Patent Publication Number: US-2019187261-A1

Title: Proximity sensing

Description:
TECHNICAL FIELD 
     Embodiments of the present disclosure relate to proximity sensing, and particularly to the use of ultrasonic transducers for proximity sensing. 
     BACKGROUND 
     Proximity sensing is a well-known function enabling a device to detect the presence of nearby objects without physical contact. Many different techniques exist for proximity detection, and a significant number of those techniques involve the generation of an interrogating signal, and the detection of changes in the field or return signal. The return signal may comprise a component resulting from reflection off a nearby object, with the time between generation of the interrogating signal and detection of the reflected response giving an indication of the distance between the sensor and the object. One famous use of this principle is radar, which involves the generation of an interrogating electromagnetic radio signal and the detection of a reflected radio waves. 
     Modern handheld electronic devices also make use of proximity sensors. For example, smart phones and the like typically provide a touchscreen for user input, which covers a significant percentage of the front face of the device. When the user holds the device close to their ear, for a call or any other purpose, proximity sensors in the device can be used to detect the proximity of the user&#39;s ear (or more generally the user&#39;s head) and lock the touchscreen to touch input. In this way, accidental input from touches by the user&#39;s ear are blocked, and undesired commands via the touchscreen are avoided. 
     Currently, proximity sensing is performed by an infrared sensor in the front face of the electronic device, which generates an outgoing infrared signal, and detects changes in the field or return signal as described above. However, this limits the design options of electronic device, as a separate infrared proximity sensor must be provided. This is located in the bezel which surrounds the touchscreen, and designers of touchscreen devices are increasingly seeking to reduce the size of the bezel in order to maximize the space available on the front face for the screen. Thus the presence of the infrared proximity sensor places a limit on the percentage of the front face which can be devoted to the screen. An alternative proximity sensing solution is therefore required. 
     Ultrasonic transducers have been proposed as a solution for proximity detection in electronic devices. However, these devices present their own problems. Handheld electronic devices are typically used in noisy environments, and it is important that an ultrasonic proximity sensor is robust to such noise. For example, it is expected that users will interact with their device by tapping on a touchscreen, or even physical buttons provided in the device. The electronic device may be handled roughly, resulting in bumps and other sudden accelerations. Each of these sources of noise may be known as “impulsive noise”. Acoustic noise in the environment may also be sensed by an ultrasonic sensor. Some or all of these sources of noise can affect the correct functioning of ultrasonic-based proximity-sensing. If the performance of the ultrasonic proximity detection is to match that of infrared proximity detection, it is important for the ultrasonic system to operate correctly in such noisy environments. 
     SUMMARY 
     A first aspect of the present disclosure provides processing circuitry for ultrasonic proximity-detection. The processing circuitry comprises: a first input for receiving an indication of an interrogating ultrasonic signal; a second input for receiving a detected ultrasonic return signal; an adaptive filter, coupled to the first and second inputs, operative to estimate a transfer function between the interrogating ultrasonic signal and the detected ultrasonic return signal; a feature extract module, coupled to the adaptive filter, operative to calculate statistical moments of one or more of: the estimated transfer function; an estimated ultrasonic return signal, calculated based on the interrogating ultrasonic signal and the estimated transfer function; and an error signal, representing the error between the estimated ultrasonic return signal and the detected ultrasonic return signal; and a classifier module, coupled to the feature extract module, operative to determine the presence of a nearby object based on the statistical moments. 
     The processing circuitry may be provided in an electronic device, additionally comprising one or more ultrasonic transducers, coupled to the processing circuitry, operative to generate the interrogating ultrasonic signal and to detect the ultrasonic return signal. 
     A further aspect of the disclosure provides a method of performing proximity detection, comprising: receiving an indication of an interrogating ultrasonic signal; receiving a detected ultrasonic return signal; estimating a transfer function between the interrogating ultrasonic signal and the detected ultrasonic return signal; calculating statistical moments of one or more of: the estimated transfer function; an estimated ultrasonic return signal, calculated based on the interrogating ultrasonic signal and the estimated transfer function; and an error signal, representing the error between the estimated ultrasonic return signal and the detected ultrasonic return signal; and determining the presence of a nearby object based on the statistical moments. 
