A target detection method

Abstract

The application discloses a target detection method, which comprises the following steps: controlling at least two transmitting antennas to transmit transmitting signals according to corresponding sparse codes, wherein the sampling rates of the transmitting antennas in the speed dimension are different; obtaining at least two receiving signals received by a receiving antenna; mixing and transforming each receiving signal with the corresponding transmitting signal to obtain at least two data matrices to be processed; and determining target information according to each data matrix to be processed, so that the problem of long detection period and low refresh rate is solved. Through the design of sparse codes, each transmitting antenna transmits a transmitting signal according to the sparse codes, the signal waveforms transmitted by the transmitting antennas have different sampling rates in the speed dimension, the maximum unambiguous speed of a single antenna is not reduced, and only the different transmitting signals and receiving signals in a single frame are used to determine the target information, so that the target recognition is realized, the system period is reduced, and a large unambiguous range is obtained.

Description

Technical Field

[0001] This invention relates to the field of target detection technology, and in particular to a target detection method. Background Technology

[0002] With the continuous improvement of people's living standards, the automotive industry has experienced rapid development, and Advanced Driver Assistance Systems (ADSA) with functions such as adaptive cruise control and forward collision warning are being equipped in more and more cars. ADSA requires sensor data collection for assistance during its application. Millimeter-wave radar, with its advantages of low cost, long detection range, and immunity to weather conditions, is a crucial component in the automotive sensor field.

[0003] MIMO radars employing time-division multiplexing typically use multiple antennas to uniformly and alternately transmit the same FMCW (Frequency Modulated Continuous Wave) waveform. Then, a Fast Fourier Transform (FFT) is performed in the range-velocity dimension to obtain the corresponding target range and velocity. However, the target velocity measurement range is limited by the time interval between two chirps, and the hardware chirp repetition period cannot meet the velocity measurement range of -150 km / h to 200 km / h for vehicle-mounted radars, often resulting in velocity ambiguity. Especially when using time-division multiplexing, the time interval between two chirps on a single transmitting antenna increases exponentially due to the alternating waveform transmission by multiple antennas, leading to a significant reduction in the maximum unambiguous velocity measurement range per frame. The commonly used staggered repetition rate method for velocity ambiguity resolution requires multiple frames as a system period, with different chirp repetition periods for each frame. In this case, the maximum unambiguous velocity range is the least common multiple of the maximum velocity measurement ranges of all frames within the system period. Therefore, time-division multiplexing suffers from the significant reduction in the maximum unambiguous velocity measurement range per frame. When using traditional methods to detect targets, more frames are needed in a single cycle to reach the required speed measurement range, resulting in a longer system cycle and a reduced refresh rate. Summary of the Invention

[0004] This invention provides a target detection method to solve the problems of long detection cycle and low refresh rate.

[0005] According to one aspect of the present invention, a target detection method is provided, comprising:

[0006] Control at least two transmitting antennas to transmit signals according to corresponding sparse codes, wherein each transmitting antenna has a different sampling rate in the velocity dimension;

[0007] Acquire at least two received signals received by the receiving antenna;

[0008] The received signals are mixed and transformed with the corresponding transmitted signals to obtain at least two data matrices to be processed;

[0009] The target information is determined based on each of the data matrices to be processed.

[0010] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0011] At least one processor; and

[0012] A memory communicatively connected to the at least one processor; wherein,

[0013] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the target detection method according to any embodiment of the present invention.

[0014] The technical solution of this invention addresses the problems of long detection cycles and low refresh rates by controlling at least two transmitting antennas to transmit signals according to corresponding sparse codes, with each transmitting antenna having a different sampling rate in the velocity dimension; acquiring at least two received signals received by a receiving antenna; mixing and transforming each received signal with its corresponding transmitted signal to obtain at least two data matrices to be processed; and determining target information based on each data matrix to be processed. This solution utilizes sparse coding to control each transmitting antenna to transmit signals according to the sparse code, with the transmitted signal waveforms having different sampling rates in the velocity dimension. This does not reduce the maximum unambiguous speed of a single antenna and eliminates the need for multi-frame cascading; target information is determined using only the different transmitted and received signals within a single frame, thus achieving target identification, reducing system cycles, and providing a larger unambiguous range.

[0015] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart of a target detection method provided in Embodiment 1 of the present invention;

[0018] Figure 2 This is a flowchart of a target detection method provided according to Embodiment 2 of the present invention;

[0019] Figure 3 This is an example diagram illustrating the implementation of a sparse coding-based transmission waveform according to Embodiment 2 of the present invention.

