LIDAR phase noise removal system

Abstract

To improve accuracy of FMCW LIDAR signals.SOLUTION: A light detection and ranging (LIDAR) system includes a LIDAR measurement unit, a reference measurement unit, and a phase cancellation unit. The LIDAR measurement unit estimates a travel time of a laser beam. The reference measurement unit determines a phase of a laser source. The phase cancellation unit identifies phase noise and cancels the phase noise from the laser beam, based at least partially on the phase of the laser source and the travel time of the laser beam. The denoised signal is used to determine a distance between a laser source and a target.SELECTED DRAWING: Figure 3a

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to U.S. Patent Application No. 17 / 463,263, filed August 31, 2021, which claims priority to U.S. Provisional Application No. 63 / 074,832, filed September 4, 2020. Applications Nos. 17 / 463,263 and 63 / 074,832 are incorporated herein by reference.

[0002] The present disclosure relates generally to light detection and ranging (LIDAR). [Background technology]

[0003] Frequency Modulated Continuous Wave (FMCW) LIDAR directs a frequency-modulated collimated beam of light at a target to directly measure the object's range and velocity. Target range and velocity information can be derived from the FMCW LIDAR signal. Designs and techniques that increase the accuracy of the LIDAR signal are preferred.

[0004] The automotive industry is currently developing autonomous capabilities to control vehicles in certain situations. According to SAE International standard J3016, there are six levels of autonomy, ranging from level 0 (no autonomy) to level 5 (a vehicle that can operate without driver input in all conditions). Vehicles with autonomous capabilities use sensors to sense the environment in which the vehicle travels. By collecting and processing data from the sensors, the vehicle can explore its surrounding environment. An autonomous vehicle may include one or more FMCW LIDAR devices to sense the environment. Summary of the Invention

[0005] An embodiment of the present disclosure includes a LIDAR (Light Detection and Ranging) system including a LIDAR measurement unit, a reference measurement unit, and a phase removal unit. The LIDAR measurement unit is configured to estimate a time for a laser beam to travel between a laser source and a target. The reference measurement unit is configured to determine a phase of the laser source. The phase removal unit is configured to remove phase noise from a signal representing the laser beam based at least in part on the phase of the laser source and the time for the laser beam to travel.

[0006] In one embodiment, the LIDAR system further includes a free-space interferometer and a fixed-length interferometer. The signal representing the laser beam is a first beat signal received from the free-space interferometer. The phase of the laser source is calculated from a second beat signal received from the fixed-length interferometer. The laser source simultaneously provides the laser beam to the free-space interferometer and the fixed-length interferometer.

[0007] In one embodiment, the free-space interferometer combines a first local oscillator signal with a target reflected signal to generate a first beat signal, and the fixed-length interferometer combines a second local oscillator signal with a fixed-length signal delayed by a fixed-length optical delay line to generate a second beat signal.

[0008] In one embodiment, the phase removal unit generates a delayed phase of the laser source through a delay operation configured to delay the phase of the laser source by the travel time of the laser beam estimated by the LIDAR measurement unit.

[0009] In one embodiment, the phase removal unit subtracts the delayed phase of the laser source from the phase of the laser source to generate a delta phase of the laser source, where the delta phase of the laser source represents phase noise in the signal representing the laser beam.

[0010] In one embodiment, the phase removal unit multiplies the signal representing the laser beam by the complex conjugate of the delta phase to remove phase noise.

[0011] In one embodiment, the LIDAR system further includes a distance calculation unit configured to calculate a distance between the laser source and the target from a denoised signal, which is a signal representing the laser beam with phase noise removed.

[0012] In one embodiment, the distance calculation unit determines the frequency of the denoised signal, the frequency of the denoised signal being determined based on a peak amplitude of the frequency representation of the denoised signal.

[0013] In one embodiment, the LIDAR system is a Frequency Modulated Continuous Wave (FMCW) LIDAR system.

[0014] In one embodiment, the reference measurement unit determines the phase of the laser source based at least in part on an in-phase signal and a quadrature signal from a fixed length interferometer.

[0015] In one embodiment, to determine the phase of the laser source, the reference measurement unit is configured to apply an arctangent operation to the quadrature signal divided by the in-phase signal and to apply an integration operation to the output from the arctangent operation.

[0016] In one embodiment, to estimate the travel time of the laser beam, the LIDAR measurement unit is configured to determine a frequency of a beat signal from the free-space interferometer, the frequency of the beat signal being determined based on at least one peak amplitude of a frequency representation of the beat signal.

[0017] An embodiment of the present disclosure includes an autonomous vehicle control system including a LIDAR system. The LIDAR system includes a LIDAR measurement unit, a reference measurement unit, and a phase removal unit. The LIDAR measurement unit is configured to estimate a time for a laser beam to travel between a laser source and a target. The reference measurement unit is configured to determine a phase of the laser source. The phase removal unit is configured to remove phase noise from a signal representing the laser beam based at least in part on the phase of the laser source and the time for the laser beam to travel. The autonomous vehicle control system includes one or more processors for controlling the autonomous vehicle control system in response to a signal output from the phase removal unit.

[0018] In one embodiment, the LIDAR system further includes a free-space interferometer and a fixed-length interferometer. The signal representing the laser beam is a first beat signal received from the free-space interferometer. The phase of the laser source is calculated from a second beat signal received from the fixed-length interferometer. The laser source simultaneously provides the laser beam to the free-space interferometer and the fixed-length interferometer.

[0019] In one embodiment, the phase removal unit generates a delayed phase of the laser source through a delay operation configured to delay the phase of the laser source by the travel time of the laser beam estimated by the LIDAR measurement unit.

[0020] In one embodiment, the phase removal unit subtracts the delayed phase of the laser source from the phase of the laser source to generate a delta phase of the laser source, the delta phase of the laser source representing phase noise in the signal representing the laser beam, and the phase removal unit is configured to multiply the signal representing the laser beam by a complex conjugate of the delta phase to remove the phase noise.

[0021] An embodiment of the present disclosure includes an autonomous vehicle system for an autonomous vehicle including a LIDAR system. The LIDAR system includes a LIDAR measurement unit, a reference measurement unit, and a phase removal unit. The LIDAR measurement unit is configured to estimate a time for a laser beam to travel between a laser source and a target. The reference measurement unit is configured to determine a phase of the laser source. The phase removal unit is configured to remove phase noise from a signal representing the laser beam based at least in part on the phase of the laser source and the time for the laser beam to travel. The autonomous vehicle includes one or more processors for controlling the autonomous vehicle in response to a signal output by the phase removal unit.

[0022] In one embodiment, the LIDAR system further includes a free-space interferometer and a fixed-length interferometer. The signal representing the laser beam is a first beat signal received from the free-space interferometer. The phase of the laser source is calculated from a second beat signal received from the fixed-length interferometer. The laser source simultaneously supplies the laser beam to the free-space interferometer and the fixed-length interferometer.

[0023] In one embodiment, the phase removal unit is configured to generate a delayed phase of the laser source through a delay operation configured to delay the phase of the laser source by a travel time of the laser beam estimated by the LIDAR measurement unit.

[0024] In one embodiment, the phase removal unit is configured to subtract the delayed phase of the laser source from the phase of the laser source to generate a delta phase of the laser source, the delta phase of the laser source representing phase noise in the signal representing the laser beam, and the phase removal unit is configured to multiply the signal representing the laser beam by a complex conjugate of the delta phase to remove the phase noise.

[0025] An embodiment of the present disclosure includes a LIDAR system including a laser waveform function, a parameter set, and a calibration unit. The laser waveform function defines a laser waveform. The parameter set at least partially defines the laser waveform. The calibration unit is configured to estimate partial derivatives of a frequency response with respect to each parameter of the parameter set. The frequency response is measured at a laser output driven by the laser waveform. The calibration unit is configured to update the parameter set so that the frequency response of the laser satisfies a condition defined by the laser waveform function.

[0026] In one embodiment, the LIDAR system further includes a fixed length interferometer, the calibration unit is configured to receive the in-phase and quadrature signals from the fixed length interferometer, and the calibration unit is configured to determine a frequency response of the laser based on the in-phase and quadrature signals.

[0027] In one embodiment, the calibration unit iteratively configures the laser waveform based on the laser waveform function and the parameter set, the parameter set including an initial version of the parameter set that is replaced with one or more updated versions of the parameter set.

[0028] In one embodiment, the calibration unit is configured to repeatedly evaluate the frequency response of the laser using updated versions of the parameter set.

[0029] In one embodiment, to repeatedly evaluate the frequency response of the laser, the calibration unit is configured to load the laser waveform into a digital-to-analog converter, wait until the laser stabilizes, measure the output from the interferometer, and calculate the frequency response from the output from the interferometer.

[0030] In one embodiment, the calibration unit is configured to estimate the slope of the laser waveform function.

[0031] In one embodiment, to estimate the slope of the laser waveform function, the calibration unit is configured to calculate a perturbed version of the laser waveform, load the perturbed version of the laser waveform into a digital-to-analog converter, measure the output from the laser, and evaluate the perturbed version of the laser waveform function.

[0032] In one embodiment, the perturbed version of the laser waveform comprises a difference between a first parameter of the set of parameters and a second parameter of the set of parameters.

[0033] In one embodiment, the calibration unit is configured to update the parameter set based on partial derivatives of the frequency response with respect to each parameter of the parameter set.

[0034] In one embodiment, the calibration unit is configured to update the parameter set to compensate for distortion characteristics of the laser.

[0035] An embodiment of the present disclosure includes an autonomous vehicle control system including a LIDAR system. The LIDAR system includes a laser waveform function that defines a laser waveform, a parameter set that at least partially defines the laser waveform, and a calibration unit configured to estimate partial derivatives of a frequency response with respect to each parameter in the parameter set. The frequency response is measured from an output of a laser driven by the laser waveform. The calibration unit is configured to update the parameter set so that the frequency response of the laser satisfies a condition defined by the laser waveform function. The autonomous vehicle control system includes one or more processors for controlling the autonomous vehicle control system in response to the laser waveform at least partially defined by the calibration unit.

