Target measurement system and method for ultra-wideband lfmcw millimeter wave radar

Abstract

This invention discloses a target measurement system and method for ultra-wideband LFMCW millimeter-wave radar, relating to the field of digital signal processing. It includes: a signal generation module, a wireless transceiver module, an echo preprocessing module, a high-precision processing module, and a parameter estimation module. This invention utilizes spectral super-resolution technology, which helps to achieve high-precision measurement of moving targets. It can process received data without changing the existing radar system and operating mode, achieving a plug-and-play effect.

Description

Technical Field

[0001] This invention relates to the field of digital signal processing, and in particular to a high-precision measurement system and method for moving targets using ultra-wideband LFMCW millimeter-wave radar. Background Technology

[0002] Ultra-wideband linear frequency modulated continuous wave (LFMCW) millimeter-wave radar refers to radar with a bandwidth of several GHz, transmitting a continuously linearly frequency modulated signal, and a carrier signal electromagnetic wave wavelength of 0.1–1 cm. Its characteristics include less susceptibility to adverse weather conditions such as sandstorms, rain, snow, and smoke; strong signal penetration; all-weather, 24 / 7 operation; high accuracy in ranging, velocity, and angle measurement; less susceptibility to ground clutter and multipath effects; strong long-range detection capabilities; and a certain degree of anti-interference capability in complex electromagnetic environments. Due to technological advancements, millimeter-wave radar has achieved miniaturization, integration, and modularization, allowing it to be installed and adapted to most sensor platforms. Therefore, millimeter-wave radar has been widely used in many fields.

[0003] In real-world scenarios, moving target detection is a crucial task. In the field of autonomous driving, vehicle sensors face complex and diverse traffic conditions, requiring them to detect the position, size, and motion status of different vehicles, pedestrians, road signs, and road obstacles on a moving platform. Simultaneously, they must maintain a detection range of approximately 100 meters to provide a safe distance, ensure sensor reliability under varying natural environments, and possess a certain degree of anti-interference capability. Therefore, millimeter-wave radar, meeting these conditions, has become one of the primary sensors for detecting moving targets in autonomous vehicles. In the smart home sector, sensors need to accurately identify the movement and attitude information of users inside the house. Infrared sensors suffer from limited detection range and low accuracy, while optical sensors pose a risk of privacy leaks. Millimeter-wave radar, however, can detect subtle movement and attitude features without the shortcomings of the aforementioned sensors. Therefore, the smart home market based on millimeter-wave radar is continuously expanding. In urban security, millimeter-wave radar can continuously detect specific airspaces, capturing the presence of unknown flying objects such as small drones, and performing target identification and continuous tracking using high-resolution information. Furthermore, in fields such as intelligent transportation, industrial measurement, and elderly care, millimeter-wave radar is continuously expanding its market share due to its superior performance.

[0004] In traditional radar measurement, the received and transmitted signals are mixed and passed through a low-pass amplifier to obtain an intermediate frequency (IF) signal. After A / D sampling, a Fast Fourier Transform (FFT) is performed on the discrete sequence to compress the pulse, and the peak value corresponds to the target range information. Since the radar signal transmission interval is short, the target is approximately considered stationary. Therefore, multiple discrete sequences can be collected and assembled into a two-dimensional matrix. Performing an FFT on the second dimension yields the velocity information. Furthermore, if the radar uses multiple channels for transmitting and receiving signals, multiple two-dimensional echo matrices will form a three-dimensional data cube. Performing an FFT on the third dimension yields the target's angle information relative to the radar system. Traditional processes have the following problems: First, the ranging accuracy is not high. In traditional narrowband radar, distance movement is not obvious, and the target can be approximated as stationary. However, as the bandwidth increases, the range resolution improves, and even small distance changes will lead to relatively obvious movement, thus causing ranging errors. Second, as the range resolution increases, the main lobe width of the scatterer echo narrows. When the number of sampling points is small, peak values ​​are easily lost, resulting in a decrease in the signal-to-noise ratio and further leading to velocity measurement errors. Third, due to the limitation of actual size, the aperture of the millimeter-wave radar transmitting antenna cannot be too large, which results in a low radar angular resolution. Super-resolution processing methods must be used at the signal processing end to obtain an angular resolution that exceeds the actual aperture.

