Patent Description:
Applications in the millimeter-wave frequency regime have gained significant interest in the past few years due to the rapid advancement in low cost semiconductor technologies, such as silicon germanium (SiGe) and fine geometry complementary metal-oxide semiconductor (CMOS) processes. Availability of high-speed bipolar and metal-oxide semiconductor (MOS) transistors has led to a growing demand for integrated circuits for millimeter-wave applications at e.g., <NUM>, <NUM>, <NUM>, and <NUM> and also beyond <NUM>. Such applications include, for example, automotive radar systems and multi-gigabit communication systems.

In some radar systems, the distance between the radar and a target is determined by transmitting a frequency modulated signal, receiving a reflection of the frequency modulated signal (also referred to as the echo), and determining a distance based on a time delay and/or frequency difference between the transmission and reception of the frequency modulated signal. Accordingly, some radar systems include a transmit antenna to transmit the radio-frequency (RF) signal, and a receive antenna to receive the reflected RF signal, as well as the associated RF circuits used to generate the transmitted signal and to receive the RF signal. In some cases, multiple antennas may be used to implement directional beams using phased array techniques. A multiple-input and multiple-output (MIMO) configuration with multiple chipsets can be used to perform coherent and non-coherent signal processing as well.

Document <CIT> proposes a system for monitoring fatigue of a driver using a radar sensor and a Kalman filter. Further, document <CIT> proposes to vary, based on the heart rate, characteristics of a bandpass filter used for detecting a signal corresponding to a heartbeat from a detected signal of a heartbeat sensor. Document <CIT> proposes a system for monitoring the vital signs of multiple people using millimeter-wave signals.

There may be a demand for providing an improved concept for a method and a device.

Such a demand may be satisfied by the subject matter of any of the claims.

In accordance with an embodiment, a method includes: transmitting radar signals with the millimeter-wave radar; receiving reflected radar signals with the millimeter-wave radar; generating a displacement signal indicative of a displacement of a target based on the reflected radar signals; filtering the displacement signal using a bandpass filter to generate a filtered displacement signal; determining a first rate indicative of a heartbeat rate of the target based on the filtered displacement signal; tracking a second rate indicative of the heartbeat rate of the target with a track using a Kalman filter; updating the track based on the first rate; and updating a setting of the bandpass filter based on the updated track, wherein generating the displacement signal comprises: generating range data based on the reflected radar signals; performing target detection based on the range data to detect the target; and generating the displacement signal based on in-phase (I) and quadrature (Q) signals associated with the detected target.

In accordance with an embodiment, a device includes: a millimeter-wave radar configured to transmit chirps and receive reflected chirps; and a processor configured to: generate a displacement signal indicative of a displacement of a target based on the reflected chirps, filter the displacement signal using a bandpass filter to generate a filtered displacement signal, determine a first rate indicative of a heartbeat rate of the target based on the filtered displacement signal, track a second rate indicative of the heartbeat rate of the target with a track using a Kalman filter, update the track based on the first rate, and update a setting of the bandpass filter based on the updated track, wherein the processor is configured to generate the displacement signal based on the reflected chirps by: generating range data based on the reflected radar chirps; performing target detection based on the range data to detect the target; and generating the displacement signal based on in-phase (I) and quadrature (Q) signals associated with the detected target.

The making and using of the embodiments disclosed are discussed in detail below.

The description below illustrates the various specific details to provide an in-depth understanding of several example embodiments according to the description. References to "an embodiment" in this description indicate that a particular configuration, structure or feature described in relation to the embodiment is included in at least one embodiment. Consequently, phrases such as "in one embodiment" that may appear at different points of the present description do not necessarily refer exactly to the same embodiment.

Embodiments of the present invention will be described in a specific context, a system and method for heartbeat tracking of a human using a Kalman Filter. Embodiments of the present invention may be used for other targets, such as a dog, or other animals. Examples not according to the invention may be used for other vital signs tracking, such as respiration.