[0036] In one embodiment, the autonomous vehicle control system further includes a fixed length interferometer, the calibration unit is configured to receive the in-phase and quadrature signals from the fixed length interferometer, and the calibration unit is configured to determine a frequency response of the laser based on the in-phase and quadrature signals.

[0037] In one embodiment, the calibration unit iteratively configures the laser waveform based on the laser waveform function and the parameter set, the parameter set including an initial version of the parameter set that is replaced with one or more updated versions of the parameter set.

[0038] In one embodiment, the calibration unit is configured to repeatedly evaluate the frequency response of the laser using updated versions of the parameter set.

[0039] In one embodiment, to repeatedly evaluate the frequency response of the laser, the calibration unit is configured to load the laser waveform into a digital-to-analog converter, wait until the laser stabilizes, measure the output from the interferometer, and calculate the frequency response from the output from the interferometer.

[0040] In one embodiment, the calibration unit is configured to estimate the slope of the laser waveform function.

[0041] In one embodiment, to estimate the slope of the laser waveform function, the calibration unit is configured to calculate a perturbed version of the laser waveform, load the perturbed version of the laser waveform into a digital-to-analog converter, measure the output from the laser, and evaluate the perturbed version of the laser waveform function.

[0042] In one embodiment, an autonomous vehicle includes a LIDAR system. The LIDAR system includes a laser waveform function that defines a laser waveform, a parameter set that at least partially defines the laser waveform, and a calibration unit configured to estimate partial derivatives of a frequency response with respect to each parameter in the parameter set. The frequency response is measured from an output of a laser driven by the laser waveform. The calibration unit is configured to update the parameter set so that the frequency response of the laser satisfies a condition defined by the laser waveform function. The autonomous vehicle includes one or more processors for controlling the autonomous vehicle in response to the laser waveform at least partially defined by the calibration unit.

[0043] In one embodiment, the calibration unit is configured to repeatedly evaluate the frequency response of the laser using updated versions of the parameter set.

[0044] In one embodiment, the calibration unit is configured to estimate the slope of the laser waveform function.

[0045] An embodiment of the present disclosure includes a LIDAR system including a reference measurement unit and a LIDAR measurement unit. The reference measurement unit is configured to determine the phase of a reference beat signal from a fixed-length interferometer driven by a laser source. The LIDAR measurement unit is configured to determine ranges of multiple targets based at least in part on a first frequency spectral peak from an upward frequency chirp of the laser source paired with a second frequency spectral peak from a downward frequency chirp of the laser source. The LIDAR measurement unit is configured to determine ranges of the multiple targets based at least in part on the phase of the reference beat signal.

[0046] In one embodiment, the LIDAR measurement unit is configured to estimate a time for the free-space laser signal to travel to a plurality of targets using sequential pairings of peaks between a first frequency spectral peak and a second frequency spectral peak, the first pair of peaks including a first peak of the first frequency spectral peak and a first peak of the second frequency spectral peak.

[0047] In one embodiment, the LIDAR measurement unit is configured to repeatedly determine ranges to a plurality of targets, each of which is associated with a travel time estimate determined from a peak pair of one of the first frequency spectrum peaks and one of the second frequency spectrum peaks.

[0048] In one embodiment, the LIDAR measurement unit is configured to delay the phase of the reference beat signal by a duration equal to the travel time estimate to identify phase noise of the laser source.

[0049] In one embodiment, the LIDAR measurement unit is configured to multiply the free-space beat signal by a complex conjugate of the phase noise to remove phase noise from the free-space beat signal and generate a denoised free-space beat signal.

[0050] In one embodiment, the LIDAR measurement unit is configured to remove phase noise that occurs during an upward frequency chirp, and the reference measurement unit is configured to remove phase noise that occurs during a downward frequency chirp.

[0051] In one embodiment, the first frequency spectral peak is generated from a first beat signal from the free-space interferometer, and the second frequency spectral peak is generated from a second beat signal from the free-space interferometer.

[0052] In one embodiment, the free space interferometer combines a first local oscillator signal with a first target return signal to generate a first beat signal from an upward frequency chirp, and a second local oscillator signal with a second target return signal to generate a second beat signal from a downward frequency chirp.

[0053] In one embodiment, the LIDAR system is a frequency modulated continuous wave (FMCW) LIDAR system.

[0054] In one embodiment, the reference measurement unit determines the phase of the reference beat signal based at least in part on the in-phase and quadrature signals from the fixed length interferometer.

[0055] In one embodiment, to determine the phase of the reference beat signal, the reference measurement unit is configured to apply an arctangent operation to the quadrature signal divided by the in-phase signal, and to apply an integral operation to the output from the arctangent operation.

[0056] In one embodiment, the reference beat signal is a first reference beat signal, the phase of which is generated from an upward frequency chirp, and the reference measurement unit is configured to estimate the phase of a second reference beat signal, which is generated from a downward frequency chirp.

[0057] An embodiment of the present disclosure includes an autonomous vehicle control system including a LIDAR system. The LIDAR system includes a LIDAR measurement unit and a reference measurement unit. The reference measurement unit is configured to determine the phase of a reference beat signal from a fixed-length interferometer driven by a laser source. The LIDAR measurement unit is configured to determine the range of a plurality of targets based at least in part on a first frequency spectral peak from an upward frequency chirp of the laser source paired with a second frequency spectral peak from a downward frequency chirp of the laser source. The LIDAR measurement unit is configured to determine the range of the plurality of targets based at least in part on the phase of the reference beat signal. The autonomous vehicle control system includes one or more processors for controlling the autonomous vehicle control system in response to signals output by at least one of the LIDAR measurement unit and the reference measurement unit.

[0058] In one embodiment, the LIDAR measurement unit is configured to estimate a time for the free-space laser signal to travel to a plurality of targets using sequential pairings of peaks between a first frequency spectral peak and a second frequency spectral peak, the first pair of peaks including a first peak of the first frequency spectral peak and a first peak of the second frequency spectral peak.

[0059] In one embodiment, the LIDAR measurement unit is configured to repeatedly determine ranges to a plurality of targets, each of which is associated with a travel time estimate determined from a peak pair of one of the first frequency spectrum peaks and one of the second frequency spectrum peaks.

[0060] In one embodiment, the LIDAR measurement unit is configured to delay the phase of the reference beat signal by a duration equal to the travel time estimate to identify phase noise of the laser source, and the LIDAR measurement unit is configured to multiply the free-space beat signal by a complex conjugate of the phase noise to remove the phase noise of the free-space beat signal and generate a denoised free-space beat signal.

[0061] An embodiment of the present disclosure includes an autonomous vehicle including a LIDAR system. The LIDAR system includes a LIDAR measurement unit and a reference measurement unit. The reference measurement unit is configured to determine the phase of a reference beat signal from a fixed-length interferometer driven by a laser source. The LIDAR measurement unit is configured to determine the range of a plurality of targets based at least in part on a first frequency spectral peak from an upward frequency chirp of the laser source paired with a second frequency spectral peak from a downward frequency chirp of the laser source. The LIDAR measurement unit is configured to determine the range of the plurality of targets based at least in part on the phase of the reference beat signal. The autonomous vehicle includes one or more processors for controlling the autonomous vehicle in response to signals output by at least one of the LIDAR measurement unit or the reference measurement unit.

[0062] In one embodiment, the LIDAR measurement unit is configured to estimate a time for the free-space laser signal to travel to a plurality of targets using sequential pairings of peaks between a first frequency spectral peak and a second frequency spectral peak, the first pair of peaks including a first peak of the first frequency spectral peaks and a first peak of the second frequency spectral peaks.

[0063] In one embodiment, the LIDAR measurement unit is configured to repeatedly determine ranges to a plurality of targets, each of which is associated with a travel time estimate determined from a peak pair of one of the first frequency spectrum peaks and one of the second frequency spectrum peaks.

[0064] In one embodiment, the LIDAR measurement unit is configured to delay the phase of the reference beat signal by a duration equal to the travel time estimate to identify phase noise of the laser source, and the LIDAR measurement unit is configured to multiply the free-space beat signal by a complex conjugate of the phase noise to remove the phase noise of the free-space beat signal and generate a denoised free-space beat signal. [Brief explanation of the drawings]

[0065] Non-limiting and non-exhaustive embodiments of the present disclosure are described with reference to the following drawings, in which like reference numerals refer to like parts throughout the various drawings unless otherwise stated.

[0066] [Figure 1] 1 illustrates a light measurement device that supports phase estimation, active phase removal, predistortion waveform generation, and peak pairing in a LIDAR system according to an embodiment of the present disclosure.

[0067] [Figure 2] 1 illustrates a LIDAR system capable of integrating phase estimation, active phase removal, predistortion waveform generation, and peak pairing according to an embodiment of the present disclosure.

[0068] [Figure 3a] 1 illustrates an exemplary phase noise removal system according to an embodiment of the present disclosure. [Figure 3b] 1 illustrates an exemplary phase noise removal system according to an embodiment of the present disclosure.

[0069] [Figure 4a] 1 illustrates an example of a predistortion waveform generator according to an embodiment of the present disclosure. [Figure 4b] 1 illustrates an example of a predistortion waveform generator according to an embodiment of the present disclosure.

[0070] [Figure 5a]1 illustrates an example of a multi-target identification system for an FMCW LIDAR system according to an embodiment of the present disclosure. [Figure 5b] 1 illustrates an example of a multi-target identification system for an FMCW LIDAR system according to an embodiment of the present disclosure.

[0071] [Figure 6a] 1 illustrates an example operation cycle of a LIDAR system that integrates phase estimation, active phase cancellation, predistortion waveform generation, and peak pairing according to various embodiments of the present disclosure. [Figure 6b] 1 illustrates an example operation cycle of a LIDAR system that integrates phase estimation, active phase cancellation, predistortion waveform generation, and peak pairing according to various embodiments of the present disclosure.

