Wheel speed sensor noise filtering

Abstract

Wheel speed sensor noise filtering is provided. Noise filtering is performed on a wheel speed or other sensor, such as a wheel speed sensor configured to sense a rotational speed of a wheel included on a vehicle. The filtering process may include determining sensor data generated by a wheel speed sensor, the sensor data representing a rotational speed of a wheel; determining a stable interval of wheel rotation and an unstable interval of wheel rotation; and filtering noise from the sensor data associated with the stable interval according to an adaptive filtering process.

Description

Technical Field

[0001] The present disclosure relates to noise filtering, such as but not necessarily limited to noise filtering of wheel speed sensors included on a vehicle for measuring wheel rotation. Background Art

[0002] A vehicle can include multiple wheels to facilitate movement, typically one or more wheels are undriven and / or one or more wheels are driven. The undriven wheels can correspond to those configured to rotate passively in accordance with the movement of the vehicle, while the driven wheels can correspond to those configured to be driven or mechanically rotated for the purpose of propelling the vehicle. A vehicle can include a variety of vehicle systems to facilitate its operation, including those that may rely on or otherwise be affected by an accurate and precise representation of wheel speed. For example, an advanced driver assistance system (ADAS) can be one such system, whereby its ability to operate as needed can be at least partially based on accurately and precisely ascertaining wheel speed. A vehicle can include wheel speed sensors to facilitate measuring wheel speed, rotation, acceleration, etc. However, due to different road conditions, manufacturing variability, and other inherent or induced inconsistencies, wheel speed sensors may periodically provide different or varying speed values, and in some cases, may provide different values when the wheels can be rotating at relatively the same speed. Although contrasting road conditions, different suppliers, inconsistent calibration, and other differences can cause some wheels to rotate at different speeds periodically, a common source of inconsistency can be noise in the sensor data collected by the wheel sensors, i.e., unwanted or extraneous variations in the sensor data that may not be directly related to the actual movement or rotation of the wheels in motion. Summary of the Invention

[0003] A non-limiting aspect of the present disclosure relates to filtering noise for a wheel speed sensor, such as minimizing or otherwise improving variations caused by unwanted or extraneous variations in sensor data that may not be directly related to the actual movement or rotation of the sensed wheels. The noise filtering can be provided in an adaptive manner, optionally on a per-wheel basis, so as to increase and / or decrease the level of noise filtering as needed to compensate for variations that may be present or more pronounced at some wheel sensors than others. The ability to filter out, attenuate, or otherwise compensate for noise can be beneficial in providing more accurate and precise measurements, which can in turn be particularly valuable in optimizing the operation of vehicle systems that rely on or are otherwise affected by an appropriate representation of wheel speed.

[0004] One aspect of the present disclosure relates to a method for filtering noise for a wheel speed sensor configured to sense the rotational speed of a wheel included on a vehicle. The method may include: determining sensor data generated by the wheel speed sensor, the sensor data representing the rotational speed of the wheel; determining a stable interval of wheel rotation and an unstable interval of wheel rotation, optionally, the stable interval corresponds to the wheel rotating in a stable state, and the unstable interval corresponds to the wheel rotating in an unstable state; and filtering noise from the sensor data associated with the stable interval according to an adaptive filtering process.

[0005] The method may include performing an adaptive filtering process based on a variability calculation, the variability calculation generating a noise value to represent the amount of noise within the associated sensor data.

[0006] The method may include performing a filter selection process to select a noise filter for the adaptive filtering process from a plurality of available filters based on the noise value.

[0007] The method may include a filter selection process that includes cross-referencing the noise value relative to a filter selection graph to determine the noise filter, optionally, the filter selection graph depicts the available filters relative to a plurality of possible noise values.

[0008] The method may include selecting a noise filter to correspond to one of the available filters, the one of the available filters cross-referenced with a relevant noise value among the possible noise values that is most closely aligned with the noise value.

[0009] The method may include that the available filters correspond to a plurality of low-pass filters arranged in a filter selection graph such that the lowest frequency filter of the low-pass filters corresponds to the lowest one among the possible noise values, and the highest frequency filter of the low-pass filters corresponds to the highest one among the possible noise values.

[0010] The method may include that the available filters in the filter graph are dispersed linearly between the lowest frequency filter and the highest frequency filter.