     The disclosure also provides a computer program product, comprising a non-transitory machine-readable medium containing instructions which, when executed by processing circuitry, cause the processing circuitry to perform the method recited above. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       For a better understanding of examples of the present disclosure, and to show more clearly how the examples may be carried into effect, reference will now be made, by way of example only, to the following drawings in which: 
         FIG. 1  is a schematic diagram of an electronic device comprising processing circuitry according to embodiments of the disclosure; 
         FIG. 2  is a schematic illustration of an adaptive filter according to embodiments of the disclosure; 
         FIG. 3  is a schematic diagram of a feature extract module according to embodiments of the disclosure; and 
         FIG. 4  illustrates classification of a feature vector according to embodiments of the disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     Terms such as “first”, “second” etc. may be used herein as labels to distinguish between features which are otherwise similar, unless the context otherwise dictates. In particular, in the present disclosure, references to first, second, third and fourth statistical moments instead describe the “first moment”, “second moment”, and so on, in a mathematical sense. In the present context, the first statistical moment of a collection of data samples may be considered as the mean value of those data samples, the second statistical moment of a collection of data samples may be considered as the variance of those data samples, the third statistical moment of a collection of data samples may be considered as the skewness of those data samples, and the fourth statistical moment of a collection of data samples may be considered as the kurtosis of those data samples. 
       FIG. 1  is a schematic diagram of an electronic device  100  comprising processing circuitry  102  according to embodiments of the disclosure. The electronic device  100  may be any suitable device which may require, or make use of, proximity detection (e.g. a motion sensor). In one embodiment, the device  100  may be a handheld or mobile device, such as smartphone, camera, monitor, laptop or tablet computer, games console, or games console peripheral device. The processing circuitry  102  may be provided on a single integrated circuit or chip, or may be distributed across multiple integrated circuits. 
     Proximity detection may comprise, at a basic level, detecting the presence or absence of an object which is near the electronic device  100 . Parameters may be set within the processing circuitry  102 , as will be described below, so as to detect the presence of an object within a given distance of the device  100 . The distance at which detection occurs may be varied in accordance with the design requirements. Thus, the output of the proximity detection may be a binary decision indicating the presence or absence of a nearby object. Alternatively or additionally, proximity detection may comprise measurement of the distance between the electronic device  100  and any nearby objects, e.g., based on a measured round-trip time between the emitted interrogating signal and the detected reflected signal. The output of the proximity detection in this case may be an indication of the distance between the electronic device  100  (or rather the proximity detection system within the device  100 ) and the nearby object. Still further, proximity detection may comprise measurement of the distance between the electronic device  100  and different parts of a nearby object, so as to detect an orientation of the object with respect to the electronic device  100 . This latter embodiment entails the highest processing complexity, but may be used, for example, for gesture recognition and consequential user input to the electronic device  100 . Embodiments of the present disclosure are relevant to each of these methods. 
     The electronic device  100  further comprises a loudspeaker  104  and a microphone  106 , both coupled to the processing circuitry  102 . In the illustrated embodiment, the loudspeaker  104  is operative to output an interrogating ultrasonic signal, and the microphone  106  is operative to detect an ultrasonic return signal (e.g. as reflected from a nearby object). It will be understood by those skilled in the art that loudspeakers and microphones have similar components for the generation and detection of acoustic or ultrasonic signals. In the former case, the loudspeaker is driven by an electronic signal to output an acoustic or ultrasonic signal; in the latter case, the microphone is driven by an acoustic or ultrasonic signal to output an electronic signal. The term “ultrasonic transducer” is used herein to describe any device which is capable of generating or detecting an ultrasonic signal. 
     In embodiments where the electronic device  100  is a smartphone, the loudspeaker  104  may comprise an earpiece, used in a normal mode to output an audio signal (such as a voice signal) while the user is on a call. The microphone  106  may comprise a “top microphone” (i.e. arranged at or near the top of the smartphone), and conventionally used for the detection of sounds in the environment of the device  100 . Alternatively, the microphone  106  may comprise an error microphone, positioned at or close to the loudspeaker  104 , and used for the purposes of feedback active noise cancellation. In each of these embodiments, physical components of the device  100  which are already present in the device for other purposes may be re-purposed for ultrasonic proximity-detection. Thus, additional dedicated sensors may not be required, and real-estate on the front face of the device  100  can be used for other purposes, such as the screen. 
