WO2023047174A1 - Detection and removal of pulse-like interference in an amcw lidar sensor - Google Patents

Detection and removal of pulse-like interference in an amcw lidar sensor Download PDF

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Publication number
WO2023047174A1
WO2023047174A1 PCT/IB2021/058844 IB2021058844W WO2023047174A1 WO 2023047174 A1 WO2023047174 A1 WO 2023047174A1 IB 2021058844 W IB2021058844 W IB 2021058844W WO 2023047174 A1 WO2023047174 A1 WO 2023047174A1
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WIPO (PCT)
Prior art keywords
amcw
signal
interfering
pulse
previous
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PCT/IB2021/058844
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French (fr)
Inventor
Miguel Vidal Drummond
Daniel António MACEDO BASTOS
Abel LORENCES RIESGO
Paulo Miguel NEPOMUCENO PEREIRA MONTEIRO
Dionísio ALVES PEREIRA
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Bosch Car Multimedia Portugal, S.A.
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Publication of WO2023047174A1 publication Critical patent/WO2023047174A1/en

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
    • G01S7/491—Details of non-pulse systems
    • G01S7/493—Extracting wanted echo signals
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/02—Systems using the reflection of electromagnetic waves other than radio waves
    • G01S17/06—Systems determining position data of a target
    • G01S17/08—Systems determining position data of a target for measuring distance only
    • G01S17/32—Systems determining position data of a target for measuring distance only using transmission of continuous waves, whether amplitude-, frequency-, or phase-modulated, or unmodulated
    • G01S17/36—Systems determining position data of a target for measuring distance only using transmission of continuous waves, whether amplitude-, frequency-, or phase-modulated, or unmodulated with phase comparison between the received signal and the contemporaneously transmitted signal
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications
    • G01S17/93—Lidar systems specially adapted for specific applications for anti-collision purposes
    • G01S17/931—Lidar systems specially adapted for specific applications for anti-collision purposes of land vehicles

