ULTRASOUND SENSORS WITH ADAPTIVE DATA COMPRESSION

Abstract

Ultrasonic sensors, sensor controllers, and acquisition methods can employ adaptive data compression to improve key aspects of measurement signal fidelity while maintaining the main benefits of data compression. An illustrative sensor includes: a piezoelectric transducer; and a sensor controller.The sensor control includes: a receiver coupled to an ultrasonic transducer to obtain a received signal that has one or more reflections of an acoustic burst within a measurement interval associated with the acoustic burst; a correlator configured to generate an output signal with a peak for each of the one or more reflections; and a compressor configured to determine a digital representation of the output signal for communication over a bus using at least one coding parameter that varies within the measurement interval.

Description

CROSS-REFERENCE TO RELATED REGISTRATIONS

[0001] The present application claims the benefit of the preliminary US application 63 / 700,271, filed on September 27, 2024, entitled “Ultrasonic sensor with adaptive sampling and adaptive data interface” by the inventor M. Hustava. The aforementioned application is hereby incorporated by reference herein. BACKGROUND

[0002] Modern vehicles are equipped with an impressive number and variety of sensors. For example, cars are now routinely fitted with ultrasonic sensor arrays to monitor distances between the car and nearby people, animals, vehicles, or obstacles. Due to ambient noise and safety considerations, each sensor may need to provide dozens of measurements per second, with each measurement requiring the analysis of hundreds of multi-bit signal samples for each channel.When the analysis for several such sensors is performed by a so-called fusion processor, which collects the various sensor data streams to enable cross-sensor and combined sensor measurements to improve detection accuracy and reliability, it becomes quite difficult to provide the required communication bandwidth in a cost-effective manner while adhering to industry-standard limits on electromagnetic emissions.

[0003] Some proposed solutions use lossy data compression to reduce the number of bits transmitted over the communication bus. Such compression introduces a certain loss of measurement signal fidelity, which can negatively impact the accuracy of the fusion processor's obstacle detection. However, without such compression, existing vehicle networks would have to be completely redesigned, with corresponding impacts on their cost and performance. SUMMARY

[0004] Accordingly, ultrasonic sensors, sensor controllers, and acquisition methods are disclosed that employ adaptive data compression to improve key aspects of measurement signal fidelity while maintaining the main advantages of data compression. An illustrative sensor includes: a piezoelectric transducer; and a sensor controller.The sensor control includes: a receiver coupled to an ultrasonic transducer to obtain a received signal that has one or more reflections of an acoustic burst within a measurement interval associated with the acoustic burst; a correlator configured to generate an output signal with a peak for each of the one or more reflections; and a compressor configured to determine a digital representation of the output signal for communication over a bus using at least one coding parameter that varies within the measurement interval.

[0005] An illustrative procedure includes: receiving a received signal that exhibits one or more reflections of an acoustic burst within a measurement interval associated with the acoustic burst; applying a correlation filter to produce an output signal with a peak for each of the one or more reflections; and determining a digital representation of the output signal using at least one coding parameter that varies within the measurement interval.

[0006] Each of the aforementioned examples can be used individually or in combination and can include one or more of the following features in any suitable combination: 1. The at least one coding parameter is one of: a signal decimation rate, a bit resolution, a quantization threshold, and a signal type. 2. The signal type is selectable from a set that includes at least: output signal strength; and output signal value, expressed as in-phase and quadrature components or as strength and phase. 3. The at least one coding parameter varies based on the elapsed time within the measurement interval. 4. The at least one coding parameter is the signal decimation rate. 5. The signal decimation rate increases during the measurement interval. 6. The at least one coding parameter is the bit resolution. 7. The bit resolution decreases during the measurement interval. 8. The at least one coding parameter is the signal type. 9.The signal type is initially the output signal value and changes to the output signal strength during the measurement interval. 10. The compressor identifies a corresponding output signal segment for each of these peaks. 11. At least one coding parameter varies based on the content of the output signal segment. 12. The coding parameter is, by default, a higher signal decimation rate, which changes to a lower signal decimation rate if the output signal segment includes a phase shift or interpolation error above a predetermined threshold. 13. The coding parameter is the signal type, which is, by default, the output signal strength and changes to the output signal value if the output signal segment includes a phase shift or interpolation error above a predetermined threshold. 14.The coding parameter is, by default, a lower bit resolution that switches to a higher bit resolution if the output signal segment includes a marginal signal below a lowest quantization threshold. 15. The coding parameter is, by default, a default lowest quantization threshold that switches to a noise threshold if the output signal segment includes a marginal signal below the default lowest quantization threshold. 16. The measurement interval includes at least one noise monitoring period and one echo detection period. 17. The at least one coding parameter is a signal decimation rate that alternates between 1:1 during the noise monitoring period and at least 2:1 for at least part of the echo detection period. BRIEF DESCRIPTION OF THE DRAWINGS Fig. Figure 1 is a top view of an illustrative vehicle equipped with driver assistance sensors. Fig. Figure 2 is a block diagram of an illustrative driver assistance system. Fig. Figure 3 is a circuit diagram of an illustrative ultrasonic sensor. Fig. Figure 4 is a block diagram of an illustrative ultrasonic sensor. Fig. Figure 5 is a graph that compares signals that are useful for echo detection. Fig. Figure 6 is a block diagram for generating the output strength signal of Fig. 5. Fig. 7A is a graph illustrating the correlation strength for overlapping echoes. Fig. 7B is a graph of an illustrative curve interpolated from a decimated signal. Fig. 7C is a graph of an illustrative curve interpolated from an adaptively compressed data stream. Fig. Figure 8A is a graph illustrating the correlation strength with a marginal signal. Fig. 8B is a graph of an illustrative curve interpolated from a decimated signal. Fig. 8C is a graph of an illustrative curve interpolated from an adaptively compressed data stream. Fig. 8D is a graph of an illustrative curve interpolated from an alternative adaptively compressed data stream. Fig. Figure 9 is a flowchart illustrating a data collection procedure. Fig. Figure 10 is a diagram of an illustrative digital representation for a compressed output signal segment. DETAILED DESCRIPTION

[0007] The drawings and the following description do not restrict the disclosure, but on the contrary, they provide the basis for the person skilled in the art to understand all modifications, equivalents and alternatives that fall within the scope of protection of the wording of the claims.

