Automotive radar apparatus and method for determining unambiguous radial velocity

Abstract

A car radar apparatus (100) for determining unambiguous radial velocity is provided. The car radar apparatus comprises a waveform generator (102), a transmit antenna (104), a receive antenna (106), a mixer (108), an analogue to digital converter (ADC) (110), and a signal processing unit (112). The waveform generator generates a transmit signal comprising a sequence of ramp frequency chirps. The transmit antenna transmits radio waves in response to being driven by the transmit signal. The receive antenna generates a receive signal in response to receiving reflected radio waves. The mixer generates an intermediate frequency (IF) signal by mixing the transmit signal with the receive signal. The ADC generates samples of the IF signal by sampling the IF signal within each of a sequence of time windows. The signal processing unit is configured to calculate the unambiguous radial velocity from ambiguous radial velocity and a period index.

Description

Technical Field

[0001] This invention generally relates to automotive radar, and more specifically, to a method for determining unambiguous radial velocities in an automotive radar system. Background Technology

[0002] Automotive radar is a sensor system that detects targets and estimates their positions. The target's position is estimated based on distance and angle; the radial velocity is estimated relative to the vehicle. For example, in the automotive industry, the radial velocity of a target is estimated based on resolution and a certain unambiguous speed range of the sensor system. If the target's velocity is outside this unambiguous speed range, the observed velocity sensed by the sensor system will be a value encompassed by the target's velocity and the sensor system's unambiguous speed range. For example, if the sensor system has an unambiguous speed range from -50 km / h to +50 km / h, and the target's relative speed is 100 km / h, the observed velocity sensed by the sensor system will be 0 km / h. In practice, to allow the required speed to cover the required unambiguous speed range, the sensor system's resolution may require a large amount of memory, which is impractical for automotive products.

[0003] Existing methods use waveforms with a series of chirps to enhance a sensor system over a certain unambiguous radial velocity range, where the chirps exhibit interleaved frequency shifts. Processing odd and even chirps separately and correlating the phase difference of the detected target with the number of wrapper cycles results in a signal-to-noise ratio (SNR) loss of approximately 3 dB. This means that some weak targets, such as pedestrians, may be missed, especially in automotive radar systems.

[0004] Furthermore, some existing methods use different waveforms across several scans. However, these methods must match the target's velocity across the scans. Due to noise or scattering characteristics, these scans may show targets appearing and disappearing. Additionally, in dense point clouds, there is the problem of different scans matching the same target. To estimate the correct target velocity, the sensor system needs to perform at least two or three scans, introducing a delay into the sensor system. This delay can be critical, especially in automotive safety functions such as emergency braking.

[0005] Existing methods provide phase difference quality by determining the number of cycles that depends only on a single channel. Therefore, estimating unambiguous velocities across multiple channels may not be robust enough. Furthermore, no processing such as trackers or classifications is used to evaluate the estimation quality.

[0006] Therefore, it is necessary to address the aforementioned technical deficiencies in existing systems or technologies regarding the determination of unambiguous radial velocities. Summary of the Invention

[0007] The purpose of this invention is to provide an automotive radar device and a method for determining unambiguous radial velocity in an automotive radar system while avoiding one or more disadvantages of prior art methods.

[0008] This objective is achieved through the features of the independent claim. Other implementations are apparent from the dependent claims, the description, and the drawings.

[0009] The present invention provides an automotive radar device and a method for determining unambiguous radial velocity in an automotive radar system.

[0010] According to a first aspect, an automotive radar device is provided. The automotive radar device includes a waveform generator, a transmitting antenna, a receiving antenna, a mixer, an analog-to-digital converter (ADC), and a signal processing unit. The waveform generator is used to generate a transmitted signal including a chirped sequence of ramp frequencies. The chirped sequence includes a first chirped time sequence interleaved with a second chirped time sequence. The transmitting antenna is used to transmit radio waves in response to being driven by the transmitted signal. The receiving antenna is used to generate a received signal in response to receiving reflected radio waves. The mixer is used to generate an intermediate frequency (IF) signal by mixing the transmitted signal with the received signal. The ADC is used to generate samples of the IF signal by sampling the IF signal within each time window in a time window sequence associated with the chirped sequence. The time windows have equidistant midpoints. The signal processing unit is used to: for each corresponding time window, calculate a range profile by applying a Fourier transform to the samples associated with the corresponding time window. The signal processing unit is configured to: calculate a blurred radial velocity matrix by applying a Fourier transform to the range image. The blurred radial velocity matrix includes a first submatrix and a second submatrix corresponding to a lower velocity and a higher velocity, respectively. The signal processing unit is configured to: calculate a sum matrix and a difference matrix. The sum matrix is ​​the sum of the first submatrix and the second submatrix, and the difference matrix is ​​the difference between the first submatrix and the second submatrix. The signal processing unit is configured to: determine a first phase difference matrix between elements in the sum matrix and elements in the difference matrix. The signal processing unit is configured to: determine a second phase difference matrix between elements in the first phase difference matrix and phase components based on the measured range position and the blurred measurement velocity, such that the second phase difference is a function that depends only on the period index of each position. The signal processing unit is configured to: determine the period index n of the blurred radial velocity at each position. cyc (I ′ The signal processing unit is used to calculate the unambiguous radial velocity based on the ambiguous radial velocity and the period index.

[0011] The described automotive radar device improves target velocity estimation in a single radar scan, thereby reducing network latency, especially in automotive safety functions such as emergency braking. The device estimates target velocity across multiple channels in a single scan, enhancing its robustness. It estimates target velocity without signal-to-noise ratio (SNR) loss, improving phase quality. The device offers flexibility in waveform design and received signal recording. It can detect potential errors during target velocity estimation and maintain the tracker accordingly.

[0012] Optionally, the ADC is used to generate a sample RxTime(i,j) of the IF signal by sampling the IF signal in each time window W(i) in the time window sequence, i = 1, ..., N, j = 1, ..., M, where RxTime(i,j) is the j-th sample in the i-th time window.

[0013] Optionally, the signal processing unit is configured to: calculate the range profile by applying a Fourier transform to the samples RxTime(i,j), j=1,…,M for each i from 1 to N. The signal processing unit may also be configured to: calculate the fuzzy radial velocity by applying the Fourier transform to the range profiles Range_SlowTime(i,J), i=1,…,N for each J from 1 to M. This results in the calculation of the matrix RgVel(I,J), I=1,…,N. The first submatrix RgVel... S1 (I ′ J ′ ) = RgVel(I′,J′), I′ = 1,…,N / 2, J′ = 1,…,M, the second submatrix I'=1,...,N / 2, J'=1,...,M.