Embodiments of the present invention may be used in a variety of applications. For example, some embodiments may be used in patient monitoring in hospitals, sleep apnea detection, presence sensing in homes and offices, driver monitoring in autonomous cars, and physiological monitoring in surveillance and earthquake rescue operations. Other applications are also possible.

In an embodiment of the present invention, a millimeter-wave radar system enables a contactless, non-invasive method to monitor and track the heartbeat of a human target using a Kalman filter that applies a band-pass filter to a time-domain heartbeat signal to generate a filtered signal from which the heartbeat rate is estimated. After a coarse estimation of the heartbeat rate, a bandwidth of the applied band-pass filter is successively updated (e.g., narrowed) based on respective successive estimations of the heartbeat rate. In some embodiments, measurement segments with random body movements are identified and ignored for the Kalman filter update.

Advantages of some embodiments include high accuracy heartbeat rate determination during only a short observation window. Some embodiments advantageously prevent jumps in the determined heartbeat rate.

Monitoring the vital signs of, e.g., human targets finds wide usage in the fields of, e.g., consumer electronics, medical care, surveillance, driver assistance, and industrial applications.

A radar, such as a millimeter-wave radar, may be used to detect and track humans. Once the human targets are identified, the radar may be used to monitor vital signs such as the heartbeat rate of the identified human targets. In embodiments, therefore, a millimeter-wave radar enables a contactless, non-invasive method for vital sensing, which may advantageously increase the comfort of the human target during the vital signs monitoring.

<FIG> shows radar system <NUM>, according to an embodiment of the present invention. Radar system <NUM> includes millimeter-wave radar <NUM> and processor <NUM>. In some embodiments, millimeter-wave radar <NUM> includes processor <NUM>.

During normal operation, millimeter-wave radar <NUM> transmits a plurality of radiation pulses <NUM>, such as chirps, towards scene <NUM> with transmitter (TX) circuit <NUM>. In some embodiments the chirps are linear chirps (i.e., the instantaneous frequency of the chirp varies linearly with time).

The transmitted radiation pulses <NUM> are reflected by objects in scene <NUM>. The reflected radiation pulses (not shown in <FIG>), which are also referred to as the echo signal, are received by millimeter-wave radar <NUM> using receiver (RX) circuit <NUM> and processed by processor <NUM> to, for example, detect and track targets such as humans.

The objects in scene <NUM> may include static humans, such as lying human <NUM>, humans exhibiting low and infrequent motions, such as standing human <NUM>, and moving humans, such as running or walking humans <NUM> and <NUM>. The objects in scene <NUM> may also include static objects (not shown), such as furniture, and periodic movement equipment. Other objects may also be present in scene <NUM>.

Processor <NUM> analyses the echo data to determine the location of humans using signal processing techniques. For example, in some embodiments, a range FFT is used for estimating the range component of the location of a detected human (e.g., with respect to the location of the millimeter-wave radar). The azimuth component of the location of the detected human may be determined using angle estimation techniques.

In some embodiments, a range-Doppler map (image) is generated from the echo data, and a two-dimensional (2D) moving target identification (MTI) is performed on the range-Doppler map to detect moving targets.

Processor <NUM> may be implemented as a general purpose processor, controller or digital signal processor (DSP) that includes, for example, combinatorial circuits coupled to a memory. In some embodiments, the DSP may be implemented with an ARM architecture, for example. In some embodiments, processor <NUM> may be implemented as a custom application specific integrated circuit (ASIC). Some embodiments may be implemented as a combination of hardware accelerator and software running on a DSP or general purpose micro-controller. Other implementations are also possible.

Millimeter-wave radar <NUM> operates as a frequency-modulated continuous-wave (FMCW) radar that includes a millimeter-wave radar sensor circuit, and one or more antenna(s). Millimeter-wave radar <NUM> transmits (using TX <NUM>) and receives (using RX <NUM>) signals in the <NUM> to <NUM> range via the one or more antenna(s) (not shown). Some embodiments may use frequencies outside of this range, such as frequencies between <NUM>, and <NUM>. Examples not according to the invention may use frequencies between <NUM> and <NUM>.