Definitions

  • the present application describes an innovative technology for detecting and removing interfering pulses from an Amplitude Modulated Continuous Wave (AMCW) signal received by a AMCW Light Detection and Ranging (LIDAR) sensor .
  • AMCW Amplitude Modulated Continuous Wave
  • LIDAR Light Detection and Ranging
  • an Amplitude Modulated Continuous Wave (AMCW ) of a Light Detection and Ranging sensor LIDAR
  • AMCW Amplitude Modulated Continuous Wave
  • LIDAR Light Detection and Ranging sensor
  • an AMCW LIDAR sensor is af fected by interfering pulses .
  • the range estimation provided by an AMCW LIDAR sensor can be disrupted by in-band interfering signals .
  • An in-band AMCW interfering signal is an obvious example of such an interfering signal ; however, so is a short pulse interfering signal , as such a pulse has a wide spectral occupancy .
  • the present invention describes a method for detecting and removing interfering pulses from an AMCW LiDAR sensor signal comprising the steps of : detecting the existence of at least one interfering pulse in the AMCW signal received by a LIDAR sensor through the estimation of the peak-to-average power ratio of the AMCW signal .
  • the method comprises the steps of : determining a threshold value for the AMCW signal ; removing the at least one interfering pulse above the determined threshold value of the AMCW signal .
  • determining the threshold value for the AMCW signal comprises determining the mean value of the AMCW signal considering the existence of at least one interfering pulse .
  • the AMCW signal comprises at least one interfering pulse i f the estimation of the peak-to-average power ratio is greater than l OdB .
  • the at least one interfering pulse comprises pulses with equal amplitudes .
  • the at least one interfering pulse comprises pulses with di f ferent amplitudes .
  • removing the at least one interfering pulse above the determined threshold value of the AMCW signal is performed repeatedly until the estimation of the peak-to-average power ratio of the AMCW signal is lower than a given threshold value .
  • determining a threshold value for the AMCW signal comprises the steps of : a . Calculating a histogram of sample amplitudes of the received AMCW signal ; b . Searching for a first gap in the histogram, which separates samples from the AMCW s ignal from samples of the weakest interfering pulse ; c . Defining the threshold value as the middle point within the limits of the first gap .
  • the method comprises a filtering step for reconstructing the obtained signal after removing the interfering pulses .
  • the AMCW LiDAR sensor produces a distance estimation which is discarded i f at least one interfering pulse is detected .
  • the present application describes a technology for detecting and removing interfering pulses from an AMCW signal received by an AMCW LIDAR sensor .
  • AMCW signal can have any given number of tones , and can be continuous or not , i . e . , a burst .
  • the presently disclosed technology is particularly useful in the autonomous vehicles field .
  • the main and obvious advantage of the disclosed method is related with the fact that an AMCW LIDAR employing such technology becomes capable of detecting and even removing interfering pulses from the received signals , thus becoming impervious to pulsed interference .
  • Another important advantage is that the disclosed technology is complementary to interference avoidance known methods , as well as to AMCW interference detection methods , further increasing the robustness of an AMCW LIDAR to interference in general .
  • the used method cons ists in analysing an AMCW signal , depicted by a sinusoidal wave , which comprises an interfering short pulse located at about hal f the observation window .
  • the interfering short pulse does not need to be necessarily at hal f of the burst , since the AMCW signal does not need to be non-continuous .
  • Such a short pulse is distinguishable as , even though it has the same average power as the AMCW signal , it has a much higher peak power .
  • Such an assumption is valid in reality as the maximum average power of a ToF LIDAR sensor should be about the same as an AMCW LIDAR sensor .
  • the short pulse can be identified simply by defining a threshold, such that samples above such a threshold value will be considered and assumed to be originated by an interfering signal .
  • the interfering pulse Once the interfering pulse is located, removing it is done through resetting the samples above the threshold to zero , as well as a few samples adj acent to those ones .
  • it can be used an interpolating method.
  • the output signal thus has a much-mitigated interfering short pulse .
  • Fig. 1 - illustrates the AMCW signal (1) received by a LIDAR sensor which comprises one interference pulse (2) .
  • Fig. 2 - illustrates the AMCW signal (1) received by a LIDAR sensor which comprises one interfering pulse (2) , and where it is detected said pulse (2) through the definition of the threshold amplitude range (3) .
  • the threshold value (3) is the mean value of the AMCW signal (1) considering the existence of the interference pulse (2) .
  • Fig. 3 - illustrates the recovered AMCW signal (4) with the interfering pulse (2) having been removed.
  • Fig. 4 - illustrates the peak-to-average power ratio (PAPR) between the received AMCW signal and the sent AMCW signal.
  • PAPR peak-to-average power ratio
  • curve (1) the received AMCW signal contain at least one interfering pulse
  • the second case (4) the received AMCW signal does not contain interfering signals.
  • the only considered noise source is quantization error due to the digitalization of the received signal.
  • Fig.5 - illustrates the estimated ToF error results without pulsed interference (2) removal (left graph A) , and with the pulsed interference (2) removal (right graph B) . In Graph B, all noise sources are deactivated, but there is quantization noise from the analog to digital conversion (ADC) .
  • ADC analog to digital conversion
  • the detection of interfering pulses (2) in the AMCW signal (1) received by a LIDAR sensor can be obtained by estimating the peak-to-average power ratio (PAPR) of said received signal.
  • An AMCW signal (1) typically has a low PAPR.
  • a square-wave single-tone AMCW signal (1) has a PAPR of 2.
  • an interfering short pulse (2) has an extremely high PAPR, about the inverse of its duty cycle, thus reaching a ratio of ⁇ 10000.
  • PAPR an excellent metric for identifying interfering pulses ( 2 ) .
  • the PAPR remains low when there are no interfering pulses. Conversely, the PAPR becomes much higher when there are interfering pulses.
  • the reason why the PAPR difference between signals without and with an interfering pulse is lower for channel delays shorter than 0.5ps is due to the saturation of the receiver. Realistically, receivers saturate for very high peak powers, which artificially decreases the PAPR of the received signals that include interfering pulses for short ranges (i.e., short channel delays) .
  • a typical white Gaussian noise signal with infinite duration has an infinite PAPR. This means that the PAPR of the AMCW signal (1) is expected to increase over distance. However, if the PAPR of the AMCW signal (1) is significant, without the presence of interfering pulses (2) , it means that noise is dominant over signal and therefore an erroneous range estimation would likely be obtained. In summary, given that the gap in PAPR between absence and presence of interfering pulses is so wide, noise is not expected to significantly affect the performance of the detection of interfering pulses for acceptable input signal powers .
  • Detecting interfering pulses (2) does not require obtaining any range (or time) estimation, but only estimating the PAPR. This means that it is possible to move on to range estimation and AMCW interference detection only after interfering pulses (2) are detected and removed from the received waveform ( 1 ) .
  • the method as herein described is capable of removing more than one interfering pulse (2) , in particular if these have about the same amplitude. However, if there are multiple interfering pulses (2) with widely different amplitudes, one of the following possible methods can be used.
  • the first one comprises repeating the disclosed threshold method until the PAPR of the received signal (1) falls within an interference-free range.
  • the second is a Histogram-based method which comprises: a. Calculating a histogram of sample amplitudes; b. Search for the first gap in the histogram, which separates samples from the AMCW signal (1) from samples of the weakest interfering pulses (2) ; c. Samples beyond such a gap are classified as interfering samples and are reset to a new amplitude equal to the one corresponding to the first histogram bin (lowest amplitude) , which represents the noise floor .
  • a filtering stage can be performed for smoothing the resulting waveform (4) .