[0072] [Figure 7a] 1 illustrates an autonomous vehicle including an exemplary array of sensors according to an embodiment of the present disclosure.

[0073] [Figure 7b] FIG. 1 illustrates a plan view of an autonomous vehicle including an exemplary array of sensors according to an embodiment of the present disclosure.

[0074] [Figure 7c] 1 illustrates an exemplary vehicle control system including a sensor, a drivetrain, and a control system according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0075] Implementations of phase estimation, active phase cancellation, predistortion waveform generation, and peak pairing for a LIDAR (Light Detection and Ranging) system are described herein. In the following description, several details are presented to provide a thorough understanding of the implementations. However, those skilled in the relevant art will recognize that the techniques described herein may be implemented without one or more specific details or using other methods, elements, or materials. In other cases, well-known structures, materials, or operations have not been shown or described in detail to avoid obscuring certain aspects.

[0076] References throughout this specification to "one embodiment" or "an embodiment" mean that a particular feature, structure, or characteristic described in connection with an embodiment is included in at least one embodiment of the invention. Thus, the appearances of the phrase "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Also, particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0077] Throughout this specification, several technical terms are used. These terms should take their general meaning in the technical field from which they originate, unless specifically defined herein or the context in which they are used clearly dictates otherwise. For purposes of this disclosure, the term "autonomous vehicle" includes vehicles having autonomous capabilities at the SAE International Standard J3016 autonomy level.

[0078] In an embodiment of the present disclosure, visible light can be defined as having a wavelength range of approximately 380 nm to 700 nm. Invisible light can be defined as light having a wavelength outside the visible light range, such as ultraviolet light and infrared light. Infrared light having a wavelength range of approximately 700 nm to 1 mm includes near-infrared light. In an embodiment of the present disclosure, near-infrared light can be defined as having a wavelength range of approximately 700 nm to 1.6 μm.

[0079] In aspects of the present disclosure, the term "transparent" can be defined as having a light transmittance of greater than 90%. In some embodiments, the term "transparent" can be defined as a material having a visible light transmittance of greater than 90%.

[0080] Coherent LIDAR systems directly measure the distance and velocity of an object by directing a modulated and collimated beam of light at the object. Light reflected from the object is combined with a tapped version of the beam. The frequency of the resulting beat tone, once corrected for Doppler shift, is proportional to the distance from the LIDAR system to the object, which may require a second measurement. Both measurements, which may or may not be performed simultaneously, provide both range and velocity information. This application describes frequency modulated continuous wave (FMCW) LIDAR as an example of coherent LIDAR. However, the implementations and examples described herein are applicable to any type of coherent LIDAR.

[0081] In some embodiments, FMCW LIDAR, a type of coherent LIDAR, can be used. Specifically, FMCW LIDAR modulates the frequency of a light beam emitted from a laser source. FMCW LIDAR can take advantage of integrated photonics for improved manufacturability and performance. Integrated photonic systems can manipulate single optical modes using micron-scale waveguide devices.

[0082] Integrated FMCW LIDAR systems rely on one or more laser sources to provide optical power to the system. The optical fields generated by such lasers typically exhibit deterministic and stochastic phase fluctuations, which can degrade system performance by broadening the returned FMCW beat signal.

[0083] FMCW LIDAR systems emit light that can reflect off one or more objects in a scene before returning to the unit. These multiple returns produce a spectral beat signal with multiple peaks. Two measurements of the beat signal can be made to determine the range and velocity of each of these returns. The two spectral peaks are precisely paired to accurately determine the multiple ranges and velocities.

[0084] FMCW LIDAR systems use a linear frequency chirp to achieve superior performance. This linear frequency chirp can be achieved by driving a laser with a laser drive waveform. To compensate for the distortion characteristics of the laser, the laser drive waveform can be defined to compensate for the characteristics of the transmitting laser. As the system ages, the linearity of the frequency chirp can degrade. Some embodiments of the present disclosure provide in situ recalibration of the laser drive waveform to assist with system degradation or modification.

[0085] A system is described for directly measuring the frequency excursion of a laser in an integrated FMCW LIDAR system. The measured frequency excursion can be integrated to determine the phase optical signal generated by the laser.

[0086] The system includes a short (fixed length) integrated interferometer connected in parallel or coupled to a main free-space interferometer that at least partially defines the FMCW LIDAR system. A single laser source feeds both interferometers.

[0087] An initial estimate of the target distance from the main free-space interferometer can be used to estimate and subtract unwanted optical phase fluctuations (phase noise) in the beat signal from the beat signal, thereby improving the measurement capabilities of the main free-space interferometer.

[0088] The estimated beat signal phase can further be used to accurately pair spectral peaks when multiple returns (multiple targets) are present.

[0089] The measured frequency deviation can equally be used for in situ generation and calibration of predistortion waveforms to improve the linearity of the laser frequency chirp.

[0090] The systems and methods described herein for identifying and removing optical phase fluctuations ("phase noise"), distinguishing between multiple targets, and generating predistortion waveforms can be used (collectively or individually) to assist in the autonomous operation of a vehicle. These and other embodiments are described in more detail in connection with Figures 1-7c.

[0091] 1 illustrates an optical phase measurement device 100 that supports phase estimation, active phase removal, predistortion waveform generation, and peak pairing in an FMCW LIDAR system according to an embodiment of the present disclosure. The optical phase measurement device 100 includes a laser source 101, a splitter 102, a free-space interferometer 103, and a fixed-length interferometer 104, and supports phase estimation, phase noise removal, predistortion waveform generation, and peak pairing in the LIDAR system.

[0092] Light emitted by laser source 101 enters splitter 102, which splits the optical output of laser source 101 into two separate optical channels. The split ratio achieved by splitter 102 can be the same (50:50) or several different ratios (e.g., 80:20). In effect, the majority of the optical power is split and routed to free-space interferometer 103, while the remaining small portion of the optical power is routed to fixed-length interferometer 104.

[0093] Fixed-length interferometer 104 is configured to measure or estimate the instantaneous laser frequency of laser source 101. Fixed-length interferometer 104 may include splitter 105, fixed-length optical delay line 106, optical hybrid 107, balanced photodiode pair 108, and balanced photodiode pair 109. Fixed-length interferometer 104 may include a short optical delay in fixed-length optical delay line 106, for example, in the range of 10 cm to 30 cm. Therefore, fixed-length interferometer 104 may be referred to as a short fixed-length interferometer or a reference interferometer.

[0094] Light entering the fixed-length interferometer 104 passes through a splitter 105, which may have an identical or unequal splitting ratio. The upper output of the splitter 105 is connected to a fixed-length optical delay line 106. The fixed-length optical delay line 106 delays the optical signal by a short time compared to the light exiting the lower output of the splitter 105. These two optical paths (i.e., the upper and lower optical paths) are connected to an optical hybrid 107, which may be implemented as a 2x4 optical hybrid. The optical hybrid 107 mixes the lower signal with the upper signal delayed through the fixed-length optical delay line 106. The laser source 101 can be driven to output a time-based linearly varying frequency (i.e., chirp) so that the frequency of the upper signal arriving at the optical hybrid 107 is slightly different (e.g., faster or slower) than the frequency of the lower signal arriving at the optical hybrid 107. When signals of different frequencies are mixed or combined, a beat tone or beat signal is generated with a beat frequency equal to the difference between the two frequencies.

[0095] The beat signal from the optical hybrid 107 is measured and converted to an electrical signal using a balanced photodiode pair 108 and a balanced photodiode pair 109. The balanced photodiode pair 108 converts the in-phase signal I ref and the balanced photodiode pair 109 generates an electrical signal corresponding to the quadrature signal Q ref For a sufficiently short (e.g., 20 cm) implementation of the fixed-length optical delay line 106, the in-phase signal I ref and quadrature signal Q ref The phase of the measurement is proportional to the instantaneous frequency of the laser source 101. The instantaneous frequency of the laser source 101 can be integrated to calculate the instantaneous phase of the laser source 101. The instantaneous phase of the laser source 101 can be used to separate the phase noise of the laser source 101, which can be defined by deterministic and stochastic phase fluctuations.

[0096] The free-space interferometer 103 is configured to measure or estimate the distance between the laser source 101 and the target. The free-space interferometer 103 may include a splitter 110, a variable distance optical delay line 111, an optical hybrid 112, a balanced photodiode pair 113, and a balanced photodiode pair 114.

[0097] Light entering the free-space interferometer 103 enters splitter 110. Splitter 110 separates the "local oscillator" field (i.e., the illustrated lower path, "lower signal" and / or "local oscillator signal") from the "signal" field (i.e., the illustrated upper path, "upper signal" and / or "delayed signal"). The upper signal power is coupled into free space. This light strikes a target and propagates over different or variable distances before being reflected back towards the LIDAR unit (e.g., free-space interferometer 103). This light is received by the free-space interferometer 103, effectively forming a variable-distance optical delay line 111. The delayed signal and the local oscillator signal are mixed together by optical hybrid 112. The output of optical hybrid 112 is converted to an electrical signal using a balanced photodiode. The resulting electrical signals are then converted into an in-phase signal I, which is a component of the FMCW LIDAR beat signal. FS and quadrature signal Q FS The phase fluctuations of the measured beat signal are time-correlated with the phase fluctuations of the beat signal measured by the fixed-length interferometer 104 because the interferometer is simultaneously fed by the laser source 101.

[0098] 2 illustrates an example FMCW LIDAR system 200 that may be configured to integrate phase estimation, active phase cancellation, predistortion waveform generation, and peak pairing according to embodiments of the present disclosure. The FMCW LIDAR system 200 includes a LIDAR processing engine 201 and a Focal Plane Array (FPA) system 202. In other embodiments, other forms of beam steering may be used.