     The discussion above and the illustrated embodiment assumes the use of separate physical components for the generation of the interrogating ultrasonic signal and the detection of the ultrasonic return signal. In other embodiments, however, the same ultrasonic transducer may be used for both signal generation and signal detection. For example, the transducer may be time-multiplexed between signal generation and signal detection. In one particular example, a screen of the electronic device  100  may be re-purposed for the generation and/or detection of ultrasonic signals, e.g., through the use of piezoelectric or other high-speed actuators coupled to the screen—either driven to generate ultrasonic signals, or non-driven to detect incoming ultrasonic signals. 
     The processing circuitry  102  comprises a chirp module  108 , coupled to the speaker  104 . The chirp module  108  is operative to generate a chirp, pulse or any other ultrasonic signal (e.g., single tones, dual-tones, multi-tones, etc.) and output the signal to the speaker  104  to be output from the device  100 . The output signal may comprise a plurality of consecutive samples at an output frequency which is above the limit of human hearing. 
     In one particular example, where the output signal is a chirp, the signal may comprise 4096 samples at a sampling frequency of 96 kHz, and a waveform ramping between first and second ultrasonic frequencies (e.g. from 25 kHz to 35 kHz). Multiple such chirps may be generated by the chirp module  108  and transmitted by the speaker  104  in succession or concurrently. 
     In one embodiment, the ultrasonic output signal may be output simultaneously with an audio signal (e.g. a voice signal while the user is on a call). 
     The interrogating ultrasonic signal is thus output from the device  100  by the speaker  104 . An ultrasonic return signal is detected by the microphone  106 , and the detected signal is passed to the processing circuitry  102 . The detected return signal is input to a high-pass filter  110 , to remove low-frequency noise from the signal. It will be appreciated that the band of interest in this context is an ultrasonic frequency band, and thus the high-pass filter  110  may filter signals below (or significantly below) the transmission frequency of the interrogating ultrasonic signal. For example, where the frequency is between 25 and 35 kHz (as with the chirp described above), the cut-off frequency of the high-pass filter  110  may be set at or around 20 kHz. It will be understood by those skilled in the art that the precise cut-off frequency of the high-pass filter  110  may be determined by a designer of the proximity-detection system in accordance with the needs of the system. 
     The filtered signal is output from the high-pass filter  110  to an averaging module  112 , which averages data samples in the signal in order to reduce noise further, and enhance the desired signal (e.g., the reflection of the interrogating ultrasonic signal). The return signal may be captured in consecutive blocks of samples, with each block comprising a plurality of data samples. In one embodiment, each block comprises the same number of data samples as the ultrasonic output signal (i.e., in the chirp example above, 4096 samples), such that each data block comprises one chirp or pulse at most. The averaging module  112  may be operative to average a plurality of consecutive blocks of samples (e.g., two blocks). 
     The block-averaged return signal is output from the averaging module  112  to an adaptive filter  114 . In one embodiment, the adaptive filter  114  is a least-mean-squares (LMS) filter, i.e. an adaptive filter that employs a LMS adaptation algorithm. The adaptive filter is further operative to receive an indication of the ultrasonic interrogating signal. In the illustrated embodiment, the indication of the ultrasonic interrogating signal is received from a high-pass filter  116 , which is coupled to the chirp module  108 . The high-pass filter  116  receives the ultrasonic output signal from the chirp module  108 , and applies a high-pass filter with the same cut-off frequency as the high-pass filter  110  which acts of the detected ultrasonic return signal. The high-pass filter  116  thus applies the same processing to the output signal as the high-pass filter  110  applies to the detected signal. 
     The adaptive filter  114  is operative to determine or estimate the transfer function (e.g., the acoustic transfer function) between the transducers emitting and receiving the ultrasonic signal. 
       FIG. 2  is a schematic illustration of an adaptive filter  200  according to embodiments of the disclosure. The adaptive filter  200  may be implemented as the adaptive filter  114  described above with respect to  FIG. 1 . The adaptive filter  200  comprises a filter  202  (which may be a finite impulse response (FIR) filter), an adaptation module  204 , and an add/subtract module  206 . 