In some embodiments, the echo signals received by millimeter-wave radar <NUM> are filtered and amplified using band-pass filter (BPFs), low-pass filter (LPFs), mixers, low-noise amplifier (LNAs), and intermediate frequency (IF) amplifiers in ways known in the art. The echo signals are then digitized using one or more analog-to-digital converters (ADCs) for further processing. Other implementations are also possible.

Generally, monitoring a heartbeat signal of a human target with a radar-based system is a complex endeavor. For example, the amplitude of the heartbeat signal is generally smaller than the amplitude of the respiration signal of the human target. The amplitude of the heartbeat signal is also smaller than the amplitude caused by the movement of the human target (e.g., when walking), as well as random body movements of the human target (e.g., such lifting an arm, twisting the torso, etc.). Additionally, the signal shape of a single heartbeat may be dependent on the subject, the chosen measurement spot, and the distance to the antenna.

In an embodiment of the present invention, a pre-processing step is performed in which data inflicted by random body movements are identified. During an adaptive heartbeat rate filtering and monitoring step, a band-pass filter is applied to a time-domain heartbeat signal to generate a filtered signal, from which the heartbeat rate is determined. The determined heartbeat rate per time instance is stabilized and smoothed by applying a Kalman filter, which additionally updates the bandpass filter limits. In some embodiments, when data inflicted by random body movement is detected, the Kalman filter is not updated with the new measurements and, instead, the state prediction of the Kalman filter is applied.

In some embodiments, identifying data inflicted by random body movements advantageously allows for preventing jumps in the determined heartbeat rate. Performing a time-domain (band-pass) filtering of the time-domain heartbeat signal advantageously allows for fast and accurate identification of the heartbeat signal from other signals, such as the respiration signal.

<FIG> shows a flow chart of embodiment method <NUM> for determining a heartbeat rate of a human target, according to an embodiment of the present invention. Method <NUM> may be implemented, for example, by FMCW radar system <NUM>.

During step <NUM>, millimeter-wave radar <NUM> transmits a frame of chirps (<NUM>) towards scene <NUM>. The time between chirps of a frame is generally referred to as pulse repetition time (PRT). In some embodiments, the time interval between the end of the last chirp of a frame and the start of the first chirp of the next frame is the same as the PRT so that all chirps are transmitted (and received) equidistantly.

In some embodiments, the chirps have a bandwidth of <NUM> within the <NUM> UWB band, the frame time has a duration of <NUM>, and the PRT is <NUM> (corresponding to an effective sampling rate of <NUM>).

During step <NUM>, raw data is generated based on the reflected chirps received by millimeter-wave radar <NUM>. For example, in some embodiments, during step <NUM>, the received radar signal is mixed with the transmitted radar signals to generate an IF signal that is low-pass filtered and digitized with an ADC to generate the raw data.

During step <NUM>, a displacement signal of a human target is generated, where the displacement signal is a time-domain signal. The displacement signal is indicative of a vital sign, such as heartbeat or respiration, for example. For example, small movements in the chest of a human target may be indicative of a vital sign of the human target. In some embodiments, and as explained later, e.g., with respect to <FIG>, the displacement signal of a human target is generated by calculating arctangent function of the baseband signal from the millimeter-wave radar, as arctan(Q/I), e.g., as given by Equation <NUM>.

During step <NUM>, the displacement signal generated during step <NUM> is processed to determine the heartbeat of a human target. As shown in <FIG>, step <NUM> includes steps <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, and <NUM>.

As will be explained in greater detail below, during step <NUM>, a first rate indicative of the heartbeat of the human target is determined (e.g., measured) from the displacement signal (in step <NUM>). A second rate indicative of the heartbeat of the human target is predicted by predicting the heartbeat rate based on a track of a Kalman filter (i.e., based on the history of previous heartbeat rate determinations for the human target by the Kalman filter) but without considering the current first rate (in step <NUM>). If it is determined based on the predicted second rate that the first rate is acceptable (in step <NUM>), the track of the Kalman filter is updated (in step <NUM>) to include information from the first rate and the bandpass filter is updated (in step <NUM>) based on the updated track. If it is determined based on the predicted second rate that the first rate is not acceptable (in step <NUM>), the track of the Kalman filter is not updated (in step <NUM>) to include information from the first rate.