[0011] The method may include filtering noise from the sensor data associated with the unstable interval according to a non-adaptive filtering process, optionally, the non-adaptive filtering process includes using a static filter to filter noise from the sensor data.

[0012] The method may include selecting a static filter from a look-up table configured to cross-reference a plurality of speed-based filters relative to the vehicle speed of the vehicle, the static filter corresponding to a speed-based filter that is most closely aligned with the vehicle speed associated with the sensor data to be filtered.

[0013] The method may include determining stable and unstable intervals based on a variability process. Optionally, the variability process determines the stable interval to be consistent with sensor data indicating a speed change in a stable range during wheel rotation.

[0014] The method may include determining that the wheel rotation is within a stable range based on a two-factor authentication process. The two-factor authentication process may include evaluating that the speed change is within a stable range by both a shorter window aggregation evaluation and a longer window aggregation evaluation in response to associated sensor data.

[0015] The method may include determining a shorter window aggregation evaluation to pass in response to the sensor data indicating that the speed change occurring throughout a shorter sampling window is less than a first threshold.

[0016] The method may include determining a longer window aggregation evaluation to pass in response to the sensor data indicating that the speed change occurring throughout a longer sampling window is less than a second threshold, where the longer sampling window is larger than the shorter sampling window.

[0017] One aspect of the present disclosure relates to a computer-readable storage medium having stored thereon a plurality of non-transitory instructions that, when executed by one or more processors, are operable to filter noise of a wheel speed sensor configured to sense a rotational speed of a wheel included in a vehicle. The non-transitory instructions are operable to: determine sensor data generated to represent the rotational speed of the wheel; determine a stable interval of wheel rotation and an unstable interval of wheel rotation. Optionally, the stable interval corresponds to the wheel rotating in a stable state, and the unstable interval corresponds to the wheel rotating in an unstable state; characterize sensor data associated with the stable interval as stable state sensor data and characterize sensor data associated with the unstable interval as unstable state sensor data; generate a stable state noise characterization for the stable state sensor data; select a noise filter from a plurality of available filters based on the stable state noise characterization; and filter noise from the stable state sensor data according to an adaptive filtering process.

[0018] The non-transitory instructions may be operable to determine the stable state noise characterization based on a variability calculation configured to characterize the amount of noise within the sensor data according to the standard deviation of the sensor data.

[0019] The non-transitory instructions may be operable to select a noise filter from a filter selection graph configured to depict available filters relative to a plurality of possible stable state noise characterizations. Selecting a noise filter from the filter selection graph includes selecting a noise filter to correspond to an available filter cross-referenced with the stable state noise characterization that is most closely aligned with the standard deviation.

[0020] The non-transitory instructions can be operable to select a noise filter corresponding to one of a plurality of low-pass filters linearly arranged in a filter selection graph from a lowest-frequency low-pass filter to a highest-frequency low-pass filter, with a plurality of intermediate low-pass filters therebetween.

[0021] One aspect of the present disclosure relates to a vehicle. The vehicle can include: a plurality of wheels operable to facilitate vehicle movement; a powertrain operable to rotate one or more wheels in response to mechanical power generated by an internal combustion engine and / or an electric motor; a plurality of wheel sensors configured to generate sensor data representing the rotational speed of the associated wheels; and a noise filter controller configured to adaptively filter noise from the sensor data using a noise filter separately selected for each wheel sensor from a plurality of available filters, wherein the noise filter controller can be configured to select the noise filter based on the noise characterization of the associated wheel sensor.

[0022] The available filters can include a plurality of low-pass filters configured to perform low-pass filtering according to different frequencies among a plurality of filter frequencies. The noise filter control can be configured to select a noise filter corresponding to the low-pass filter that is most closely aligned with the noise characterization of the associated wheel sensor.

[0023] The vehicle can include low-pass filters linearly arranged in a filter selection graph from a lowest-frequency low-pass filter to a highest-frequency low-pass filter with respect to the noise characterization, with a plurality of intermediate low-pass filters therebetween.

[0024] The following solutions are provided:

[0025] 1. A method for filtering noise for a wheel speed sensor configured to sense the rotational speed of a wheel included on a vehicle, the method comprising:

[0026] Determining sensor data generated by the wheel speed sensor, the sensor data representing the rotational speed of the wheel;

[0027] Determining a stable interval of wheel rotation and an unstable interval of wheel rotation, the stable interval corresponding to the wheel rotating in a stable state and the unstable interval corresponding to the wheel rotating in an unstable state; and

[0028] Filtering noise from the sensor data associated with the stable interval according to an adaptive filtering process.