[0005] Therefore, how to process the received data without changing the existing radar system and operating mode to achieve high-precision measurement of moving targets is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0006] In view of this, the present invention provides a target measurement system and method for ultra-wideband LFMCW millimeter-wave radar, which can achieve the effect of high-precision measurement of moving targets by processing received data without changing the existing radar system and working mode.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] A target measurement system for ultra-wideband LFMCW millimeter-wave radar includes: an external platform MCU, a signal generation module, a wireless transceiver module, an echo preprocessing module, a high-precision processing module, and a parameter estimation module;

[0009] An external platform MCU connects to the input / output terminals of the signal generation module and is used to send RF signal parameters to the signal generation module via a standard communication protocol to control it to generate a linear frequency modulated RF band transmission signal.

[0010] An external platform MCU connects to the input / output terminals of the wireless transceiver module and is used to control the wireless transceiver module's transmission and reception of radio frequency signals.

[0011] The signal generation module is connected to the input terminal of the wireless transceiver module and is used to generate a linear frequency modulated radio frequency band transmission signal and send it to the wireless transceiver module.

[0012] The signal generation module is connected to the first input terminal of the echo preprocessing module and is used to generate the reference signal required for demodulation by the echo preprocessing module.

[0013] The wireless transceiver module, connected to the second input of the echo preprocessing module, is used to convert the linearly frequency modulated radio frequency digital signal into an analog signal and transmit it into the environment via the transmitting antenna within the module; at the same time, it captures reflected echo signals from objects in the environment via the receiving antenna within the module and transmits them to the subsequent echo preprocessing module for preprocessing.

[0014] The echo preprocessing module is connected to the input of the high-precision processing module and is used to convert the analog echo signal into a baseband digital signal.

[0015] The high-precision processing module is connected to the input of the parameter estimation module. It is used to receive the baseband digital signal sent by the echo preprocessing module and perform high-precision processing to obtain the processed echo data. The processed echo data is then transmitted to the parameter estimation module.

[0016] The parameter estimation module is used to extract measured values ​​from the processed echo data, which are the parameters of the moving target.

[0017] Optionally, the echo preprocessing module of the above system includes a low-noise amplifier, an echo mixer, a low-pass filter, and an A / D sampling circuit connected in sequence.

[0018] The echo preprocessing module converts analog echo signals into baseband digital signals in the following ways:

[0019] First, the analog echo signal is amplified by a low-noise amplifier, then mixed with a reference signal generated by the signal generation module, and then passed through a low-pass filter to obtain the baseband signal. Subsequently, the baseband analog signal is converted into a baseband digital signal by an A / D sampling circuit and reassembled to form a three-dimensional discrete echo data matrix.

[0020] The above-mentioned system, optionally, includes a high-precision processing module comprising a pulse compression unit, a spectrum refinement unit, and a distance correction unit connected in sequence;

[0021] The pulse compression unit is used to perform pulse compression on fast-time discrete echo data to obtain a preliminary slow-time-range image;

[0022] The spectrum refinement unit is used to refine the spectrum obtained by the range-dimensional FFT of fast-time discrete echo data by performing Chirp-z transform using a fast algorithm, and obtain the refined slow-time-range image.

[0023] The distance correction unit is used to calculate the average phase value of the peak phase due to inherent errors that cause periodic phase changes. The average phase value is used to estimate the distance deviation and compensate for the echo phase so that the echo phase contains accurate distance information.

[0024] The above system optionally includes a parameter estimation module, which performs constant false alarm rate detection on the echo data processed by the high-precision processing module to distinguish the target from the noisy environment. Then, it performs maximum likelihood estimation on each fast time series to obtain the estimated value of the target position, extracts the slow time series, and uses a time-frequency analysis algorithm to obtain the estimated value of the target velocity. Finally, it uses a spectral super-resolution algorithm on the channel-dimensional sequence to obtain the estimated value of the target angle.

[0025] A target measurement method for ultra-wideband LFMCW millimeter-wave radar, applied to any of the target measurement systems for ultra-wideband LFMCW millimeter-wave radars described above, includes the following steps:

[0026] S1. The external platform MCU sends the radio frequency signal parameters to the signal generation module through the standard communication protocol and generates a linear frequency modulated radio frequency band transmission signal.

[0027] S2. The wireless transceiver module receives the radio frequency band transmitted signal and detects whether the wireless transceiver module has completed transmission and reception.

