Measuring distance changes using radar
Patent Information
- Application Number
- CN202610171940.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Priority Date
- 2025-02-17
- Filing Date
- 2026-02-06
- Publication Date
- 2026-08-18
Smart Images
Figure CN122592379A_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to using radar to measure changes in distance over time. Background Technology
[0002] Radar systems are used to locate and measure the distance and relative velocity of target objects. In automotive applications, radar systems are used to detect objects around a vehicle, such as determining the distance and relative velocity of other objects to avoid collisions, for speed control, and for navigation. Radar systems can also be used inside vehicles, sometimes called in-cabin vehicle radar (IVR), to determine the presence and vital signs of occupants, such as the driver or passengers. Similar radar systems can also be used to determine vital signs in other situations, such as for non-contact detection of heart rate and respiration in patient care. Radar systems used for such applications can use frequencies in the millimeter-wave region, specifically in the range of approximately 30 to 300 GHz. Summary of the Invention
[0003] According to a first aspect, a computer-implemented method is provided for measuring the change of distance over time using FMCW radar signals, the method comprising: transmitting a series of chirped signals; receiving a series of reflected chirped signals from a target; and processing the series of reflected chirped signals to determine the change of distance to the target over time based on the phase difference between the transmitted chirped signals and the received chirped signals, wherein the phase difference between consecutive frames derived from the received chirped signals is fitted to a function to correct any phase difference between the consecutive frames greater than 2π.
[0004] The steps of processing the series of reflected chirped signals can be performed on one or more range partitions of the range response derived from the series of reflected chirped signals.
[0005] The function can be fitted to a first series of phase measurements in the first frame. A second series of phase measurements in the second frame can be offset by, for example, an integer multiple of 2π, to fit the second series of phase measurements to the function. In some examples, the offset can be any number, i.e., not necessarily an integer multiple of 2π.
[0006] The function can be a polynomial function, a sine function, or a combination of a polynomial function and a sine function. For example, the function can be a first-order polynomial function, a second-order polynomial function, or a third-order polynomial function.
[0007] The method may include providing an output signal that provides displacement toward the target over time.
[0008] The method may include applying a bandpass filter to the output signal and determining the amplitude and frequency of the output signal within a frequency range defined by the bandpass filter.
[0009] Each frame may include two or more chirps, and consecutive frames may be separated by an interframe interval longer than each frame.
[0010] According to a second aspect, a method for determining the vital signs of a user in a vehicle is provided, the method comprising performing the method according to a first aspect, wherein the target is the user, and the output signal is the user's heart rate and / or respiratory rate.
[0011] According to a third aspect, an FMCW radar system is provided, comprising: a transmitter configured to generate and transmit a series of chirped signals; a receiver configured to receive reflected chirped signals from a target; and a signal processor configured to process the series of reflected chirped signals to determine a time-varying distance to the target based on a phase difference between the transmitted and received chirped signals, wherein the signal processor is configured to fit a function to the phase difference between consecutive frames containing the received chirped signals to correct any phase difference between the consecutive frames greater than 2π.
[0012] The signal processor can be configured to fit the function to a first series of phase measurements in a first frame. The signal processor can also be configured to offset a second series of phase measurements in a second frame by an integer multiple of 2π to fit the second series of phase measurements to the function.
[0013] The function can be a polynomial function, a sine function, or a combination of a polynomial function and a sine function. The signal processor can be configured to provide an output signal of displacement toward the target over time.
[0014] Other features related to the first aspect can also be applied to the functions of the signal processor described in the third aspect.
[0015] According to a fourth aspect, a computer program is provided, the computer program including instructions for causing a signal processor of an FMCW radar system to execute the method according to the first aspect.
[0016] A computer program may be provided that, when executed on a computer, causes the computer to configure the computer to include any device comprising the circuits, controllers, sensors, filters, or means disclosed herein, or to perform any of the methods disclosed herein. The computer program may be a software implementation, and the computer may be considered any suitable hardware, including digital signal processors, microcontrollers, and implementations in read-only memory (ROM), erasable programmable read-only memory (EPROM), or electrically erasable programmable read-only memory (EEPROM), these being non-limiting examples. The software implementation may be an assembler.
[0017] The computer program may be provided on a non-transitory computer-readable medium, which may be a physical computer-readable medium, such as an optical disc or storage device, or may be embodied as a transient signal. Such a transient signal may be a network download, including an internet download.
