Select the smaller value between the first deviation and the second deviation as the individual deviation score of the candidate spacing.

[0093] Step S3313: Accumulate the individual deviation scores of all candidate spacings in the candidate spacing sequence to obtain the deviation score of the candidate waveform.

[0094] Step S3311 generates two deviation candidate values ​​for each candidate spacing, representing the deviation distance of the spacing relative to the two valid protocol widths (0.5 bit and 1.0 bit).

[0095] In Manchester encoding, a valid pulse or level interval is either half a bit wide or one bit wide. By selecting the minimum of the two, this interval is matched to the legal protocol transition interval with the closest absolute value. Even if the signal has phase jitter, the minimum value operation in step S3312 can automatically align it to the closest standard timing feature.

[0096] Step S3313 obtains the overall deviation score of the candidate waveform by summing all spacing deviations within the sequence.

[0097] Because each candidate spacing is independently determined through a "two-choice" repositioning process, even if a particular spacing in the candidate waveform has a large deviation, it can still be accurately identified as long as the cumulative score of the overall sequence is the lowest. This method enhances the algorithm's tolerance to random phase jitter, ensuring that the target waveform can still be reconstructed through the logical path with the highest cumulative probability even under non-ideal channel conditions.

[0098] In one embodiment, the first deviation is the absolute value of the difference between the candidate spacing and the half-bit width, and the second deviation is the absolute value of the difference between the candidate spacing and the one-bit width.

[0099] This embodiment ignores the positive or negative directionality of the offset. During digital signal sampling, due to inconsistencies in timing characteristics or gradual changes in the rising / falling edge of the signal, the actual sampling spacing is often slightly longer or shorter than the ideal width. For logic verification, whether the spacing is "leading" or "lagging" does not affect its determination as interference; the key parameter is the absolute physical distance of the spacing from the standard timing characteristics.

[0100] by Figure 4-6Taking a candidate waveform with two candidate spacings as an example, i.e., only two candidate spacings A and B exist, then the overall deviation score distance for each candidate waveform is... total It can be calculated using the following formula: distance A =MIN(|A / 0.5OverSampleRate-1|,|A / 0.5OverSampleRate–2|) (Formula 1) distance B =MIN(|A / 0.5OverSampleRate-1|,|B / 0.5OverSampleRate–2|) (Formula 2) distance total =distance A +distance B (Formula 3) Formula 1 and Formula 2 calculate the two individual deviation scores respectively, and Formula 3 is solved by combining Formula 1 and Formula 2. OverSampleRate is the bit width.

[0101] For example, Figures 4-6 The corresponding bit width OverSampleRate is 8. Figure 4 The candidate spacing A1 of candidate waveform 1 has a length of 9, and the candidate spacing B1 has a length of 9, while the candidate spacing A2 of candidate waveform 2 has a length of 11, and the candidate spacing B2 has a length of 7. Therefore, after calculation, the overall deviation score of candidate waveform 1 is 1 / 2, and the overall deviation score of candidate waveform 2 is 1. Finally, candidate waveform 1 is the target waveform.

[0102] Figure 5 The candidate spacing A1 of candidate waveform 1 has a length of 7, the candidate spacing B1 has a length of 11, the candidate spacing A2 of candidate waveform 2 has a length of 11, and the candidate spacing B2 has a length of 7. Therefore, after calculation, the overall deviation score of candidate waveform 1 is 1, the overall deviation score of candidate waveform 2 is 1 / 2, and finally, candidate waveform 2 is the target waveform.

[0103] Figure 6 The candidate spacing A1 of candidate waveform 1 has a length of 7, and the candidate spacing B1 has a length of 11. The candidate spacing A2 of candidate waveform 2 has a length of 9, and the candidate spacing B2 has a length of 9. The candidate spacing A3 of candidate waveform 3 has a length of 11, and the candidate spacing B3 has a length of 7. Therefore, after calculation, the overall deviation score of candidate waveform 1 is 1, the overall deviation score of candidate waveform 2 is 1 / 2, and the overall deviation score of candidate waveform 3 is 1. Finally, candidate waveform 2 is the target waveform.

[0104] If there is a third or more candidate spacing, then the distance is further increased based on Formula 3. C distance D etc.