[0028] S3, the echo preprocessing module converts the radio frequency band transmitted signal into a digital signal to obtain three-dimensional discrete echo data;

[0029] S4. The pulse compression unit performs pulse compression on the discrete echo data to obtain a preliminary slow time-range image.

[0030] S5, the spectrum refinement unit performs spectrum refinement processing on the discrete echo data to obtain an accurate slow time-range image;

[0031] S6. The distance correction unit performs distance correction on the discrete echo data to obtain distance-corrected echo data.

[0032] S7. The parameter estimation module performs CFAR detection processing on the processed echo data to separate the target from the noisy environment.

[0033] S8, the parameter estimation module extracts the amplitude and phase of the 3D radar echo after CFAR detection and processing, and converts them into the moving target parameters corresponding to the target by extracting the information on the changes of the amplitude and phase of the 3D radar echo with time and space.

[0034] Optionally, in S4, the pulse compression processing method described above is to perform an FFT operation on the fast time series. In this case, the original time-domain signal is compressed into an amplitude spike in the frequency domain, which can be distinguished from the noise.

[0035] Optionally, the method for spectral refinement in S5 is as follows: First, based on the result of pulse compression, find the peak position of the first column of fast-time pulse compression, and with this peak position as the center, within the frequency range that can contain all the peaks of fast-time pulse compression, sequentially apply a fast algorithm to each column of fast-time sequence before pulse compression to achieve Chirp-z transform. After the calculation is completed, the refined spectral sequence can be obtained.

[0036] Optionally, the distance correction method in S6 is as follows: For each fast time series, extract the phase corresponding to its peak point to obtain a sequence with a slow time period variation, calculate the average value of the sequence, and then multiply it by a constant related to the signal parameters to obtain the average distance deviation. Use this distance deviation to compensate for all fast time series to obtain the distance-corrected echo data.

[0037] Optionally, the CFAR detection processing method in S7 is as follows: based on the preset false alarm probability and detection performance requirements, select the CFAR detection algorithm and generate a detector, apply the detector to the high-precision processed echo data, and separate the target from the noisy environment.

[0038] Optionally, in S8, the parameter estimation method described above is as follows: For distance estimation, the target distance is obtained by searching for the frequency corresponding to the peak value after fast-time pulse compression and distance correction, and multiplying it by a constant related to the signal parameters; for velocity estimation, the target velocity is obtained by extracting the fast-time peak value and performing time-frequency analysis or using a domain transformation algorithm on the sequence; for angle estimation, the target angle is obtained by extracting a two-dimensional matrix composed of the fast time and channel dimensions, and performing peak search on the obtained spatial pseudospectrum using a spectral super-resolution algorithm.

[0039] As can be seen from the above technical solution, compared with the prior art, the present invention provides a target measurement system and method for ultra-wideband LFMCW millimeter-wave radar, which has the following beneficial effects: 1) The present invention performs data processing without changing the existing radar system, and is a plug-and-play processing step, reflecting the convenience of the invention. 2) The signal processing of the present invention can all be implemented through fast algorithms, reflecting the real-time processing performance of the invention. 3) The high-precision processing step of the present invention has no special restrictions on target type, usage environment and mounting platform, reflecting the versatility of the invention. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0041] Figure 1 This is a structural diagram of an LFMCW millimeter-wave radar moving target measurement system disclosed in this invention;

[0042] Figure 2 This is a flowchart of a moving target measurement method using LFMCW millimeter-wave radar disclosed in this invention;

[0043] Figure 3 This is a schematic diagram of the radar system transmitting and receiving radio frequency signals disclosed in this embodiment;

[0044] Figure 4 This is a schematic diagram of the echo signal preprocessing disclosed in this embodiment;

[0045] Figure 5 This is a schematic diagram of the pulse compression processing disclosed in this embodiment;

[0046] Figure 6 This is a schematic diagram of the spectrum refinement processing disclosed in this embodiment;

[0047] Figure 7 This is a schematic diagram of the distance correction processing disclosed in this embodiment;

[0048] Figure 8 This is a schematic diagram of the CFAR detection processing disclosed in this embodiment;

[0049] Figure 9 This is a schematic diagram illustrating the parameter estimation principle disclosed in this embodiment.