[0018] These and other aspects of the invention will become apparent from the embodiments described below, and these aspects will be illustrated with reference to the embodiments. Attached Figure Description
[0019] Embodiments will be described by way of example only with reference to the accompanying drawings, in which:
[0020] Figure 1 This is a schematic diagram of an example frequency modulated continuous wave (FMCW) radar transceiver;
[0021] Figure 2 This is a simplified block diagram illustrating an example conventional system for monitoring vital signs using radar;
[0022] Figure 3 This is a simplified block diagram illustrating an example system for monitoring vital signs using radar with phase stitching;
[0023] Figure 4a It is a schematic representation of a series of radar chirped signals transmitted as a continuous stream;
[0024] Figure 4b It is a schematic representation of a series of radar chirps transmitted as a discontinuous stream;
[0025] Figure 5 This is an image of an example in-vehicle scene where the driver is being monitored by a radar system;
[0026] Figure 6 This is an example plot showing the time displacement of the chest at different sampling times;
[0027] Figure 7 This is an example plot of the phase measurements over time, showing the difference between the unadjusted and adjusted phase measurements;
[0028] Figure 8 This is an example plot of the distance between estimated and actual measurements over time;
[0029] Figure 9 It is a plot of the signal-to-noise ratio (SNR) as a function of frequency for an exemplary series of respiratory rate measurements;
[0030] Figure 10This is a plot of the normalized amplitude of the expanded phase signal as a function of frequency for an exemplary series of respiratory rate measurements with an input noise power of -140 dBm.
[0031] Figure 11 This is a plot of the normalized amplitude of the expanded phase signal as a function of frequency for an exemplary series of respiratory rate measurements with an input noise power of -125 dBm.
[0032] Figure 12 It is a plot of the average SNR as a function of input noise power;
[0033] Figure 13 It is a plot of the unfolded phase that changes over time before and after splicing;
[0034] Figure 14a It is a plot of the spliced and corrected phase that varies over time with the application of a passband filter;
[0035] Figure 14b It is a plot of the uncorrected phase that expands over time with the application of a passband filter;
[0036] Figure 15 It is a plot of the absolute amplitude of a series of respiratory rate measurements as a function of frequency when splicing is applied;
[0037] Figure 16 It is a plot of the absolute amplitude of a series of respiratory rate measurements as a function of frequency without splicing; and
[0038] Figure 17 This is a flowchart illustrating an example method for obtaining displacement measurements from radar signals.
[0039] It should be noted that the figures are illustrative and not drawn to scale. For clarity and convenience in the figures, the relative dimensions and proportions of the parts have been shown by enlarging or reducing their size. The same reference numerals are generally used to refer to corresponding or similar features in modified and different embodiments. Detailed Implementation
[0040] This disclosure primarily relates to an automotive onboard vital signs monitoring system based on FMCW radar-associated signal processing. In such systems, target displacement is, for example, displacement of a surface such as the torso or neck of a vehicle occupant, where movement originating from breathing and heartbeat is detected by identifying phase changes in a beat signal derived from a combination of transmitted and received signals.
[0041] In existing systems for vital sign detection, which typically operate at 60 GHz, changes in distance or displacement cannot be accurately detected if the phase difference between consecutive chirps exceeds 2π radians (or 360°) due to the indeterminacy of this discontinuity. Therefore, the pulse repetition interval (PRI) is often configured based on the maximum expected displacement, but this results in sending more chirps. This consumes more power and may exceed the safety limits for transmitting radar signals used in vehicles.
[0042] The method and system disclosed herein overcome the aforementioned problem by estimating the target displacement based on a function (e.g., multinomial regression) derived from a set of chirped target distance information through a process that can be termed "stitching," as this process essentially stitches together the originally discontinuous expanded output phase (or distance) information originating from different sets of chirped ...
[0043] The specific application of the methods and systems disclosed herein is in-cabin FMCW radar systems. Such radar systems are used to detect the presence and vital signs (e.g., respiratory rate and heart rate) of the vehicle's driver and / or one or more passengers. Several radar-based technologies are currently available for in-cabin sensing, including UWB or 60 GHz radar systems. Other systems based on 140 GHz radar, which are more sensitive to displacement measurements, are also under development and may become available.