During step <NUM>, the displacement signal is filtered with a band-pass filter. In some embodiments, a fourth order Butterworth digital filter is applied to the displacement signal. A filter of different order and/or different type may also be applied.

In some embodiments, the default pass-band of the band-pass filter is from <NUM> to <NUM>. In some embodiments, the passband of the band-pass filter may be dynamically changed within the initial default range (e.g., between <NUM> and <NUM>). In some embodiments, the pass-band may be outside this frequency range, such as having a lower cutoff frequency fL of <NUM> or lower and/or higher cutoff frequency fH higher than <NUM>, such as <NUM>, or higher.

During step <NUM>, the first rate zk+<NUM> indicative of the heartbeat rate of the human target is determined, e.g., estimated, based on the filtered displacement signal, where k+<NUM> is indicative of the next time step of the Kalman filter, which is currently being evaluated. For example, in some embodiments, the first rate zk+<NUM> is estimated by counting the number of peaks in the filtered displacement signal. In some embodiments, the first rate zk+<NUM> may be estimated based on the time between peaks of the filtered displacement signal. In some embodiments, the first rate zk+<NUM> may also be estimated through template matching and estimating the frequency peaks. Other implementations are also possible.

When the first rate zk+<NUM> is recorded during the first time step k = <NUM> (in step <NUM>), a track of a Kalman filter is initialized during step <NUM>. In some embodiments, k may correspond to a single frame or to a group of frames (such as <NUM> frames, for example). As will be explained in greater detail later, the Kalman filter keeps track of the state of the heartbeat rate of the target for every kth update.

Kalman filtering may be understood as a recursive Bayesian process, which may be applied when the measured values contain unpredictable or random errors, uncertainties or variations. With respect to <FIG>, the process of Kalman filtering includes steps <NUM>, <NUM>, <NUM>, and <NUM>. The process of Kalman filtering may also include steps <NUM> and <NUM>.

The state vector x used by the Kalman filter may be expressed as <MAT> where h, ḣ , and ḧ represents the second rate indicative of the heartbeat rate of the human target in Hz, its first order derivative, and its second order derivative, respectively.

The state vector in the Kalman filter at time step k may be represented as a Gaussian process with mean xk|k and covariance or uncertainty Pk|k. The vector xk|k includes the second rate hk.

In some embodiments, the Kalman filter used is designed with a constant acceleration model, in which predicting the state in time step k+<NUM> from state vector in time step k is given by <MAT> where <MAT> where Δt is the time elapsed between two time steps of the Kalman filter.

The observation model may be given by <MAT> where <MAT>.

In some embodiments, the uncertainty Q of the state prediction originates from process noise, which may be modeled as <MAT> where <MAT> represents the acceleration process noise, and where <MAT>.

The variance of the measurement noise may be given by R = δh<NUM> , which represents the expected square of the estimation error.

During step <NUM>, the track may be initialized with default Q, R noise matrices and initial state and covariance xo|o, Po|o, along with default band-pass filter settings.

During step <NUM>, for each time step k, the Kalman filter makes a state prediction based on the track associated with the heartbeat rate of the human target. In some embodiment, the state prediction may be determined by <MAT> where the state prediction includes a prediction of the second rate (hk+<NUM>) as part of the state prediction xk+<NUM>|k, e.g., as shown in Equations <NUM> and <NUM>. The prediction of the second rate (hk+<NUM>) may also be referred to as the heartbeat rate prediction.

In some embodiments, when k><NUM>, step <NUM> may be performed, before, after, or concurrently with step <NUM>.