[0050] In the figure, the symbols are explained as follows: MCU stands for Microcontroller Unit; CFAR stands for Constant False Alarm Rate; and FFT stands for Fast Fourier Transform. Detailed Implementation

[0051] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] In this application, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0053] Reference Figure 1 As shown, a target measurement system for ultra-wideband LFMCW millimeter-wave radar includes: an external platform MCU, a signal generation module, a wireless transceiver module, an echo preprocessing module, a high-precision processing module, and a parameter estimation module.

[0054] An external platform MCU connects to the input / output terminals of the signal generation module and is used to send RF signal parameters to the signal generation module via a standard communication protocol to control it to generate a linear frequency modulated RF band transmission signal.

[0055] An external platform MCU connects to the input / output terminals of the wireless transceiver module and is used to control the wireless transceiver module's transmission and reception of radio frequency signals.

[0056] The signal generation module is connected to the input terminal of the wireless transceiver module and is used to generate a linear frequency modulated radio frequency band transmission signal and send it to the wireless transceiver module.

[0057] The signal generation module is connected to the first input terminal of the echo preprocessing module and is used to generate the reference signal required for demodulation by the echo preprocessing module.

[0058] The wireless transceiver module, connected to the second input of the echo preprocessing module, is used to convert the linearly frequency modulated radio frequency digital signal into an analog signal and transmit it into the environment via the transmitting antenna within the module; at the same time, it captures reflected echo signals from objects in the environment via the receiving antenna within the module and transmits them to the subsequent echo preprocessing module for preprocessing.

[0059] The echo preprocessing module is connected to the input of the high-precision processing module and is used to convert the analog echo signal into a baseband digital signal.

[0060] The high-precision processing module is connected to the input of the parameter estimation module. It is used to receive the baseband digital signal sent by the echo preprocessing module and perform high-precision processing to obtain the processed echo data. The processed echo data is then transmitted to the parameter estimation module.

[0061] The parameter estimation module is used to extract measured values ​​from the processed echo data, which are the parameters of the moving target.

[0062] Furthermore, the echo preprocessing module includes a low-noise amplifier, an echo mixer, a low-pass filter, and an A / D sampling circuit connected in sequence;

[0063] The echo preprocessing module converts analog echo signals into baseband digital signals in the following ways:

[0064] First, the analog echo signal is amplified by a low-noise amplifier, then mixed with a reference signal generated by the signal generation module, and then passed through a low-pass filter to obtain the baseband signal. Subsequently, the baseband analog signal is converted into a baseband digital signal by an A / D sampling circuit and reassembled to form a three-dimensional discrete echo data matrix.

[0065] Furthermore, the high-precision processing module includes a pulse compression unit, a spectrum refinement unit, and a distance correction unit connected in sequence;

[0066] The pulse compression unit is used to perform pulse compression on fast-time discrete echo data to obtain a preliminary slow-time-range image;

[0067] The spectrum refinement unit is used to refine the spectrum obtained by the range-dimensional FFT of fast-time discrete echo data by performing Chirp-z transform using a fast algorithm, and obtain the refined slow-time-range image.

[0068] The distance correction unit is used to calculate the average phase value of the peak phase due to inherent errors that cause periodic phase changes. The average phase value is used to estimate the distance deviation and compensate for the echo phase so that the echo phase contains accurate distance information.

[0069] Furthermore, the parameter estimation module is used to perform constant false alarm rate detection on the echo data processed by the high-precision processing module, distinguish the target from the noisy environment, then perform maximum likelihood estimation on each fast time series to obtain the estimated value of the target position, extract the slow time series, and use time-frequency analysis algorithm to obtain the estimated value of the target velocity; and use spectral super-resolution algorithm on the channel dimension sequence to obtain the estimated value of the target angle.

[0070] Reference Figure 2 As shown, a target measurement method for ultra-wideband LFMCW millimeter-wave radar, applied to any of the target measurement systems for ultra-wideband LFMCW millimeter-wave radars described above, includes the following steps:

[0071] S1. The external platform MCU sends the radio frequency signal parameters to the signal generation module through the standard communication protocol and generates a linear frequency modulated radio frequency band transmission signal.

[0072] S2. The wireless transceiver module receives the radio frequency band transmitted signal and detects whether the wireless transceiver module has completed transmission and reception.

[0073] S3, the echo preprocessing module converts the radio frequency band transmitted signal into a digital signal to obtain three-dimensional discrete echo data;

[0074] S4. The pulse compression unit performs pulse compression on the discrete echo data to obtain a preliminary slow time-range image.