[0029] 2. The method according to solution 1, further comprising:

[0030] Perform an adaptive filtering process based on variability calculations, the variability calculations generating a noise value to represent the amount of noise within the sensor data associated therewith.

[0031] 3. The method according to embodiment 2 further comprises:

[0032] Perform a filter selection process to select a noise filter for the adaptive filtering process from a plurality of available filters, including selecting the noise filter based on the noise value.

[0033] 4. The method according to embodiment 3 further comprises:

[0034] The filter selection process includes cross-referencing the noise value with respect to a filter selection graph to determine the noise filter, the filter selection graph depicting the available filters with respect to a plurality of possible noise values.

[0035] 5. The method according to embodiment 4 further comprises:

[0036] Select the noise filter to correspond to one of the available filters, the one of the available filters being cross-referenced with a relevant noise value that is most closely aligned with the noise value among the possible noise values.

[0037] 6. The method according to embodiment 5 further comprises:

[0038] The available filters correspond to a plurality of low-pass filters arranged in a filter selection graph such that the lowest frequency filter of the low-pass filters corresponds to the lowest one among the possible noise values, and the highest frequency filter of the low-pass filters corresponds to the highest one among the possible noise values.

[0039] 7. The method according to embodiment 6 further comprises:

[0040] The available filters in the filter graph are linearly distributed between the lowest frequency filter and the highest frequency filter.

[0041] 8. The method according to embodiment 1 further comprises:

[0042] Filter noise from the sensor data associated with the non-stable interval according to a non-adaptive filtering process, the non-adaptive filtering process including filtering noise from the sensor data using a static filter.

[0043] 9. The method according to embodiment 8 further comprises:

[0044] Select a static filter from a look-up table, the look-up table being configured to cross-reference a plurality of speed-based filters with respect to the vehicle speed of the vehicle, the static filter corresponding to a speed-based filter that is most closely aligned with the vehicle speed associated with the sensor data to be filtered.

[0045] 10. The method according to Scheme 1 further includes:

[0046] Determine the stable and unstable intervals according to the variability process, and the variability process determines the stable interval to be consistent with the sensor data indicating that the speed change during wheel rotation is within the stable range.

[0047] 11. The method according to Scheme 10 further includes:

[0048] Determine that the wheel rotation is within the stable range based on a two-factor authentication process, and the two-factor authentication process evaluates that the speed change is within the stable range by both a shorter window aggregation evaluation and a longer window aggregation evaluation in response to the associated sensor data.

[0049] 12. The method according to Scheme 11 further includes:

[0050] Determine the shorter window aggregation evaluation to be passed in response to the sensor data indicating that the speed change occurring in the entire shorter sampling window is less than the first threshold.

[0051] 13. The method according to Scheme 12 further includes:

[0052] Determine the longer window aggregation evaluation to be passed in response to the sensor data indicating that the speed change occurring in the entire longer sampling window is less than the second threshold, where the longer sampling window is larger than the shorter sampling window.

[0053] A computer-readable storage medium storing a plurality of non-transitory instructions, which when executed by one or more processors, the non-transitory instructions are operable to filter the noise of a wheel speed sensor, and the wheel speed sensor is configured to sense the rotational speed of a wheel included on a vehicle, wherein the non-transitory instructions are operable to:

[0054] Determine the sensor data generated by the wheel speed sensor, and the sensor data represents the rotational speed of the wheel;

[0055] Determine the stable interval of wheel rotation and the unstable interval of wheel rotation, the stable interval corresponds to the wheel rotating in a stable state, and the unstable interval corresponds to the wheel rotating in an unstable state;

[0056] Characterize the sensor data associated with the stable interval as stable state sensor data, and characterize the sensor data associated with the unstable interval as non-stable state sensor data;

[0057] Generate a stable state noise characterization for the stable state sensor data;

[0058] Select a noise filter from multiple available filters based on a steady-state noise characterization; and

[0059] Filter noise from the steady-state sensor data according to an adaptive filtering process.

[0060] 15. The computer-readable storage medium according to claim 14, wherein the non-transitory instructions are operable to:[[]]

[0061] Determine a steady-state noise characterization based on a variability calculation, the variability calculation being configured to characterize the amount of noise within the sensor data according to the standard deviation of the sensor data.