[0020] Figure 4 This is an example diagram illustrating the implementation of determining a data matrix to be processed according to Embodiment 2 of the present invention;

[0021] Figure 5 This is an example diagram illustrating the display of target information according to Embodiment 2 of the present invention;

[0022] Figure 6 This is a schematic diagram of a MIMO array according to Embodiment 2 of the present invention;

[0023] Figure 7 This is a schematic diagram of a virtual equivalent array provided according to Embodiment 2 of the present invention;

[0024] Figure 8 This is a schematic diagram of the structure of an electronic device that implements the target detection method of this invention. Detailed Implementation

[0025] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a 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 those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0027] Example 1

[0028] Figure 1The flowchart below provides a target detection method according to Embodiment 1 of the present invention. This embodiment is applicable to the detection of targets using MIMO radar. The method can be executed by a target detection device, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0029] S101. Control at least two transmitting antennas to transmit signals according to the corresponding sparse coding, with each transmitting antenna having a different sampling rate in the velocity dimension.

[0030] In this embodiment, the transmitting antenna is the transmitting antenna of a radar array, such as a multiple-input multiple-output (MIMO) radar array. The radar array in this embodiment can be installed on equipment such as vehicles and robots; sparse coding uses two different numbers for encoding.

[0031] Specifically, sparse coding is determined in advance through intelligent optimization algorithms. Taking two transmitting antennas TX1 and TX2 as an example, the sparse coding of transmitting antenna TX1 is [1 0 0 1……0 1 0], and the sparse coding of transmitting antenna TX2 is [0 0 1 1……10 1]. Here, 1 represents transmitting a signal and 0 represents not transmitting a signal. Therefore, the 1s in the sparse coding of transmitting antenna TX1 and transmitting antenna TX2 are staggered and will not appear at the same time. That is, transmitting antenna TX1 and transmitting antenna TX2 will not transmit signals at the same time.

[0032] When transmitting signals, this application uses a MIMO radar array and employs time-division multiplexing (TDM) technology to ensure that the transmitted FMCW waveforms from each transmitting antenna do not overlap in time. This means that only one transmitting antenna operates at any given time, and one frame constitutes one system cycle. This application uses the transmitted and received signals within one frame to determine motion information; that is, the sparse coding provided in this embodiment is equivalent to one system cycle. After determining the sparse code for each transmitting antenna, each transmitting antenna is controlled to transmit signals according to the corresponding sparse code. Each transmitting antenna has a different sampling rate in the velocity dimension.

[0033] Different transmitting antennas have different sampling rates in the velocity dimension. Taking a dual transmitting antenna as an example, the interval between two chirps of transmitting antenna TX1 is Ts1, and the corresponding sampling rate in the velocity dimension is fs1; the interval between two chirps of transmitting antenna TX2 is Ts2, and the corresponding sampling rate in the velocity dimension is fs2.

[0034] For conventional, non-velocity-dimensional sparse time-division multiplexing waveforms, when the number of transmit antennas is M, assuming the time interval between two chirps is T, the time interval between adjacent chirps with the same transmit antenna becomes M*T. Therefore, the maximum unambiguous velocity is... For time-division multiplexing waveforms with sparse velocity dimensions, when the number of transmit antennas is M, due to the sparse arrangement of the velocity dimension, the minimum time interval between adjacent chirps of the same transmit antenna is still T. Furthermore, with good signal-to-noise ratio optimization through intelligent algorithms, the maximum unambiguous velocity remains T. This greatly extends the maximum unambiguous speed.

[0035] S102. Acquire at least two received signals received by the receiving antenna.

[0036] In this embodiment, the received signal is the signal obtained by reflecting the transmitted signal off a target, such as a person, vehicle, building, or tree. After transmitting the transmitted signal, the receiving antenna receives the signal reflected back from the target. Upon receiving the signal, the receiving antenna transmits it to the executing entity.

[0037] S103. Mix and transform each received signal with its corresponding transmitted signal to obtain at least two data matrices to be processed.

[0038] In this embodiment, the data matrix to be processed can be specifically understood as a data matrix used for target detection. Each received signal and its corresponding transmitted signal are determined, and the signals are processed using techniques such as frequency mixing and fast Fourier transform to obtain the data matrix to be processed for each transmitting antenna.

[0039] S104. Determine the target information based on each data matrix to be processed.