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  • Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Electromagnetism (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • General Physics & Mathematics (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Radar Systems Or Details Thereof (AREA)

Abstract

The present application describes a method for detecting and removing interfering pulses from an Amplitude Modulated Continuous Wave (AMCW) signal (1) received by an AMCW Light Detection and Ranging (LIDAR) sensor. The method is based on detecting the existence of interfering pulses (2) in the AMCW signal (1) received by a LIDAR sensor through the estimation of the peak-to-average power ratio of the AMCW signal.

Description

DESCRIPTION "Detection and removal of pulse-like interference in an AMCW LIDAR sensor"
Technical Field
The present application describes an innovative technology for detecting and removing interfering pulses from an Amplitude Modulated Continuous Wave (AMCW) signal received by a AMCW Light Detection and Ranging ( LIDAR) sensor .
Background art
In general , one should assume that an Amplitude Modulated Continuous Wave (AMCW ) of a Light Detection and Ranging sensor ( LIDAR) can be subj ected not only to AMCW interference , originated by the AMCW LIDAR s ensors , but also due to interfering pulses originated by time of flight ( ToF) of LIDAR sensors .
The question that naturally arises is whether an AMCW LIDAR sensor is af fected by interfering pulses . In general , the range estimation provided by an AMCW LIDAR sensor can be disrupted by in-band interfering signals . An in-band AMCW interfering signal is an obvious example of such an interfering signal ; however, so is a short pulse interfering signal , as such a pulse has a wide spectral occupancy .
Known prior art does not disclose any methods that deal with interfering pulses captured by an AMCW LIDAR sensor . Therefore , a pioneering method proposed for detecting and consequently removing interfering pulses from an AMCW signal is herein disclosed . Summary
The present invention describes a method for detecting and removing interfering pulses from an AMCW LiDAR sensor signal comprising the steps of : detecting the existence of at least one interfering pulse in the AMCW signal received by a LIDAR sensor through the estimation of the peak-to-average power ratio of the AMCW signal .
In a proposed embodiment of present invention, the method comprises the steps of : determining a threshold value for the AMCW signal ; removing the at least one interfering pulse above the determined threshold value of the AMCW signal .
Yet in another proposed embodiment of present invention, determining the threshold value for the AMCW signal comprises determining the mean value of the AMCW signal considering the existence of at least one interfering pulse .
Yet in another proposed embodiment of present invention, the AMCW signal comprises at least one interfering pulse i f the estimation of the peak-to-average power ratio is greater than l OdB .
Yet in another proposed embodiment of present invention, the at least one interfering pulse comprises pulses with equal amplitudes .
Yet in another proposed embodiment of present invention, the at least one interfering pulse comprises pulses with di f ferent amplitudes . Yet in another proposed embodiment of present invention, removing the at least one interfering pulse above the determined threshold value of the AMCW signal , is performed repeatedly until the estimation of the peak-to-average power ratio of the AMCW signal is lower than a given threshold value .
Yet in another proposed embodiment of present invention, determining a threshold value for the AMCW signal comprises the steps of : a . Calculating a histogram of sample amplitudes of the received AMCW signal ; b . Searching for a first gap in the histogram, which separates samples from the AMCW s ignal from samples of the weakest interfering pulse ; c . Defining the threshold value as the middle point within the limits of the first gap .
Yet in another proposed embodiment of present invention, the method comprises a filtering step for reconstructing the obtained signal after removing the interfering pulses .
Yet in another proposed embodiment of present invention, the AMCW LiDAR sensor produces a distance estimation which is discarded i f at least one interfering pulse is detected .
General Description
The present application describes a technology for detecting and removing interfering pulses from an AMCW signal received by an AMCW LIDAR sensor . Such AMCW signal can have any given number of tones , and can be continuous or not , i . e . , a burst .
The presently disclosed technology is particularly useful in the autonomous vehicles field . The main and obvious advantage of the disclosed method is related with the fact that an AMCW LIDAR employing such technology becomes capable of detecting and even removing interfering pulses from the received signals , thus becoming impervious to pulsed interference .
Another important advantage is that the disclosed technology is complementary to interference avoidance known methods , as well as to AMCW interference detection methods , further increasing the robustness of an AMCW LIDAR to interference in general .
The used method cons ists in analysing an AMCW signal , depicted by a sinusoidal wave , which comprises an interfering short pulse located at about hal f the observation window . The interfering short pulse does not need to be necessarily at hal f of the burst , since the AMCW signal does not need to be non-continuous . Such a short pulse is distinguishable as , even though it has the same average power as the AMCW signal , it has a much higher peak power . Such an assumption is valid in reality as the maximum average power of a ToF LIDAR sensor should be about the same as an AMCW LIDAR sensor .