[0044] Typical respiratory rate can be approximately 12-20 bpm (0.2-0.33 Hz), while typical heart rate can be approximately 60-120 bpm (1-2 Hz). When measuring such low-frequency signals and small changes in distance between the radar and the target (approximately a few millimeters or less), checking the distance from a range / Doppler image is often insufficiently accurate. Instead, the (unfolded) phase can be used to track movement, and then spectral analysis (e.g., FFT) can be used to detect the frequency components of the desired signal to isolate the desired displacement signal indicative of vital signs. The unfolded phase is then converted to range.
[0045] Current regulations (e.g., for 60 GHz and 140 GHz radar systems) indicate lower maximum average EIRP for automotive applications. The problem is that the signal-to-noise ratio (SNR) may be insufficient to achieve the required performance in certain scenarios. Heartbeat monitoring, in particular, requires a high SNR due to the smaller detected movement, lower received signal, and propagation losses (which are even higher at 140 GHz). Energy consumption can also be an issue, especially for electric vehicles, as these sensors may need to operate over long activity cycles, not just while the vehicle is in motion.
[0046] A key feature of the method disclosed herein is a technique that coherently connects output data from processing multiple chirps, even when the phase rotation between chirps or chirp groups is greater than 2π. This adds a degree of freedom to the system by relaxing PRI constraints, thereby enabling different chirp and frame scheduling strategies. The new scheduling strategies can be used, for example, to concentrate energy into fewer chirps by reducing the duty cycle, to optimize energy consumption by reducing the number of chirps, or a trade-off between the two. The method can be used in in-cabin vehicle FMCW radar systems within the millimeter-wave frequency range, particularly at specific frequencies such as 140 GHz, 60 GHz, and 77 GHz.
[0047] Figure 1 This is a schematic diagram of an example FMCW radar transceiver 100 used to detect the distance to target 104. The transceiver 100 includes a transmitter 101 and a receiver 102. The transmitter 101 generates and transmits radar signals 105, typically in the form of a series of chirps, and the receiver 102 receives signals 106 reflected from one or more targets 104. The received time-series signal r(t) is combined with the signal s(t) generated by the transmitter 101, and the resulting signal y(t) is converted into a digital signal by an ADC and then processed by a signal processor 103.
[0048] Figure 2An exemplary series of modules that can constitute a signal processor 103 are shown. The signal processor 103 is arranged to acquire ADC output (or IQ) samples 201 from the ADC of the receiver 102 and process the samples to provide one or more vital sign signals 202, such as respiratory and heart rate signals. The signal processor 103 is a simplified version of an implementation of a millimeter-wave sensor processor configured to measure chest displacement due to breathing and heartbeat based on FMCW radar signals. First, the ADC output samples 201 are converted using a range FFT module 203 to provide a range response, followed by DC compensation and artifact removal performed by a compensation module 204. A range partition detection module 205 locates a selected range partition based on the range response and extracts the phase from the selected range partition. A phase unrolling module 206 then unrolls the phase from one or more partitions corresponding to the selected target measurement value. The unrolled phase measurement value is then analyzed using a corresponding vibration FFT module 207, which applies a selected bandpass filter and outputs vital sign signals 202 corresponding to desired measurements such as heart rate and respiratory rate.
[0049] When measuring low-frequency signals (e.g., vital signs with frequencies of approximately 0.1–2 Hz) and / or small range changes between radar and a target (e.g., breathing, involving changes of only a few centimeters), it is not feasible to use range information from range / Doppler maps. Instead, the unfolded phase is used to track movement, and then FFT or spectral analysis is used to detect the frequency components of the signal. Phase changes... This can be expressed as:
[0050]
[0051] Equation 1
[0052] in, It is the wavelength of the signal, and This refers to the change in distance to the target. Therefore, a shorter wavelength, i.e., a higher frequency signal, will provide better displacement sensitivity.
[0053] Figure 3 It shows that it can be located in Figure 2 The signal processor 103 shown includes a splicing module 301 between the phase unrolling module 206 and the vibration FFT module 207. The splicing module 301 allows discontinuous chirp groups to be connected together; otherwise, these discontinuous chirp groups would be too far apart and unable to be connected using conventional techniques due to the long dwell time between chirps. The first regression model 302 receives the current frame. Phase of expansion The second regression model 303 receives the unfolded phase from the previous frame. , Estimation module 304 estimates the next initial phase based on the second regression model 303. The outputs from estimation module 304 and the first regression model 302 are provided to correction module 305, which corrects the phase point through compensation, applying an offset to the expanded phase based on the estimated value of the next phase from estimation module 304. The output is then provided by the splicing module 301 to the vibration FFT module 207, and provides a life rate output 202 as in the conventional signal processor 103. The splicing module 301 may include multiple modules that operate in parallel on signals received from multiple targets, each individual module operating in the same manner, and each module providing an output signal to the corresponding vibration FFT module 207.