[0105] This embodiment not only reduces computational complexity and avoids logical redundancy caused by positive and negative sign processing, but also establishes a symmetrical judgment interval. This symmetry ensures that regardless of whether the signal is stretched as a whole due to frequency offset or shortened locally due to noise, the deviation score can reflect the degree of logical deviation, providing a standardized calculation benchmark for finally determining the target waveform with the smallest deviation score.

[0106] Example 2 This embodiment provides a filtering determination scheme based on numerical proportional relationships, focusing on the proportional characteristics of the internal structure of the signal.

[0107] Example 2 is the same as Example 1, both based on candidate waveform construction. It also requires identifying dense transition clusters and generating candidate waveforms by removing or retaining transition edges, thereby extracting candidate spacings divided sequentially by adjacent transition edges and determining the candidate spacing sequence.

[0108] The difference between Example 2 and Example 1 is that Example 2 no longer focuses on the absolute numerical deviation between the candidate spacing and the bit width, but rather on the proportional relationship between the candidate spacings. This example limits each candidate waveform to consist of two candidate spacings, for example... Figures 4-6 Candidate spacings A and B in the data.

[0109] Specifically, such as Figure 8 As shown, step S33 compares the candidate spacing sequence of each candidate waveform with the preset standard transition spacing to determine the target waveform from the multiple candidate waveforms, including: Step S3321: Determine the numerical ratio between two candidate spacings in the candidate spacing sequence.

[0110] Step S3322: Compare the numerical ratio with the standard ratio defined by the Manchester encoding transition rule, wherein the standard ratio includes 1:1, 1:2 and 2:1.

[0111] Step S3323: Based on the comparison results, select the candidate waveform whose numerical ratio is closest to the standard ratio from the multiple candidate waveforms as the target waveform.

[0112] For example Figures 4-6 In step S3321, the ratios of A1 / B1, A2 / B2, and A3 / B3 are calculated respectively.

[0113] Due to the protocol characteristics of Manchester encoding, the width ratio between adjacent transition edges can theoretically only exist as 1:1, 1:2, and 2:1 in step S3322. Here, 1:1 corresponds to 0.5 bit to 0.5 bit or 1.0 bit to 1.0 bit, 1:2 corresponds to 0.5 bit to 1.0 bit, and 2:1 corresponds to 1.0 bit to 0.5 bit.

[0114] Step S3323 is used to determine which value among A1 / B1, A2 / B2, A3 / B3... is closer to 1:1, 1:2, and 2:1. For example... Figure 4 The ratio of candidate waveform 1 to candidate waveform 2 is 11 / 7. Candidate waveform 1 is closer to 1:1, so candidate waveform 1 is selected as the target waveform. Figure 5 The ratio of candidate waveform 1 is 7 / 11, and the ratio of candidate waveform 2 is 1. Candidate waveform 2 is closer to 1:1, so candidate waveform 2 is selected as the target waveform. Figure 6 The ratio of candidate waveform 1 is 7 / 11, the ratio of candidate waveform 2 is 1, and the ratio of candidate waveform 3 is 11 / 7. Candidate waveform 2 is closer to 1:1, so candidate waveform 2 is selected as the target waveform.

[0115] Example 2 demonstrates strong anti-interference capabilities against frequency offset and clock jitter. In communication systems, if the clock frequencies of the transmitting and receiving ends are inconsistent, the sampled absolute pulse width (number of points) will be either too large or too small overall. However, regardless of the overall frequency offset, the pulse width ratio within the Manchester code remains consistently at 1:1 or 1:2. By determining the ratio using a numerical measure, fluctuations in absolute width can be ignored, and the true signal can be locked in solely based on the inherent logical consistency of the waveform structure, thus improving the robustness of the filter in non-ideal channels.

[0116] Example 3 This embodiment provides a filtering and judgment scheme based on historical statistical features and probability weighting, which utilizes the continuity of signal transmission to dynamically intervene in the current judgment logic through historical data.

[0117] Example 3 is similar to Example 1 in that it first acquires sampled data, identifies dense transition clusters, and constructs multiple candidate waveforms based on different combinations of transition edges. Then, for each candidate waveform, its corresponding candidate spacing sequence is extracted. In the waveform preprocessing and candidate spacing extraction stages, this example uses the same physical process as the aforementioned scheme for calculating deviation scores.

[0118] The difference between Example 3 and Example 1 is that the statistical regularity of historical sampling data is introduced as a balancing indicator.