[0075] S5, the spectrum refinement unit performs spectrum refinement processing on the discrete echo data to obtain an accurate slow time-range image;

[0076] S6. The distance correction unit performs distance correction on the discrete echo data to obtain distance-corrected echo data.

[0077] S7. The parameter estimation module performs CFAR detection processing on the processed echo data to separate the target from the noisy environment.

[0078] S8, the parameter estimation module extracts the amplitude and phase of the 3D radar echo after CFAR detection and processing, and converts them into the moving target parameters corresponding to the target by extracting the information on the changes of the amplitude and phase of the 3D radar echo with time and space.

[0079] Furthermore, the pulse compression processing method in S4 is to perform an FFT operation on the fast time series. At this time, the original time domain signal is compressed into an amplitude spike in the frequency domain, which can be distinguished from the noise.

[0080] Furthermore, the method for spectrum refinement in S5 is as follows: First, based on the results of pulse compression, find the peak position of the first column of fast-time pulse compression, and with this peak position as the center, within the frequency range that can contain all the peaks of fast-time pulse compression, apply a fast algorithm to each column of fast-time sequence before pulse compression to achieve Chirp-z transform. After the calculation is completed, the refined spectrum sequence can be obtained.

[0081] Furthermore, the distance correction method in S6 is as follows: for each fast time series, extract the phase corresponding to its peak point to obtain a sequence with a slow time period variation, calculate the average value of the sequence, and then multiply it by a constant related to the signal parameters to obtain the average distance deviation. Use this distance deviation to compensate for all fast time series to obtain the distance-corrected echo data.

[0082] Furthermore, the CFAR detection processing method in S7 is as follows: based on the preset false alarm probability and detection performance requirements, a CFAR detection algorithm is selected and a detector is generated. The detector is then applied to the high-precision processed echo data to separate the target from the noisy environment.

[0083] Furthermore, in S8, the parameter estimation method is as follows: For distance estimation, the target distance is obtained by searching for the frequency corresponding to the peak value after fast-time pulse compression and distance correction, and multiplying it by a constant related to the signal parameters; for velocity estimation, the slow-time series is obtained by extracting the fast-time peak value, and the target velocity is obtained by performing time-frequency analysis on the series or by using a domain transformation algorithm; for angle estimation, the target angle is obtained by extracting a two-dimensional matrix composed of the fast time and channel dimensions, and by using a spectrum super-resolution algorithm to perform peak search on the obtained spatial pseudospectrum.

[0084] Reference Figure 3 As shown, radar detection involves emitting electromagnetic waves of a specific type and then extracting the amplitude and phase information of the electromagnetic waves reflected back from the target. The electromagnetic wave transmission and reception process requires the coordinated operation of a signal generator, transmitter, receiver, and antenna. The method for transmitting and receiving radio frequency (RF) signals in a radar system is as follows: the microcontroller unit (MCU) of the external platform sends RF signal parameters to the signal generation unit via a standard communication protocol, generating a specific LFMCW signal. Each antenna of the radar transmitter transmits RF signals under the control of the radar MCU. LFMCW millimeter-wave radars are often synchronous in transmission and reception; therefore, the receiving antenna can receive the echo signal scattered from the target within microseconds and immediately send it to the echo preprocessing module for further processing.

[0085] Reference Figure 4As shown, the signal received by the receiving antenna is an analog signal, that is, a signal that changes continuously with time. However, computers can only process digital signals, that is, discrete sequences. Therefore, the main task of preprocessing is to convert the echo into a digital signal. The radar echo digital signal is usually a three-dimensional data. Each column is a sampled signal of a single transmitted waveform echo, called a fast time series. Multiple fast time series are combined into a two-dimensional matrix, and the row direction is called the slow time direction. Such a two-dimensional matrix is ​​also called a frame. Since radar often has multiple receiving antennas, each receiving antenna forms a frame of signal, which can be superimposed into a three-dimensional data block. The height of the data block is called the channel direction. The method for receiving echo signals is as follows: For the radio frequency band echo of each antenna, the signal amplitude is first amplified by a low-noise amplifier (LNA), then mixed with a reference signal from the signal generation module in a mixer, and then filtered by a low-pass filter (LPF) to obtain a low-frequency baseband signal with the carrier frequency removed. Finally, the analog signal is sampled by an analog-to-digital converter (ADC) to obtain a digital signal.