[0014] Optionally, the signal processing unit is configured to: calculate the sum matrix RgVel SUM The difference matrix RgVel DIFF , so that:

[0015] RgVel SUM (I ′ J ′ )=RgVel S1 (I ′ J ′ )+RgVel S2 (I ′J ′ )and

[0016] RgVel DIFF (I ′ J ′ )=RgVel S1 (I ′ J ′ )-RgVel S2 (I ′ J ′ ),

[0017] I'=1,...,N / 2, J'=1,...,M.

[0018] The automotive radar device can detect one or more targets using the sum matrix or the difference matrix based on the full processing gain. The automotive radar device can improve the robustness of the estimated unambiguous velocity by analyzing the interferometric phases of a large number of channels to estimate the unambiguous velocity of the detected target.

[0019] Optionally, the signal processing unit is configured to: determine the first phase difference Phase(I) ′ J ′ ), I'=1,…,N / 2, J'=1,…,M, the first phase difference Phase(I ′ J ′ ) is the element RgVel in the sum matrix. SUM (I ′ J ′ The element RgVel in the difference matrix DIFF (I ′ J ′ The signal processing unit can be used to determine the phase difference between the two phases. VelCylce (I ′ J ′ ) = Phase(I ′ J ′ )-Phase known (I ′ J ′ ), I'=1,…,N / 2, J'=1,…,M, where Phase known (I ′ J ′ The phase component depends on the measured distance position and the fuzzy measurement velocity, such that Phase known (I ′ J ′ ) is a function of I' and J', Phase known (I ′ J′ )=f(I ′ The second phase difference is determined solely by the period index n for each I'J' position. cyc The function Phase of (I′, J′) VelCylce (I ′ J ′ )=f(n cyc (I ′ J ′ )).

[0020] The first phase difference can also be referred to as the interferometric phase. The interferometric phase is calculated for each moving target and each channel. Phase compensation with residuals is derived for each channel, and its average value is calculated. Therefore, the phase quality becomes equivalent to the coherence gain of all available channels. Thus, the automotive radar device is robust for unambiguous speed estimation.

[0021] Optionally, the frequency ramp slope is the same for the first chirped time series and the second chirped time series; and wherein the ramp time and idle time between chirps of the first time series and the second time series are adjusted such that the window from which the ADC samples from the first chirped time series includes a different center frequency than the window from which it samples from the second chirped time series.

[0022] Optionally, the frequency ramp slopes of the first chirped time series and the second chirped time series are different, such that the window from which the ADC samples from the first chirped time series includes a different center frequency than the window from which it samples from the second chirped time series; and wherein the size of the window from which the ADC samples is adjusted to sample over a constant bandwidth. The automotive radar device can thus offer flexibility in waveform design and even in recording the received signals.

[0023] Optionally, the signal processing unit is further configured to: detect multiple targets based on the calculated sum or difference matrix. For each target, the first phase difference is determined and averaged across multiple channels. The signal processing unit is further configured to: estimate the direction of arrival for each detected target. The signal processing unit can improve the robustness of estimating unambiguous velocities across a large number of channels and reduce network latency by analyzing the interference phases on a large number of channels to estimate the unambiguous velocity of one or more detected targets.

[0024] Optionally, the signal processing unit is further configured to: estimate the direction of arrival based on the sum and difference matrices calculated using all channels. The signal processing unit is also configured to: detect multiple targets based on the direction of arrival estimated using the sum or difference matrices. If the direction of arrival (DoA) estimation of the detected targets is readily available, the phase quality can be improved with zero SNR loss.

[0025] Optionally, a potential error indication flag is generated based on the value of the second phase difference. The probability of detecting an error when determining the radial velocity can be estimated and recorded in the tracker.

[0026] According to a second aspect, a method is provided for determining unambiguous radial velocity in an automotive radar system. The method includes: using a waveform generator to generate a transmitted signal comprising a ramp frequency chirp sequence. The chirp sequence includes a first chirp time sequence interleaved with a second chirp time sequence. The method includes: using the transmitted signal to drive a transmitted antenna to transmit radio waves. The method includes: using a received antenna in response to receiving reflected radio waves to generate a received signal. The method includes: mixing the transmitted signal with the received signal to generate an intermediate frequency (IF) signal. The method includes: using an analog-to-digital converter (ADC) to generate samples of the IF signal by sampling the IF signal within each time window in a time window sequence associated with the chirp sequence. The time windows have equidistant midpoints. The method includes: processing the samples of the IF signal using a signal processing unit such that: (i) for each corresponding time window, a range profile is calculated by applying a Fourier transform to the samples associated with the corresponding time window; (ii) a blurred radial velocity matrix is ​​calculated by applying a Fourier transform to the range profile, the blurred radial velocity matrix comprising a first submatrix and a second submatrix corresponding to a lower velocity and a higher velocity, respectively; (iii) a sum matrix and a difference matrix are calculated, the sum matrix being the sum of the first submatrix and the second submatrix, and the difference matrix being the difference between the first submatrix and the second submatrix; (iv) a first phase difference matrix is ​​determined between the elements in the sum matrix and the elements in the difference matrix; (v) a second phase difference matrix is ​​determined between the elements in the first phase difference matrix and the phase components based on the measured distance position and the blurred measurement velocity, such that the second phase difference is a function that depends only on the period index of each position; and (vi) the period index n of the blurred radial velocity at each position is determined. cyc (I ′(vii) Calculate the unambiguous radial velocity based on the ambiguous radial velocity and the periodic index.

[0027] The method estimates target velocity without incurring SNR loss, thereby improving phase quality. In automotive radar systems, this method improves target velocity estimation in a single radar scan, thereby reducing network latency, especially in automotive safety functions such as emergency braking. The method estimates target velocity in a single scan across multiple channels, enhancing the robustness of the automotive radar device. The method offers flexibility in waveform design and received signal recording. The method can detect potential errors during target velocity estimation and maintain the tracker for such errors.

[0028] Optionally, generating samples of the IF signal includes: sampling the IF signal within each time window W(i) in the time window sequence to generate a sample RxTime(i,j) of the IF signal, i = 1, ..., N, j = 1, ..., M, where RxTime(i,j) is the j-th sample in the i-th time window.

[0029] Optionally, processing the samples of the IF signal includes: (i) calculating the range profile by applying a Fourier transform to the samples RxTime(i,j), j=1,…,M for each i from 1 to N; and (ii) calculating the fuzzy radial velocity by applying the Fourier transform to the samples RxTime(i,j), j=1,…,N for each J from 1 to M. This results in the matrix RgVel(I,J), I=1,…,N. The first submatrix RgVel... S1 (I ′ J ′ ) = RgVel(I′,J′), I′ = 1,…,N / 2, J′ = 1,…,M, the second submatrix I'=1,...,N / 2, J'=1,...,M.