     The acoustic transfer function from the speaker  104  to the microphone  106  provides information about how the transmitted signal was modified in order to receive the signal y(n), assuming no external noise sources are present. It therefore provides information of the properties of the acoustic path between the transmitted signal and the detected signal, which can be indicative of nearby objects. 
     As used herein, x(n) is the transmitted output signal corresponding to the nth block (e.g., a vector comprising a set of samples with length equal to the block size), y(n) is the detected return signal for the nth block (e.g. a vector comprising a set of samples with length equal to the block size), h(n) is the transfer function, and ĥ(n) and h(n) are the estimates of the detected return signal and the transfer function respectively (e.g., in the latter case, a vector comprising a set of coefficients of the FIR filter  202 ). 
     It can be seen from  FIG. 2  that the acoustic transfer function h(n) acts on the transmitted signal to result in the detected return signal y(n). It is an objective of the adaptive filter  200  to estimate the transfer function, and thus minimize an error signal e(n) representing the difference between the detected return signal and the estimated return signal (where e(n) may also comprise a vector having a length equal to the block size): 
     
       
         
           
             
               
                 
                   
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     In particular, when implemented as a LMS filter, the filter  200  may seek to minimize the mean squared error: 
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     The filter  202  may comprise a finite impulse response (FIR) filter, with a plurality of taps, operative to apply the estimated transfer function to the output signal x(n). The adaptation module  204  may seek to minimize one or more of the error, the mean squared error or any other suitable metric (such as Kullback-Leibler divergence), by updating the taps of the filter  202  as new data blocks are received. 
     Returning to  FIG. 1 , the adaptive filter  114  thus outputs one or more signals to a feature extract module  118 . The output signals may comprise one or more of: the estimated transfer function ĥ(n); the estimated ultrasonic return signal ŷ(n); and the error signal e(n). In one embodiment, each of these three signals is output from the adaptive filter  114  to the feature extract module  118 . 
     The feature extract module  118  is operative to calculate, for the nth data block, one or more statistical moments for one or more of the estimated transfer function ĥ(n); the estimated ultrasonic return signal ŷ(n); and the error signal e(n). In one embodiment, the feature extract module is operative to calculate one or more statistical moments in respect of each of those signals. Thus the amplitude distribution of the samples of a given block (where the block may have length 4096 samples, as per the example above) is analysed statistically to generate one or more statistical moments for that block. 
     The feature extract module  118  may operate on the envelopes of the respective signals (i.e. the estimated transfer function ĥ(n); the estimated ultrasonic return signal ŷ(n); and the error signal e(n)), calculating the statistical moments based on the envelopes rather than the amplitudes. 
     The statistical moments calculated for each signal may be the first moment (akin to the mean value of the nth data block, indicative of the central tendency of the data distribution); the second moment (akin to the variance of the nth data block, indicative of the width of the data distribution); the third moment (akin to the skewness of the nth data block, indicative of the asymmetry of the data distribution); or the fourth moment (akin to the kurtosis of the nth data block, indicative of the peakedness of the data distribution). In one embodiment, the feature extract module  118  may extract first, second, third and fourth statistical moments from each signal. 
     Further, the feature extract module  118  may be operative to determine other statistical measures. Conventional acoustic feature extraction schemes like Mel-frequency cepstral coefficients (MFCC), linear frequency cepstral coefficients (LFCC), linear predictive cepstral coefficients (LPCC) or similar can be used in addition or instead. 
       FIG. 3  is a schematic diagram of a feature extract module  300  according to embodiments of the disclosure. The feature extract module  300  may be implemented as the feature extract module  118  described above with respect to  FIG. 1 . 
     The feature extract module  300  comprises a first averaging module  302 , a moment calculation module  304 , and a second averaging module  306 . It will be noted that the flow of data processing in  FIG. 3  is from right to left, in accordance with the flow illustrated in  FIG. 1 . 
     In the illustrated embodiment, the feature extract module  300  receives the estimated transfer function ĥ(n), the estimated ultrasonic return signal ŷ(n), and the error signal e(n). It will be apparent from the discussion above that, in other embodiments, only one or more of these signals may be received. 