[0119] Specifically, such as Figure 9As shown, step S33 compares the candidate spacing sequence of each candidate waveform with the preset standard transition spacing to determine the target waveform from the multiple candidate waveforms, including: Step S3331: Based on the statistical characteristics of historical sampling data, assign corresponding weight values ​​and / or probability distribution values ​​to different candidate intervals.

[0120] Step S3332: Combine the weight value and / or the probability distribution value to give a comprehensive score to the candidate spacing sequence of each candidate waveform.

[0121] Step S3333: Select the candidate waveform with the best comprehensive score as the target waveform.

[0122] During the communication process, step S3331 continuously records and statistically analyzes the distribution of transition intervals that have been confirmed as true over a period of time. For example, if historical data shows that the current bit width is stable at a certain number of sampling points, or if the interval offset caused by some kind of interference has a clear statistical tendency, a probability model is established accordingly.

[0123] Unlike the degree of calculation deviation in Examples 1 and 2, step S3332 of Example 3 assigns a higher score weight to intervals that conform to historical statistical patterns. Even if a candidate waveform deviates slightly in real-time calculation, it may still obtain a high overall score if its rhythm is highly consistent with historical patterns.

[0124] Example 3 utilizes the time stability of communication signals to effectively address asymmetric interference caused by environmental changes. By introducing historical weights, Example 3 learns from historical sampling data to identify and adapt to long-term drift caused by differences in crystal oscillators at the transmitting and receiving ends. It also filters out accidental spoofing transitions that conform to mathematical characteristics but violate historical patterns based on probabilistic statistical methods, thereby improving the accuracy of logic reconstruction in complex noise environments.

[0125] Example 4 This embodiment provides a filtering and determination scheme based on the correlation operation of ideal feature templates, which treats candidate waveforms as a whole signal and determines the target by waveform matching.

[0126] Example 4 is similar to Example 1, also involving the acquisition of sampled data and the identification of dense transition clusters. In the candidate waveform generation stage, it is also necessary to generate multiple possible candidate waveforms by retaining or discarding transition edges. Before determining the final target waveform, both Example 4 and Example 1 require obtaining a candidate set containing multiple candidate waveforms.

[0127] The difference between Example 4 and Example 1 is that the judgment criterion is no longer the summation of the deviation values, but the determination of the correlation coefficient between the overall waveform and the ideal feature template.

[0128] In this embodiment, as Figure 10 As shown, step S33 compares the candidate spacing sequence of each candidate waveform with the preset standard transition spacing to determine the target waveform from the multiple candidate waveforms, including: Step S3341: Perform correlation calculations between each candidate waveform and the ideal feature template, wherein the ideal feature template is determined based on the Manchester coding transition rule.

[0129] Step S3342: Based on the correlation coefficient generated by the calculation, select the candidate waveform with the highest correlation to the ideal feature template as the target waveform.

[0130] An ideal feature template is a standard signal sequence that conforms to Manchester coding transition rules on the time axis, with each transition point precisely located within half a bit width and / or one bit width. The ideal feature template is not a fixed, unchanging single waveform, but rather a reference sequence dynamically generated by the standard transition timing defined by the Manchester coding protocol based on the logical characteristics carried by the candidate waveform. The essence of correlation calculation is to measure the similarity between the candidate waveform's time-domain energy distribution and the ideal transition characteristics defined by the protocol standard.

[0131] Correlation calculation can be performed by multiplying the level sequence of the candidate waveform with the ideal feature template point by point in the time domain and summing the results to obtain the correlation coefficient. The magnitude of the correlation coefficient directly reflects the degree of overlap between the two waveforms in terms of phase and rhythm.

[0132] Because correlation operations mathematically suppress random noise, Example 4 exhibits strong resistance to noise and phase jitter. By comparing the overall waveform, it can ignore minor local sampling deviations and focus on the overall energy distribution and phase direction of the waveform. This allows the filter to lock onto the original signal that best conforms to the protocol logic through the peak value of the correlation coefficient, even in low signal-to-noise ratio environments, ensuring the reliability of the communication link.

[0133] Example 5 This embodiment provides a filtering and determination scheme based on a preset spacing mapping table. When dealing with complex dense jump clusters, this scheme has the same technical basis as Embodiment 1, but there are significant differences in the core determination logic.