[0062] 16. The computer-readable storage medium according to claim 15, wherein the non-transitory instructions are operable to:[[]]

[0063] Select a noise filter from a filter selection graph, the filter selection graph being configured to depict available filters relative to multiple possible steady-state noise characterizations, selecting a noise filter from the filter selection graph includes selecting a noise filter to correspond to an available filter cross-referenced with the steady-state noise characterization, the steady-state noise characterization being most closely aligned with the standard deviation.

[0064] 17. The computer-readable storage medium according to claim 16, wherein the non-transitory instructions are operable to:[[]]

[0065] Select a noise filter to correspond to one of a plurality of low-pass filters linearly arranged in the filter selection graph from a lowest-frequency low-pass filter to a highest-frequency low-pass filter, with a plurality of intermediate low-pass filters therebetween.

[0066] 18. A vehicle, comprising:[[]]

[0067] A plurality of wheels operable to facilitate vehicle movement;

[0068] A powertrain operable to rotate one or more wheels in response to mechanical power generated by an internal combustion engine and / or an electric motor;

[0069] A plurality of wheel sensors operable to sense the rotational speed of a corresponding one of the wheels, the wheel sensors being configured to generate sensor data indicative of the rotational speed of the wheel associated therewith; and

[0070] A noise filter controller configured to adaptively filter noise from the sensor data using a noise filter separately selected for each wheel sensor from a plurality of available filters, wherein the noise filter controller is configured to select a noise filter based on the noise characterization of the wheel sensor associated therewith.

[0071] 19. The vehicle according to aspect 18, wherein:

[0072] The available filters include a plurality of low - pass filters configured to perform low - pass filtering according to different frequencies among a plurality of filter frequencies; and

[0073] The noise filter control is configured to select a noise filter corresponding to the low - pass filter that is most closely aligned with the noise characterization of the associated wheel sensor.

[0074] 20. The vehicle according to aspect 19, wherein:

[0075] The low - pass filters are arranged in a filter selection graph in a linear manner from the lowest - frequency low - pass filter to the highest - frequency low - pass filter with respect to the noise characterization, with a plurality of intermediate low - pass filters therebetween.

[0076] When taken in conjunction with the accompanying drawings, from the following detailed description of the modes of practicing the teachings, these features and advantages of the teachings, along with other features and advantages, will be readily apparent. It should be understood that although the following drawings and embodiments may be described separately, their individual features may be combined into additional embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] The drawings, which can be incorporated into the specification and form a part thereof, illustrate implementations of the present disclosure and, together with the description, are used to explain the principles of the present disclosure.

[0078] Figure 1 A vehicle having a wheel sensor according to an aspect of the present disclosure is shown.

[0079] Figure 2 Unfiltered wheel sensor data according to an aspect of the present disclosure is shown.

[0080] Figure 3 Filtered wheel sensor data according to an aspect of the present disclosure is shown.

[0081] Figure 4 A flowchart of a method for filtering noise according to an aspect of the present disclosure is shown.

[0082] Figure 5 A graph of sampling unfiltered wheel sensor data according to an aspect of the present disclosure is shown.

[0083] Figure 6 A graph of a noise filter according to a non - limiting aspect of the present disclosure is shown. DETAILED DESCRIPTION

[0084] As needed, this document may disclose detailed embodiments of the present disclosure; however, it is understood that the disclosed embodiments may merely be examples of the present disclosure, and the present disclosure may be embodied in various and alternative forms. The drawings may not necessarily be to scale; some features may be enlarged or minimized to show details of particular components. Accordingly, the specific structural and functional details disclosed herein are not to be construed as limiting, but merely as a representative basis for teaching one skilled in the art to employ the present disclosure in different ways.