[0099] The LIDAR processing engine 201 includes a microcomputer 203 configured to drive a digital-to-analog converter (DAC) 204, which generates modulation signals for a laser controller 205. The laser controller 205 modulates the frequency of a Q-channel laser array 206. The optical output emitted by the laser array 206 is split and routed to a switchable coherent pixel array 208 and a laser phase reference interferometer 207 (which may include the fixed-length interferometer 104 of FIG. 1). The light entering the switchable coherent pixel array 208 is controlled by an FPA driver 209. The light emitted from different positions of the switchable coherent pixel array 208 is collimated at different angles by a lens 210 and emitted into free space 211.

[0100] Light 211 emitted into free space is reflected from the target, propagates again through lens 210, and is coupled back into switchable coherent pixel array 208. The received light is measured using an N-channel receiver 212, which may be integrated into optical hybrid 107 and / or optical hybrid 112 (shown in FIG. 1). The resulting current is digitized using one or more M-channel analog-to-digital converters (ADCs) 213, and these signals are processed by microcomputer 203.

[0101] In parallel with the free-space measurement, the optical field passing through the laser phase reference interferometer 207 is measured using a P-channel receiver 214, which generates a current that is converted to a digital signal using an R-channel analog-to-digital converter (ADC) 215. The resulting digital signal is processed by the microcomputer 203 to estimate the phase fluctuations (phase noise) of the laser. The estimated or determined phase noise can then be used to remove ("denoise") the phase noise from the free-space distance measurement signal.

[0102] 3a and 3b illustrate an exemplary phase noise removal system that can be used by optical phase measurement apparatus 100 (shown in FIG. 1) and FMCW LIDAR system 200 (shown in FIG. 2) according to embodiments of the present disclosure to actively remove unwanted phase fluctuations (“phase noise”) to assist in FMCW LIDAR range and velocity measurements.

[0103] FIG. 3a illustrates a phase noise removal system 300 according to an embodiment of the present disclosure. The phase noise removal system 300 may include a LIDAR measurement unit 301, a reference measurement unit 302, a phase removal unit 303, and a distance calculation unit 304. The LIDAR measurement unit 301 may be configured to estimate the time it takes for light to travel between a light source and a target (i.e., travel time) to perform FMCW distance measurements. In this application, the time it takes for light to travel between a light source (e.g., a laser source) and a target (e.g., an object in the environment in which the LIDAR system is located) is defined as time-of-flight. At the same time, the reference measurement unit 302 may be configured to determine a phase estimate of the light (e.g., laser light) using a reference or fixed-length interferometer. The time-of-flight estimate τ from the LIDAR measurement unit 301 may be calculated as: est and the optical phase φ from the reference measurement unit 302 ex (t) is provided to a phase removal unit 303, which removes the time-of-flight estimate τ est and the optical phase φ ex (t) is used to represent the signal representing the light (e.g., the in-phase signal I FS and / or quadrature signal Q FS The phase removal unit 303 estimates and removes the phase noise from the denoised signal V(t), which is used by the distance calculation unit 304 to estimate the distance to the target distance. dn The range calculation unit 304 may also be configured to calculate the velocity of the target, for example, by performing a Doppler shift calculation or measurement.

[0104] 3b illustrates an example of a phase noise removal system 340 according to an embodiment of the present disclosure. The phase noise removal system 340 is an exemplary implementation of the phase noise removal system 300. One or more components or operations within the phase noise removal system 340 may be implemented in a photonic integrated circuit and / or an FMCW LIDAR system.

[0105] The LIDAR measurement unit 301 receives a signal 305 (e.g., a voltage signal) and calculates a time-of-flight estimate τ est The signal 305 is a signal representing light that has traveled to and from at least one target in free space. The signal 305 may be a beat signal that is a combination of a local oscillator signal and a free-space optical signal. The signal 305 is an in-phase signal I FS and / or quadrature signal Q FS , which may be received from free-space interferometer 103 (shown in FIG. 1).

[0106] The LIDAR measurement unit 301 includes a frequency conversion block 306, a filter block 307, and a peak search block 308. The frequency conversion block 306 converts the signal 305 into a frequency representation of the signal 305. The LIDAR measurement unit 301 performs this task using a Fourier transform (e.g., a fast Fourier transform (FFT)). The frequency conversion block 306 can digitize the signal 305 and use the Fourier transform to calculate the power spectral density (PSD) of the signal 305. The filter block 307 filters the output of the frequency conversion block 306 to improve the signal-to-noise ratio. The peak search block 308 can identify the highest peak in the filtered frequency spectrum of the signal 305. Based on system parameters, this peak location can be used to calculate a time-of-flight estimate τ of the optical signal. esr can be converted to

[0107] Simultaneously with the operation of the LIDAR measurement unit 301, the reference measurement unit 302 measures the light phase φ ex(t). The reference measurement unit 302 may include a division block 311, an arctangent block 312, a phase unwrap block 313, and an integration block 314. The reference measurement unit 302 receives an in-phase signal 310 and a quadrature signal 309 as inputs from the fixed length interferometer. In one embodiment, the in-phase signal 310 and the quadrature signal 309 are obtained by dividing the in-phase signal I from the fixed length interferometer 104. ref and quadrature signal Q ref ##EQU00002## Divide block 311 involves dividing quadrature signal 309 by in-phase signal 310. Arctangent block 312 performs an arctangent on the output of divide block 311 to estimate the phase of the beat signal represented by at least one of in-phase signal 310 and / or quadrature signal 309. Phase unwrap block 313 applies phase unwrapping to the output of arctangent block 312. The output of phase unwrap block 313 is integrated (over time) in integrate block 314 to estimate the phase fluctuation of the system laser over time.

[0108] The phase removal unit 303 removes the phase φ of the light. ex and time of flight τ est The phase removal unit 303 is configured to remove phase noise from the signal 305 based at least in part on the phase φ of the light. The phase removal unit 303 includes a delay block 315, a subtraction block 316, an exponent block 317, and a multiplication block 318. The delay block 315 is configured to remove phase noise from the signal 305 based at least in part on the phase φ of the light. ex The delayed phase φ is a time-delayed estimate of ex (t-τ est ) The delay may be a digital delay, and the duration of the delay is equal to the time of flight τ est The duration of the flight is τ est The phase of light φ for the duration ex By delaying (t), the phase removal unit 303 removes the optical phase φ associated with the optical transmission that defines the signal 305. ex (t) and subtraction block 316 identifies the delayed phase φ ex (t-τest ) and the phase of light φ ex (t) to obtain the delta phase Δφ, which defines the phase fluctuation or phase noise at the time the laser source transmitted the signal 305. ex (τ est ) is separated. Exponential block 317 separates the delta phase Δφ ex (τ est ) to construct a conjugate phasor. Multiplication block 318 multiplies the conjugate phasor by signal 305 to obtain the denoised signal V(t). dn Generate the denoised signal V(t) dn is the signal from which unwanted phase fluctuations have been removed or the noise has been removed 305. The denoised signal V(t) dn is the resulting “clean” beat signal that can be conveyed to the distance calculation unit 304.

[0109] The distance calculation unit 304 calculates the denoised signal V(t) dn The distance calculation unit 304 is configured to determine the distance between the light source and the target using a frequency transform block 319, a filter block 320, and a peak search block 321. The frequency transform block 319 is configured to convert the denoised signal V(t) dn The denoised signal V(t) dn The distance calculation unit 304 may perform this operation using a Fourier transform (e.g., a fast Fourier transform (FFT)). The frequency transformation block 319 converts the denoised signal V(t) dn can be digitized and the denoised signal V(t) dn A Fourier transform may be used to calculate the power spectral density (PSD) of V(t). A filter block 320 filters the output of the frequency transform block 319 to improve the signal-to-noise ratio. A peak search block 321 filters the noise-reduced signal V(t). dnOne or more peaks can be identified in the filtered frequency spectrum of the phase noise removal system 340. This peak information is used to estimate the position and velocity of the target. At block 322, the phase noise removal system 340 terminates operation.

[0110] 4a and 4b illustrate an example of a predistortion waveform generator according to an embodiment of the present disclosure. The predistortion waveform generator may generate a predistortion waveform for driving a LIDAR system laser using the optical phase measurement device 100 (shown in FIG. 1) and the FMCW LIDAR system 200 (shown in FIG. 2) or may improve (“calibrate”) an existing predistortion waveform. Predistortion waveform generation may be beneficial in LIDAR systems depending on the distortion characteristics of the laser. For example, to linearly change the laser frequency up and / or down, the LIDAR system may be configured to drive the laser frequency with a waveform such as a triangular waveform. A triangular waveform linearly increases and decreases in value. However, due to the distortion characteristics of the laser, the laser's frequency response may produce an output that does not have linearly increasing and decreasing frequencies. Because FMCW LIDAR systems rely on frequency modulation (e.g., chirping), such systems may benefit from a predistortion waveform that compensates for the distortion characteristics of the laser integrated into the particular LIDAR system. In-Place or In Situ Adjustment, Refinement, or Calibration of the Predistortion Waveform provides the advantage of compensating for minor unique operating characteristics of each laser.

[0111] FIG. 4a illustrates a predistortion waveform generator 400 according to an embodiment of the present disclosure. The predistortion waveform generator 400 includes a function definition block 401, a parameter set block 402, and a calibration unit 403. Prior to operation of the LIDAR system, the function definition block 401 defines a merit function F(f). The merit function F(f) defines or quantifies the linearity (or shape) of the chirped laser frequency used to drive the laser. Similarly, prior to operation of the LIDAR system, the parameter set block 402 defines a parameter p, which includes a set of numbers that define the shape / behavior of the laser drive waveform. The merit function F(f) may explicitly be a function of the time-dependent frequency used to chirp the LIDAR laser and may explicitly or implicitly depend on the parameter p.