[0008] As an illustrative application context, it shows Fig. 1. A vehicle 102 equipped with a set of ultrasonic sensors 1 to 6 on the rear and a similar set on the front. The ultrasonic sensors are transceivers, meaning that each sensor can both transmit and receive ultrasonic bursts. Emitted bursts propagate outward from the vehicle until they encounter and reflect an object or other form of acoustic impedance mismatch. The reflected bursts return to the vehicle as "echoes" of the emitted bursts. The times between the emitted bursts and received echoes indicate the distances to the reflection points. The number and configuration of sensors in the sensor array vary.

[0009] In the illustrated arrangement, sensor 1 is the left sensor, sensor 2 the left intermediate sensor, sensor 3 the left center sensor, sensor 4 the right center sensor, sensor 5 the right intermediate sensor, and sensor 6 the right sensor. On the sensor array on the front side, arrows 104 represent the acoustic bursts emitted by sensors 1, 3, 4, and 6. The bursts are intended to be emitted simultaneously, with sensors 1 and 4 transmitting in the low channel and sensors 3 and 6 in the high channel. The low channel and the high channel can represent non-overlapping frequency bands that lie within the frequency band detectable by the ultrasonic transducers.

[0010] Arrows 106 represent the reflections of the acoustic bursts returning to the sensor array. Sensor 1 can be configured to detect direct reflections of its transmitted acoustic bursts. Sensor 2 can be configured to detect indirect reflections of the acoustic bursts transmitted by Sensor 3. Sensor 3 can be configured to detect direct reflections of its transmitted acoustic bursts and indirect reflections of the acoustic bursts transmitted by Sensor 4. Sensor 4 can be configured to detect direct reflections of its acoustic bursts and indirect reflections of acoustic bursts from Sensor 3. Sensor 5 can be configured to detect indirect reflections of acoustic bursts from Sensor 4. Sensor 6 can be configured to detect direct reflections of its acoustic bursts.In total, each sensor array can monitor eight propagation paths, thus enabling the fusion processor to detect and measure distances to objects in the various detection zones, potentially using the sensors for single measurements as well as for collaborative measurements (e.g., triangulation, multi-receiver measurements).

[0011] Fig. Figure 2 is a block diagram of an illustrative automated driver assistance system. The illustrative system includes a set of interconnected ECUs (electronic control units) 201 to 203, organized in a hierarchical bus topology. Of course, other topologies, including serial, parallel, star, and ring topologies, are also suitable and are considered for use according to the principles disclosed herein. Although several ECUs are shown and described here, in some cases it may be preferable to implement the various functions with a single ECU.

[0012] A first ECU 201, also referred to herein as the fusion processor, is coupled to bus controllers for two arrays of ultrasonic sensors 204 and 206 to detect and monitor objects around the vehicle. A serial bus 205 for automotive applications, such as those provided in the DSI3, LIN, and CAN standards, can be used for the sensor arrays. To provide automated parking or other advanced functions, a second ECU 202 can be connected to a set of actuators, such as door locking actuators 210, an accelerator pedal actuator 212, a brake actuator 214, a steering actuator 216, and turn signal actuators 218. The third ECU 203 can be coupled to an interactive user interface 220 to accept user input and provide a display of the various measurements and system status.Using the interface, sensors and actuators, the ECUs 201 to 203 can provide automated parking, assisted parking, lane change assistance, obstacle and blind spot detection and other desirable features.

[0013] One possible sensor configuration will now be discussed with reference to Fig. Figure 3 describes a three-connector configuration with two power supply connections and one I / O connection. The communication and power supply technologies under consideration, including those provided in the DSI3, LIN, and CAN standards, would also be suitable and are considered for use according to the principles disclosed herein. In addition to the two power supply connections (Vbat and GND) described in the embodiment of Fig. As shown in Figure 3, each of the illustrative ultrasonic sensors is connected to the bus controller via a single input / output line (“I / O” or “EA” line). The I / O line can be biased to the supply voltage by a pull-up resistor (“disabled” state) when it is not actively driven “low” by the bus controller or by the sensor controller 302 (“enabled” state). The communication protocol is designed so that only one of the two controllers activates the I / O line at any given time.

[0014] The sensor controller 302 includes an I / O interface 303 which, when placed in a recessive mode, monitors the I / O line for activation by the bus controller and, when placed in a dominant mode, controls the state of the I / O line. The bus controller transmits a command to the sensor by activating the I / O line to transmit a command word or a frame initiation pulse. The sensor controller 302 includes a core logic 304 which operates according to firmware and parameters stored in a non-volatile memory 305 to analyze received commands and perform the corresponding operations, including the transmission of ultrasonic bursts and the reception of reflection signals. To transmit an ultrasonic burst, the core logic 304 is coupled to a transmitter 306, which drives a set of transmit terminals on the sensor controller 302 with a suitably modulated local oscillator signal.The transmitter connections can be coupled to a piezoelectric element PZ via a transformer M1. The transformer windings have an inherent inductance LP and a resistance RLP. The transformer M1 steps up the voltage from the sensor control (e.g., 12 volts) to a suitable level for driving the piezoelectric element (e.g., tens of volts). A parallel resistor RP can be provided to dampen residual vibrations of the piezoelectric element.