[0040] In this embodiment, target information can be specifically understood as information used to describe the target, such as distance, speed, etc., where distance is the distance between the radar (or vehicle or other equipment) and the target, and speed is the relative speed between the target and the radar.

[0041] The data matrix to be processed is processed using methods such as incoherent integration and constant false alarm rate (CFAR) detection to determine the range and velocity cells of the detected target. Simultaneously, the maximum unambiguous velocity of the transmitting antenna is determined. Velocity deambiguation is then performed using the maximum unambiguous velocity and the velocity cells to obtain the target's velocity. Further, the distance to the target is calculated based on the velocity and range cells. This application can also determine the angle between the target and the radar (or vehicle, etc.) based on the transmitted and received waveforms. The angle is used to determine the relative position between the target and the executing entity, enabling target evasion or other actions.

[0042] The target detection method of this invention controls at least two transmitting antennas to transmit signals according to corresponding sparse codes, with each transmitting antenna having a different sampling rate in the velocity dimension; acquires at least two received signals received by a receiving antenna; mixes and transforms each received signal with its corresponding transmitted signal to obtain at least two data matrices to be processed; and determines target information based on each data matrix to be processed. This solves the problems of long detection cycles and low refresh rates. By designing sparse codes and controlling each transmitting antenna to transmit signals according to the sparse codes, the waveforms of the signals transmitted by the transmitting antennas have different sampling rates in the velocity dimension, which can maintain the maximum unambiguous speed of a single antenna and eliminate the need for multi-frame cascading, using only the different transmitted and received signals within a single frame to determine target information, thereby achieving target recognition, reducing system cycles, and having a large unambiguous range.

[0043] Example 2

[0044] Figure 2 This is a flowchart of a target detection method provided in Embodiment 2 of the present invention. This embodiment is a refinement based on the above embodiments. Figure 2 As shown, the method includes:

[0045] S201. Control at least two transmitting antennas to transmit signals according to the corresponding sparse coding, with each transmitting antenna having a different sampling rate in the velocity dimension.

[0046] Optionally, the number of virtual ramps included in the sparse coding corresponding to each transmit antenna is different. The virtual ramps include effective ramps and static regions. The number of effective ramps in the sparse coding of each transmit antenna is the same.

[0047] Sparse coding is obtained through encoding optimization using a genetic algorithm. The genetic algorithm uses the encoding of the transmitting antenna and the start time difference as variables to be optimized, uses whether the waveforms of the transmitting antenna overlap in the time domain as a penalty function, and takes the optimal signal-to-noise ratio as the optimization objective to perform encoding optimization.

[0048] Taking two transmitting antennas TX1 and TX2 as examples, the sparse coding of transmitting antenna TX1 is [1 0 0 1 ……0 10], and the sparse coding of transmitting antenna TX2 is [0 0 1 1……1 0 1]. In the sparse coding of each transmitting antenna, 0 and 1 each represent a virtual ramp. Here, 1 represents the signal persistence region, the interval where the signal is continuously transmitted, i.e., the effective ramp; 0 represents the static region, where no signal is transmitted. The number of effective ramps q in the sparse coding of each transmitting antenna is the same, that is, the number of 1s in the sparse coding is the same (i.e., there are q effective ramps), but the number of 0s is different. Different transmitting antennas have different sampling rates in the velocity dimension, and the number of virtual ramps of different transmitting antennas is also different, namely q1 and q2, respectively. q1 and q2 have the following relationship:

[0049] T frame =Ts1*q1=Ts2*q2

[0050] In the above formula, T frame This is the total duration of the transmitted signal within a single frame, i.e., the system period.

[0051] To ensure that the transmit antennas do not overlap in time slots, the virtual ramps of each transmit antenna have different start times within a single frame, with a start time difference of t0. This application uses a genetic algorithm from intelligent optimization algorithms to obtain a waveform with relatively optimal performance. The genetic algorithm uses the encoding of the transmit antennas and the start time difference t0 as variables to be optimized, uses whether the waveforms of the transmit antennas overlap in the time domain as a penalty function, and uses the best signal-to-noise ratio as the optimization objective to perform encoding optimization, thereby obtaining a sparse code for the velocity dimension that does not overlap in time and has a high signal-to-noise ratio. For example, Figure 3 An example diagram of transmitting waveforms based on sparse coding is provided. The diagram uses two transmit antennas TX1 and TX2 as an example, where ts is the ramp duration, and the start time difference of the first virtual ramp of TX1 and TX2 is t0. The diagram takes the start time difference of TX2 as 0 as an example, the start time of the first virtual ramp of TX2 is 0, and the start time of the first virtual ramp of TX1 is t0.