During step <NUM>, an ellipsoidal gating function checks whether the first rate zk+<NUM> (also referred to as the heartbeat rate estimate zk+<NUM>) is within a gating window. The gating function may be given by <MAT> where yk+<NUM> is given by Equation <NUM>, and where γ is the gate threshold. When the heartbeat rate estimate zk+<NUM> is outside the gating region (i.e., when the gating function is higher than γ ), the state of the Kalman filter is not updated (during step <NUM>) based on the heartbeat rate estimate zk+<NUM> (since the new heartbeat rate estimate zk+<NUM> is an outlier, and, therefore there is no target detected associated to the track).

During step <NUM>, a counter is incremented to keep track of the number of time steps in which the first rate zk+<NUM> falls outside the gating region. If it is determined during step <NUM> that there have been more than M consecutive instances in which the first rate zk+<NUM> is outside the gating region (e.g., the counter count is higher than M), then the band-pass filter settings are updated during step <NUM>. For example, in some embodiments, the band-pass filter pass-band is set to a default value, such as from <NUM> to <NUM>. In other embodiments, the pass-band is incrementally broadened each time step <NUM> is performed (e.g., by increasing the pass-band by a predetermined amount, such as <NUM>, or a predetermined percentage, such as <NUM>%, until reaching a predetermined maximum, such as the default value).

In some embodiments, the settings of the band-pass filter are updated based on the state prediction (e.g., from Equation <NUM>) during step <NUM>. For example, in some embodiments, during step <NUM>, the lower cutoff frequency fL and higher cutoff frequency fH of the band-pass filter are updated as <MAT>.

In some embodiments, updating the pass-band filter based on Equation <NUM> advantageously allows for detecting a heartbeat rate that might have drifted during the period that there were no updates. In some embodiments, the track is killed and the filter settings reset to default values when there is not target detections for N consecutive time steps of the Kalman filter. In some embodiments, M is equal to <NUM> and N is equal to <NUM>. Other values may also be used.

If it is determined during step <NUM> that the counter count is not higher than M during step <NUM>, then the band-pass filter settings are not updated during step <NUM>.

When the heartbeat rate estimate zk+<NUM> is inside the gating region (i.e., when the gating function is lower than γ ), the state of the Kalman filter is updated (during step <NUM>) based on the first rate zk+<NUM>. For example, in some embodiments, the state of the Kalman filter is updated by <MAT> where I is the identify matrix and Kk+<NUM> is given by <MAT>.

During step <NUM>, the counter is reset. As shown, steps <NUM>, <NUM>, and <NUM> are used to keep track of consecutive instances in which the first rate zk+<NUM> is outside the gating region. Some embodiments may keep track of consecutive instances in which the first rate zk+<NUM> is outside the gating region in other ways.

During step <NUM>, the settings of the band-pass filter are updated based on the updated state (e.g., from Equation <NUM>). For example, in some embodiments, during step <NUM>, the lower cutoff frequency fL and higher cutoff frequency fH of the band-pass filter are updated as <MAT>.

In some embodiments, the lower cutoff frequency fL and higher cutoff frequency fH of the band-pass filter are updated as <MAT> where α is a real number, such as between, e.g., <NUM> and <NUM>.

In some embodiments, the pass-band of the band-pass filter is updated so that it is centered at the frequency of the current heartbeat rate estimate zk+<NUM>.

As illustrated in <FIG>, embodiments use an adaptive bandpass filtering to extract the heartbeat rate signal along with Kalman filter-based tracking procedure. In some embodiments, the combination of these steps leads to narrowing of the pass-band range of the band-pass filter while simultaneously approximating the actual heartbeat rate as the center frequency of the pass-band.

In some embodiments, performing adaptive bandpass filtering in combination with Kalman filter-based tracking and the use of, e.g., ellipsoidal gating advantageously allows for minimizing the impact of intermodulation product, e.g., caused by the combination of the heartbeat signal, the respiration signal, and harmonics thereof.

<FIG> shows a flow chart of embodiment method <NUM> for generating a displacement signal for determining a heartbeat rate of a human target, according to an embodiment of the present invention. Step <NUM> may be implemented as method <NUM>.