[0015] As used here, the term "piezoelectric transducer" includes not only the piezoelectric element itself, but also the supporting circuitry for tuning, driving, and sensing the piezoelectric element. However, the use of the term "piezoelectric transducer" does not necessarily require the presence of supporting circuitry, because a piezoelectric element can be used on its own, without such supporting elements.

[0016] Several implementations employ chirp-modulated signals, for example, a linear frequency-modulated chirp (LFM chirp). A chirp is a pulse that changes frequency during transmission. An upward chirp is a signal pulse whose frequency increases during transmission, and a downward chirp is a signal pulse whose frequency decreases during transmission. For clarity, the examples used herein assume a linear increase or decrease, but in several alternative implementations considered, the increase or decrease is non-linear. The echo of a chirp can be compressed in a correlator without introducing much or any correlation noise. This facilitates peak detection of the echo without reducing the temporal resolution. Furthermore, LFM chirps withstand Doppler frequency shift with little or no increase in correlation noise.LFM chirps can be used as transmit pulses to measure the distance to an obstacle or object located within the detection range of a sensor system.

[0017] Other implementations use amplitude-modulated (AM) signals, for example, a shaped pulse of a fixed-frequency carrier. The AM signaling mode can allow the use of shorter bursts (e.g., on the order of 200 to 300 microseconds), thereby reducing transmission time and increasing sensitivity to nearby obstacles. Other implementations can employ pulses with modulated carriers, such as those modulated by binary phase-shift keying (BPSK). For clarity, the term "burst," as used herein, refers to an AM (fixed-frequency), BPSK (modulated), or chirp (wobbled) pulse, which may be one of a series of bursts generated by driving a piezoelectric element or other ultrasonic transducer.Chirp-modulated pulses can have a longer duration than a typical AM pulse, for example, more than 1 millisecond, as in the 2 to 3 millisecond range. It should be noted that burst lengths can be varied, with shorter bursts used to facilitate the detection of nearby obstacles and longer bursts used to increase the burst energy (and echo energy) for more distant obstacles. Burst lengths for detecting nearby obstacles may be half or perhaps a quarter of the burst lengths used for more distant obstacles. The sensor can be switched between modes for different detection distances.

[0018] Although systematically varying a characteristic frequency (e.g., the start frequency, or correspondingly the center or end frequency) of the chirp-modulated pulses in a series is considered particularly useful, this frequency variation can also be applied to the carrier frequency of the AM pulses in a series. The frequency variation can be expressed for each pulse as a frequency shift from a nominal characteristic frequency (e.g., a nominal start frequency or a nominal carrier frequency).

[0019] To transmit an acoustic burst, the core logic configures the transmitter to drive the output pins for the ultrasonic transducer, which is coupled to a piezoelectric element PZ. A transformer M1 and / or a resonant tuning network may be provided for voltage gain and control of the transducer's resonant frequency. The transmitter can accept a carrier frequency signal from the oscillator with a nominal frequency of, for example, 50 kHz. The transmitter can use the carrier frequency signal to generate a series of AM (amplitude modulated) or chirp pulses, each pulse corresponding to an acoustic burst. An example of a chirp pulse could be one whose frequency is swept upwards from 7 kHz below the carrier frequency to the carrier frequency, an upward chirp of the lower channel.Another example is an upward chirp of the high channel, where the chirp frequency is swept from the carrier frequency to 7 kHz above the carrier frequency. Alternatively, downward chirps can be used, where the frequency is swept linearly downwards instead of upwards.

[0020] To receive an acoustic signal, the core logic configures the ADC 310 to digitize the electrical received signal from the piezoelectric element PZ. The digitized signal can be provided directly to the DSP for real-time processing or buffered in memory for later processing by the DSP. To reduce the EA bandwidth requirements, the DSP can implement data compression to reduce the number of bits needed to represent the received signal data or the strength of the baseband signals. To further reduce bandwidth requirements, the DSP can optionally perform on-chip processing for peak detection and distance estimation.Various suitable processing techniques for detecting reflections of the acoustic burst are known in the field, including the jointly owned US patent publication 2024 / 0069192 “Motion-compensated distance sensing with concurrent up-chirp downchirp waveforms”, which is hereby incorporated by reference.

[0021] Since the received electrical signals are typically in the millivolt or microvolt range, a receive amplifier 308 can be included to buffer and amplify the signal from the receive terminals. Analog or digital mixers can be included to down-convert the received signals to the baseband for further filtering and processing by the DSP. In one implementation, the mixer is a digital in-phase / quadrature mixer (I / Q mixer) that provides zero intermediate frequency (ZIF) IQ data as its output. (Although the term "ZIF" is used here, in practice the down-converted signal may be a low intermediate frequency or "baseband-near" signal.) The DSP 304 employs programmable procedures to monitor the piezoelectric transducer during the transmission of a burst and to detect any echoes and measure their parameters, such as time-of-flight (ToF), duration, and peak amplitude.Such methods can employ threshold comparisons, minimum intervals, peak detection, zero-crossing detection and counting, noise level determination, and other customizable techniques aimed at improving reliability and accuracy. Alternatively, the DSP can perform initial processing steps and transmit the processed signal to the fusion processor for further analysis.

[0022] Fig. Figure 4 is a block diagram illustrating a signal processing path, which can be embodied as firmware modules, i.e., instructions retrieved from memory for execution by the DSP. Alternatively, the modules can be implemented as application-specific integrated circuits, a field-programmable gate array (FPGA), or another form of programmable logic device (PLD).