[0112] A calibration unit 403 is applied to the LIDAR system to find parameters p that minimize a merit function F(f). The calibration unit 403 is configured to generate a predistortion waveform to compensate for the distortion characteristics of the laser. The calibration unit 403 generates the predistortion waveform by applying a partial derivative to the merit function F(f) for each parameter p in the set of parameters p. By iteratively identifying the distortion characteristics of the laser, the calibration unit 403 redefines the set of parameters p, which are stored to define the laser drive waveform for future use.

[0113] Predistortion waveform generator 400 terminates operation at block 419 .

[0114] 4b illustrates an example of a predistortion waveform generator 430 according to an embodiment of the present disclosure. The predistortion waveform generator 430 is an exemplary implementation of the predistortion waveform generator 400 (shown in FIG. 4a).

[0115] The calibration unit 403 includes several operations or processing blocks to support predistortion waveform generation. The calibration unit 403 includes a waveform construction block 404, a function evaluation block 405, a gradient estimation block 411, and an update block 417. In the waveform construction block 404, the calibration unit 403 constructs an initial drive waveform V(t,p) from the merit function F(f) and parameters p defined in the function definition block 401 and parameter set block 402.

[0116] Next, the value of the merit function F(f) is evaluated in function evaluation block 405. Function evaluation block 405 may include several sub-operations. In block 406, the current version of the drive waveform V(t,p) is loaded into a digital-to-analog converter (DAC) 406. In block 407, the laser driven by the drive waveform V(t,p) is allowed to settle to steady-state operation. In block 408, the in-phase signal I ref and quadrature signal Q ref is measured at the output of a short reference interferometer (e.g., fixed length interferometer 104 shown in FIG. 1). In block 409, the in-phase signal I ref and quadrature signal Q ref is used to calculate an estimate of the time-dependent laser frequency f, for example, as described for reference measurement unit 302 in FIG. 3b. In block 409, the in-phase signal I ref and quadrature signal Q ref is the quadrature signal Q ref the in-phase signal I ref The time-dependent laser frequency f is used to calculate an estimate of the time-dependent laser frequency f by dividing by , taking the arctangent of the result, unwrapping the arctangent result, and dividing the value by the relative delay τ of the fixed-length interferometer. In block 410, a current value of the merit function F(f) is calculated using the time-dependent frequency from block 409.

[0117] After the current value of the merit function F(f) is evaluated in function evaluation block 405, gradient estimation block 411 is configured to estimate the gradient of the merit function F(f). Gradient estimation block 411 is configured to determine the gradient by calculating the partial derivative of the merit function F(f) with respect to each parameter p. Block 412 perturbs the j-th element of parameter p and calculates a perturbed version of the drive waveform (V(p i +Δp j )), calculating a perturbed version of the driving waveform (V(p i +Δp j Block 413 involves uploading the corresponding (perturbed) value of the merit function F(p) to the DAC, for example, using the sub-operations of function evaluation block 405. i +Δp j ) at block 414. i +Δp j ) partial derivative ((∂F / ∂p j )) is estimated with respect to the j-th element of parameter p. j ) is approximated using finite differences. Block 415 determines whether there are additional parameters p for perturbation. If there are additional elements in parameter p, block 415 proceeds to block 416, where the value of j is incremented and gradient estimation block 411 is repeated. When each element of parameter p has been evaluated, block 415 proceeds to block 417.

[0118] In update block 417, calibration unit 403 updates parameters p based on the merit function F(f) estimate (from function evaluation block 405) and the gradient estimate of merit function F(f) (from gradient estimation block 411). In block 418, calibration unit 403 performs a convergence check. The convergence check evaluates how closely the frequency response of the laser matches the defined merit function F(f) when driven with parameters p. Once the merit function F(f) has converged, a final version of parameters p and a corresponding optimized drive signal V(t,p) are selected, and calibration unit 403 proceeds to block 419 and terminates.

[0119] 5a and 5b illustrate an example of a multi-target identification system that uses a reference phase measurement to identify multiple targets in an FMCW LIDAR system according to an embodiment of the present disclosure. The multi-target identification system applies phase measurements to multiple frequency spectrum return peak pairs of an FMCW LIDAR beat signal.

[0120] 5a shows an example of a multi-target identification system 500 for using reference phase measurements to identify multiple targets in an FMCW LIDAR system. The multi-target identification system 500 includes a LIDAR measurement unit 551 and a reference measurement unit 552. According to one embodiment, the LIDAR measurement unit 551 includes some features of the LIDAR measurement unit 301 (shown in FIGS. 3a and 3b), and the reference measurement unit 552 includes some features of the reference measurement unit 302 (shown in FIGS. 3a and 3b).

[0121] The LIDAR measurement unit 551 is configured to determine the range of a plurality of targets. The LIDAR measurement unit 551 is configured to determine the range of a plurality of targets by identifying a first set of frequency spectral peaks generated from an upward frequency chirp of a laser source. The LIDAR measurement unit 551 is configured to determine the range of a plurality of targets by identifying a second set of frequency spectral peaks generated from a downward frequency chirp of the laser source. The LIDAR measurement unit 551 confirms the presence of each of the plurality of targets and estimates the time of flight for each of the plurality of targets by pairing a peak from the first set of frequency spectral peaks with a peak from the second set of frequency spectral peaks. The LIDAR measurement unit 551 adjusts the phase φ of the reference beat signal to denoise the free-space beat signal from which the frequency spectral peaks are derived. ex (t) is configured to be used.

[0122] The reference measurement unit 552 is configured to provide a phase measurement of the laser source using a fixed length interferometer. The reference measurement unit 552 measures the phase φ of the reference beat signal from the fixed length interferometer. ex (t) and the phase φ of the reference beat signal so that the LIDAR measurement unit 551 can remove the phase noise. ex The reference measurement unit 552 is configured to provide the reference beat signal (t) to the LIDAR measurement unit 551. The reference measurement unit 552 can calculate a first phase from a first reference beat signal generated by an upward frequency chirp. The reference measurement unit 552 can calculate a second phase from a second reference beat signal generated by a downward frequency chirp. The reference measurement unit 552 is configured to provide the first phase from the first reference beat to the LIDAR measurement unit 551 to enable phase noise removal from the free-space beat signal of the upward frequency chirp. The reference measurement unit 552 is configured to remove phase noise from the free-space beat signal of the downward frequency chirp using the second phase from the second reference beat.

[0123] The operation of the multi-target identification system 500 ends at block 553 .

[0124] 5b illustrates an example of a multi-target identification system 570 that uses reference phase measurements to identify multiple targets in an FMCW LIDAR system according to an embodiment of the present disclosure. The multi-target identification system 570 is an exemplary implementation of the multi-target identification system 500.

[0125] First, a beat signal is generated from a laser source. In block 501, a free-space beat signal is generated from a free-space interferometer using an upward frequency chirp (upward ramp). In block 502, a free-space beat signal is generated from a free-space interferometer using a downward frequency chirp (downward ramp). Both free-space beat signals are collected using an FMCW LIDAR system. Such beat signals correspond to distance and velocity measurements through free space. In block 503, the power spectral density (PSD) of the up-ramp beat signal is calculated, and the positions (frequencies) of the highest N peaks are located in the frequency spectrum relative to the up-ramp. In block 504, the power spectral density (PSD) of the down-ramp beat signal is calculated, and the positions (frequencies) of the highest N peaks are located in the frequency spectrum relative to the down-ramp.

[0126] In parallel with the free-space interferometer measurements, a reference beat signal is generated from the same laser source in blocks 505 and 506. In block 505, a reference beat signal is generated from a reference (fixed length) interferometer using an upward frequency chirp (upward ramp). In block 506, a reference beat signal is generated from a reference interferometer using a downward frequency chirp (downward ramp). In block 507, the phase φ of the up-ramp beat signal is calculated. ex (t) is calculated. At block 508, the phase φ of the down ramp beat signal is calculated. ex (t) is calculated.

[0127] The N frequency spectrum peaks in the up-ramp and down-ramp PSDs correspond to N different return paths in free space. By properly pairing each peak in the up-ramp PSD with each peak in the down-ramp PSD, the length of such a path and the rate at which that path length changes (i.e., the relative velocity of the target) can be calculated. In block 509, the first peak in the up-ramp PSD is paired with the first peak in the down-ramp PSD. This pairing provides the target range, velocity, and time-of-flight τ est The time-dependent phase φ of the laser up-ramp obtained from the reference interferometer in block 510 is ex (t) is delayed by the phase φ ex The delay applied to (t) is the delayed phase φ ex (t-τ est ) to generate the estimated flight time τ est The phase φ delayed in block 511 is the duration of ex (t-τ est ) is the delta phase Δ φ The non-delay time-dependent phase φ of the laser up-ramp to generate (t) ex Subtract from (t). Delta phase Δ φ (t) is an estimate of the phase noise and nonlinearity contributions for the free space beat signal generated in block 501. In block 512, the conjugate phasor is multiplied by the delta phase Δ φ (t). In block 513, the conjugate phasor is multiplied with the free-space up-ramp beat signal to remove phase noise and generate a denoised beat signal. In block 514, the PSD of the denoised beat signal is calculated and the peak of the result is located.

[0128] Each peak set identified in block 509 is evaluated. In block 515, the peak pairs are examined to determine if more (unassessed) peak pairs remain in the up- and down-ramp PSD. If peak pairs remain, blocks 509-515 are repeated for each remaining pair. If all pairs have been tested, block 515 proceeds to block 516. A comparison is made against the calculated PSD peak value for each pair to determine which pair was correct in block 516 (the correct pair is the one whose PSD peak value is maximized on the up-ramp). A peak pair is selected after verifying whether the pairing is correct in block 517.