During step <NUM>, a range FFT is performed on the raw data, where the maximum unambiguousness range for the range FFT is based on the PRT, the number of samples per chirp, chirp time, and sampling rate of the analog-to-digital converter (ADC). In some embodiments, the ADC has <NUM> bits. ADC's with different resolution, such as <NUM> bits, <NUM> bits, or <NUM> bits, for example, can also be used.

In some embodiments, the range FFT is applied on all samples of the observation window. The observation window may be implemented as consecutive windows or as sliding windows and may have a length of one or more frames. For example, in some embodiments, the observation window is implemented as a sliding window in which the length of the observation window is a plurality of time steps which are evaluated during each time step. For example, in an embodiment in which the time step is equal to <NUM> frame, and the observation window is a sliding window with <NUM> frames, then, for each frame, the last <NUM> frames are used as the observation window. In an embodiment, an observation window with a duration of <NUM> frames has a duration of about <NUM>.

In some embodiments, the observation window is equal to the duration of the time step k (which may be one or more frames).

Range data, such as a range image, such as a range-Doppler image or a range cross-range image is generated during step <NUM>.

During step <NUM>, detection of potential targets is performed. For example, in some embodiments, an order statistics (OS) constant false alarm rate (CFAR) (OS-CFAR) detector is performed during step <NUM>. The CFAR detector generates target detection data (also referred to as target data) in which, e.g., "ones" represent targets and "zeros" represent non-targets based, e.g., on the power levels of the range image. For example, in some embodiments, the CFAR detector compares the power levels of the range-Doppler image with a threshold, and points above the threshold are labeled as targets while points below the threshold are labeled as non-targets. Although targets may be indicated by ones and non-targets may be indicated by zeros, it is understood that other values may be used to indicate targets and non-targets.

Targets present in the target data are clustered during step <NUM> to generate clustered targets (since, e.g., a human target may occupy more than one range bin). For example, in an embodiment, a density-based spatial clustering of applications with noise (DBSCAN) algorithm is used to associate targets to clusters during step <NUM>. The output of DBSCAN is a grouping of the detected points into particular targets. DBSCAN is a popular unsupervised algorithm, which uses minimum points and minimum distance criteria to cluster targets, and may be implemented in any way known in the art. Other clustering algorithms may also be used.

During step <NUM>, movement detection of the clustered targets is performed. For example, in some embodiments, the complex FFT output is stored in a sliding window. Then, the amplitude of each range bin is summed up along the complete sliding window. The peak in the complete sliding window within chosen minimum and maximum ranges is the target range bin for the current frame.

In some embodiments, data is not further processed (e.g., with respect to method <NUM>, go from step <NUM> directly to step <NUM>) if the standard deviation in the target range bin along the sliding window is above a predetermined threshold (movement detection). For example, in some embodiments, if it determined during step <NUM> that the human target is moving (if the standard deviation in the target range bin along the sliding window is above a predetermined threshold), data from the current frame may not be further processed (e.g., and step <NUM> of <FIG> may be performed next).

If it is determined during step <NUM> that the human target is not moving (if the standard deviation in the target range bin along the sliding window is below the predetermined threshold), the displacement signal may be determined, e.g., during steps <NUM> and <NUM>. It is understood that when it is determined during step <NUM> that the target is not moving, the target may be exhibiting some movement, such as movements of the target's hands outside the field of view of the radar, or any other movement that results in a standard deviation below the predetermined threshold. Such may be the case, for example, of a human target that is sitting or standing, for example.

During step <NUM>, the angle of the compensated target data is calculated by arctangent demodulation of the signal from the selected range bin selected during step <NUM> (the detected target) and that is determined to be not moving during step <NUM>. The resulting phase values in the range of [-π,+ π] are unwrapped between two consecutive data points during step <NUM>. For example, during step <NUM>, the phase is unwrapped by adding or subtracting 2π for phase jumps larger than -π or +π, respectively.