[0030] Optionally, processing the samples of the IF signal includes: calculating the sum matrix RgVel SUM The difference matrix RgVel DIFF , so that:

[0031] RgVel SUM (I ′ J ′ )=RgVel S1 (I ′ J ′ )+RgVelS2 (I ′ J ′ )and

[0032] RgVel DIFF (I ′ J ′ )=RgVel S1 (I ′ J ′ )-RgVel S2 (I ′ J ′ ),

[0033] I'=1,...,N / 2, J'=1,...,M.

[0034] Optionally, processing the sample of the IF signal includes: (i) determining the first phase difference Phase(I) ′ J ′ ), I'=1,…,N / 2, J'=1,…,M, the first phase difference Phase(I ′ J ′ ) is the element RgVel in the sum matrix. SUM (I ′ J ′ The element RgVel in the difference matrix DIFF (I ′ J ′ (ii) Determine the phase difference between the two phases; VelCylce (I ′ J ′ ) = Phase(I ′ J ′ )-Phase known (I ′ J ′ ), I'=1,…,N / 2, J'=1,…,M, where Phase known (I ′ J ′ The phase component depends on the measured distance position and the fuzzy measurement velocity, such that Phase known (I ′ J ′ ) is a function of I' and J', Phase known (I ′ J ′ )=f(I ′ The second phase difference is determined solely by the period index n for each I'J' position. cyc The function Phase of (I′, J′) VelCylce (I′ J ′ )=f(n cyc (I ′ J ′ )).

[0035] Optionally, the frequency ramp slope is the same for the first chirped time series and the second chirped time series; and the ramp time and idle time between chirps of the first time series and the second time series are adjusted such that the window sampled from the first chirped time series includes a different center frequency than the window sampled from the second chirped time series.

[0036] Optionally, the frequency ramp slopes of the first chirped time series and the second chirped time series are different, such that the window sampled from the first chirped time series includes a different center frequency than the window sampled from the second chirped time series; and wherein the size of the sampling window is adjusted to sample a constant bandwidth.

[0037] Optionally, the method further includes: detecting multiple targets based on the calculated sum or difference matrix. The first phase difference is determined for each target, and averaged over multiple channels. Optionally, the method includes: estimating the direction of arrival for each detected target. This method can improve unambiguous velocity estimation for a single scan without affecting memory requirements.

[0038] Optionally, the method further includes: estimating the direction of arrival based on a sum matrix and a difference matrix calculated using all channels. Optionally, the method includes: detecting multiple targets based on the direction of arrival estimated using the sum matrix or the difference matrix. Optionally, generating a potential error indication flag based on the value of the second phase difference.

[0039] According to a third aspect, a computer-readable medium including instructions that, when executed by a processor, cause the processor to perform the method.

[0040] This invention solves the technical problems in the prior art, which include insufficient efficiency of unambiguous velocity estimation, insufficient flexibility of chirped transmission, insufficient flexibility of received chirped records, signal-to-noise ratio loss, and lack of robustness of estimation for multiple channels.

[0041] Therefore, compared to existing technologies, the automotive radar device and the method for determining unambiguous radial velocity in the automotive radar system improve unambiguous velocity estimation in a single scan without affecting memory requirements. The automotive radar device eliminates signal-to-noise ratio (SNR) loss, making data processing easier and weak target detection easier. Therefore, due to the robustness of the automotive radar system, an efficient method for estimating unambiguous velocity is implemented across multiple available channels. The automotive radar device can be used to identify possible erroneous velocity estimates, thereby improving the efficiency of the automotive radar system. Therefore, computational costs can be minimized and effectively managed.

[0042] These and other aspects of the invention will become apparent from one or more implementations described below. Attached Figure Description

[0043] The various implementations of the present invention will now be described by way of example and in conjunction with the accompanying drawings, wherein:

[0044] Figure 1 A block diagram of an automotive radar device provided in one implementation of the present invention is shown;

[0045] Figure 2 A graphical illustration is shown of an exemplary sequence of ramp frequency chirps provided in one implementation of the present invention, the exemplary sequence representing related time parameters having ramp slopes of the same frequency;

[0046] Figure 3 A graphical illustration is shown of an exemplary sequence of ramp frequency chirps provided in one implementation of the present invention, the exemplary sequence representing relevant time parameters with ramp slopes of different frequencies;

[0047] Figure 4 A graphical illustration of an exemplary comparison diagram of phase and phase residuals for a moving target at different Doppler velocities, provided by one implementation of the present invention, is shown.

[0048] Figure 5 An exemplary representation of the Doppler enhancement factor as a sector of an automotive radar device is shown in one implementation of the present invention;

[0049] Figure 6 A graphical illustration of an exemplary comparison between the estimated unwrapping speed and the ground truth speed of an automotive radar device provided in one implementation of the present invention is shown.

[0050] Figure 7A flowchart is shown, illustrating a method for determining unambiguous radial velocity in an automotive radar system before detecting the direction of arrival, according to one implementation of the present invention.

[0051] Figure 8 A flowchart is shown, illustrating a method for determining an unambiguous radial velocity in an automotive radar system after detecting the direction of arrival, according to one implementation of the present invention.

[0052] Figures 9A to 9D A flowchart is shown illustrating a method for determining unambiguous radial velocity in an automotive radar system, provided by one implementation of the present invention. Detailed Implementation

[0053] Various implementations of the present invention provide an automotive radar device and a method for determining unambiguous radial velocity in an automotive radar system. The automotive radar device may be a frequency-modulated continuous wave radar.

[0054] To enable those skilled in the art to more easily understand the present invention, the following implementation of the present invention is described in conjunction with the accompanying drawings.

[0055] The terms “first,” “second,” “third,” and “fourth” (if any) used in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a particular sequence or order. It should be understood that such terms are interchangeable where appropriate, for example, to allow implementations of the invention described herein to be implemented in sequences other than those shown or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to the steps or units expressly listed, but may include other steps or units not expressly listed or inherent to such processes, methods, products, or apparatuses.