[0023] A transmit control module 402 combines a waveform template from a waveform control module 404 with a carrier signal from a digital carrier generation module 406 to form a digital burst signal. In at least some implementations under consideration, the digital burst signal is a linear frequency-modulated chirp lasting approximately 2.5 milliseconds, during which the frequency is swept upward from 7 kHz below the carrier frequency to 1 kHz below the carrier frequency (lower sideband up chirp) or from 1 kHz above the carrier frequency to 7 kHz above the carrier frequency (upper sideband up chirp). One or both of the up chirps can be replaced by down chirps, in which the frequency is swept from the higher value to the lower value.The exact duration and frequency range can be individually adjusted, and some implementations under consideration divide the useful transducer frequency range into more than two channels, each of which can be selected for insertion by the transmit control module 402.

[0024] A driver 408 converts the digital burst signal into a drive signal for the piezoelectric transducer 410. The piezoelectric transducer 410 oscillates in response to the analog burst signal, generating an acoustic burst signal that propagates outward from the transducer. Reflections of the acoustic burst cause the piezoelectric transducer to oscillate, inducing a detectable analog receive signal. A receiver 412 amplifies and digitizes the analog receive signal, generating a digital receive signal. The sampling rate is at least twice the highest expected frequency component in the signal, and preferably higher, e.g., an integer multiple such as 8, which enables efficient digital down-conversion.Based on the digital received signal, a gain control 414 adjusts the gain of the driver 408 and / or the receiver 412 to optimize performance while preventing saturation of the receiver's analog-to-digital converter (ADC). A diagnostic module 416, alone or in combination with a reverberation monitor 418, analyzes the digital received signal to detect and diagnose any transducer fault conditions. Some fault conditions may be indicated, for example, by reverberation periods that are too short (which may be due to a disconnected or defective transducer, suppressed vibration, or the like), while others may be indicated by a reverberation period that is too long (faulty mounting, insufficient damping resistance, or the like).The diagnostic module 416 can detect and classify several of these transducer fault conditions, storing the corresponding fault codes in internal registers or non-volatile memory 305. The reverberation monitor 418 detects and signals the end of the transducer's reverberation period. A broadband noise detector 419 monitors noise levels and operates outside the measurement period to detect possible disturbances in the operation of the sensor assembly.

[0025] A mixer 420 combines the digital receive signal with the digital carrier signal to downconvert the digital receive signal to a zero intermediate frequency (ZIF) representation containing in-phase and quadrature signal components. A low-pass filter 422 filters the ZIF signal components to eliminate modulation byproducts that could otherwise cause aliasing. Optionally, a strength module 424 combines the in-phase and quadrature signal components to obtain a strength signal representing the strength of the received signal. A decimation or down-sampling module 426 reduces the sampling rate of the low-pass filtered ZIF signal components to decrease the computational load in downstream components. In at least some implementations under consideration, the down-sampling module 426 reduces the rate to approximately 20 kHz.

[0026] A Correlator 428 filters the downsampled ZIF signal components using correlation filters that exhibit impulse responses matching waveform templates for each channel, such as upward chirps in the upper and lower sidebands. The correlation filters generate channel correlation signals in which the burst echoes are represented as peaks. In at least some implementations, the correlation filter output signals are converted from in-phase and quadrature component representations to amplitude and phase representations to facilitate subsequent processing. The correlation filters can employ modified waveform templates (e.g., an upward chirp combined with a Gaussian window function) to narrow the peaks in the correlation signals.In some cases, correlation filters can vary their impulse responses depending on the time elapsed since the end of the reverberation period, thereby improving detection performance at short distances while still suppressing channel crosstalk at medium or long distances. For example, the applied window function can be varied to reduce the bandwidth of the chirp waveform as a function of elapsed time. The larger bandwidth allows partial echoes to pass through at short distances. Partial echoes are those whose initial segments arrive before the measurement start time, which corresponds to the end of a transducer reverberation. This impulse response variation enables the correlation filters to provide peaks for these partial echoes in the correlation signal.Over time, the partial echoes gradually become full echoes, and the bandwidth of each correlation filter is narrowed to provide better separation between the frequency channels. Interpolation can be used to determine the filter coefficients between the values ​​initially used for correlation with partial echoes and those used for correlation with full echoes.

[0027] It should be noted, however, that any correlation signal peaks associated with partial echoes are attenuated relative to peaks representing full echoes, not only because the partial echoes are shorter, but also because their frequency content varies relative to the full echoes. The Chirp Attenuation Control Module 430 can scale the channel correlation signals based on the time elapsed since the measurement start time, applying channel-dependent gain to compensate for partial echoes and the associated frequency dependence of the transducer response, thereby significantly improving near-field detection performance. The Module 430 can also apply time-dependent gain to compensate for echo attenuation due to propagation to and from obstacles.

[0028] As described below, a noise detection / suppression module 432 applies a nonlinear function to the attenuation-compensated channel correlation signals to suppress noise and amplify the peaks representing echoes. In some implementations under consideration, the module's output signals 433 are fed to a compressor 434 to reduce the number of bits required to transmit the correlation signals to the fusion processor for further processing. Other implementations under consideration include an echo detection module that detects the peaks in the channel correlation signals, determines the peak strength, and calculates the time of flight (or equivalently, determines the distance) associated with each peak. In particular, the time-of-flight calculation takes into account the delay caused by the correlation filtering process.The echo detection module can store the strength and flight time information for the echoes detected in each channel in memory 305. The sensor interface module 436 transmits the required information to the bus controller, which then forwards it to the ECU.