     The first averaging block  302  averages the received signals over a plurality of data blocks (e.g., four blocks), in order to reduce noise in the estimated values. The moment calculation module  304  calculated the statistical moments of the averaged values output from the block  302 . These statistical moments, or features, are computed for the nth data block. Thus, features for the estimated ultrasonic return signal ŷ( n ) and the error signal e(n) are calculated over the number of data samples in the data block (e.g., 4096 samples), while features for the estimated transfer function ĥ(n) are calculated over the number of taps of the adaptive filter  114  (e.g.  128 ). 
     As noted above, the statistical moments may be calculated on the basis of the envelopes of the respective signals. For example, the first to fourth statistical moments may be calculated as follows:
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     where Z i  is the ith sample position of a total of N samples and P Z     i    is the probability density function at A i . The probability density function may be defined as the envelopes of the signals normalized such that 
     
       
         
           
             
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     where the envelopes may be calculated using the Taeger-Kaiser method (or any other suitable method). 
     The output of the moment calculation module  304  is thus one or more calculated statistical moments. In the illustrated embodiment, first to fourth statistical moments are calculated for each of the estimated transfer function ĥ(n), the estimated ultrasonic return signal ŷ(n), and the error signal e(n), and therefore the moment calculation module  304  outputs twelve statistical moments. 
     The second averaging block  306  receives the statistical moment(s), and averages them over a plurality of data blocks (e.g.,  20  blocks) in order to smoothen variation in the statistical moments from block to block. 
     Returning to  FIG. 1 , and the calculated statistical moments are output to a classifier module  120 , which is operative to determine the presence of a nearby object based on the calculated statistical moments. The calculated statistical moments may be considered as a vector of extracted features, also called a “feature vector” herein. Thus, the feature vector for the nth data block may comprise up to twelve parameters as follows:
         {F 1,n , F 2,n , F 3,n , F 4,n }: the first four moments of the envelope distribution of the estimated transfer function h(n);   {F 5,n , F 6,n , F 7,n , F 8,n }: the first four moments of the envelope distribution of the estimated ultrasonic return signal ŷ(n);   {F 9,n , F 10,n , F 11,n , F 12,n }: the first four moments of the envelope distribution of the error signal e(n).       

     In one embodiment, the classifier module  120  is operative to classify the feature vector for each data block into one of a plurality of classifications. The plurality of classifications may comprise at least a first classification indicating the presence of a nearby object and a second classification indication the absence of a nearby object. In this way, the classifier module  120  may provide a binary output indicating the presence or absence of a nearby object. In further embodiments, the plurality of classifications may comprise additional classifications for different scenarios. For example, the plurality of classifications may comprise multiple classifications indicating the presence of an object at multiple, different distances from the device  100 . For example, the plurality of classifications may comprise classifications which take account of an angle of orientation of the nearby object with respect to the electronic device. In this case, the classifier module  120  may be able to distinguish between situations in which the electronic device  100  is held parallel to the user&#39;s ear (for example), and in which the electronic device  100  is held at a skewed angle to the user&#39;s ear. 
       FIG. 4  illustrates classification of a feature vector according to embodiments of the disclosure. The illustration is limited to classification based on two features, F 1  and F 2 , owing to the limitations of the printed medium; however, those skilled in the art will appreciate that the concept may be extended to more than two dimensions. 
     Crosses in the illustrated graph represent feature vectors for particular data blocks. The crosses fall on either side of a line (or, in general, a hyperplane)  400 , which thus serves to classify the crosses into one of two classifications  404   a ,  404   b  in the illustrated embodiment. The hyperplane may be found by a machine-learning algorithm, based on training data comprising known samples for the different classifications (e.g., the presence or absence of a nearby object, etc). In one example, the classifier module  120  implements a support vector machine (SVM). 
     Thus, the classifier module  120  may classify a data block based on the relationship between the feature vector for that data block and the hyperplane. 