[0056] Figure 1A block diagram of an automotive radar device 100 according to one implementation of the present invention is shown. The automotive radar device 100 includes a waveform generator 102, a transmitting antenna 104, a receiving antenna 106, a mixer 108, an analog-to-digital converter (ADC) 110, and a signal processing unit 112. The waveform generator 102 is used to generate a transmitting signal including a chirped sequence of ramp frequencies. The chirped sequence includes a first chirped time sequence interleaved with a second chirped time sequence. The transmitting antenna 104 is used to transmit radio waves in response to being driven by the transmitting signal. The receiving antenna 106 is used to generate a received signal in response to receiving reflected radio waves. The mixer 108 is used to generate an intermediate frequency (IF) signal by mixing the transmitting signal with the received signal. The analog-to-digital converter (ADC) 110 is used to generate samples of the IF signal by sampling the IF signal within each time window of a time window sequence associated with the chirped sequence. The time windows have equidistant midpoints. The signal processing unit 112 is configured to: calculate a range profile for each time window by applying a Fourier transform to the samples. The signal processing unit 112 is configured to: calculate a blurred radial velocity matrix by applying a Fourier transform to the range profile. The blurred radial velocity matrix includes a first submatrix and a second submatrix corresponding to a lower velocity and a higher velocity, respectively. The signal processing unit 112 is configured to: calculate a sum matrix and a difference matrix. The sum matrix is ​​the sum of the first submatrix and the second submatrix, and the difference matrix is ​​the difference between the first submatrix and the second submatrix. The signal processing unit 112 is configured to: determine a first phase difference matrix between elements in the sum matrix and elements in the difference matrix. The signal processing unit 112 is configured to: determine a second phase difference matrix between elements in the first phase difference matrix and phase components based on the measured distance position and the blurred measurement velocity, such that the second phase difference is a function that depends only on the period index of each position. The signal processing unit 112 is configured to: determine the period index n of the blurred radial velocity at each position. cyc (I ′ The signal processing unit 112 is used to: calculate the unambiguous radial velocity based on the ambiguous radial velocity and the period index.

[0057] The automotive radar device 100 improves target velocity estimation in a single radar scan, thereby reducing network latency, especially in automotive safety functions such as emergency braking. The automotive radar device 100 estimates target velocity in a single scan across multiple channels, thus enhancing the robustness of the device. The automotive radar device 100 estimates target velocity without incurring SNR loss, thereby improving phase quality. The automotive radar device 100 offers flexibility in waveform design and received signal recording. The automotive radar device 100 can detect potential errors during estimation and maintain the tracker for such errors.

[0058] Optionally, the analog-to-digital converter (ADC) 110 is used to generate a sample RxTime(i,j) of the IF signal by sampling the IF signal in each time window W(i) in the time window sequence, i = 1, ..., N, j = 1, ..., M, where RxTime(i,j) is the j-th sample in the i-th time window.

[0059] Optionally, in the RxTime(i,j) of the IF signal, index i represents the i-th chirp, the i-th row is the recorded data of each transmitted chirp within its time recording window, and the column corresponding to the sample is recorded within its specific time window. The i-th index is also called the slow time dimension.

[0060] Optionally, the index j in RxTime(i,j) of the IF signal represents the j-th acquisition window corresponding to the recorded sample within each specific acquisition window. The j-th index is also called the fast time dimension.

[0061] Optionally, the signal processing unit 112 is configured to: calculate the range profile by applying a Fourier transform to the samples RxTime(i,j), j=1,…,M for each i from 1 to N. Optionally, the signal processing unit 112 is configured to: calculate the blurred radial velocity by applying the Fourier transform to the range profiles Range_SlowTime(i,J) (i=1,…,N) for each J from 1 to M. The first submatrix RgVel... S1 (I ′ J ′ ) = RgVel(I′,J′), I′ = 1,…,N / 2, J′ = 1,…,M, the second submatrix I' = 1, ..., N / 2, J' = 1, ..., M. The Fourier transform applied to the range image can also calculate the spectrum, i.e., the frequency range. The spectrum may include two frequency peaks (P1, P2). The values ​​of P1 and P2 can be obtained by the following equation.

[0062]

[0063]

[0064] Where n is the fuzzy period number, V m It measures speed, R m This is for measuring distance. The signal processing unit 112 can divide the spectrum into a first sub-spectrum and a second sub-spectrum. The first sub-spectrum and the second sub-spectrum can be two sub-apertures in the Doppler domain. The first sub-spectrum can include an ambiguous radial velocity from -0.25 to 0.25. The second sub-spectrum can include the remainder of the spectrum. The signal processing unit 112 can calculate the sum peak (P1+P2) and difference peak (P1-P2) of the first peak and the second peak of the spectrum. The phase difference between the sum peak and the difference peak includes information about the number of ambiguous periods. The following equation represents the phase difference between the sum peak and the difference peak:

[0065] ∠((P1-P2)·(P1+P2) * )=φ(n,V m ,R m )

[0066] The phase difference may depend on the number of cycles (n) of the moving target and the measurement distance (R). m ) and the measured speed (V) m After removing the phase components related to the measured distance and the measured velocity of the moving target, the remaining phase can be correlated with the number of cycles relative to the unambiguous velocity range.

[0067] Optionally, the signal processing unit 112 is configured to: calculate the sum matrix RgVel SUM The difference matrix RgVel DIFF , so that:

[0068] RgVel SUM (I ′ J ′ )=RgVel S1 (I ′ J ′ )+RgVel S2 (I ′ J ′ )and

[0069] RgVel DIFF (I ′ J ′ )=RgVel S1 (I ′ J ′ )-RgVel S2 (I ′ J ′ ), I'=1,...,N / 2, J'=1,...,M.

[0070] The automotive radar device 100 can detect one or more targets using the sum matrix or the difference matrix based on the full processing gain. The automotive radar device 100 can improve the robustness of the estimated unambiguous velocity by analyzing the interferometric phases of a large number of channels to estimate the unambiguous velocity of the detected target.

[0071] Optionally, the signal processing unit 112 is configured to: determine the first phase difference Phase(I) ′ J ′ ), I'=1,…,N / 2, J'=1,…,M, the first phase difference Phase(I ′ J ′ ) is the element RgVel in the sum matrix. SUM (I ′ J ′ The element RgVel in the difference matrix DIFF (I ′ J ′ The phase difference between ( ). Optionally, the signal processing unit 112 is configured to: determine the second phase difference Phase VelCylce (I ′ J ′ ) = Phase(I ′ J ′ )-Phase known (I ′ J ′ ), I'=1,…,N / 2, J'=1,…,M, where Phase known (I ′ J ′ The phase component depends on the measured distance position and the fuzzy measurement velocity, such that Phase known (I ′ J ′ ) is a function of I' and J', Phase known (I ′ J ′ )=f(I ′The second phase difference is determined solely by the period index n for each I'J' position. cyc The function Phase of (I′, J′) VelCylce (I ′ J ′ )=f(n cyc (I ′ J ′ )).

[0072] Optionally, the first phase difference is defined as Where ∠ is the angle operator, * is the complex product operator, and * is the conjugate operator. Using matrix notation: Phase =

[0073] ∠(RgVel SUM ⊙RgVel DIFF ), where ∠ is the angle operator, ⊙ is the Hadamard product, and is the conjugate operator.