[0029] In practice, the received and digitized response includes not only any reflections of the distance signal emitted by the acoustic transducer, but also noise. Such noise can have various causes. Fig. Figure 5 shows a thin solid line representing an illustrative correlation strength curve (“MAGN”). Also shown is a dash-dotted line representing an average noise level (“NOISE”) derived from the non-peak regions of the correlation strength curve, as explained below. A dashed line represents an illustrative threshold curve (“THR”), as described in the context of Fig. This was explained in section 6. Finally, a thick solid line represents one of the output signals 433 (“OUT”) of the noise detection / suppression module 432. In the peak regions (where the strength curve exceeds the threshold curve), this output signal follows the strength curve. Outside these peak regions, this output signal is zero.

[0030] Fig. Section 6 provides additional details for an illustrative implementation of the noise detection / suppression module 432. The correlator 428 convolves the downconverted signal with the waveform for the selected channel. The attenuation control 430 applies a time-dependent gain to compensate for the expected attenuation during the measurement period. A strength element 604 combines the in-phase and quadrature components of the signal to determine the signal strength. An arctangent module 606 can combine the in-phase and quadrature components to determine the phase of the correlation signal.

[0031] A CFAR element 608 processes the correlation strength signal to provide a CFAR threshold signal (CT signal) according to a constant false alarm rate (CFAR) algorithm. Various CFAR algorithms are described in the literature, including the jointly held U.S. patent application 16 / 530,654, filed on August 2, 2019, entitled "Ultrasonic Sensor Having Edge-Based Echo Detection," by inventors M. Hustava and J. Kantor (referring to U.S. patent 5793326 ("Hofele")). Suitable CFAR algorithm variants include, for example, CASH-CFAR (Cell Averaging Statistic Hofele CFAR) and Ordered Statistical CFAR (OS-CFAR).In short, CFAR algorithms perform statistical processing within a sliding window to determine a threshold representing background noise, excluding any strong spikes that would likely represent a valid echo from the threshold determination. CFAR variants differ in the precise nature of the statistical processing, such as whether a min-max sum, ranking, or averaging is used in combination with appropriate weighting or scaling to allow sufficient differentiation between valid echoes and background noise. Various algorithm parameters (e.g., block size, window size) can be adjusted to optimize the threshold's adaptability.A CFAR offset value can be stored in memory and added to the algorithm-based threshold to provide further tuning of the CT signal.

[0032] The CFAR element 608 can process a symmetrical or asymmetrical window around a "current" sample of the correlation strength signal. A delay element 610 can accordingly be used to provide a suitable time offset between the "early" correlation strength signal fed to the CFAR element 608 and the "current" correlation strength signal 612 fed to the other elements of the processing circuit. A comparator 614 compares the current correlation strength signal 612 with the CFAR threshold signal CT and activates a selection signal for a multiplexer 616 to indicate when the correlation strength signal is above the threshold (a "peak range"), and deactivates the selection signal to indicate when the correlation strength signal is below the threshold (a "non-peak range").

[0033] A noise averaging block 620 receives the selection signal at an inverted activation input ( / EN input), also known as a deactivation input, which disables the operation of the noise averaging block 620 while the comparator output is enabled. In this way, the averaging block 620 processes the non-peak regions of the signal and ignores the peak regions of the correlation strength signal when determining the average noise level (“NOISE” in ). Fig. 5) The noise averaging block 620 calculates an average within a given section or moving window of the non-peak correlation strength signal. The averaging block can, for example, be configured to sum signals of the non-peak regions within the signal section and divide by the duration of the non-peak regions of the signal section. While the present application refers to an average, it is understood that the resulting average can be any type of average known to a person skilled in the art, including the median, the arithmetic mean, the mode, the geometric mean, and / or a weighted average, as well as the exponential moving average.

[0034] For each peak region in the current correlation strength signal, a peak measuring element 618 determines the signal strength by identifying the peak value (local maximum). A signal-to-noise ratio (SNR) block 622 accepts each peak value from the peak measuring element 618 and uses a corresponding noise average from the noise averaging block to calculate an SNR value for that peak. It should be noted here that block 622 does not rely on a definition formula such as SNR = 20 log 10(Signal / noise) is limited. In fact, given the hardware complexity typically associated with logarithmic calculations, it may be preferable to use a simple ratio or other calculation that is monotonically related to the defining formula in the range of interest. The SNR Block 622 generates a peak detection threshold that is inversely proportional to the SNR value. The peak detection threshold may be low in areas with relatively high SNR and may be relatively high in areas with low SNR. The SNR Block 622 can use a lookup table to convert the SNR value into a peak detection threshold.

[0035] A summing element 624 adds the peak detection threshold to the average noise level to calculate the threshold curve THR of Fig. 5. A comparator 626 compares the current correlation strength signal 612 with the threshold THR and outputs a selection signal only if the signal strength exceeds the threshold. In response to the selection signal, the multiplexer 628 selects the current correlation strength signal 612 when the selection signal is enabled and selects a zero signal to suppress the output signal OUT when the selection signal is disabled. Another multiplexer 630 can also respond to the selection signal by outputting the phase of the correlation signal when the selection signal is enabled and suppressing the phase output when the selection signal is disabled.

[0036] In light of the above, we now turn to the operation of the 434 compressor. In certain implementations under consideration, the compressor performs downsampling of the correlation strength signal, for example, by dropping every second sample before scaling and quantizing to a lower bit resolution. The compressor can treat the correlation strength signal as a series of signal segments, each containing a predetermined number of samples. A scaling factor is determined for each segment, which (unless it reaches its maximum) normalizes the peak value in that segment to one. For example, the scaling factor might have a five-bit representation. In some cases, the scaling factor uses a non-linear scale. The compressor then quantizes the scaled samples with a reduced bit resolution.As a concrete example, the compressor can use a three-bit resolution for each of the samples in the segment.