[0054] Figure 4a and 4b The differences between a conventional continuous sequence of chirps and a set of chirps or chirps of an exemplary discontinuous series according to this disclosure are shown. Figure 4a In the process, the chirp signal 401 has an on-time T of each chirp signal 401. on The break time T between the continuous chirping signal and the break time T off A defined pulse repetition interval (PRI) is used. Therefore, PRI can be defined as the time between the start of consecutive chirps in a frame. Signal processing of these chirped signals 401 requires a moving window 402 in which coherent integration of the chirps is performed. For a typical vehicle-mounted vital signs monitoring application, the moving window 402 can be, for example, approximately 20 seconds. The minimum slow-time sampling frequency, which is the reciprocal of the maximum PRI, can be derived from the maximum distance displacement between chirps; otherwise, signal processing might be unable to distinguish which modulus 2π the phase rotation corresponds to.
[0055] Figure 4b An exemplary series of chirps according to this disclosure is shown, wherein the chirp signal 401 is provided as a discontinuous series. In this example, PRI and Figure 4a The same applies to both, meaning the disconnect and connect times are the same. The difference lies in the interval T provided between consecutive chirp groups. off,frameEach group of chirps can be referred to as a frame. In this case, two groups of chirps are provided, with an interval between each group. In general, a series of chirped signals comprises groups with two or more chirps, separated by intervals, where each interval is longer than the pulse repetition interval in a group with two or more chirps. As described above, after the phase unrolling operation, a splicing process is applied to the chirps in the inter-frame moving window 402. Using splicing allows for different chirp scheduling strategies because some chirps (e.g., 50%) can be omitted while still being able to distinguish the phase rotation of the target signal. The benefit of doing so is the ability to remove or relax the maximum PRI constraint between some chirps, thus allowing for a trade-off between power consumption and SNR. For example, about half the chirps used in a conventional arrangement can be transmitted with about half the power consumption. Alternatively, individual chirps can be transmitted at higher instantaneous power while maintaining similar or reduced total power consumption.
[0056] Figure 5 This is a diagram of an exemplary vehicle-mounted experimental setup, in which the FMCW radar 501 is located inside the vehicle to monitor the vital signs of the driver 502. The following description... Figure 13-17 The measurements shown are derived from this arrangement. Radar 501 is located in front of the pilot 502, allowing the processing of reflected signals from the pilot 502 to obtain vital sign measurements, such as respiration and heart rate.
[0057] Figure 6 A series of measurements of chest movement in a simulated static breathing scenario are shown, where the distance between the user's chest and the radar is plotted over time. In this example, each frame 601 includes 375 chimes with a 50% duty cycle, meaning the duration of the inter-frame interval is the same as each frame of the chime. Figure 6 The diagram also plots the continuous function 602, which represents the function to which the discontinuous series of frames 601 are fitted. Figure 6 The distance results are derived from the phase expansion of the signal within the partition with the highest power.
[0058] Figure 7 Showing from Figure 6 A more detailed representation of the first two frames of the plot, showing the first frame 701 and the second frame 702 before the splicing process. Because, as stated above, it is only possible to determine... The absolute phase is within the range, so the expanded phase of the second frame 702 is discontinuous with the expanded phase of the first frame 701. The phase of the second frame 702 can be adjusted by fitting the first frame 701 to a predefined function, in this case, a polynomial function 703. The adjusted second frame 704 is obtained by multiplying the first frame 701 by a factor of 703, such that both the first frame 701 and the adjusted second frame 704 fit the function 703. This process can be performed continuously using a moving window, where the function is applied across the entire window based on the previous number of frames fitted to the function, and each subsequent frame is adjusted to fit the function. This process effectively "stitches" each subsequent frame to the previous frame, thus providing a continuous series of unfolded phase measurements. Therefore, it is not necessary to make the frame interval less than 703. The polynomial regression performed on the previous frame is used to estimate the initial phase of the next frame, even if the phase discontinuity between frames is greater than [value missing]. Therefore, the technique ensures signal continuity in an explicit manner by evaluating the polynomial based on the timestamp of the start of the current frame.