[0074] Optionally, the second phase difference removes the phase component from the known terms, which depend on Rg and the fuzzy velocity directly related to indices I' and J'. Therefore, the resulting phase depends only on the desired unknown parameters of the periodic index.

[0075] Optionally, the signal processing unit 112 is further configured to: detect one or more targets based on the calculated sum or difference matrix. For each target, the first phase difference is determined and averaged over one or more channels. Optionally, the signal processing unit 112 is further configured to: estimate the direction of arrival for each detected target. The signal processing unit 112 can estimate the unambiguous velocity of one or more detected targets by analyzing the interference phases over a large number of channels, thereby improving the robustness of estimating unambiguous velocities over a large number of channels and reducing network latency.

[0076] Optionally, the signal processing unit 112 is further configured to: estimate the direction of arrival based on the sum and difference matrices calculated using all channels. The signal processing unit 112 may be configured to: detect one or more targets based on the direction of arrival estimated using the sum or difference matrices. If the direction of arrival (DoA) estimation of the detected target is readily performed, the phase quality can be improved with zero SNR loss. The one or more targets can be detected by estimating all sum or difference matrices. The unambiguous radial velocity is estimated using the phase difference between the DoA from the sum of the sub-matrices (i.e., the velocity sub-matrices) and the DoA from the differences between the sub-matrices.

[0077] The first phase difference can also be referred to as the interferometric phase. The interferometric phase is calculated for each moving target and each channel. Phase compensation with residuals is derived for each channel, and its average value is calculated. Therefore, the phase quality becomes equivalent to the coherence gain of all channels. Thus, the automotive radar device 100 is robust to unambiguous velocity estimation for all channels. Furthermore, the direction of arrival (DoA) estimation for the detected target is performed. Optionally, the value of the second phase difference can be used to generate a potential error indication. The automotive radar device 100 can improve unambiguous velocity estimation for a single scan without affecting memory requirements.

[0078] Figure 2 A graphical illustration is shown of an exemplary sequence of ramp frequency chirps provided in one implementation of the present invention, the exemplary sequence representing related time parameters having ramp slopes of the same frequency. The graphical illustration shows the frequency of the ramp frequency chirps on the Y-axis and the time (t) of the ramp frequency chirps on the X-axis. At 202, the graphical illustration shows the idle time of the first chirp time sequence 214. At position 204, the graphical representation illustrates the ramp time of the first chirped time series 214. At position 206, the graphical representation illustrates the idle time of the second chirped time series 216. At 208, the graphical representation of the exemplary comparison plot illustrates the ramp time of the second chirped time series 216. At 210, the graphical representation of the exemplary comparison plot illustrates the ADC time of the first chirped time series. The ADC time of the first chirped time series 214 can be the time taken at the midpoint of the ramp in the first chirped time series 214. At 212, the graphical representation shows the ADC time of the second chirped time series 216. The ADC time of the second chirped time series 216 may be the time taken at the midpoint of the ramp in the second chirped time series 216.

[0079] Optionally, the frequency ramp slope is the same for both the first chirped time series 214 and the second chirped time series 216. The ramp times and idle times between chirps in the first and second time series are adjusted such that the window from which the ADC samples from the first chirped time series 214 includes a different center frequency than the window from which it samples from the second chirped time series 216.

[0080] The first chirped time series 214 and the second chirped time series 216 can be interleaved. During sample recording, a frequency shift can be introduced for the first chirped time series 214 and the second chirped time series 216. For the first chirped time series 214 and the second chirped time series 216, the time between the midpoints of the sample recordings can always be the same, i.e. Therefore, for the slope of the same frequency ramp, the following equation holds:

[0081]

[0082] Based on the relevant time parameters, the following can be given relative to the original unambiguous Doppler range (DE). f Doppler enhancement factor:

[0083]

[0084] For example, with an enhancement factor of 5.5, the first chirped time series 214 can have The second chirped time series 216 can have

[0085] For example, if the pulse repetition interval (PRI) of the automotive radar device is 66 μs and the carrier frequency is 76.5 GHz, the total speed range is 106.9 km / h. With this configuration, the enhanced unambiguous range can be 587.8 km / h.

[0086] In the exemplary scenario, the two ramps can have the same duration. Then the previous equation is simplified to:

[0087]

[0088] Figure 3 A graphical illustration is shown of an exemplary sequence of ramp frequency chirps provided in one implementation of the present invention, the exemplary sequence representing relevant time parameters with ramp slopes of different frequencies. The graphical illustration shows the frequency of the ramp frequency chirps on the Y-axis and the time (t) of the ramp frequency chirps on the X-axis. At 302, the graphical illustration shows the idle time of the first chirp time sequence 314. At position 304, the graphical representation illustrates the ramp time of the first chirped time series 314. At position 306, the graphical representation illustrates the idle time of the second chirped time series 316. At 308, the graphical representation illustrates the ramp time of the second chirped time series 316. At 310, the graphical representation illustrates the bandwidth (Bw) of the first chirped time series 314. A At 312, the graphical representation illustrates the bandwidth (Bw) of the second chirped time series 316. B The first chirped time series 314 and the second chirped time series 316 may be interleaved. During sample recording, a frequency shift may be introduced for the first chirped time series 314 and the second chirped time series 316.

[0089] Optionally, the frequency ramp slopes of the first chirped time series 314 and the second chirped time series 316 are different, such that the window from which the ADC samples from the first chirped time series 314 includes a different center frequency than the window from which it samples from the second chirped time series 316; and wherein the size of the window sampled by the ADC is adjusted to sample over a constant bandwidth. For the first chirped time series 314 and the second chirped time series 316, the time between the midpoints of the sample records can always be the same, i.e.

[0090] In an exemplary scenario, the first chirped time series 314 may have a slope k. A The second chirped time series can have a slope k. B The recording times of the first chirped time series 314 and the second chirped time series 316 can be defined as follows: Where N Rg It is the number of distance samples. These are the sampling frequencies of the first chirped time series 314 and the second chirped time series 316, respectively. Therefore, for the first chirped time series 314 and the second chirped time series 316, which have different frequency ramp slopes, the following equation holds:

[0091]

[0092] For example, for the first chirped time series, if Then N Rg =12.8, For the second chirped time series, if Then N Rg =128, These two records cover a bandwidth of 320MHz, resulting in a distance resolution of 46.8cm.