[0085] Figure 1 Vehicle 12 is shown in accordance with a non - limiting aspect of the present disclosure. Vehicle 12 may include an electric traction motor 14 that is operable to convert electrical power into mechanical power for work purposes, such as for mechanically actuating a powertrain 16 to propel the vehicle. Since the powertrain 16 optionally includes an internal combustion engine (ICE) 18 for generating mechanical power, vehicle 12 is shown as a hybrid type. Alternatively, vehicle 12 may omit the electric motor 14 and instead be propelled solely by the ICE 18. The powertrain 16 may include components that facilitate the transfer of mechanical rotational force from the traction motor 14 and / or the ICE 18 to one or more of the wheels 20, 22, 24, 26. Vehicle 12 may include a rechargeable energy storage system (RESS) 30 to store and supply electrical power for the traction motor 14 and / or other components, systems, etc. 32 on vehicle 12, for example, via a first bus 34 (such as a main bus or HV bus) and a second bus 36 (such as an auxiliary bus or LV bus). Vehicle 12 may include a vehicle controller 38 to facilitate monitoring, controlling, measuring, and otherwise directing operations, performance, etc. on vehicle 12, which may include performing measurements, taking readings, or otherwise collecting data to facilitate operations. The vehicle controller 38 may include additional controllers that optionally perform associated operations in accordance with corresponding non - transitory instructions executed by one or more processors stored on one or more computer - readable storage media.

[0086] One non - limiting aspect of the present disclosure relates to a noise filter controller 42 and / or a vehicle controller 38 that operates in conjunction with the noise filter controller 42. The noise filter controller 42 may be configured to filter noise from a plurality of wheel speed sensors 44 included on a vehicle to sense, measure, record, or otherwise evaluate the rotational speed or other movement of the wheels. The noise filter controller 42 may be configured to filter noise from the sensor data collected from the speed sensors 44, which may include evaluating acceleration, angular position, rotation, and / or other aspects associated with wheel use, which for simplicity and brevity may be collectively referred to as rotational speed or wheel speed. The noise filter controller 42 is operable to filter noise from the wheel speed sensors 44 in order to minimize or otherwise improve variations caused by unwanted or extraneous variations in the sensor data that may not be directly related to the actual movement or rotation of the sensed wheels. The noise filtering may be provided in an adaptive manner, optionally on a wheel - by - wheel basis, in order to increase and / or decrease the level of noise filtering as needed to compensate for variations that may be present or more pronounced at some wheel sensors 44 than others. The ability to filter out, attenuate, or otherwise compensate for noise may be beneficial in providing more accurate and precise measurements, which in turn may be particularly valuable in optimizing the operation of vehicle systems that rely on or are otherwise affected by an appropriate representation of wheel speed.

[0087] Due to different road conditions, manufacturing variability, and other inherent or induced inconsistencies, the wheel speed sensors 44 may periodically provide different or varying speed values, and in some cases, may provide different values when the wheels are rotating at relatively the same speed. Although contrasting road conditions, different suppliers, inconsistent calibration, and other differences may cause some wheels to periodically rotate at different speeds, a common source of inconsistency may be noise in the sensor data collected by the wheel sensors 44, i.e., unwanted or extraneous variations in the sensor data that may not be directly related to the actual movement or rotation of the traveling wheels. By way of example, Figure 2 and Figure 3 provides an unfiltered or raw wheel speed data graph 48 in accordance with the present disclosure ( Figure 2 ) filtered with the noise filter controller 42 to provide a filtered wheel speed sensor data graph 50 ( Figure 3) Exemplary comparison. Data graphs 48, 50 are presented for non-limiting purposes as representing sensor data associated with wheel sensor 44. However, as will be understood by those skilled in the art, the noise filtering described herein can contribute to facilitating noise filtering of other types of sensor data generated by other types of sensors 44 in addition to wheel sensor 44. Data graphs 48, 50 may each include a vertical axis 52 representing wheel speed and a horizontal axis 54 representing time, and each data graph includes a wheel speed data set 56 for the left front wheel and another wheel speed data set 58 for the right front wheel.

[0088] Compared to the unfiltered wheel speed data graph 48, the filtered wheel speed data graph 50 is shown to include less wheel speed variation. This reduction in variation can be attributed to the noise filter controller 42 filtering out noise from the unfiltered wheel speed data. Minimizing the noise can be beneficial for providing a more accurate and precise representation of the wheel speed, which in turn can help support a wide variety of vehicle systems (not shown) that rely on or are otherwise affected by an accurate and precise representation of the wheel speed. For example, an advanced driver assistance system (ADAS) can be one such system, whereby, as opposed to phantom variations in sensor data caused by noise, steering and other related controls can be accurately and precisely adjusted based on the actual variations in the wheel speed to improve the system's ability to operate as desired, e.g., to autonomously steer the vehicle in a straight line or along a desired path. In addition to facilitating the operation of the ADAS, the ability of the noise filter controller 42 to filter out noise can be beneficial for a wide variety of other applications and purposes, e.g., the filtered sensor data can be used to update sensing alignment criteria for robust parameter learning and better control performance and / or to simplify the complexity of calibration while also enhancing the robustness of dynamic bias learning / sensing alignment towards robust control features.