     The hyperplane  400  may be configured with a level of hysteresis, to prevent rapid switching between classifications when feature vectors fall close to the hyperplane. In the illustrated embodiment, two further hyperplanes  402   a ,  402   b  are defined on either side of the hyperplane  400 . Where a feature vector falls between the two hyperplanes  402   a ,  402   b , the classifier module  120  maintains the classification from a previous data block. 
     Consider an example in which the feature vector for the nth data block falls within a first classification  404   a , indicating the absence of a nearby object. The feature vector for the (n+1)th data block falls the other side of the first hyperplane  402   a , and may even fall the other side of the hyperplane  400 . However, it is not classified within a second classification  404   b  unless it also crosses the second hyperplane  402   b . Thus the classification of consecutive data blocks should not change rapidly, even in cases where the feature vectors fall close to the hyperplane  400 . 
     Returning to  FIG. 1 , and the classification for the data block is output from the processing circuitry  102 . For example, the classification may be output to further processing circuitry of the device  100  (not illustrated), such as a general-purpose or applications processor for further handling (e.g., locking of a touchscreen, generation of an alert signal, etc.). 
     In the illustrated embodiment, the processing circuitry  102  further comprises a first ratio module  122  coupled in parallel with the high-pass filter  110  (i.e. with separate inputs coupled to the input and the output of the high-pass filter  110 ). The first ratio module  122  is operative to determine a ratio of out-of-band energy to in-band energy (or out-of-band energy to total energy), and thus determine a level of noise in the ultrasonic return signal detected by the microphone  106 . If the ratio is above a threshold for a data block (i.e., the noise is too high), the corresponding data block may not be used in the proximity detection process. For example, the data block may be discarded without further processing (e.g. in the adaptive filter  114  or the feature extract module  118 ), or the results of that processing may be ignored or discarded. In this way, poor-quality data is not relied upon to detect nearby objects. 
     The processing circuitry  102  also comprises a second ratio module  124  coupled in parallel with the filter  114 . The second ratio module  124  is operative to determine a ratio of the root mean square deviation of e(n) to the root mean square deviation of Y(n). If the ratio is above a threshold for a data block (i.e., the error is too high), the corresponding data block may not be used in the proximity detection process. For example, the data block may be discarded without further processing (e.g. in the feature extract module  118 ), or the results of that processing may be ignored or discarded. In this way, similarly to the first ratio module, poor-quality data is not relied upon to detect nearby objects. 
     Embodiments of the present disclosure thus provide apparatus, systems and methods for proximity detection using ultrasonic signals. Processing of the signals is robust to noise, and has the potential to classify detected signals into a plurality of different classifications. 
     As used herein, the term ‘module’ shall be used to at least refer to a functional unit or block of an apparatus or device. The functional unit or block may be implemented at least partly by dedicated hardware components such as custom defined circuitry and/or at least partly be implemented by one or more software processors or appropriate code running on a suitable general purpose processor or the like. A module may itself comprise other modules or functional units. 
     This software code may be stored in the device as firmware on some non-volatile memory e.g. EEPROM (to preserve program data when the battery becomes discharged or is removed for replacement). 
     It should be understood—especially by those having ordinary skill in the art with the benefit of this disclosure—that that the various operations described herein, particularly in connection with the figures, may be implemented by other circuitry or other hardware components. The order in which each operation of a given method is performed may be changed, and various elements of the systems illustrated herein may be added, reordered, combined, omitted, modified, etc. It is intended that this disclosure embrace all such modifications and changes and, accordingly, the above description should be regarded in an illustrative rather than a restrictive sense. 
     It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design many alternative embodiments without departing from the scope of the appended claims. The word “comprising” does not exclude the presence of elements or steps other than those listed in a claim, “a” or “an” does not exclude a plurality, and a single feature or other unit may fulfil the functions of several units recited in the claims. Any reference numerals or labels in the claims shall not be construed so as to limit their scope. 
     Similarly, although this disclosure makes reference to specific embodiments, certain modifications and changes can be made to those embodiments without departing from the scope and coverage of this disclosure. Moreover, any benefits, advantages, or solutions to problems that are described herein with regard to specific embodiments are not intended to be construed as a critical, required, or essential feature or element. 
     Further embodiments likewise, with the benefit of this disclosure, will be apparent to those having ordinary skill in the art, and such embodiments should be deemed as being encompassed herein.