[0037] Using the scaling factor and quantized samples for each segment, the fusion processor can rescale and interpolate the data to reconstruct the correlation strength signal with sufficient accuracy to detect and monitor reflectors within the measurement zone of ultrasonic sensor arrays. However, with increasingly sophisticated processing, system developers want to be able to better distinguish closely spaced peaks and / or detect edge signal energies around a main peak, potentially providing additional information useful for differentiating between various types of objects, such as a person from a curb. The Compressor 434 can adjust the coding parameters associated with selected segments of the correlation strength signal accordingly.

[0038] Fig. Figure 7A shows an illustrative output signal before the compression process. (For simplicity, it is assumed that the samples in the illustrated output signal segment have been scaled so that the peak value is one.) Correlation strength signal samples are represented by open circles. Samples outside the peak region (i.e., below the noise threshold 704) have been set to zero. Within the peak region, a sample 702 indicates a dip that can distinguish between two closely spaced peaks. The dip is usually associated with a significant shift in the signal phase but can also be recognized as a significant interpolation error. Fig. In Figure 7B, the black diamonds represent, for example, the downsampled and quantized samples. The solid line is a linear interpolation between these compressed samples, and it should be noted that sample 702 deviates from the line by more than two quantization steps. (Linear interpolation is used for simplicity, but any suitable interpolation method can be used, including, for example, cosine interpolation, cubic interpolation, and Hermite interpolation.) The threshold for detecting excessive interpolation error is preferably at least half a quantization step, but can be adjusted to optimize system performance.

[0039] Upon detecting an interpolation error, or alternatively upon detecting a phase shift that exceeds a predetermined threshold (e.g., 30 degrees), the compressor 434 can reduce the downsampling rate, e.g., from 2:1 to 1:1. Fig. Figure 7C shows the compressed representation with the reduced sampling rate. Note that the maximum interpolation error has decreased to approximately half a quantization step. Alternatively, the compressor can maintain the downsampling rate but change the signal type by sending the strength and phase of the correlation signal (or in-phase and quadrature phase components) instead of just the correlation strength signal. The additional information is expected to substantially eliminate or at least reduce the interpolation error.

[0040] Fig. Figure 8A shows another illustrative output signal before the compression process. Note the presence of a sample 802, which represents an edge signal, i.e., a signal that is above the noise threshold 804 but has a small amplitude after peak normalization of its output signal segment. If the sample has a strength below the lowest quantization threshold, which is usually half a quantization step, the quantization process suppresses the edge signal, as shown in Fig. Figure 8B shows that if the compressor 434 detects such a circumstance, it can adjust the bit resolution used to quantize the samples within the given signal segment, e.g. by increasing the bit resolution from 3 bits to 4 bits. Fig. Figure 8C shows an example of compressed sampling using increased bit resolution.

[0041] Alternatively, the compressor 434 can maintain the bit resolution and instead adjust the lowest quantization threshold for the signal segment, e.g. by lowering the threshold from half a quantization step to the noise threshold, so that any sample with a strength above the noise threshold is quantized to a non-zero value. Fig. Figure 8D shows an example of the compressed samples obtained with this approach.

[0042] When such an adjustment of the coding parameters is performed, the compressor 434 can provide a field to indicate the current value of the given parameter for each segment. However, the described cases are expected to tend to occur more frequently in the short measurement range, and in longer measurement ranges, attenuation is expected to make such effects rare or at least undetectable. Accordingly, the compressor can be configured to adjust the coding parameters depending on the elapsed time within the measurement intervals after each acoustic burst. For example, the compressor can use one set of parameters (signal type, downsampling rate, bit resolution, lowest quantization threshold) for the first 12 milliseconds of the measurement interval and then use a second set of parameters for the remainder of the measurement interval.

[0043] The signal type can progress from correlation strength and phase to correlation strength only, and the bit resolution can decrease from 4 bits to 3 bits. As another example, the downsampling rate can increase from 1:1 to 2:1 if the lowest quantization threshold is adjusted from a quarter of a quantization step to half a quantization step, while keeping the remaining parameters constant. Alternatively, the downsampling rate and bit resolution can be adjusted while maintaining the other parameters. Of course, individual parameters can also be set independently of the others.

[0044] In at least some of the implementations under consideration, a portion of the measurement interval is dedicated to noise monitoring. For example, during the last millisecond of the measurement period immediately preceding a subsequent acoustic burst, the correlation strength signal can be analyzed by the fusion processor to estimate an ambient noise level. The noise detection / suppression module can be disabled during this period, allowing the 434 compressor to process the raw correlation strength data. The coding parameters used by the compressor can be selected to minimize additional distortion; for example, the downsampling rate can be minimized (set to 1:1), and the bit resolution can be maximized (e.g., by using a higher bit depth).(set to 4 bits), and / or the signal type can be set so that the complex-valued correlation signal (in-phase and quadrature components, or strength and phase) is transmitted instead of just the strength.

[0045] Fig. Figure 9 is a flowchart of an illustrative acquisition procedure that uses adaptive compression. The various operations can be implemented by the sensor controller described above. In block 902, the sensor controller receives a receive signal from an ultrasonic transducer. The receive operation can immediately follow the transmission of an acoustic burst by the same transducer or by another transducer in the sensor array. The receive operation can include digitization, downmixing, and filtering to obtain a digital baseband receive signal. To reduce the processing load in subsequent components in the chain, downsampling can optionally be performed. In block 904, the controller filters the digital receive signal with one or more correlation filters to obtain a correlation signal for each channel. The output of the correlation filter can be complex-valued, i.e.,It can exhibit in-phase and quadrature phase components, and accordingly, the control system in block 906 can calculate the correlation strength signal. Optionally, a correlation phase signal can also be determined in this block.