[0089] Figure 4 A flowchart 60 of a method for filtering noise in accordance with a non-limiting aspect of the present disclosure is shown. The method can be facilitated by the noise filter controller 42 and optionally the vehicle controller 38 and / or additional controllers included on and / or external to the vehicle 12, which operate in accordance with one or more processors executing corresponding multiple non-transitory instructions stored on an associated computer-readable storage medium. Block 62 involves a data process for detecting sensor data 64 from the wheel speed sensor 44, which sensor data 64 can be used, for example, to represent the rotational speed of the wheel associated therewith. The sensor data 64 can be considered raw or unfiltered data, e.g., direct feedback from the wheel sensor 44, including that associated with generating Figure 2Values, data, and / or other comparable information required for an unfiltered wheel speed data plot. The data process can include generating sensor data 64 as individual data sets for each wheel sensor 44. For illustrative purposes, noise filtering of sensor data 64 is described in terms of filtering noise from a subset of sensor data 64, i.e., the noise filter controller 42 can be operable to filter noise for multiple sets of sensor data 64 simultaneously. As described above, the noise filtering contemplated herein can be described with respect to wheel speed sensors 44. However, the present disclosure is not necessarily so limited and its use and application in filtering noise from other types of sensors 44 is fully contemplated.

[0090] Block 66 relates to a state determination process for characterizing the state of unfiltered sensor data 64. State determination can be used to facilitate characterizing portions of the unfiltered sensor data 64 that are consistent with a stable state of wheel operation and an unstable state of wheel operation. The stable state can correspond to a stable interval of wheel rotation and the unstable state can correspond to an unstable interval of wheel rotation. The ability to distinguish portions of the unfiltered sensor data 64 that correspond to stable and unstable states of wheel operation can be beneficial for adjusting noise filtering to account for different operating conditions, and in particular for separating noise filtering for accounting for noise caused by transient or varying operating conditions (i.e., during the unstable state) from noise filtering for accounting for noise caused by spurious changes in the sensor data 64, where spurious changes in the sensor data 64 are caused by unwanted or extraneous changes in the sensor data 64 that may not be directly related to the actual movement or rotation of the vehicle wheels (i.e., during the stable state). The presentation of the distinction between stable and unstable operating states is for the purpose of highlighting a beneficial aspect of the present disclosure in providing different types of noise filtering. However, this is done for non-limiting purposes as the present disclosure fully contemplates performing noise filtering without distinguishing based on stable and unstable states.

[0091] As described in more detail below, different types of noise filtering can correspond to adaptive filtering and non - adaptive filtering. Optionally, adaptive filtering corresponds to a stable operating state, and non - adaptive filtering corresponds to an unstable operating state. One aspect of the present disclosure relates to performing a two - factor authentication process as part of the state determination process in block 66, whereby unfiltered sensor data 64 can be determined to be stable - state sensor data 64, i.e., sensor data 64 associated with a stable wheel rotation interval, if the unfiltered sensor data 64 associated therewith exceeds the two - factor authentication process, and otherwise determined to be non - stable - state sensor data 64, i.e., sensor data 64 associated with an unstable wheel rotation interval. The two - factor authentication process can be used to evaluate the speed change within the unfiltered sensor data 64 as being within a stable range, i.e., within a stable interval or associated with a stable state, in response to the unfiltered sensor data 64 passing both a first aggregation assessment 70 and a second aggregation assessment 72 associated therewith. The first aggregation assessment 70 can be characterized as a shorter - window aggregation assessment 70, and the second aggregation assessment 72 can be characterized as a longer - window aggregation assessment 72.