[0059] The above process is repeated for a large number of frames, and the phase information is converted into displacement (according to Equation 1 above) to obtain a measure of chest displacement showing characteristic frequency and displacement change. This is in Figure 8 As shown in the figure, the estimated displacement 801 based on the spliced frame fitted to the polynomial function is shown together with the actual displacement 802, demonstrating a close match.
[0060] The function used to fit phase measurements across multiple frames can be, for example, a first-, second-, third-, or higher-order polynomial function. Other functions, such as sine functions, can also be used, which are particularly suitable for signals with periodic variations. The function can be fitted to the entirety of each frame, or it can be fitted to only a portion of each frame to save memory requirements.
[0061] FFT is used to detect the frequency of displacement measurements. Figure 9 An example FFT output is shown, where amplitude is plotted as a function of vibrational frequency in cycles per minute (60 cycles per minute corresponds to a frequency of 1 Hz). In this example, the respiratory rate is detected by the peak measurement 901, which is approximately 15 bpm. The SNR 902 of the vibrational spectrum is given by the difference between the amplitude of the peak measurement 901 and the average background amplitude 903. SNR can be used as a metric to compare different configurations or algorithms. The higher the SNR, the easier it is to detect vital signal frequencies. The peak-to-sidelobe ratio (PSR) 904 is given by the difference between the amplitude of the peak measurement 901 and the amplitude of the sidelobes 905.
[0062] Figure 10The simulation results are shown, illustrating the spectrum of the amplitude of the unfolded phase signal from the first set of simulated measurements 1001 and the second set of simulated measurements 1002. The first measurement 1001 was obtained from a baseline using a conventional series of chirps with an average EIRP of approximately 3 dBm. The second measurement was obtained from a series of measurements obtained using phase splicing as described above, where the transmit power is adapted to maintain a similar EIRP by having a higher transmit power per frame and, in this case, a duty cycle of approximately 30%. In both cases, a target signal of approximately 30 bpm (0.5 Hz) represents breathing. A measure of the SNR of the target signal is that the SNR value should be above approximately 10 dB to enable signal detection, which is achieved in both cases. In this example, the noise power is relatively low at -140 dBm. In this low-noise configuration, both techniques provide sufficient SNR, significantly exceeding 10 dB, to enable easy detection of the target vibration signal. It should be understood that the SNR values provided in these results are illustrative only and should not be construed as limiting.
[0063] Figure 11 Further results are shown, where the noise power increases to approximately -120 dBm, generating a power comparable to... Figure 10 The lower SNR measure of both the first measurement 1101 and the second measurement 1102 was obtained in a similar manner to the first measurement 1001 and the second measurement 1002. In this example, the second measurement 1102 is significantly better than the first measurement obtained by conventional methods by about 17 dB.
[0064] Figure 12 The differences between conventional measurements and measurements obtained using the stitching method described herein are further illustrated, with the average SNR plotted as a function of input noise power. A first set of baseline measurements 1201 is obtained from a conventional radar system. Second measurements 1202, third measurements 1203, fourth measurements 1204, and fifth measurements 1205 are obtained from phase stitching using different duty cycles of 0.1, 0.3, 0.5, and 0.7, respectively. The parameters for each case are as described above regarding... Figure 10 and 11 As described above, for lower input noise power below approximately -133 dBm, the conventional measurement 1201 shows a higher SNR than all those measurements obtained using the stitching method. For higher input noise power, the stitching method, especially at lower duty cycles, shows an improved SNR than the baseline measurement. In summary, the new phase stitching technique performs better than the conventional method, particularly in scenarios with higher input noise power and especially with low duty cycle configurations. Similar results were obtained by measuring the PSR metric as a function of input noise power.
[0065] Figure 13The results shown are based on measurements obtained using a 140 GHz FMCW radar configured with a transmit scan time of 25.6 microseconds, a PRI of 150.4 microseconds, an off time of 73 ms, and a chirp bandwidth of 10 GHz. Figure 13 The expanded phase result shown in the upper plot illustrates the discontinuity between the first frame 1301 and the second frame 1302 before splicing. The first frame 1301 is fitted to a polynomial function 1303, and after splicing, the expanded phase of the adjusted second frame 1302' is fitted to the polynomial function 1303, as shown below. Figure 13 As shown in the lower part of the diagram.