[0129] After the correct peak pairs are selected in block 517, the multi-target identification system 570 can proceed to block 553 and end operation. Alternatively, after block 517, the multi-target identification system 570 can repeat the phase noise removal to improve the signal-to-noise ratio (SNR) of the downramp signal. The distances of all path lengths measured using the peak pairs are estimated. Based on such path lengths or estimated time-of-flight τ for the path lengths, est Based on this, in block 518, a delayed phase φ ex (t-τ est ) is the estimated time-dependent phase φ for the down-ramp ex (t) is generated by delaying the delayed phase φ ex (t-τ est ) is the delta phase Δ φ The non-delay time-dependent phase φ of the laser down-ramp to generate (t) ex Subtract from (t). Delta phase Δ φ (t) is an estimate of the phase noise and nonlinearity contributions for the free space beat signal generated by the down ramp in block 502. In block 520, the conjugate phasor is multiplied by the delta phase Δ φ(t). In block 521, the conjugate phasor is multiplied with the free-space downward ramp beat signal to remove phase noise and generate a denoised beat signal. In block 522, the PSD of the denoised beat signal is calculated and the resulting peaks are located. In block 523, a check is made to determine if any peak pairs remain. If so, blocks 518 through 523 are repeated. If all peaks have been processed, the final ranges and velocities of the multiple targets can be calculated and block 523 proceeds to block 553 to end operation of the multi-target identification system 570.

[0130] 6a and 6b illustrate an example operation cycle 600 of an FMCW LIDAR system that integrates phase estimation, active phase cancellation, predistortion waveform generation, and Peak Pairing Fit according to various embodiments of the present disclosure.

[0131] In block 601, power is applied to the system and laser. In block 602, the laser temperature is allowed to stabilize using data generated by temperature sensor 603. Once the laser temperature has stabilized, in block 605, the system loads an existing laser drive waveform 604 to modulate the laser frequency. Due to system wear, changes in environmental conditions, etc., the loaded laser drive waveform 604 may not be optimal. In block 606, a check may be performed to determine whether the laser's frequency characteristics (e.g., chirp rate and chirp nonlinearity) sufficiently meet specifications. If the laser characteristics are within specifications, block 606 proceeds to block 609 (shown in FIG. 6b). If the laser characteristics deviate from specifications, block 606 proceeds to block 607. In situ tuning of the laser drive waveform 604 is performed in block 607. The in situ tuning may be performed by predistortion waveform generator 400 and / or predistortion waveform generator 430 (shown in FIGS. 4a and 4b). Block 607 proceeds to both block 608 and block 609 (shown in FIG. 6b). The waveform updated in block 608 is stored for the next power cycle.

[0132] In FIG. 6b, once the existing version of the laser drive waveform 604 meets specifications, or alternatively, field modifications are completed, block 609 initiates the process of capturing frame data. Block 609 may include multiple sub-operations. Once the laser frequency is modulated, the LIDAR system waits for a trigger indicating that an upward ramp (frequency chirp increase) has begun in block 610. In response, performance of a free-space FMCW LIDAR measurement is triggered in block 611, which simultaneously triggers measurement of time-dependent laser phase fluctuations (noise and nonlinearity) in block 612. The operations associated with blocks 611 and 612 may correspond to optical phase measurement apparatus 100 (shown in FIG. 1). The measurement results of blocks 611 and 612 are combined in block 613. The operation of block 613 may represent the operation of phase noise cancellation system 300 (shown in FIG. 3a), phase noise cancellation system 340 (shown in FIG. 3b), multi-target identification system 500 (shown in FIG. 5a), and / or multi-target identification system 570 (shown in FIG. 5b). The operation of block 613 may improve the fidelity of the free-space LIDAR measurement. In block 614, a beat signal spectrum is calculated. Blocks 610-614 are repeated for the down-ramp. In block 615, the distance and velocity of points in the scene may be calculated based on the filtered result PSD.

[0133] Typically, a LIDAR frame contains one or more points. In block 616, the LIDAR system determines whether more points remain in the frame. If more points remain in the frame, block 616 proceeds to block 617, where the position of the beam emitted by the FMCW LIDAR system is corrected, and the up / down ramp capture process of blocks 610-615 is repeated. Once all points in the frame have been captured, block 616 proceeds to block 618, where a point cloud is assembled, completing work cycle 600.

[0134] The order in which some or all of the process blocks appear in the systems and processes 300, 340, 400, 430, 500, 570 and / or 600 should not be considered limiting. Rather, one of ordinary skill in the art having the benefit of this disclosure will understand that some of the process blocks may occur in various orders not illustrated or in parallel.

[0135] FIG. 7a illustrates an example autonomous vehicle 700 that may include the LIDAR designs of FIGS. 1-6 according to embodiments of the present disclosure. The illustrated autonomous vehicle 700 includes a sensor array configured to capture one or more objects in the autonomous vehicle's external environment and generate sensor data related to the captured one or more objects for the purpose of controlling the operation of the autonomous vehicle 700. FIG. 7a illustrates sensors 733A, 733B, 733C, 733D, and 733E. FIG. 7b illustrates a top view of the autonomous vehicle 700 that includes sensors 733A, 733B, 733C, 733D, and 733E, as well as sensors 733F, 733G, 733H, and 733I. Any of sensors 733A, 733B, 733C, 733D, 733E, 733F, 733G, 733H, and / or 733I may include a LIDAR device that includes the designs of FIGS. 1-6. 7c shows a block diagram of an example system 799 for an autonomous vehicle 700. For example, the autonomous vehicle 700 may include a powertrain 702 including a prime mover 704 that may be powered by an energy source 706 and provide electrical power to a drivetrain 708. The autonomous vehicle 700 may further include a control system 710 that includes directional control 712, powertrain control 714, and brake control 716. The autonomous vehicle 700 may be embodied as any number of different vehicles, including vehicles that may transport people and / or cargo and that may operate in a variety of different environments. It will be understood that the components 702-716 described above may vary widely depending on the type of vehicle in which these components are used.

[0136] For example, the embodiments described below focus on wheeled land vehicles such as cars, vans, trucks, or buses. In such embodiments, prime mover 704 may include one or more electric motors and / or internal combustion engines (among other things). Energy sources may include, for example, a fuel system (e.g., providing gasoline, diesel, hydrogen), a battery system, solar panels or other renewable energy sources, and / or a fuel cell system. Drivetrain 708 may include wheels and / or tires along with a transmission and / or any other mechanical drive components suitable for converting the power output of prime mover 704 into vehicle motion, as well as one or more brakes configured to controllably stop or slow autonomous vehicle 700 and directional or steering components suitable for controlling the trajectory of autonomous vehicle 700 (e.g., a rack-and-pinion steering connection that allows one or more wheels of autonomous vehicle 700 to pivot about a generally vertical axis to change the angle of the wheel's plane of rotation relative to the vehicle's longitudinal axis). In some embodiments, a combination of powertrain and energy source may be used (e.g., in the case of an electric / gas hybrid vehicle). In some embodiments, multiple electric motors (e.g., dedicated to individual wheels or axles) can be used as prime movers.

[0137] Directional control 712 may include one or more actuators and / or sensors for controlling and receiving feedback from directional or steering components to enable autonomous vehicle 700 to follow a desired trajectory. Powertrain control 714 may be configured to control the output of powertrain 702, such as by controlling the output power of prime mover 704 and controlling the transmission gears of drivetrain 708, thereby controlling the speed and / or direction of autonomous vehicle 700. Brake control 716 may be configured to control one or more brakes, such as disc or drum brakes coupled to the vehicle's wheels, to slow or stop autonomous vehicle 700.

[0138] Other vehicle types, including, but not limited to, off-road vehicles, all-terrain or track vehicles, or construction equipment, will necessarily utilize other powertrains, drivetrains, energy sources, directional control, powertrain control, and braking control as would be understood by one of ordinary skill in the art having the benefit of this disclosure. Also, in some embodiments, some components may be combined; for example, vehicle directional control may be handled primarily by modifying the output of one or more prime movers. Accordingly, the embodiments disclosed herein are not limited to the specific application of the technology described herein in wheeled, land-based autonomous vehicles.

[0139] In the illustrated embodiment, autonomous control for the autonomous vehicle 700 is embodied in a vehicle control system 720, which may include one or more processors and one or more memories 724 within processing logic 722, where the processing logic 722 is configured to execute program code (e.g., instructions 726) stored in the memory 724. The processing logic 722 may include, for example, a graphics processing unit (GPU) and / or a central processing unit (CPU). The vehicle control system 720 may be configured to control the powertrain 702 of the autonomous vehicle 700 in response to output from the optical mixers of the LIDAR pixels. The vehicle control system 720 may be configured to control the powertrain 702 of the autonomous vehicle 700 in response to output from the plurality of LIDAR pixels. The vehicle control system 720 may be configured to control the powertrain 702 of the autonomous vehicle 700 in response to output from the microcomputer 203 generated based on signals received from the FPA system 202.

[0140] Sensors 733A-733I may include various sensors suitable for collecting data from the autonomous vehicle's surrounding environment for use in controlling the operation of the autonomous vehicle. For example, sensors 733A-733I may include a RADAR unit 734, a LIDAR unit 736, a 3D positioning sensor 738, and a satellite navigation system such as GPS, GLONASS, BeiDou, Galileo, or Compass. The LIDAR design of FIGS. 1-6 may be included in LIDAR unit 736. LIDAR unit 736 may include, for example, multiple LIDAR sensors distributed around the autonomous vehicle 700. In some embodiments, 3D positioning sensor 738 may determine the vehicle's position on Earth using satellite signals. Sensors 733A-733I may optionally include one or more ultrasonic sensors, one or more cameras 740, and / or an inertial measurement unit (IMU) 742. In some embodiments, camera 740 may be a monographic or stereographic camera capable of recording still and / or video images. Camera 740 may include a complementary metal-oxide-semiconductor (CMOS) image sensor configured to capture images of one or more objects in the environment external to autonomous vehicle 700. IMU 742 may include multiple gyroscopes and accelerometers capable of detecting linear and rotational motion of autonomous vehicle 700 in three directions. One or more encoders (not shown), such as wheel encoders, may be used to monitor the rotation of one or more wheels of autonomous vehicle 700.