[0134] Example 5 is similar to Example 1; both first acquire sampled data, identify dense transition clusters, and generate candidate waveforms. Similarly, in determining the target waveform, it is necessary to determine the corresponding candidate spacing sequence for each candidate waveform.

[0135] The difference between Example 5 and Example 1 is that this example no longer performs specific deviation score accumulation calculation, but directly determines the target waveform by retrieving a preset spacing mapping table.

[0136] Specifically, such as Figure 11 As shown, step S33 compares the candidate spacing sequence of each candidate waveform with the preset standard transition spacing to determine the target waveform from the multiple candidate waveforms, including: Step S331: Use the candidate spacing sequence of each candidate waveform as a query index to search in a preset spacing mapping table, wherein the preset spacing mapping table stores preset spacing combinations, and the preset spacing combinations are determined based on the Manchester encoding transition rules.

[0137] Step S332: Based on the search results, select the candidate waveform whose candidate spacing sequence matches the preset spacing combination from the multiple candidate waveforms as the target waveform.

[0138] The preset spacing combinations in the preset spacing mapping table are predetermined based on the standard timing characteristics (such as the range of sampling points corresponding to standard 0.5 bits or 1.0 bits) defined by the Manchester encoding transition rules.

[0139] by Figure 4 For example, candidate waveform 1 contains two candidate spacings with values ​​of 9 and 9 respectively, so the combination "9,9" is used as the index value; candidate waveform 2 contains two candidate spacings with values ​​of 11 and 7 respectively, so the combination "11,7" is used as the index value. However, the preset spacing combination in the preset spacing mapping table only contains "9,9" and does not contain "11,7", so candidate waveform 1 is selected as the target waveform.

[0140] The preset spacing mapping table transforms the logical judgments and verifications that would normally need to be performed in real-time during signal processing into static correspondences in the storage space through offline calculations. The generation process and implementation logic of the preset spacing mapping table are explained below: The preset spacing mapping table is generated by pre-evaluating the validity of various possible spacing combinations. Essentially, it shifts the computational overhead that would otherwise belong to the real-time filtering stage to the initialization or offline simulation stage. When constructing the preset spacing mapping table, any one or a combination of the above embodiments 1-4 can be used to determine the mapping relationship.

[0141] In actual implementation, after extracting the candidate spacing sequence, Example 5 does not repeat the calculation process in Example 1. That is, it does not require two subtractions, one minimum operation, and one addition for each candidate spacing; instead, it only requires one index retrieval operation to directly obtain the matching result through a table lookup. Example 5 significantly reduces the computational power requirements while ensuring the filtering accuracy remains consistent with the aforementioned calculation scheme. In hardware environments with low sampling rates and relatively limited jump combinations, it significantly shortens response latency and reduces system power consumption.

[0142] Example 6 This embodiment provides a filtering determination scheme based on direct mapping of waveform sequences.

[0143] Example 6 is the same as Example 1. Both require acquiring sampled data of the Manchester encoded signal and trigger filtering based on the identification of dense transition clusters.

[0144] The difference between Example 6 and Example 1 is that it does not require generating multiple candidate schemes or evaluating the spacing sequence one by one. Instead, it directly completes waveform replacement through the original sampling features captured by the shift register.

[0145] Specifically, such as Figure 12 As shown, step S30, which determines the target waveform corresponding to the dense transition cluster, includes: Step S34: Take the sampled data containing the dense jump cluster as the input sequence, wherein the input sequence is obtained through a shift register.

[0146] Step S35: Use the input sequence to retrieve a preset waveform mapping table. The waveform mapping table is determined based on the Manchester encoding transition rule and records the correspondence between various abnormal sampling sequence features and standard protocol waveform sequences.

[0147] Step S36: When the input sequence matches a specific abnormal sampling sequence feature in the waveform mapping table, obtain the corresponding standard protocol waveform sequence as the target waveform.

[0148] Step S34 uses a shift register to continuously capture and temporarily store the input signal. Each logic value (such as 0 or 1) in the shift register constitutes a digital sequence reflecting the current waveform characteristics, such as 11111010000. Each mapping relationship in the waveform mapping table represents a preset repair logic for a specific interference mode. For example, the mapping relationship between abnormal sampling sequence characteristics and standard protocol waveform sequences stored in the table includes "11111010000, 11111000000". The standard protocol waveform sequence corresponding to 11111010000 is 11111000000, and this sequence is output as the target waveform.