[0066] Figure 14a It is a plot of the expanded phase after splicing and after applying a passband filter to isolate the breathing signal, which can be clearly seen in the phase plot over time. Figure 14b The result without splicing is shown, in which a clear signal cannot be seen. This is in Figure 15 The FFT spectrum further illustrates this, where a clear peak can be seen at approximately 11 bpm for the phase-stitched signal, while... Figure 16 In the FFT spectrum of the unspliced signal, no clear peaks are displayed.
[0067] Figure 17 This is a flowchart illustrating a simplified method according to the present disclosure. In a first step 1701, a series of chirped signals are transmitted. In a second step 1702, a series of reflected chirped signals are received from the target. In a third step, frames of reflected chirped signals are processed to extract phase information from the transmitted and reflected chirped signals and to obtain the unfolded phase from the signals. In a fourth step 1704, frames are fitted to a function. In a fifth step 1705, the displacement or distance change to the target is determined from the fitted frames. In practice, the method operates iteratively, continuously transmitting and receiving chirps and processing the received frames using a moving window to fit each successive frame to a function fitted to one or more previous frames, i.e., previously received and processed frames. Each frame is fitted to the function to correct for any phase difference greater than 2π compared to the immediately preceding frame.
[0068] Other variations and modifications will become apparent to those skilled in the art upon reading this disclosure. Such variations and modifications may involve equivalent and other features known in the field of FMCW radar systems and used in place of or in addition to the features described herein.
[0069] Although the appended claims relate to specific combinations of features, it should be understood that the scope of the disclosure of this invention also includes any novel feature or combination of novel features or any generalized form thereof explicitly or implicitly disclosed herein, regardless of whether it relates to the same invention claimed in any claim or whether it alleviates any or all of the same technical problems as those alleviated by this invention.
[0070] Features described in the context of different embodiments may be combined and provided in a single embodiment. Conversely, various features described in the context of a single embodiment for the sake of brevity may also be provided individually or in any sub-combination. The applicant hereby reminds that new claims may be formulated based on such features and / or combinations of such features during the examination of this application or any other application derived therefrom.
[0071] For the sake of completeness, it is also stipulated that the term "comprising" does not exclude other elements or steps, the term "a (a or an)" does not exclude that a plurality of, a single processor or other unit may perform the functions of the several components described in the claims, and the reference numerals in the claims should not be interpreted as limiting the scope of the claims.
Claims
1. A computer-implemented method for measuring distance changes over time using FMCW radar signals, characterized in that, The method includes: Send a series of chirping signals; Receive a series of reflected chirp signals from the target; and The series of reflected chirped signals are processed to determine the time-varying distance to the target based on the phase difference between the transmitted and received chirped signals. The phase difference between consecutive frames derived from the received chirped signal is fitted to a function to correct any phase difference between the consecutive frames greater than 2π.
2. The method according to claim 1, characterized in that, The function is fitted to the first series of phase measurements in the first frame.
3. The method according to any one of the preceding claims, characterized in that, This includes providing an output signal that indicates the displacement toward the target over time.
4. The method according to claim 3, characterized in that, This includes applying a bandpass filter to the output signal and determining the amplitude and frequency of the output signal within a frequency range defined by the bandpass filter.
5. The method according to any one of the preceding claims, characterized in that, Each frame includes two or more chirps, and consecutive frames are separated by an inter-frame interval longer than the pulse repetition interval of the two or more chirps.
6. A method for determining the vital signs of a user in a vehicle, characterized in that, The method includes performing the method according to any one of the preceding claims, wherein the target is the user, and the output signal is the user's heart rate and / or respiratory rate.
7. An FMCW radar system, characterized in that, include: A transmitter configured to generate and transmit a series of chirped signals; A receiver configured to receive reflected chirped signals from a target; as well as A signal processor, configured to process the series of reflected chirped signals, to determine the time-varying distance to the target based on the phase difference between the transmitted and received chirped signals. The signal processor is configured to fit the phase difference between consecutive frames containing the received chirped signal to a function to correct any phase difference between the consecutive frames greater than 2π.
8. The FMCW radar system according to claim 7, characterized in that, The signal processor is configured to fit the function to a first series of phase measurements in the first frame.
9. The FMCW radar system according to claim 8, characterized in that, The signal processor is configured to offset a second series of phase measurements from the second frame to fit the second series of phase measurements to the function, the offset being optionally an integer multiple of 2π.
10. A computer program product, characterized in that, Includes instructions for causing the signal processor of the FMCW radar system to execute the method according to any one of claims 1 to 6.