[0141] The outputs of sensors 733A-733I may be provided to a control subsystem 750, which includes a localization subsystem 752, a trajectory subsystem 756, a perception subsystem 754, and a control system interface 758. The localization subsystem 752 may be configured to determine the position and orientation (also sometimes referred to as “attitude”) of the autonomous vehicle 700 within the surrounding environment, and generally within a particular geographic region. The autonomous vehicle's position can be compared to the positions of additional vehicles in the same environment as part of labeled autonomous vehicle data generation. The perception subsystem 754 may be configured to detect, track, classify, and / or determine objects in the environment surrounding the autonomous vehicle 700. The trajectory subsystem 756 may be configured to generate trajectories for stationary and moving objects in the environment, as well as trajectories for the autonomous vehicle 700 given a desired destination over a particular time frame. Machine learning models, according to some embodiments, may be utilized to generate the vehicle trajectory. The control system interface 758 is configured to communicate with the control system 710 to implement the trajectory of the autonomous vehicle 700. In some embodiments, machine learning models can be utilized to control an autonomous vehicle to execute a planned trajectory.

[0142] It will be understood that the collection of components for vehicle control system 720 shown in FIG. 7 is merely exemplary in nature. Individual sensors may be omitted in some embodiments. In some embodiments, the different types of sensors shown in FIG. 7c may be used redundantly and / or to cover different areas in the environment surrounding the autonomous vehicle. In some embodiments, different types and / or combinations of control subsystems may be used. Also, while subsystems 752-758 are shown as separate from processing logic 722 and memory 724, it will be understood that in some embodiments, some or all of the functionality of subsystems 752-758 may be embodied in program code, such as instructions 726, resident in memory 724 and executed by processing logic 722, and that these subsystems 752-758 may, in some cases, be embodied using the same processor and / or memory. In some embodiments, the subsystems may be embodied in various dedicated circuit logic, various processors, various field programmable gate arrays (FPGAs), various application-specific integrated circuits (ASICs), various real-time controllers, etc., and as previously described, multiple subsystems may utilize circuits, processors, sensors, and / or other components. Additionally, the various components of vehicle control system 720 may be networked in various ways.

[0143] In some embodiments, autonomous vehicle 700 may also include a secondary vehicle control system (not shown) that can be used as a redundant or backup control system for autonomous vehicle 700. In some embodiments, the secondary vehicle control system can operate autonomous vehicle 700 in response to specific events. The secondary vehicle control system may have only limited functionality in response to specific events detected by primary vehicle control system 720. In yet other embodiments, the secondary vehicle control system may be omitted.

[0144] In some embodiments, different architectures including various combinations of software, hardware, circuit logic, sensors, and networks can be used to implement the various components shown in FIG. 7c. Each processor can be embodied, for example, as a microprocessor, and each memory can refer to not only main storage but also any auxiliary levels of memory, such as cache memory, non-volatile or backup memory (e.g., programmable or flash memory), or read-only memory. Each memory can also be considered to include memory storage physically located elsewhere in autonomous vehicle 700, such as any cache memory of the processor, as well as any storage capacity used as virtual memory, such as that stored in a mass storage device or other computer controller. Processing logic 722 shown in FIG. 7c, or entirely separate processing logic, can be used to perform additional functions in autonomous vehicle 700 beyond those of autonomous control, such as controlling an entertainment system or operating doors, lights, or convenience features.

[0145] For additional storage, autonomous vehicle 700 may also include one or more mass storage devices, such as a removable disk drive, a hard disk drive, a direct access storage device ("DASD"), an optical drive (e.g., a CD drive, a DVD drive), a solid state storage drive (SSD), a network attached storage, a storage area network, and / or a tape drive. Autonomous vehicle 700 may also include a user interface 764, such as one or more displays, touchscreens, voice and / or gesture interfaces, buttons, and other tactile controls, through which autonomous vehicle 700 receives inputs from and generates outputs for the passenger. In some embodiments, inputs from the passenger may be received via another computer or electronic device, such as via an app on a mobile device or via a web interface.

[0146] In some embodiments, autonomous vehicle 700 may include one or more network interfaces, e.g., network interface 762, suitable for communicating with one or more networks 770 (e.g., a local area network (“LAN”), a wide area network (“WAN”), a wireless network, and / or the Internet, etc.), which may enable communication of information with other computers and electronic devices, including, for example, a central service such as a cloud service for autonomous vehicle 700 to receive environmental and other data for use in autonomous control. In some embodiments, data collected by one or more sensors 733A-733I may be uploaded via network 770 to computing system 772 for further processing. In such embodiments, a timestamp may be associated with each instance of vehicle data prior to uploading.

[0147] 7c, as well as the various additional controllers and subsystems disclosed herein, generally operate and execute under the control of an operating system or otherwise rely on various computer software applications, components, programs, objects, modules, or data structures, as described in detail below. Additionally, the various applications, components, programs, objects, or modules may execute on one or more processors of other computers coupled to the autonomous vehicle 700 via a network 770, for example, in a distributed, cloud-based, or client-server computing environment, whereby the processing required to implement the functionality of a computer program is allocated across multiple computers and / or services via the network.

[0148] The routines executed to implement the various embodiments described herein are referred to herein as "program code," whether embodied as part of an operating system or as part of a specific application, component, program, object, module, or instruction sequence, or a subset thereof. Program code generally comprises one or more instructions resident in various memory and storage devices that, when read and executed by one or more processors, perform the steps necessary to carry out the steps or elements embodying various aspects of the present disclosure. Furthermore, while the embodiments have been and will continue to be described in the context of fully functional computers and systems, it will be understood that the various embodiments described herein can be distributed as program products in various forms and can be embodied regardless of the particular type of computer-readable medium used to actually carry out the distribution. Examples of computer-readable media include types of non-transitory media, such as volatile and non-volatile memory devices, floppy and other removable disks, solid-state drives, hard disk drives, magnetic tape, and optical disks (e.g., CD-ROM, DVD).

[0149] Additionally, various program code described below may be identified based on the application in which it is implemented in a particular embodiment. However, it should be understood that any particular program nomenclature below is used merely for convenience, and thus the present invention should not be limited to use with only any particular application identified and / or implied by such nomenclature. Furthermore, given the generally infinite number of ways in which a computer program may be organized into routines, procedures, methods, modules, objects, etc., and the various ways in which program functionality may be allocated among the various software layers resident within a typical computer (e.g., operating system, libraries, APIs, applications, applets), it should be understood that the present disclosure is not limited to the specific organization and allocation of program functionality described herein.

[0150] Those skilled in the art having the benefit of this disclosure will recognize that the exemplary environment illustrated in Figure 7c is not intended to limit the embodiments disclosed herein. Indeed, those skilled in the art will recognize that other alternative hardware and / or software environments may be used without departing from the scope of the embodiments disclosed herein.

[0151] As used herein, the term "processing logic" (e.g., processing logic 722) may include one or more processors, microprocessors, multi-core processors, application specific integrated circuits (ASICs), and / or field programmable gate arrays (FPGAs) for performing the operations disclosed herein. In some embodiments, memory (not shown) is integrated with the processing logic for storing instructions for performing operations and / or storing data. Additionally, the processing logic may include analog or digital circuitry for performing operations according to embodiments of the present disclosure.

[0152] A "unit" herein may be comprised of hardware components (e.g., AND, OR, NOR, XOR gates), implemented as circuitry embedded in one or more processors, ASICs, FPGAs, or photonic integrated circuits (PICs), and / or defined in part as software instructions stored in one or more memories within a LIDAR system. For example, various units disclosed herein according to embodiments of the present disclosure may be at least partially embodied in the LIDAR processing engine 201, microcomputer 203, laser controller 205, and / or FPA driver 209 (shown in FIG. 2).

[0153] "Memory" or "memories" as described herein may include one or more volatile or non-volatile memory architectures. "Memory" or "memories" may be removable and non-removable media embodied in any method or technology for storage of information such as computer-readable instructions, data structures, program modules, or other data. Example memory technologies may include RAM, ROM, EEPROM, flash memory, CD-ROM, DVD, high-definition multimedia / data storage disks or other optical storage devices, magnetic cassettes, magnetic tape, magnetic disk storage devices, or other non-transmission media that can be used to store information so that it can be accessed by a computing device.

[0154] The network includes any network or network system, such as, but not limited to, a peer-to-peer network, a local area network (LAN), a wide area network (WAN), a public network such as the Internet, a private network, a cellular network, a wireless network, a wired network, a combined wired and wireless network, and a satellite network.

[0155] The communication channel is IEEE802.11 protocol, SPI (Serial Peripheral Interface), I2 It may include or be routed by one or more wired or wireless communications using an Inter-Integrated Circuit (C), Universal Serial Port (USB), Controller Area Network (CAN), cellular data protocols (e.g., 3G, 4G, LTE, 5G), optical communications networks, Internet Service Providers (ISPs), peer-to-peer networks, LANs, WANs, public networks (e.g., the "Internet"), private networks, satellite networks, or others.

[0156] The computing devices may include desktop computers, laptop computers, tablets, phablets, smartphones, feature phones, server computers, etc. The server computers may be located remotely in a data center or stored locally.

[0157] The processes described above are described in terms of computer software and hardware. The described techniques may constitute machine-executable instructions embodied in a tangible or non-transitory machine (e.g., computer) readable storage medium that, when executed by a machine, causes the machine to perform the described operations. Additionally, the processes may be embodied in hardware, such as an application-specific integrated circuit ("ASIC").

[0158] A tangible, non-transitory, machine-readable storage medium may include any mechanism for providing (e.g., storing) information in a form accessible by a machine (e.g., a computer, a network device, a PDA, a manufacturing tool, any device having one or more processor sets, etc.). For example, machine-readable storage media include recordable / non-recordable media (e.g., Read Only Memory (ROM), Random Access Memory (RAM), magnetic disk storage media, optical storage media, flash memory devices, etc.).

[0159] The foregoing description of illustrated embodiments of the present disclosure, including what is described in the Abstract, is not intended to be exhaustive or to limit the disclosure to the precise form disclosed. While specific embodiments of and examples for the present disclosure have been described herein for illustrative purposes, various modifications are possible within the scope of the present disclosure, as those skilled in the relevant art will recognize.