[0086] Reference Figure 5 As shown, according to the radar equation

[0087]

[0088] In the formula, P r P represents the receiver's received power. t Let G represent the transmitter's transmit power, λ represent the antenna gain, σ represent the RF signal wavelength, σ represent the target's cross-sectional area, and R represent the target's distance. On the one hand, as the formula shows, the receiver's received power gradually decreases with increasing distance. On the other hand, radar waves are modulated by noise, clutter, and some interference during propagation, ultimately leading to a severe reduction in the signal-to-noise ratio (SNR), making the target indistinguishable. Therefore, pulse compression is needed to improve the SNR and locate the target in noisy environments. The pulse compression method involves performing an FFT on the fast time series. Since the time-domain signal is approximately rectangular, the envelope of the signal in the frequency domain after the FFT approximates a sinc function, and its main lobe width is inversely proportional to the width of the rectangular time-domain signal, increasing the SNR by a factor of Bτ, where B is the signal bandwidth and τ is the signal pulse width. At this point, the original time-domain signal is compressed into a high-amplitude spike in the frequency domain, making it distinguishable from noise.

[0089] Reference Figure 6As shown, the FFT performed by pulse compression is a fast algorithm of the Discrete Fourier Transform (DFT), which converts a discrete time-domain signal into a discrete frequency-domain signal. Its spectrum can be regarded as the sampling result of the continuous Fourier transform. However, because the main lobe width of the sinc function obtained after pulse compression is very narrow, the number of sampling points within the main lobe is very small, and it is very likely that the position of the theoretical peak point will not be captured. On the one hand, this leads to a reduction in the height of the sampling peak point, resulting in a decrease in the signal-to-noise ratio and affecting signal detection. Moreover, sampling at non-theoretical peak points will generate additional phase modulation, leading to a decrease in the accuracy of ranging and velocity measurement. On the other hand, directly increasing the number of FFT calculation points to refine the spectrum is not only inefficient but also very uneconomical for hardware. Therefore, the Chirp-z transform is needed for spectrum refinement. The principle of the Chirp-z transform is as follows:

[0090]

[0091] In the formula, N is the number of points in the input sequence x(n), and M is the output sequence X(z) of the Chirp-z transform. k The number of points (A0 and W0) is constant, θ0 represents the refinement starting angle, and φ0 represents the refinement interval angle. Typically, A0 = W0 = 1 and M > N are set, and then θ0 and φ0 are set to refine the spectrum within a specified frequency range. For the same refinement result, the Chirp-z transform is computationally much less computationally intensive than the direct FFT, and a fast FFT-based algorithm can be used, thus ensuring measurement efficiency. The method for spectral refinement is as follows: First, based on the pulse compression result, find the peak position of the first fast-time pulse compression sequence. Then, using this peak position as the center, within a frequency range that includes all fast-time pulse compression peaks, sequentially apply a fast algorithm to each fast-time sequence before pulse compression to achieve the Chirp-z transform. The fast algorithm works as follows: For a known input sequence, the starting angle and interval angle on the complex plane are determined according to the required refinement level. Based on these parameters, a linear frequency modulated complex sequence and a complex exponential weight sequence of the same length as the input sequence are constructed. Both are then inner-producted with the input sequence. The resulting sequence and the constructed linear frequency modulated complex sequence are then subjected to FFT and inner-producted. The inner-product result is then subjected to Inverse Fast Fourier Transform (IFFT), and the result is again inner-producted with the original linear frequency modulated complex sequence, thus completing the fast algorithm. After calculation, the refined spectral sequence is obtained.

[0092] Reference Figure 7As shown, sampling at non-theoretical peak points introduces additional phase modulation. This phase modulation originates from the small difference between the sampled peak point and the theoretical peak point, i.e., the difference between the distance estimated by the fast-time pulse compression peak and the actual distance. Theoretical derivation reveals that the difference between the sampled peak point and the theoretical peak point exhibits periodic variation around a certain average value, while the Doppler phase caused by the target velocity varies periodically around 0. Therefore, the average of the two is the average value of the additional phase modulation. Extracting this average value allows for the reverse derivation of the average distance difference. By compensating the measurement results for this average distance difference, a more accurate estimation result can be obtained.