[0149] Example 6 directly converts the original sampled sequence captured by the shift register into a standard protocol waveform through a mapping table. The essence of this technique is to shift the complex waveform reconstruction workload to the pre-design stage of the algorithm. The waveform mapping table records the one-to-one or many-to-one mapping relationship between the characteristics of abnormal sampled sequences and standard protocol waveform sequences. The method for constructing the waveform mapping table is to pre-perform logical reconstruction calculations for all possible binary sequence combinations under a specific sampling length (e.g., the number of sampling points corresponding to a bit width not less than one bit), and then solidify the optimal reconstruction result in the waveform mapping table. The specific generation process of the waveform mapping table may include steps S51-S53.

[0150] Step S51: Exhaustively enumerate all possible anomalous sampling sequences that may occur at this sampling length, i.e., the original sampling data containing dense jump clusters.

[0151] Step S52: For each abnormal sampling sequence, perform optimal restoration determination using any one or a combination of the calculation models in Examples 1-4 above. For example, for an abnormal sequence "11111010000", the deviation score algorithm calculates that among all possible legal Manchester waveforms, "11111000000" has the lowest logical deviation score. Then, in the mapping table, the index "11111010000" is pointed to the target result "11111000000".

[0152] Step S53: Organize all abnormal sequences and their corresponding optimal restored waveform sequences into a waveform mapping table.

[0153] During filtering, Example 6 uses a shift register to capture the current sampling window data in real time and uses it as input features to search the waveform mapping table. Once a match is found, the pre-stored standard protocol waveform sequence in the waveform mapping table is directly called to replace the output.

[0154] This embodiment eliminates the intermediate steps of constructing candidate waveforms, extracting spacing sequences, and performing mathematical operations, resulting in higher processing efficiency and lower logic latency. This improves the real-time performance of the filter in processing high-speed Manchester signals and performs exceptionally well in resource-constrained or real-time-critical application scenarios. In particular, in communication environments with low sampling rates and relatively fixed interference patterns, this direct mapping method can achieve high-quality waveform restoration at a lower power cost, ensuring that the output signal strictly conforms to the physical form of the Manchester protocol at each sampling point.

[0155] Compared with commonly used technologies, this embodiment has the following advantages: The filtering method for Manchester encoded signals acquires sampling data of at least one bit width, places dense transition clusters between two relatively stable reference segments for consideration, and utilizes the logical background before and after the Manchester encoded signal as a reference benchmark to establish a global observation window with temporal continuity. This allows for more accurate identification of real logical transitions in dense interference regions, improves the accuracy of transition edge identification, and solves the problem of misjudgment near transition edges in traditional local filtering.

[0156] In one embodiment, a filtering device for Manchester-coded signals is provided, such as... Figure 13 As shown. The filtering device for this Manchester encoded signal includes the following modules, and the specific functions of each module are as follows: The data acquisition module is used to acquire sampled data of the Manchester encoded signal, wherein the time length of the sampled data is not less than one bit width.

[0157] A transition cluster determination module is used to determine whether there are dense transition clusters in the sampled data. The dense transition clusters are located between two stable reference segments. The spacing between adjacent transition edges in the dense transition clusters is less than a preset threshold. The duration of the reference segments is greater than the preset threshold. The preset threshold is determined based on the bit width.

[0158] The target waveform determination module is used to determine the target waveform corresponding to the dense transition cluster based on the Manchester encoding transition rule when the judgment result of the transition cluster judgment module is yes.

[0159] The filtering output module is used to output the target waveform as the filtering result.

[0160] It should be noted that for details not disclosed in the filtering device for Manchester encoded signals in the embodiments of this application, please refer to the details disclosed in the filtering method for Manchester encoded signals in the embodiments of this application.

[0161] The filtering device for Manchester encoded signals may further include computing devices such as computers, laptops, PDAs, and cloud servers, as well as, but not limited to, processing modules, storage modules, and computer programs stored in the storage modules and executable on the processing modules, such as the Manchester encoded signal filtering method program described above. When the processing module executes the computer program, it implements the steps in the various Manchester encoded signal filtering method embodiments described above, for example... Figure 2 The steps are shown.

[0162] In addition, this application also proposes an electronic device, which includes a storage module and a processing module. When the processing module executes the computer program, it can implement the steps in the above-mentioned Manchester encoded signal filtering method, that is, implement the steps in any of the above-mentioned Manchester encoded signal filtering methods.