[0092] The shorter aggregation assessment can include random filtering 76 or other filtering processes combined with absolute - value determination 78, whereby the associated short aggregation assessment 80 can be output to a comparison process 82 for comparison with a corresponding coefficient or test threshold 84. In response to the unfiltered sensor data 64 indicating that the speed change occurring over the entire shorter sampling window is less than a first threshold specified in the test threshold 84, the shorter - window aggregation assessment 70 can be considered to pass. The longer aggregation assessment 72 can include a buffer 88, etc., combined with a variability and / or noise characterization process 90, whereby the associated longer aggregation assessment 92 can be output to a comparison process 94 for comparison with a corresponding coefficient or test threshold 96. In response to the unfiltered sensor data 64 indicating that the speed change occurring over the entire longer sampling window is less than a second threshold specified in the test threshold 96, the longer - window aggregation assessment 72 can be considered to pass. The longer - window aggregation assessment 72 can process the unfiltered sensor data 64 over an entire sampling window that is longer than the shorter sampling window of the shorter - window aggregation assessment, such that two - factor authentication is provided by sampling the unfiltered sensor data 64 across different - sized sampling windows.

[0093] Figure 5FIG. 100 shows an unfiltered sensor data 64 with respect to a vertical axis 102 representing wheel speed and a horizontal axis 104 representing time, where an exemplary annotation 106 for a shorter sampling size window is located within another annotation 108 for a longer sampling size window. The ability to evaluate the unfiltered sensor data 64 with respect to different sized sampling windows and / or actually different first and second thresholds can be advantageous in robustly evaluating whether the associated unfiltered sensor data 64 corresponds to a stable or non - stable interval of wheel rotation. The variability process for the longer window aggregated evaluation can include a variability calculation for characterizing the amount of noise within the unfiltered sensor data 64 according to its standard deviation or other statistical modeling, optionally having a self - learning aspect, whereby the variability process 90 can learn over time the different nuances of the noise differences between wheel sensors. The standard deviation or other statistical modeling can be used to generate a noise value 110 representing the amount of noise within the associated unfiltered sensor data 64, which, as described in more detail below, can be used in conjunction with the two - factor output 112 of a two - factor authentication process to facilitate the filter selection process shown in block 114. The filter selection process can cooperate with the filtering process shown in block 116 to facilitate selectively filtering the unfiltered sensor data 64 into unfiltered sensor data 64 that can operate with various systems on and / or outside the vehicle 12. One aspect of the present disclosure contemplates that the filter selection process includes determining an adaptive and / or non - adaptive filtering of the unfiltered sensor data 64.

[0094] The filtering process can include filtering noise from the sensor data 64 associated with non - stable intervals according to a non - adaptive filtering process, such as by utilizing a static filter selected from a plurality of speed - based filters. The static filter selection process in block 120 can include selecting a static filter from a look - up table configured to cross - reference speed - based filters with respect to the vehicle speed of the vehicle. The static filter can correspond to one of the speed - based filters that most closely aligns with the wheel or vehicle speed associated with the sensor data 64 being filtered. The selection of the static filter based on speed can be used to customize the filtering according to predefined or predetermined filters that have been tested to be operable to account for noise caused by transient or varying operating conditions (i.e., during non - stable states while the wheel is in operation). The filtering process can include filtering noise from the unfiltered sensor data 64 associated with stable intervals according to an adaptive filtering process, such as by utilizing a noise filter selected from a plurality of possible noise filters. The adaptive filter selection process in block 122 can include selecting a noise filter based on the noise value 110 determined in the variability calculation of block 90, such that the noise filter is selected additionally based on the amount of noise within the unfiltered data and not just the wheel speed or vehicle speed.

[0095] The adaptive filter selection process may include a filter analysis process that is operable to cross-reference the noise value 110 relative to a filter selection feature, i.e., the standard deviation or other statistical representation of the amount of noise in the unfiltered sensor data 64 that has passed through the two-factor authentication process, to determine the noise filter to be used in the adaptive filtering process. The filter selection feature may be operable to depict the available filters relative to a plurality of possible noise values. Figure 6 A filter selection graph 128 is shown in accordance with a non-limiting aspect of the present disclosure. The filter selection graph 128 may correspond to a selection feature that is operable to depict a plurality of possible noise filters available for an adaptive filtering process relative to a filter line 130, where the filter line 130 is defined relative to a vertical axis 132 representing a filter coefficient and / or other selectable ranges of possible filter values and a horizontal axis 134 representing possible noise values. While other filter lines 130 may be utilized, the filter line 130 is shown depicting a linear spread of filter coefficients that generally extends in a linear manner from a lowest coefficient 136 for the lowest amount of noise and a highest coefficient 138 for the highest amount of noise, with a plurality of intermediate coefficients 140 therebetween.