[0093] The method for distance correction is as follows: for each fast time series, extract the phase corresponding to its peak point to obtain a sequence with a slow time period variation, calculate the average value of the sequence, and then multiply it by a constant related to the signal parameters to obtain the average distance deviation. Use this distance deviation to compensate for all fast time series to obtain the distance-corrected echo data.

[0094] Reference Figure 8 As shown, after pulse compression processing of the radar echo, the target signal-to-noise ratio is significantly improved, but the target's peak value is still surrounded by noise, requiring detection of the target peak value within the noise environment. In the field of radar signal detection, CFAR detection refers to designing a detector to maximize the detection probability under a certain false alarm probability. The CFAR detection processing method is as follows: based on the preset false alarm probability and detection performance requirements, a CFAR detection algorithm is selected and a detector is generated. The detector is then applied to the high-precision processed echo data to separate the target from the noise environment.

[0095] Reference Figure 9 As shown, after high-precision processing and CFAR processing, parameter estimation is required to obtain the final measurement results. Parameter estimation mainly involves extracting the amplitude and phase variations of the three-dimensional radar echo over time and space, and then converting them into information about the target's corresponding motion parameters. The parameter estimation methods are as follows: For range estimation, the target range is obtained by searching for the frequency corresponding to the peak value after fast-time pulse compression and range correction, and multiplying it by a constant related to the signal parameters; for velocity estimation, the slow-time series is obtained by extracting the fast-time peak value, and the target velocity is obtained by performing time-frequency analysis or using a domain transformation algorithm on this series; for angle estimation, the target angle is obtained by extracting a two-dimensional matrix composed of the fast-time and channel dimensions, and peak searching is performed on the obtained spatial pseudospectrum using a spectral super-resolution algorithm.

[0096] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on its differences from other embodiments. In particular, for system or system embodiments, since they are fundamentally similar to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. The systems and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. Components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0097] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A target measurement system for ultra-wideband LFMCW millimeter-wave radar, characterized in that, include: External platform MCU, signal generation module, wireless transceiver module, echo preprocessing module, high-precision processing module, parameter estimation module; An external platform MCU connects to the input / output terminals of the signal generation module and is used to send RF signal parameters to the signal generation module via a standard communication protocol to control it to generate a linear frequency modulated RF band transmission signal. An external platform MCU connects to the input / output terminals of the wireless transceiver module and is used to control the wireless transceiver module's transmission and reception of radio frequency signals. The signal generation module is connected to the input terminal of the wireless transceiver module and is used to generate a linear frequency modulated radio frequency band transmission signal and send it to the wireless transceiver module. The signal generation module is connected to the first input terminal of the echo preprocessing module and is used to generate the reference signal required for demodulation by the echo preprocessing module. The wireless transceiver module, connected to the second input of the echo preprocessing module, is used to convert the linearly frequency modulated radio frequency digital signal into an analog signal and transmit it into the environment via the transmitting antenna within the module; at the same time, it captures reflected echo signals from objects in the environment via the receiving antenna within the module and transmits them to the subsequent echo preprocessing module for preprocessing. The echo preprocessing module is connected to the input of the high-precision processing module and is used to convert the analog echo signal into a baseband digital signal. The high-precision processing module is connected to the input of the parameter estimation module. It is used to receive the baseband digital signal sent by the echo preprocessing module and perform high-precision processing to obtain the processed echo data. The processed echo data is then transmitted to the parameter estimation module. The parameter estimation module is used to extract measured values ​​from the processed echo data, which are the parameters of the moving target. The high-precision processing module includes a pulse compression unit, a spectrum refinement unit, and a distance correction unit connected in sequence. The pulse compression unit is used to perform pulse compression on fast-time discrete echo data to obtain a preliminary slow-time-range image; The spectrum refinement unit is used to refine the spectrum obtained by the range-dimensional FFT of fast-time discrete echo data by performing Chirp-z transform using a fast algorithm, thus obtaining a refined slow-time range image. The distance correction unit is used to calculate the average phase value of the peak phase due to inherent errors that cause periodic phase changes. The average phase value is used to estimate the distance deviation and compensate for the echo phase so that the echo phase contains accurate distance information.

2. The target measurement system for ultra-wideband LFMCW millimeter-wave radar according to claim 1, characterized in that, The echo preprocessing module includes a low-noise amplifier, an echo mixer, a low-pass filter, and an A / D sampling circuit connected in sequence. The echo preprocessing module converts analog echo signals into baseband digital signals in the following ways: First, the analog echo signal is amplified by a low-noise amplifier, then mixed with a reference signal generated by the signal generation module, and then passed through a low-pass filter to obtain the baseband signal. Subsequently, the baseband analog signal is converted into a baseband digital signal by an A / D sampling circuit and reassembled to form a three-dimensional discrete echo data matrix.