[0163] The electronic device may be part of a filter integrated into a Manchester-encoded signal, a local terminal device, or part of a cloud server.

[0164] The processing module can be a Central Processing Unit (CPU), or other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processing module is the control center of the Manchester encoded signal filtering device, connecting all parts of the entire Manchester encoded signal filtering device through various interfaces and lines.

[0165] The storage module can be used to store the computer programs and / or modules. The processing module implements various functions of the Manchester encoded signal filtering device by running or executing the computer programs and / or modules stored in the storage module and by calling the data stored in the storage module. The storage module may mainly include a program storage area and a data storage area, wherein the program storage area may store the operating system, at least one application program required for a function, etc. In addition, the storage module may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0166] For example, the computer program may be divided into one or more modules / units, which are stored in a storage module and executed by a processing module to complete this application. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in a Manchester-encoded signal filtering device.

[0167] It should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This way of describing the specification is only for clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

[0168] The detailed descriptions listed above are merely specific descriptions of feasible implementation methods of this application, and are not intended to limit the scope of protection of this application. All equivalent implementation methods or modifications made without departing from the specific spirit of this application should be included within the scope of protection of this application.

Claims

1. A filtering method for Manchester-coded signals, characterized in that, The steps include the following: Acquire sampled data of the Manchester encoded signal, wherein the time length of the sampled data is not less than one bit width; Determine whether there is a dense transition cluster in the sampled data, wherein the dense transition cluster is located between two level-stable reference stable segments, the spacing between adjacent transition edges in the dense transition cluster is less than a preset threshold, the duration of the reference stable segment is greater than the preset threshold, and the preset threshold is determined according to the bit width; Wherein, if dense transition clusters exist in the sampled data, the target waveform corresponding to the dense transition clusters is determined based on Manchester coding transition rules; and The target waveform is output as the filtering result.

2. The filtering method for Manchester-coded signals according to claim 1, characterized in that: Determining the target waveform corresponding to the dense abrupt change cluster includes: A variety of different candidate waveforms are generated, wherein each candidate waveform is obtained by retaining or removing different transition edges in the dense transition cluster; For each candidate waveform, a plurality of candidate spacings are determined by sequentially dividing adjacent transition edges in the candidate waveform, and the plurality of candidate spacings form a candidate spacing sequence; and The target waveform is determined from the candidate waveforms by comparing the candidate spacing sequence of each candidate waveform with the Manchester encoding transition rule.

3. The filtering method for Manchester-coded signals according to claim 2, characterized in that: The dense transition cluster contains at least three transition edges; The generation of various candidate waveforms includes: The target transition direction is determined based on the signal level state preceding the dense transition cluster; Multiple candidate edges consistent with the target transition direction were identified within the dense transition cluster; and Each candidate edge is set as a real transition edge, and the logic value corresponding to the remaining edges is corrected to be consistent with its adjacent level, thereby generating multiple candidate waveforms corresponding to the number of candidate edges.

4. The filtering method for Manchester-coded signals according to claim 2, characterized in that: The Manchester encoding transition rule includes half a bit width and / or one bit width determined based on the bit width; The step of comparing the candidate spacing sequence of each candidate waveform with the preset standard transition spacing to determine the target waveform from the multiple candidate waveforms includes: For each candidate waveform, calculate the deviation score between its corresponding candidate spacing sequence and the half-bit width and / or the one-bit width, and select the candidate waveform with the smallest deviation score as the target waveform.

5. The filtering method for Manchester-coded signals according to claim 4, characterized in that: For each candidate waveform, calculating the deviation score between its corresponding candidate spacing sequence and the half-bit width and / or the one-bit width includes: Calculate the first deviation of each candidate spacing in the candidate spacing sequence from the half bit width, and the second deviation from the one bit width; The smaller value between the first deviation and the second deviation is selected as the individual deviation score for the candidate spacing; The individual deviation scores of all candidate spacings in the candidate spacing sequence are summed to obtain the deviation score of the candidate waveform.

6. The filtering method for Manchester-coded signals according to claim 5, characterized in that: The first deviation is the absolute value of the difference between the candidate spacing and the half-bit width; The second deviation is the absolute value of the difference between the candidate spacing and the one-bit width.