With λ being the wavelength of the carrier frequency and λ/<NUM> representing the unambiguousness (phase) range, the displacement of the human target can subsequently be calculated by <MAT> where I and Q are the in-phase and quadrature-phase components of the carrier, respectively.

As illustrated in <FIG>, some embodiments advantageously improve the quality of the heartbeat estimate by avoiding the update of the state of the Kalman filter when the human target is moving.

Some embodiments may track the heartbeat of multiple human targets simultaneously by using multiple tracks of the Kalman filter.

<FIG> illustrate test results of a millimeter-wave radar system implementing method <NUM>, according to an embodiment of the present invention. The test results were performed on a millimeter-wave radar system similar to millimeter-wave radar system <NUM>, and using chirps having a bandwidth of <NUM> within the <NUM> UWB band, having a PRT of <NUM>, a frame time of <NUM>, an observation window of <NUM> frames (implemented as a sliding window), an ADC having a <NUM> bit resolution, and seven human targets placed in front of the millimeter-wave radar at a distance of about <NUM> from the radar. A chest belt was used as a reference heartbeat rate sensor. For the test, all measurement rows have a length of <NUM> frames, corresponding to approximately <NUM>.

<FIG> show the heartbeat rate of seven human targets during breath-holding, and with common breathing, respectively.

As shown in <FIG>, in some embodiments, method <NUM> achieves better results (for all measurement rows) than methods using a fixed pass-band for the band-pass filter. For example, methods using a fixed pass-band for the band-pass filter show a high deviation from the reference sensor and sometimes have large jumps between consecutive values.

As shown, in some embodiments, narrowing down the bandwidth of the pass-band of the band-pass filter and tracking the heartbeat rate value advantageously prevent such jumps and smooth the estimated values (output of step <NUM>). While the first values may show a considerable deviation from the reference sensor, the Kalman filtering approximates the values towards the reference in each step. The resulting root-mean-square error (RMSE) for the embodiment tested are <NUM> bpm (<FIG>) and <NUM> bpm (<FIG>) compared to <NUM> bpm (<FIG>), and <NUM> bpm (<FIG>) for the radar system with fixed pass-band.

<FIG> show a filtered displacement signal (output of step <NUM>) with fixed pass-band limits and with adaptive pass-band limits (by applying method <NUM>), respectively. During the time window shown in <FIG>, the human target has a heartbeat rate of <NUM> bpm. As shown, the filtered displacement signal of <FIG> (which has a pass-band from <NUM> to <NUM>) results in a heartbeat rate of <NUM> bpm due to the many peak detections (which may be the result of, harmonics, the respiration rate, and/or intermodulation of between the respiration rate, heartbeat rate, and harmonics thereof, for example). In contrast, the filtered displacement signal that is adaptively filtered using method <NUM> results in more accurate heartbeat rate measurement of <NUM> bpm, as shown in <FIG>.

<FIG> shows a schematic diagram of processor <NUM>, according to an embodiment of the present invention. Processor <NUM> may be implemented as processor <NUM>.

Claim 1:
A method (<NUM>) comprising:
transmitting (<NUM>) radar signals with the millimeter-wave radar;
receiving (<NUM>) reflected radar signals with the millimeter-wave radar;
generating (<NUM>) a displacement signal indicative of a displacement of a target based on the reflected radar signals;
filtering (<NUM>) the displacement signal using a bandpass filter to generate a filtered displacement signal;
determining (<NUM>) a first rate indicative of a heartbeat rate of the target based on the filtered displacement signal;
tracking (<NUM>, <NUM>) a second rate indicative of the heartbeat rate of the target with a track using a Kalman filter;
updating (<NUM>) the track based on the first rate; and
updating (<NUM>) a setting of the bandpass filter based on the updated track,
wherein generating (<NUM>) the displacement signal comprises:
generating range data based on the reflected radar signals;
performing target detection based on the range data to detect the target; and
generating the displacement signal based on in-phase (I) and quadrature (Q) signals associated with the detected target.