3. A target measurement system for ultra-wideband LFMCW millimeter-wave radar according to claim 1, characterized in that, The parameter estimation module is used to perform constant false alarm rate detection on the echo data processed by the high-precision processing module, distinguish the target from the noisy environment, and then perform maximum likelihood estimation on each fast time series to obtain the estimated value of the target position. The slow time series is extracted and the time-frequency analysis algorithm is used to obtain the estimated value of the target velocity. The channel-dimensional sequence is processed by a spectral super-resolution algorithm to obtain the estimated value of the target angle.

4. A target measurement method for ultra-wideband LFMCW millimeter-wave radar, characterized in that, A target measurement system for an ultra-wideband LFMCW millimeter-wave radar as described in any one of claims 1-3 includes the following steps: S1. The external platform MCU sends the radio frequency signal parameters to the signal generation module through the standard communication protocol and generates a linear frequency modulated radio frequency band transmission signal. S2. The wireless transceiver module receives the radio frequency band transmitted signal and detects whether the wireless transceiver module has completed transmission and reception. S3, the echo preprocessing module converts the radio frequency band transmitted signal into a digital signal to obtain three-dimensional discrete echo data; S4. The pulse compression unit performs pulse compression on the discrete echo data to obtain a preliminary slow time-range image. S5, the spectrum refinement unit performs spectrum refinement processing on the discrete echo data to obtain an accurate slow time-range image; S6. The distance correction unit performs distance correction on the discrete echo data to obtain distance-corrected echo data. S7. The parameter estimation module performs CFAR detection processing on the processed echo data to separate the target from the noisy environment. S8, the parameter estimation module extracts the amplitude and phase of the 3D radar echo after CFAR detection and processing, and converts them into the moving target parameters corresponding to the target by extracting the information on the changes of the amplitude and phase of the 3D radar echo with time and space.

5. A target measurement method for ultra-wideband LFMCW millimeter-wave radar according to claim 4, characterized in that, In S4, the pulse compression processing method is to perform an FFT operation on the fast time series. At this time, the original time domain signal is compressed into an amplitude spike in the frequency domain, which can be distinguished from the noise.

6. A target measurement method for ultra-wideband LFMCW millimeter-wave radar according to claim 4, characterized in that, The method for spectrum refinement in S5 is as follows: First, based on the pulse compression result, find the peak position of the first fast-time pulse compression. Then, with this peak position as the center, within the frequency range that includes all fast-time pulse compression peaks, apply the fast algorithm to each fast-time sequence before pulse compression to achieve Chirp-z transform. After the calculation is completed, the refined spectrum sequence can be obtained.

7. A target measurement method for ultra-wideband LFMCW millimeter-wave radar according to claim 4, characterized in that, The distance correction method in S6 is as follows: For each fast time series, extract the phase corresponding to its peak point to obtain a sequence with a slow time period variation, calculate the average value of the sequence, and then multiply it by a constant related to the signal parameters to obtain the average distance deviation. Use this distance deviation to compensate for all fast time series to obtain the distance-corrected echo data.

8. A target measurement method for ultra-wideband LFMCW millimeter-wave radar according to claim 4, characterized in that, The CFAR detection processing method in S7 is as follows: based on the preset false alarm probability and detection performance requirements, the CFAR detection algorithm is selected and a detector is generated. The detector is then applied to the high-precision processed echo data to separate the target from the noisy environment.

9. A target measurement method for ultra-wideband LFMCW millimeter-wave radar according to claim 4, characterized in that, The parameter estimation method in S8 is as follows: For distance estimation, the frequency corresponding to the peak value after fast time pulse compression and distance correction is searched, and multiplied by a constant related to the signal parameters to obtain the target distance; for velocity estimation, the slow time series can be obtained by extracting the fast time peak value, and the target velocity can be obtained by performing time-frequency analysis on the series or using a domain transformation algorithm. For angle estimation, a two-dimensional matrix composed of fast time and channel dimension is extracted, and the target angle is obtained by peak search on the obtained spatial pseudospectrum using the spectral super-resolution algorithm.

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