The short pulse can be identified simply by defining a threshold, such that samples above such a threshold value will be considered and assumed to be originated by an interfering signal .
Once the interfering pulse is located, removing it is done through resetting the samples above the threshold to zero , as well as a few samples adj acent to those ones . For better reconstructing the signal , i . e . , not leaving gaps between samples, optionally it can be used an interpolating method. The output signal thus has a much-mitigated interfering short pulse .
Brief description of the drawings
For better understanding of the present application, figures representing preferred embodiments are herein attached which, however, are not intended to limit the technique disclosed herein.
Fig. 1 - illustrates the AMCW signal (1) received by a LIDAR sensor which comprises one interference pulse (2) .
Fig. 2 - illustrates the AMCW signal (1) received by a LIDAR sensor which comprises one interfering pulse (2) , and where it is detected said pulse (2) through the definition of the threshold amplitude range (3) . In this particular case the threshold value (3) is the mean value of the AMCW signal (1) considering the existence of the interference pulse (2) .
Fig. 3 - illustrates the recovered AMCW signal (4) with the interfering pulse (2) having been removed.
Fig. 4 - illustrates the peak-to-average power ratio (PAPR) between the received AMCW signal and the sent AMCW signal. In the first case, curve (1) the received AMCW signal contain at least one interfering pulse, whereas in the second case (4) the received AMCW signal does not contain interfering signals. The only considered noise source is quantization error due to the digitalization of the received signal. Fig.5 - illustrates the estimated ToF error results without pulsed interference (2) removal (left graph A) , and with the pulsed interference (2) removal (right graph B) . In Graph B, all noise sources are deactivated, but there is quantization noise from the analog to digital conversion (ADC) .
Description of Embodiments
With reference to the figures, some embodiments are now described in more detail, which are however not intended to limit the scope of the present application.
The detection of interfering pulses (2) in the AMCW signal (1) received by a LIDAR sensor, can be obtained by estimating the peak-to-average power ratio (PAPR) of said received signal. An AMCW signal (1) typically has a low PAPR. For instance, a square-wave single-tone AMCW signal (1) has a PAPR of 2. Conversely, an interfering short pulse (2) has an extremely high PAPR, about the inverse of its duty cycle, thus reaching a ratio of ~10000. Such a large difference makes PAPR an excellent metric for identifying interfering pulses ( 2 ) .
If the PAPR of the received signal (1) is too high, then it is very likely that the signal is impaired by at least one interfering pulse (2) . This is illustrated in Figure 4.
The PAPR remains low when there are no interfering pulses. Conversely, the PAPR becomes much higher when there are interfering pulses. The reason why the PAPR difference between signals without and with an interfering pulse is lower for channel delays shorter than 0.5ps is due to the saturation of the receiver. Realistically, receivers saturate for very high peak powers, which artificially decreases the PAPR of the received signals that include interfering pulses for short ranges (i.e., short channel delays) .
Even though noise is not considered in figure 4, it is nonetheless relevant. A typical white Gaussian noise signal with infinite duration has an infinite PAPR. This means that the PAPR of the AMCW signal (1) is expected to increase over distance. However, if the PAPR of the AMCW signal (1) is significant, without the presence of interfering pulses (2) , it means that noise is dominant over signal and therefore an erroneous range estimation would likely be obtained. In summary, given that the gap in PAPR between absence and presence of interfering pulses is so wide, noise is not expected to significantly affect the performance of the detection of interfering pulses for acceptable input signal powers .
Detecting interfering pulses (2) does not require obtaining any range (or time) estimation, but only estimating the PAPR. This means that it is possible to move on to range estimation and AMCW interference detection only after interfering pulses (2) are detected and removed from the received waveform ( 1 ) .
The method as herein described is capable of removing more than one interfering pulse (2) , in particular if these have about the same amplitude. However, if there are multiple interfering pulses (2) with widely different amplitudes, one of the following possible methods can be used. The first one comprises repeating the disclosed threshold method until the PAPR of the received signal (1) falls within an interference-free range.
The second is a Histogram-based method which comprises: a. Calculating a histogram of sample amplitudes; b. Search for the first gap in the histogram, which separates samples from the AMCW signal (1) from samples of the weakest interfering pulses (2) ; c. Samples beyond such a gap are classified as interfering samples and are reset to a new amplitude equal to the one corresponding to the first histogram bin (lowest amplitude) , which represents the noise floor .
Regardless of the method that is used for removing interference pulses, optionally a filtering stage can be performed for smoothing the resulting waveform (4) .
In order to assess the effectiveness of the proposed method, the obtained results are shown in Figure 5 and confirm that the method is indeed effective, being validated by the estimated ToF results in which graph B shows all noise sources deactivated.