[0046] In block 908, the controller analyzes the correlation strength signal to distinguish peak regions from non-peak regions, suppressing the noise in the latter. In block 910, the controller divides the correlation strength signal into segments to prepare for compression. The standard coding parameters are set to their initial values ​​in block 912. The controller can then process the segments sequentially, passing through blocks 914 to 934.

[0047] In block 914, the controller determines whether the current segment is associated with a time-based change in the coding parameters, and if so, it adjusts the parameter values ​​to the new default values. For example, the default signal type might initially be the output signal value (expressible as strength and phase) and might change to just the output signal strength after 13 milliseconds. As another example, the default bit resolution might initially be four bits and might change to a three-bit resolution after 9 milliseconds. In block 918, the controller determines whether the current segment corresponds to a noise monitoring interval. If so, in block 920, the controller adjusts the signal type and / or decimation rate before proceeding to block 930.Otherwise, in block 922, the controller determines whether the current segment has a phase shift that exceeds a predetermined threshold. Alternatively, the controller determines whether the current segment has an excessive interpolation error with the default coding parameter values. If so, in block 924, the controller changes or varies the coding parameter values ​​accordingly before proceeding to block 930. Otherwise, in block 926, the controller determines whether the current segment contains an edge signal that would be suppressed with the default coding parameters. If so, in block 928, the controller adjusts the bit resolution and / or the lowest quantization threshold.

[0048] In block 930, the controller uses the selected coding parameter values ​​to create a compressed digital representation of the current segment, which is then transmitted to the fusion processor via the bus controller. In block 932, the controller determines whether the measurement period contains further segments, and if so, it resets the coding parameters to their default values ​​in block 934 before returning to block 914 to process the next segment. After the measurement period has been fully processed, the controller returns to block 902.

[0049] Although the processes shown and described above are treated as sequential for explanatory purposes, in practice the process can be carried out by several simultaneously operating components of an integrated circuit (and perhaps even speculatively) to allow operations to be performed in parallel or out of sequence. These and numerous other modifications, equivalents, and alternatives will be apparent to the person skilled in the art once the foregoing disclosure is fully understood. The following claims are to be interpreted as including, where appropriate, all such modifications, equivalents, and alternatives.

[0050] Fig.Figure 10 shows an example of a digital representation of an output signal segment that can be generated by the 434 compressor. The illustrative digital representation is a word with, for example, 32 bits. An encoding parameter 1002 can be specified by a first bit, which indicates, for example, whether a first or second value is used for the signal type, decimation rate, bit resolution, or lowest quantization threshold. If the change in the encoding parameter value is automatic based solely on elapsed time, this field can be omitted. The illustrated representation includes a field for a scale value 1004, which specifies the scaling factor used to normalize the peak value to one. This field can, for example, have five bits. The illustrated representation also includes fields for a predetermined number of output signal samples 1006. These fields can, for example, specify the number of samples.each be three or four bits long.

[0051] While dependent claims in certain countries are formulated to refer to a single claim due to claim formulation rules, it is observed that any combination of a dependent claim with any of its preceding claims is intended by the present inventors and is deemed to be included in the complete disclosure of the present application. Furthermore, it is understood that the dependent claims stated for one claim category also apply to another claim category, but have been omitted solely for the purpose of limiting the total number of claims and the resulting claim fees.

[0052] Although the context of the driver assistance system is used herein as an example, the concepts of this disclosure can be applied to any type of obstacle monitoring or distance measurement system and may be particularly suitable for those where reliability and rapid response are paramount. QUOTES INCLUDED IN THE DESCRIPTION

[0000] This list of documents cited by the applicant was automatically generated and is included solely for the reader's convenience. The list is not part of the German patent or utility model application. The DPMA accepts no liability for any errors or omissions. Cited patent literature

[0000] US 63 / 700,271

[0001] US 2024 / 0069192

[0020] US 16 / 530,654

[0031] US 5793326

[0031]