7. The filtering method for Manchester-coded signals according to claim 2, characterized in that: The step of comparing the candidate spacing sequence of each candidate waveform with the preset standard transition spacing to determine the target waveform from the multiple candidate waveforms includes: The candidate spacing sequence of each candidate waveform is used as a query index to retrieve data in a preset spacing mapping table. The preset spacing mapping table stores preset spacing combinations, which are determined based on the Manchester encoding transition rules. Based on the search results, from the multiple candidate waveforms, the candidate waveform whose candidate spacing sequence matches the preset spacing combination is selected as the target waveform.

8. The filtering method for Manchester-coded signals according to claim 2, characterized in that: Each of the candidate waveforms consists of two candidate intervals; The step of comparing the candidate spacing sequence of each candidate waveform with the preset standard transition spacing to determine the target waveform from the multiple candidate waveforms includes: Determine the numerical ratio between two candidate spacings in the candidate spacing sequence; The numerical ratio is compared with the standard ratio defined by the Manchester encoding transition rule, wherein the standard ratio includes 1:1, 1:2 and 2:1; Based on the comparison results, the candidate waveform whose numerical ratio is closest to the standard ratio is selected from the multiple candidate waveforms as the target waveform.

9. The filtering method for Manchester-coded signals according to claim 2, characterized in that: The step of comparing the candidate spacing sequence of each candidate waveform with the preset standard transition spacing to determine the target waveform from the multiple candidate waveforms includes: Based on the statistical characteristics of historical sampling data, assign corresponding weight values ​​and / or probability distribution values ​​to different candidate intervals; The candidate spacing sequence of each candidate waveform is comprehensively scored by combining the weight value and / or the probability distribution value. The candidate waveform with the best overall score is selected as the target waveform.

10. The filtering method for Manchester-coded signals according to claim 2, characterized in that: The step of comparing the candidate spacing sequence of each candidate waveform with the preset standard transition spacing to determine the target waveform from the multiple candidate waveforms includes: Each candidate waveform is correlated with an ideal feature template, wherein the ideal feature template is determined based on the Manchester coding transition rule. Based on the correlation coefficient generated by the calculation, the candidate waveform with the highest correlation to the ideal feature template is selected as the target waveform.

11. The filtering method for Manchester-coded signals according to claim 1 or 2, characterized in that: Before determining whether a dense abrupt change cluster exists in the sampled data, the method further includes: The transition patterns in the waveform of the preset duration are statistically analyzed to calibrate or obtain the bit width of the current Manchester encoded signal in real time.

12. The filtering method for Manchester-coded signals according to claim 1, characterized in that: Determining the target waveform corresponding to the dense abrupt change cluster includes: The sampled data containing the dense jump clusters is used as the input sequence, wherein the input sequence is obtained through a shift register; The input sequence is used to retrieve a preset waveform mapping table, which is determined based on the Manchester encoding transition rule and records the correspondence between various abnormal sampling sequence features and standard protocol waveform sequences. When the input sequence matches a specific abnormal sampling sequence feature in the waveform mapping table, the corresponding standard protocol waveform sequence is obtained as the target waveform.

13. The filtering method for Manchester-coded signals according to claim 1, characterized in that: If the result of determining whether there are dense abrupt change clusters in the sampled data is no, then at least one of the following steps is performed: Return to the process of acquiring the sampled data of the Manchester encoded signal; Perform normal pulse width filtering, wherein if there are pulses in the sampled data whose duration is shorter than a preset pulse width threshold, they are filtered out; The signal deadlock monitoring is performed. If the sampled data remains at a fixed level without any jump within a preset deadlock duration, it is determined to be a communication abnormality and the corresponding error handling logic is triggered.

14. A filtering device for Manchester-coded signals, characterized in that, include: The data acquisition module is used to acquire sampled data of the Manchester encoded signal, wherein the time length of the sampled data is not less than one bit width; A transition cluster determination module is used to determine whether there are dense transition clusters in the sampled data. The dense transition cluster is located between two level-stable reference stable segments. The spacing between adjacent transition edges in the dense transition cluster is less than a preset threshold. The duration of the reference stable segment is greater than the preset threshold. The preset threshold is determined according to the bit width. The target waveform determination module is used to determine the target waveform corresponding to the dense transition cluster based on the Manchester encoding transition rule when the judgment result of the transition cluster judgment module is yes. The filtering output module is used to output the target waveform as the filtering result.

Images