[0093] Figure 4 A graphical illustration of an exemplary comparison diagram of the phase and phase residuals of a moving target at different Doppler velocities, provided by one implementation of the present invention, is shown. At 402, the graphical illustration shows the original interferometric phase of the moving target. At 404, the graphical illustration shows the phase estimation of the moving target based on its estimated range position. At 406, the graphical illustration shows the Doppler velocity phase estimation of the moving target. At 408, the graphical illustration shows phase compensation and Doppler velocity phase compensation of the moving target based on its estimated range position. Optionally, the phase difference depends on the number of cycles of the moving target, the measurement distance, and the measurement velocity. Therefore, after removing the phase components related to the measurement distance and the measurement velocity of the moving target, the remaining phase can be associated with the number of cycles relative to the unambiguous velocity range. At 410, the graphical illustration shows the phase compensation of the moving target based on its estimated distance and the Doppler velocity. The phase compensation of the moving target can fall within a certain range depending on the unambiguous velocity enhancement factor. In waveform design, the aforementioned unambiguous velocity enhancement factor can be considered.

[0094] Figure 5 An exemplary representation of the Doppler enhancement factor as a sector of an automotive radar device, provided by one implementation of the present invention, is shown. The exemplary representation of the circle is divided into sectors with a Doppler enhancement factor of 4.5. Number of periods (n) cyc The number of cycles can be between -4 and 4. Each sector in the exemplary representation shows the number of cycles. The exemplary representation shows the number of cycles that may not be set in cyclic order, so phase noise may cause jumps from one sector to another.

[0095] Figure 6 A graphical illustration of an exemplary comparison of the estimated unwrapping speed and the ground truth speed of an automotive radar device provided in one implementation of the present invention is shown. The graphical illustration shows the estimated unwrapping speed of the automotive radar device on the Y-axis and the ground truth speed of the automotive radar device on the X-axis. At 602, the graphical illustration shows the estimated speed of the automotive radar device; at 604, the graphical illustration shows the unambiguous region.

[0096] Figure 7A flowchart illustrating a method for determining unambiguous radial velocity in an automotive radar system before detecting direction of arrival, according to one implementation of the present invention, is shown. In step 702, for each time window, a range profile is calculated by applying a Fourier transform to the samples. In step 704, for one or more targets, a Doppler profile is calculated by applying a Fourier transform. In step 706, a lower velocity submatrix and a higher velocity submatrix are generated. In step 708, the generated submatrixes are summed and subdivided. A sum matrix and a difference matrix are calculated. The sum matrix is ​​the sum of the first and second submatrixes, and the difference matrix is ​​the difference between the first and second submatrixes, corresponding to the lower velocity and the higher velocity, respectively. In step 710, the one or more targets are detected using all the sums or differences of the submatrixes. In step 712, the interferometric phase of the detected targets is calculated using all channels. In step 714, a sub-spectrum or unambiguous radial velocity is calculated based on the ambiguous radial velocity and a periodic index. In step 716, the direction of arrival (DoA) of the detected targets is estimated. The method derives the residual phase for each channel and calculates its average value. Therefore, the phase quality is equivalent to the beamforming of all channels, thereby enhancing the robustness of estimating the unambiguous speed of the automotive radar device.

[0097] Figure 8 A flowchart illustrating a method for determining unambiguous radial velocity in an automotive radar system after detecting the direction of arrival, according to one implementation of the present invention, is shown. In step 802, for each time window, a range profile is calculated by applying a Fourier transform to the samples. In step 804, for one or more targets, a Doppler profile is calculated by applying a Fourier transform. In step 806, a lower velocity submatrix and a higher velocity submatrix are generated. In step 808, the generated submatrixes are summed and subdivided. A sum matrix and a difference matrix are calculated. The sum matrix is ​​the sum of the first and second submatrixes, and the difference matrix is ​​the difference between the first and second submatrixes, corresponding to the lower and higher velocities, respectively. In step 810, the direction of arrival (DoA) is estimated based on the sum matrix calculated using all channels, and the DoA is estimated based on the difference matrix calculated using all channels. In step 812, the one or more targets are detected based on the DoA estimated using the sum matrix or the difference matrix. In step 814, the interferometric phase of the detected targets is calculated using all channels. In step 816, the sub-spectral period or unambiguous velocity of the detected target is estimated. The method estimates the target velocity in a single scan across multiple channels, thereby enhancing the robustness of estimating the unambiguous velocity of the automotive radar system.

[0098] Figures 9A to 9DA flowchart illustrating a method for determining unambiguous radial velocity in an automotive radar system according to one implementation of the present invention is shown. In step 902, a waveform generator is used to generate a transmitted signal comprising a ramp frequency chirp sequence. The chirp sequence includes a first chirp time sequence interleaved with a second chirp time sequence. In step 904, the transmitted signal is used to drive a transmitted antenna to transmit radio waves. In step 906, a received antenna is used to generate a received signal in response to receiving reflected radio waves. In step 908, the transmitted signal and the received signal are mixed to generate an intermediate frequency (IF) signal. In step 910, an analog-to-digital converter (ADC) is used to generate samples of the IF signal by sampling the IF signal within each time window in a time window sequence associated with the chirp sequence. The time windows have equidistant midpoints.

[0099] In step 912, the sample of the IF signal is processed using a signal processing unit. In step 914, for each time window, a range profile is calculated by applying a Fourier transform to the sample. In step 916, a blurred radial velocity matrix is ​​calculated by applying a Fourier transform to the range profile. The blurred radial velocity matrix includes a first submatrix and a second submatrix corresponding to the lower and higher velocities, respectively. In step 918, a sum matrix and a difference matrix are calculated, respectively. The sum matrix is ​​the sum of the first submatrix and the second submatrix, and the difference matrix is ​​the difference between the first submatrix and the second submatrix. In step 920, a first phase difference matrix is ​​determined between the elements in the sum matrix and the elements in the difference matrix. In step 922, a second phase difference matrix is ​​determined between the elements in the first phase difference matrix and the phase components based on the measured distance position and the blurred measurement velocity, such that the second phase difference is a function of the period index of each position. In step 924, the period index n of the blurred radial velocity at each position is determined. cyc (I ′ In step 926, the unambiguous radial velocity is calculated based on the ambiguous radial velocity and the periodic index.

[0100] The method estimates target velocity without incurring SNR loss, thereby improving phase quality. It improves target velocity estimation in a single radar scan, reducing network latency, particularly in automotive safety functions such as emergency braking. The method estimates target velocity across multiple channels in a single scan, enhancing the robustness of the automotive radar device. It offers flexibility in waveform design and received signal recording. The method can detect potential errors during target velocity estimation and maintain the tracker to address these errors.

[0101] Optionally, generating samples of the IF signal includes: sampling the IF signal within each time window W(i) in the time window sequence to generate a sample RxTime(i,j) of the IF signal, i = 1, ..., N, j = 1, ..., M, where RxTime(i,j) is the j-th sample in the i-th time window.