[0160] Such modifications of the invention can be made in light of the foregoing detailed description. The terms used in the following claims should not be construed to limit the disclosure to the specific embodiments disclosed herein. Rather, the scope of the disclosure is to be determined entirely by the following claims, which are to be construed in accordance with established doctrines of claim interpretation.

Claims

1. 1. A LIDAR system comprising: a reference measurement unit comprising a fixed-length interferometer driven by a laser source, the reference measurement unit configured to determine a first phase of a first reference beat signal generated by the fixed-length interferometer from an upward frequency chirp of the laser source and a second phase of a second reference beat signal generated by the fixed-length interferometer from a downward frequency chirp of the laser source; a LIDAR measurement unit comprising a free-space interferometer configured to determine distances to a plurality of objects based on a plurality of peak pairs and at least one of the first phase of the first reference beat signal and the second phase of the second reference beat signal; the plurality of peak pairs are defined by a first frequency spectral peak paired with a second frequency spectral peak, the first frequency spectral peak being generated by the free-space interferometer from a first beat signal from the upward frequency chirp of the laser source, and the second frequency spectral peak being generated by the free-space interferometer from a second beat signal from the downward frequency chirp of the laser source; the LIDAR measurement unit is configured to estimate a distance to at least one of the plurality of objects using at least one of the plurality of peak pairs; the LIDAR system is configured to remove phase noise occurring during the upward frequency chirp from the first beat signal using the first phase of the first reference beat signal, or to remove phase noise occurring during the downward frequency chirp from the second beat signal using the second phase of the second reference beat signal. LIDAR system.

2. the LIDAR measurement unit is configured to estimate a distance to at least one of the plurality of objects based on a time of flight of a free-space laser signal to at least one of the plurality of objects using sequentially paired peaks of the plurality of peak pairs; a first peak pair of the plurality of peak pairs includes a first peak of the first frequency spectral peaks and a first peak of the second frequency spectral peaks; 10. The LIDAR system of claim 1.

3. the LIDAR measurement unit is configured to repeatedly determine distances to the plurality of objects; the LIDAR measurement unit is configured to estimate a distance for each of the plurality of objects based on a time-of-flight estimate associated with each of the plurality of objects; the time-of-flight estimates associated with each of the plurality of objects are determined from a respective peak pair of one of the first frequency spectral peaks and one of the second frequency spectral peaks.

10. The LIDAR system of claim 1.

4. the LIDAR measurement unit is configured to delay the first phase of the first reference beat signal by a first duration equal to a first time-of-flight estimate associated with at least one of the plurality of objects, or the LIDAR measurement unit is configured to delay the second phase of the second reference beat signal by a second duration equal to a second time-of-flight estimate associated with at least one of the plurality of objects, to identify phase noise of the laser source.

10. The LIDAR system of claim 1.

5. the LIDAR measurement unit is configured to multiply the first beat signal by a complex conjugate phasor to remove the phase noise from the first beat signal and generate a denoised first beat signal.

5. The LIDAR system of claim 4.

6. the LIDAR measurement unit is configured to remove the phase noise generated during the upward frequency chirp from the first beat signal; the reference measurement unit is configured to remove the phase noise generated during the downward frequency chirp from the second beat signal.

10. The LIDAR system of claim 1.

7. the free space interferometer combining a first local oscillator signal and a first object reflected signal to generate the first beat signal from the upward frequency chirp; the free space interferometer combining a second local oscillator signal and a second object reflection signal to generate the second beat signal from the downward frequency chirp; 10. The LIDAR system of claim 1.

8. The LIDAR system is a frequency modulated continuous wave (FMCW) LIDAR system.

10. The LIDAR system of claim 1.

9. the reference measurement unit determines the first phase of the first reference beat signal based on an in-phase signal and a quadrature signal from the fixed length interferometer.

10. The LIDAR system of claim 1.

10. to determine the first phase of the first reference beat signal, the reference measurement unit is configured to apply an arctangent operation to the quadrature signal divided by the in-phase signal and to apply an integral operation to an output from the arctangent operation.

10. The LIDAR system of claim 9.

11. 1. A LIDAR system including a LIDAR measurement unit and a reference measurement unit, the reference measurement unit comprises a fixed-length interferometer driven by a laser source and configured to determine a first phase of a first reference beat signal generated by the fixed-length interferometer from an upward frequency chirp of the laser source and a second phase of a second reference beat signal generated by the fixed-length interferometer from a downward frequency chirp of the laser source; the LIDAR measurement unit includes a free-space interferometer and is configured to determine distances to a plurality of objects based on a plurality of peak pairs and at least one of the first phase of the first reference beat signal and the second phase of the second reference beat signal; the plurality of peak pairs are defined by a first frequency spectral peak paired with a second frequency spectral peak, the first frequency spectral peak being generated by the free-space interferometer from a first beat signal from the upward frequency chirp of the laser source, and the second frequency spectral peak being generated by the free-space interferometer from a second beat signal from the downward frequency chirp of the laser source; the LIDAR measurement unit is configured to estimate a distance to at least one of the plurality of objects using at least one of the plurality of peak pairs; the LIDAR system is configured to remove phase noise occurring during the upward frequency chirp from the first beat signal using the first phase of the first reference beat signal to generate a denoised first beat signal, or to remove phase noise occurring during the downward frequency chirp from the second beat signal using the second phase of the second reference beat signal to generate a denoised second beat signal. a LIDAR system; and one or more processors for controlling an autonomous vehicle control system based on at least one of a distance measurement or a velocity measurement associated with at least one of the plurality of objects obtained based on at least one of the denoised first beat signal or the denoised second beat signal. Autonomous vehicle control systems.

12. the LIDAR measurement unit is configured to estimate a distance to at least one of the plurality of objects based on a time of flight of a free-space laser signal to at least one of the plurality of objects using sequentially paired peaks of the plurality of peak pairs; a first peak pair of the plurality of peak pairs includes a first peak of the first frequency spectral peaks and a first peak of the second frequency spectral peaks; The autonomous vehicle control system of claim 11.

13. the LIDAR measurement unit is configured to repeatedly determine distances to the plurality of objects; the LIDAR measurement unit is configured to estimate a distance for each of the plurality of objects based on a time-of-flight estimate associated with each of the plurality of objects; the time-of-flight estimates associated with each of the plurality of objects are determined from a respective peak pair of one of the first frequency spectral peaks and one of the second frequency spectral peaks. The autonomous vehicle control system of claim 11.

14. the LIDAR measurement unit is configured to delay the first phase of the first reference beat signal by a first duration equal to a first time-of-flight estimate associated with at least one of the plurality of objects; the LIDAR measurement unit multiplies the first beat signal by a complex conjugate phasor to remove the phase noise from the first beat signal and generate the denoised first beat signal. The autonomous vehicle control system of claim 11.

15. the free space interferometer combining a first local oscillator signal and a first object reflected signal to generate a first beat signal from the upward frequency chirp; the free space interferometer combining a second local oscillator signal and a second object reflected signal to generate a second beat signal from the downward frequency chirp; The autonomous vehicle control system of claim 11.

16. 1. A LIDAR system including a LIDAR measurement unit and a reference measurement unit, the reference measurement unit comprises a fixed-length interferometer driven by a laser source and configured to determine a first phase of a first reference beat signal generated by the fixed-length interferometer from an upward frequency chirp of the laser source and a second phase of a second reference beat signal generated by the fixed-length interferometer from a downward frequency chirp of the laser source; the LIDAR measurement unit includes a free-space interferometer and is configured to determine distances to a plurality of objects based on a plurality of peak pairs and at least one of the first phase of the first reference beat signal and the second phase of the second reference beat signal; the plurality of peak pairs are defined by a first frequency spectral peak paired with a second frequency spectral peak, the first frequency spectral peak being generated by the free-space interferometer from a first beat signal from the upward frequency chirp of the laser source, and the second frequency spectral peak being generated by the free-space interferometer from a second beat signal from the downward frequency chirp of the laser source; the LIDAR measurement unit is configured to estimate a distance to at least one of the plurality of objects using at least one of the plurality of peak pairs; the LIDAR system is configured to remove phase noise occurring during the upward frequency chirp from the first beat signal using the first phase of the first reference beat signal to generate a denoised first beat signal, or to remove phase noise occurring during the downward frequency chirp from the second beat signal using the second phase of the second reference beat signal to generate a denoised second beat signal. a LIDAR system; and one or more processors for controlling an autonomous vehicle based on at least one of a distance measurement or a velocity measurement associated with at least one of the plurality of objects obtained based on at least one of the denoised first beat signal or the denoised second beat signal. Autonomous vehicles.

17. the LIDAR measurement unit is configured to estimate a distance to at least one of the plurality of objects based on a time of flight of a free-space laser signal to at least one of the plurality of objects using sequentially paired peaks of the plurality of peak pairs; a first peak pair of the plurality of peak pairs includes a first peak of the first frequency spectral peaks and a first peak of the second frequency spectral peaks; 17. The autonomous vehicle of claim 16.

18. the LIDAR measurement unit is configured to repeatedly determine distances to the plurality of objects; the LIDAR measurement unit is configured to estimate a distance for each of the plurality of objects based on a time-of-flight estimate associated with each of the plurality of objects; the time-of-flight estimates associated with each of the plurality of objects are determined from a respective peak pair of one of the first frequency spectral peaks and one of the second frequency spectral peaks.

17. The autonomous vehicle of claim 16.

19. the LIDAR measurement unit is configured to delay the first phase of the first reference beat signal by a first duration equal to a first time-of-flight estimate associated with at least one of the plurality of objects; the LIDAR measurement unit multiplies the first beat signal by a complex conjugate phasor to remove the phase noise from the first beat signal and generate the denoised first beat signal.

17. The autonomous vehicle of claim 16.

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