Claims

[1] Sensor that includes: a piezoelectric transducer; and a sensor control system that includes: a receiver coupled to an ultrasonic transducer to obtain a received signal that has one or more reflections of an acoustic burst within a measurement interval associated with the acoustic burst; a correlator configured to produce an output signal with a peak for each of the one or more reflections; and a compressor configured to determine a digital representation of the output signal for communication over a bus using at least one coding parameter that varies within the measurement interval. [2] Sensor according to claim 1, wherein the at least one coding parameter is one of: a signal decimation rate, a bit resolution, a quantization threshold and a signal type, wherein the signal type is selectable from a set which includes at least: Output signal strength; and Output signal value, expressed as in-phase and quadrature components or as amplitude and phase. [3] Sensor according to claim 2, wherein the at least one coding parameter varies based on the elapsed time within the measurement interval. [4] Sensor according to claim 3, wherein the at least one coding parameter is the signal decimation rate and wherein the signal decimation rate increases during the measurement interval. [5] Sensor according to claim 3, wherein the at least one coding parameter is the bit resolution and wherein the bit resolution decreases during the measurement interval. [6] Sensor according to claim 3, wherein the at least one coding parameter is the signal type and wherein the signal type is initially the output signal value and changes to the output signal strength during the measurement interval. [7] Sensor according to claim 2, wherein the compressor identifies an output signal segment for each of the peaks and wherein the at least one coding parameter varies based on the content of the output signal segment. [8] Sensor according to claim 7, wherein the at least one coding parameter is a higher signal decimation rate by default and wherein the at least one coding parameter switches to a lower signal decimation rate when the output signal segment includes a phase shift or interpolation error above a predetermined threshold. [9] Sensor according to claim 7, wherein the at least one coding parameter is the signal type, a standard signal type being the output signal strength, and wherein the at least one coding parameter switches to the output signal value when the output signal segment includes a phase shift or interpolation error above a predetermined threshold. [10] Sensor according to claim 7, wherein the at least one coding parameter is a lower bit resolution by default and wherein the at least one coding parameter switches to a higher bit resolution when the output signal segment includes an edge signal below a lowest quantization threshold. [11] Sensor according to claim 7, wherein the at least one coding parameter is by default a default lowest quantization threshold and wherein the at least one coding parameter switches to a noise threshold when the output signal segment includes an edge signal below the default lowest quantization threshold. [12] Sensor according to claim 1, wherein the measurement interval includes at least one noise monitoring period and one echo detection period, wherein the at least one coding parameter is a signal decimation rate that alternates between 1:1 during the noise monitoring period and at least 2:1 for at least part of the echo detection period. [13] Procedure that includes: Receiving a received signal that exhibits one or more reflections of an acoustic burst within a measurement interval associated with the acoustic burst; Applying a correlation filter to generate an output signal with a peak for each of the one or more reflections; and Determining a digital representation of the output signal using at least one coding parameter that varies within the measurement interval. [14] Method according to claim 13, wherein the at least one coding parameter is one of: a signal decimation rate, a bit resolution, a quantization threshold and a signal type, wherein the signal type is selectable from a set which includes at least: Output signal strength; and Output signal value, expressed as in-phase and quadrature components or as a strength and phase. [15] Method according to claim 14, wherein the at least one coding parameter varies based on the elapsed time within the measurement interval. [16] Method according to claim 15, wherein the at least one coding parameter is the signal decimation rate and wherein the signal decimation rate increases during the measurement interval. [17] Method according to claim 15, wherein the at least one coding parameter is the bit resolution and wherein the bit resolution decreases during the measurement interval. [18] Method according to claim 15, wherein the at least one coding parameter is the signal type and wherein the signal type is initially the output signal value and changes to the output signal strength during the measurement interval. [19] Method according to claim 14, wherein the determining includes identifying an output signal segment for each of the peaks and wherein the at least one coding parameter varies based on the content of the output signal segment. [20] Method according to claim 19, wherein the at least one coding parameter is a higher signal decimation rate by default and wherein the at least one coding parameter switches to a lower signal decimation rate when the output signal segment includes a phase shift or interpolation error above a predetermined threshold. [21] Method according to claim 19, wherein the at least one coding parameter is the signal type, a standard signal type being the output signal strength, and wherein the at least one coding parameter switches to the output signal value when the output signal segment includes a phase shift or interpolation error above a predetermined threshold. [22] Method according to claim 19, wherein the at least one coding parameter is a lower bit resolution by default and wherein the at least one coding parameter switches to a higher bit resolution when the output signal segment includes an edge signal below a lowest quantization threshold. [23] Method according to claim 19, wherein the at least one coding parameter is by default a default lowest quantization threshold and wherein the at least one coding parameter switches to a noise threshold when the output signal segment includes a marginal signal below the default lowest quantization threshold. [24] Method according to claim 13, wherein the measurement interval includes at least one noise monitoring period and one echo detection period, wherein the at least one coding parameter is a signal decimation rate that alternates between 1:1 during the noise monitoring period and at least 2:1 for at least part of the echo detection period. [25] Sensor control, which includes: a receiver coupled to an ultrasonic transducer to obtain a received signal that has one or more reflections of an acoustic burst within a measurement interval associated with the acoustic burst; a correlator configured to produce an output signal with a peak for each of the one or more reflections; and a compressor configured to determine a digital representation of the output signal for communication over a bus using at least one coding parameter that varies within the measurement interval. [26] Sensor control according to claim 25, wherein the at least one coding parameter is a signal decimation rate which varies based on the elapsed time within the measurement interval. [27] Sensor control according to claim 25, wherein the at least one coding parameter is a bit resolution which varies based on the elapsed time within the measurement interval. [28] Sensor control according to claim 25, wherein the at least one coding parameter is a signal type that is initially an output signal value expressed as in-phase and quadrature components or as strength and phase, and wherein the signal type changes to an output signal strength based on the elapsed time within the measurement interval. [29] Sensor control according to claim 25, wherein the compressor identifies an output signal segment for each of the peaks and wherein the at least one coding parameter varies based on the content of the output signal segment. [30] Sensor control according to claim 29, wherein the at least one coding parameter is a higher signal decimation rate by default and wherein the at least one coding parameter switches to a lower signal decimation rate when the output signal segment includes a phase shift or interpolation error above a predetermined threshold. [31] Sensor control according to claim 29, wherein the at least one coding parameter is a signal type which by default is an output signal strength, and wherein the at least one coding parameter switches to an output signal value expressed as in-phase and quadrature components or as strength and phase when the output signal segment includes a phase shift or interpolation error above a predetermined threshold. [32] Sensor control according to claim 29, wherein the at least one coding parameter is a lower bit resolution by default and wherein the at least one coding parameter switches to a higher bit resolution when the output signal segment includes an edge signal below a lowest quantization threshold. [33] Sensor control according to claim 29, wherein the at least one coding parameter is by default a standard lowest quantization threshold and wherein the at least one coding parameter switches to a noise threshold when the output signal segment includes an edge signal below the standard lowest quantization threshold. [34] Sensor control according to claim 25, wherein the measurement interval includes at least one noise monitoring period and one echo detection period, wherein the at least one coding parameter is a signal decimation rate that alternates between 1:1 during the noise monitoring period and at least 2:1 for at least part of the echo detection period.

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