[0102] Optionally, processing the samples of the IF signal includes: (i) calculating the range profile by applying a Fourier transform to the samples RxTime(i,j), j=1,…,M for each i from 1 to N; and (ii) calculating the fuzzy radial velocity by applying the Fourier transform to the samples RxTime(i,j), j=1,…,M for each j from 1 to M, and calculating the matrix RgVel(I,J), I=1,…,N for each J from 1 to M. S1 (I ′ J ′ ) = RgVel(I′,J′), I′ = 1,…,N / 2, J′ = 1,…,M, the second submatrix I'=1,...,N / 2, J'=1,...,M.

[0103] Optionally, processing the samples of the IF signal includes: calculating the sum matrix RgVel SUM The difference matrix RgVel DIFF , so that:

[0104] RgVel SUM (I ′ J ′ )=RgVel S1 (I ′ J ′ )+RgVel S2 (I ′ J ′ )and

[0105] RgVel DIFF (I ′ J ′ )=RgVel S1 (I ′ J ′ )-RgVel S2 (I ′ J ′ ),

[0106] I'=1,...,N / 2, J'=1,...,M.

[0107] Optionally, processing the sample of the IF signal includes: (i) determining the first phase difference Phase(I) ′ J ′ ), I'=1,…,N / 2, J'=1,…,M, the first phase difference Phase(I ′ J ′ ) is the element RgVel in the sum matrix. SUM (I ′ J ′ The element RgVel in the difference matrix DIFF (I ′ J ′ (ii) Determine the phase difference between the two phases; VelCylce (I ′ J ′ ) = Phase(I ′ J ′ )-Phase known (I ′ J ′ ), I'=1,…,N / 2, J'=1,…,M, where Phase known (I ′ J ′ The phase component depends on the measured distance position and the fuzzy measurement velocity, such that Phase known (I ′ J ′ ) is a function of I' and J', Phase known (I ′ J ′ )=f(I ′ The second phase difference is determined solely by the period index n for each I'J' position. cyc The function Phase of (I′, J′) VelCylce (I ′ J ′ )=f(n cyc (I ′ J′ The fuzzy velocity is estimated using the second phase difference, which depends only on the period index (multiple fuzzy periods). The quality of the second phase difference can be improved by averaging it over all available channels, thereby enhancing the robustness of the estimated unfuzzy velocity.

[0108] Optionally, the frequency ramp slope is the same for the first chirped time series and the second chirped time series; and the ramp time and idle time between chirps of the first time series and the second time series are adjusted such that the window sampled from the first chirped time series includes a different center frequency than the window sampled from the second chirped time series.

[0109] Optionally, the frequency ramp slopes of the first chirped time series and the second chirped time series are different, such that the window sampled from the first chirped time series includes a different center frequency than the window sampled from the second chirped time series; and wherein the size of the sampling window is adjusted to sample a constant bandwidth.

[0110] Optionally, the method further includes: detecting one or more targets based on the calculated sum or difference matrix. The first phase difference is determined for each target and averaged over one or more channels. The method may include: estimating the direction of arrival for each detected target. The method can improve unambiguous velocity estimation for a single scan without affecting memory requirements.

[0111] Optionally, the method further includes estimating the direction of arrival based on a sum matrix and a difference matrix calculated using all channels. The method may include detecting one or more targets based on the direction of arrival estimated using the sum matrix or the difference matrix. Optionally, a potential error indication flag is generated based on the value of the second phase difference.

[0112] A computer-readable medium including instructions that, when executed by a processor, cause the processor to perform the methods described above.

[0113] While the invention and its advantages have been described in detail, it should be understood that various changes, substitutions and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.

Claims

1. A car radar device (100), characterized in that, include: A waveform generator (102) is used to generate a transmission signal including a ramp frequency chirp sequence, wherein the chirp sequence includes a first chirp time sequence, the first chirp time sequence being interleaved with a second chirp time sequence; A transmission antenna (104) is used to transmit radio waves in response to being driven by the transmission signal; A receiving antenna (106) is used to generate a received signal in response to receiving reflected radio waves; A mixer (108) is used to generate an intermediate frequency (IF) signal by mixing the transmitted signal with the received signal; An analog-to-digital converter (ADC) (110) is used to generate samples of the intermediate frequency signal by sampling the intermediate frequency signal within each time window in a time window sequence associated with the chirped sequence, wherein the time windows have equidistant midpoints; Signal processing unit (112), used for: For each corresponding time window, the distance profile is calculated by applying a Fourier transform to the samples associated with the corresponding time window; The fuzzy radial velocity matrix is ​​calculated by applying a Fourier transform to the range image, the fuzzy radial velocity matrix comprising a first submatrix and a second submatrix corresponding to the lower velocity and the higher velocity, respectively; Calculate the sum matrix and the difference matrix, wherein the sum matrix is ​​the sum of the first submatrix and the second submatrix, and the difference matrix is ​​the difference between the first submatrix and the second submatrix; Determine the first phase difference matrix between the elements of the sum matrix and the elements of the difference matrix; Based on the measured distance position and the fuzzy measurement speed, a second phase difference matrix is ​​determined between the elements in the first phase difference matrix and the phase components, such that the second phase difference is a function that depends only on the period index of each position; Periodic index for determining the fuzzy radial velocity at each position ; I'=1,…,N / 2,J'=1,…,M; The unambiguous radial velocity is calculated based on the ambiguous radial velocity and the periodic index.

2. The apparatus (100) according to claim 1, characterized in that, The ADC (110) is used to generate samples of the intermediate frequency signal by sampling the intermediate frequency signal within each time window W(i) in the time window sequence. i=1,…,N,j=1,…,M, It is the j-th sample in the i-th time window.

3. The apparatus (100) according to claim 2, characterized in that, The signal processing unit (112) is used for: The distance profile is calculated as follows: for each i from 1 to N, by taking the sample... For j=1,…,M, use Fourier transform to calculate the distance image. J=1,…,M; The fuzzy radial velocity is calculated as follows: for each J from 1 to M, by adjusting the distance image... The Fourier transform is applied to calculate the matrix for i=1,…,N. I=1,…,N; Wherein, the first sub-matrix I'=1,…,N / 2, J'=1,…,M, the second submatrix I'=1,…,N / 2,J'=1,…,M.

4. The apparatus (100) according to claim 3, characterized in that, The signal processing unit (112) is used to calculate the sum matrix. The difference matrix , so that: and ， I'=1,…,N / 2,J'=1,…,M.