Claims

9 CLAIMS
1. Method for detecting and removing interfering pulses from an AMCW LiDAR sensor signal comprising the steps of: detecting the existence of at least one interfering pulse (2) in the AMCW signal (1) received by a LIDAR sensor through the estimation of the peak-to-average power ratio of the AMCW signal (1) .
2. Method according to previous claim, comprising the steps of : determining a threshold value (3) for the AMCW signal (1) ; removing the at least one interfering pulse (2) above the determined threshold value (3) of the AMCW signal (1) •
3. Method according to any of the previous claims, wherein determining the threshold value (3) for the AMCW signal (1) comprises determining the mean value of the AMCW signal (1) considering the existence of at least one interfering pulse (2) .
4. Method according to any of the previous claims, wherein the AMCW signal (1) comprises at least one interfering pulse (2) if the estimation of the peak-to-average power ratio is greater than lOdB.
5. Method according to any of the previous claims, wherein the at least one interfering pulse (2) comprises pulses with equal amplitudes.
6. Method according to any of the previous claims, wherein the at least one interfering pulse (2) comprises pulses with different amplitudes.
7. Method according to any of the previous claims, wherein removing the at least one interfering pulse (2) above the determined threshold value (3) of the AMCW signal (1) is performed repeatedly until the estimation of the peak-to- average power ratio of the AMCW signal (1) is lower than a given threshold value.
8. Method according to any of the previous claims, wherein determining a threshold value (3) for the AMCW signal (1) comprises the steps of: a. Calculating a histogram of sample amplitudes of the received AMCW signal (1) ; b. Searching for a first gap in the histogram, which separates samples from the AMCW signal (1) from samples of the weakest interfering pulse (2) ; c. Defining the threshold value as the middle point within the limits of the first gap.
9. Method according to any of the previous claims, comprising a filtering step for reconstructing the obtained signal after removing the interfering pulses (4) .
10. Method according to any of the previous claims, wherein the AMCW LiDAR sensor produces a distance estimation which is discarded if at least one interfering pulse (2) is detected .
PCT/IB2021/058844 2021-09-24 2021-09-28 Detection and removal of pulse-like interference in an amcw lidar sensor WO2023047174A1 (en)

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE19851307A1 (en) * 1998-10-08 2000-04-13 Z & F Zoller & Froehlich Gmbh System for determination of one or more physical quantities especially for use with a AMCW laser type system has means for determination of phase of amplitude of mission signal
WO2020227761A1 (en) * 2019-05-10 2020-11-19 The University Of Queensland An optical beam scanner
US20210055390A1 (en) * 2019-08-20 2021-02-25 Luminar Technologies, Inc. Coherent pulsed lidar system

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE19851307A1 (en) * 1998-10-08 2000-04-13 Z & F Zoller & Froehlich Gmbh System for determination of one or more physical quantities especially for use with a AMCW laser type system has means for determination of phase of amplitude of mission signal
WO2020227761A1 (en) * 2019-05-10 2020-11-19 The University Of Queensland An optical beam scanner
US20210055390A1 (en) * 2019-08-20 2021-02-25 Luminar Technologies, Inc. Coherent pulsed lidar system

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
MORALES-FERRE RUBEN ET AL: "A Survey on Coping With Intentional Interference in Satellite Navigation for Manned and Unmanned Aircraft", IEEE COMMUNICATIONS SURVEYS & TUTORIALS, IEEE, USA, vol. 22, no. 1, 23 October 2019 (2019-10-23), pages 249 - 291, XP011778016, DOI: 10.1109/COMST.2019.2949178 *

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