[0096] The filter coefficient selected for adaptive filtering may correspond to the relevant one of the possible noise values that is closely aligned with the noise value 110. One aspect of the present disclosure contemplates available noise filters corresponding to a plurality of low-pass filters arranged in the filter selection graph 128 such that the lowest frequency filter 136 of the low-pass filter corresponds to the lowest one of the possible noise values and the highest frequency filter 138 of the low-pass filter corresponds to the highest one of the possible noise values. The use of low-pass filters, particularly frequency-selective low-pass filters, is presented for non-limiting purposes as the present disclosure fully contemplates selecting and / or defining noise filters according to a wide range of filtering values, coefficients, etc. For example, the noise filter may be adaptive based on the noise value 110 to facilitate an adaptive filtering process in accordance with selectable changes made to a cut-off frequency, filter order, time constant, adaptation step, filter initialization parameter, weighting factor, window size, adaptive smoothing parameter, convergence criterion, etc.

[0097] Although various embodiments have been described, the description is intended to be exemplary, not restrictive, and it will be clear to those of ordinary skill in the art that many more embodiments and implementations are possible within the scope of the embodiments. Unless specifically restricted, any feature of any embodiment can be used in combination with or substituted for any other feature or element in any other embodiment. Thus, the embodiments are not restricted except in accordance with the appended claims and their equivalents. Additionally, various modifications and variations are possible within the scope of the appended claims. Although several modes for carrying out many aspects of the present teachings have been described in detail, those skilled in the art familiar with the fields involved in these teachings will recognize various alternative aspects for practicing the present teachings within the scope of the appended claims. It is intended that all content included in the above description or shown in the drawings be construed as illustrative and exemplary of the entire scope of alternative embodiments, and those of ordinary skill in the art will recognize that these alternative embodiments are implied by the content included, structurally and / or functionally equivalent to the content included, or otherwise made clear based on the content included, and are not limited to those embodiments explicitly depicted and / or described.

Claims

1. A method for filtering noise for a wheel speed sensor, the wheel speed sensor being configured to sense the rotational speed of a wheel included on a vehicle, the method comprising: Determining sensor data generated by the wheel speed sensor, the sensor data representing the rotational speed of the wheel; Determining a stable interval of wheel rotation and an unstable interval of wheel rotation, the stable interval corresponding to the wheel rotating in a stable state and the unstable interval corresponding to the wheel rotating in an unstable state; and Filtering noise from the sensor data associated with the stable interval according to an adaptive filtering process.

2. The method according to claim 1, further comprising: Performing an adaptive filtering process based on a variability calculation, the variability calculation generating a noise value to represent the amount of noise within the associated sensor data.

3. The method according to claim 2, further comprising: Performing a filter selection process to select a noise filter for the adaptive filtering process from a plurality of available filters, including selecting the noise filter based on the noise value.

4. The method according to claim 3, further comprising: The filter selection process includes cross-referencing the noise value with respect to a filter selection graph to determine the noise filter, the filter selection graph depicting the available filters with respect to a plurality of possible noise values.

5. The method according to claim 4, further comprising: Selecting the noise filter to correspond to one of the available filters, the one of the available filters being cross-referenced with a relevant noise value that is most closely aligned with the noise value among the possible noise values.

6. The method according to claim 5, further comprising: The available filters correspond to a plurality of low-pass filters arranged in the filter selection graph such that the lowest frequency filter of the low-pass filters corresponds to the lowest one among the possible noise values, and the highest frequency filter of the low-pass filters corresponds to the highest one among the possible noise values.

7. The method according to claim 6, further comprising: The available filters in the filter graph are linearly dispersed between the lowest frequency filter and the highest frequency filter.

8. The method according to claim 1, further comprising: Filtering noise from the sensor data associated with the unstable interval according to a non-adaptive filtering process, the non-adaptive filtering process including filtering noise from the sensor data using a static filter.

9. The method according to claim 8, further comprising: Selecting the static filter from a look-up table, the look-up table being configured to cross-reference a plurality of speed-based filters with respect to the vehicle speed of the vehicle, the static filter corresponding to a speed-based filter that is most closely aligned with the vehicle speed associated with the sensor data to be filtered.

10. The method according to claim 1, further comprising: Determining the stable and unstable intervals according to a variability process, the variability process determining the stable interval to be consistent with the sensor data indicating that the speed change in the wheel rotation is within a stable range.

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