5. The apparatus (100) according to claim 4, characterized in that, The signal processing unit (112) is used for: Determine the first phase difference I'=1,…,N / 2, J'=1,…,M, the first phase difference The elements in the sum matrix are elements in the difference matrix The phase difference between them; Determine the second phase difference I'=1,…,N / 2, J'=1,…,M, where The phase component depends on the measured distance position and the fuzzy measurement velocity, such that It is a function of I' and J'. The second phase difference is a period index that depends only on each I'J' position. function .

6. The apparatus (100) according to any one of the preceding claims, characterized in that, For the first chirped time series and the second chirped time series, the frequency ramp slope is the same; and wherein the ramp time of the first time series and the second time series and the idle time between chirps are adjusted such that the window from which the ADC (110) samples from the first chirped time series includes a different center frequency than the window from which it samples from the second chirped time series.

7. The apparatus (100) according to any one of claims 1 to 5, characterized in that, The first chirped time series and the second chirped time series have different frequency ramp slopes, such that the window from which the ADC (110) samples from the first chirped time series includes a different center frequency than the window from which it samples from the second chirped time series; and wherein the size of the window from which the ADC (110) samples is adjusted such that sampling is performed on a constant bandwidth.

8. The apparatus (100) according to any one of claims 1 to 5, characterized in that, The signal processing unit (112) is also used for: Multiple targets are detected based on the calculated sum or difference matrix, wherein the first phase difference is determined for each target and averaged over multiple channels; Estimate the direction of arrival for each detected target.

9. The apparatus (100) according to any one of claims 1 to 5, characterized in that, The signal processing unit (112) is also used for: The direction of arrival is estimated based on the sum matrix calculated using all channels, and the direction of arrival is estimated based on the difference matrix calculated using all channels. Multiple targets are detected based on the direction of arrival estimated using the sum matrix or the difference matrix.

10. The apparatus (100) according to any one of claims 1 to 5, characterized in that, A potential error indication flag is generated based on the value of the second phase difference.

11. A method for determining unambiguous radial velocity in an automotive radar system, characterized in that, include: A waveform generator (102) is used to generate a transmission signal including a ramp frequency chirp sequence, wherein the chirp sequence includes a first chirp time sequence, the first chirp time sequence being interleaved with a second chirp time sequence; The transmission signal is used to drive the transmission antenna (104) to transmit radio waves; A receiving antenna (106) is used to generate a received signal in response to the received reflected radio waves; The transmitted signal is mixed with the received signal to generate an intermediate frequency (IF) signal; The intermediate frequency signal is sampled using an analog-to-digital converter (ADC) (110) by sampling the intermediate frequency signal within each time window in a time window sequence associated with the chirped sequence, wherein the time windows have equidistant midpoints; The signal processing unit (112) is used to process the sample of the intermediate frequency signal so that: For each corresponding time window, the distance profile is calculated by applying a Fourier transform to the samples associated with the corresponding time window; The fuzzy radial velocity matrix is ​​calculated by applying a Fourier transform to the range image, the fuzzy radial velocity matrix comprising a first submatrix and a second submatrix corresponding to the lower velocity and the higher velocity, respectively; Calculate the sum matrix and the difference matrix, wherein the sum matrix is ​​the sum of the first submatrix and the second submatrix, and the difference matrix is ​​the difference between the first submatrix and the second submatrix; Determine the first phase difference matrix between the elements of the sum matrix and the elements of the difference matrix; Based on the measured distance position and the fuzzy measurement speed, a second phase difference matrix is ​​determined between the elements in the first phase difference matrix and the phase components, such that the second phase difference is a function that depends only on the period index of each position; Periodic index for determining the fuzzy radial velocity at each position ; I'=1,…,N / 2,J'=1,…,M; The unambiguous radial velocity is calculated based on the ambiguous radial velocity and the periodic index.

12. The method according to claim 11, characterized in that, Generating samples of the intermediate frequency (IF) signal includes: sampling the IF signal within each time window W(i) of the time window sequence to generate samples of the IF signal. , i=1,…,N, j=1,…,M, It is the j-th sample in the i-th time window.

13. The method according to claim 12, characterized in that, The samples for processing the intermediate frequency signal include: The distance profile is calculated as follows: for each i from 1 to N, by taking the sample... For j=1,…,M, use Fourier transform to calculate the distance image. J=1,…,M; The fuzzy radial velocity is calculated as follows: for each J from 1 to M, by adjusting the distance image... The Fourier transform is applied to calculate the matrix for i=1,…,N. I=1,…,N; Wherein, the first sub-matrix I'=1,…,N / 2, J'=1,…,M, the second submatrix , I'=1,...,N / 2, J'=1,...,M.

14. The method according to claim 13, characterized in that, Processing the samples of the intermediate frequency signal includes: calculating the sum matrix. The difference matrix , so that: and ， I'=1,…,N / 2,J'=1,…,M.

15. The method according to claim 14, characterized in that, The samples for processing the intermediate frequency signal include: Determine the first phase difference I'=1,…,N / 2, J'=1,…,M, the first phase difference The elements in the sum matrix are elements in the difference matrix The phase difference between them; Determine the second phase difference I'=1,…,N / 2, J'=1,…,M, where The phase component depends on the measured distance position and the fuzzy measurement velocity, such that It is a function of I' and J'. The second phase difference is a period index that depends only on each I'J' position. function .

16. The method according to any one of claims 11 to 15, characterized in that, For the first chirped time series and the second chirped time series, the frequency ramp slope is the same; and wherein the ramp time of the first time series and the second time series and the idle time between chirps are adjusted such that the window sampled from the first chirped time series includes a different center frequency than the window sampled from the second chirped time series.

17. The method according to any one of claims 11 to 15, characterized in that, The first chirped time series and the second chirped time series have different frequency ramp slopes, such that the window sampled from the first chirped time series includes a different center frequency than the window sampled from the second chirped time series; and wherein the size of the sampling window is adjusted to sample a constant bandwidth.

18. The method according to any one of claims 11 to 15, characterized in that, Also includes: Multiple targets are detected based on the calculated sum or difference matrix, wherein the first phase difference is determined for each target and averaged over multiple channels; Estimate the direction of arrival for each detected target.

19. The method according to any one of claims 11 to 15, characterized in that, Also includes: The direction of arrival is estimated based on the sum and difference matrices calculated using all channels; Multiple targets are detected based on the direction of arrival estimated using the sum matrix or the difference matrix.

20. The method according to any one of claims 11 to 15, characterized in that, A potential error indication flag is generated based on the value of the second phase difference.

21. A computer program comprising an instruction set, characterized in that, When executed by a processor, the instruction set causes the processor to perform the method according to any one of claims 11 to 20.

22. A computer-readable medium for storing an instruction set, characterized in that, When executed by a processor, the instruction set causes the processor to perform the method according to any one of claims 11 to 20.

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