[0052] S202. Acquire at least two received signals received by the receiving antenna.

[0053] For each receiving antenna, the number of received ramps is the effective number of ramps. In this embodiment, taking a dual-transmitting antenna as an example, after transmitting signals through two transmitting antennas, the number of ramps received by a single receiving antenna is 2q. Adaptively, when the number of transmitting antennas is m, the number of ramps received by a single receiving antenna is m*q.

[0054] S203. Mix each received signal with its corresponding transmitted signal to obtain the first data matrix.

[0055] In this embodiment, the first data matrix can be specifically understood as a data matrix obtained by performing a Fourier transform in the distance dimension. Each received signal is mixed with its corresponding transmitted signal, and the mixed signal is processed to obtain an analog signal. The analog signal is then subjected to a Fourier transform in the distance dimension to obtain the first data matrix.

[0056] As an optional embodiment of this example, this optional embodiment further performs frequency mixing processing on each received signal and its corresponding transmitted signal to obtain a first data matrix, optimized as follows:

[0057] A1. Mix each received signal with its corresponding transmitted signal to obtain the corresponding intermediate frequency signal.

[0058] In this embodiment, the intermediate frequency (IF) signal can be specifically understood as the signal obtained after mixing the transmit and receive signals. Each received signal is mixed with its corresponding transmit signal in sequence, that is, the transmit and receive signals corresponding to each ramp are mixed to obtain the corresponding IF signal.

[0059] A2. Perform analog-to-digital conversion on each intermediate frequency signal, and sample the converted signal to determine a first preset number of digital signals.

[0060] In this embodiment, the first preset quantity can be understood as a pre-set value, representing the number of sampling points in the range dimension. Each intermediate frequency (IF) signal undergoes analog-to-digital conversion, transforming the analog signal into a digital signal. The converted signal is then sampled at a certain frequency to obtain the first preset quantity Nr of digital signals. This sampling step involves sampling the IF signal corresponding to each slope to obtain the first preset quantity Nr of digital signals. The magnitude of the first preset quantity Nr is related to the hardware performance and radar range performance indicators, and is typically 256 or 512.

[0061] A3. Perform a one-dimensional fast Fourier transform on each digital signal in the distance dimension to form the first data matrix.

[0062] Perform a one-dimensional fast Fourier transform on each digital signal in the distance dimension, and form the first data matrix based on the obtained data.

[0063] It should be noted that in the embodiments of this application, each ramp can be defined in terms of distance, and all ramps can be defined in terms of speed.

[0064] S204. Separate the first data matrix according to each transmitting antenna to determine the second data matrix corresponding to each transmitting antenna.

[0065] In this embodiment, the second data matrix can be specifically understood as the data matrix corresponding to each transmitting antenna. Each transmitting antenna is pre-encoded, thus determining the antenna's encoding. Based on the antenna's encoding, it can be determined which transmitting antenna is sending the transmitted signal. The first data matrix includes the transmitted and received signals from all transmitting antennas. This step separates the data in the first data matrix according to the different transmitting antennas using their encodings, obtaining the second data matrix corresponding to each transmitting antenna. The amount of data in the second data matrix is ​​q×Nr.

[0066] S205. Rearrange each of the second data matrices according to the corresponding sparse coding to obtain the third data matrix.

[0067] For each second data matrix, its corresponding sparse code is determined based on the corresponding transmit antenna. The second data matrix is ​​then rearranged according to the sparse code. Data is filled into the positions representing continuous transmission signals ("1") in the sparse code according to chip order, and the positions representing non-transmission signals ("0") are set to 0. Taking a second data matrix with q columns and Nr rows as an example (i.e., each column contains Nr data items), the data in each column is sequentially filled into the positions of the sparse code's "1"s, and the positions of the "0"s are set to 0, resulting in the third data matrix corresponding to each transmit antenna. Since the number of virtual ramps differs for different transmit antennas, the size of the resulting third data matrix also differs. The size of the third data matrix for TX1 is q1×N. r The size of the third data matrix of TX1 is q2×N r .

[0068] S206. Perform a two-dimensional fast Fourier transform on each third data matrix in the velocity dimension to obtain the data matrix to be processed.

[0069] For each third data matrix, perform a two-dimensional Fast Fourier Transform on Nv (a second preset number) points in the velocity dimension. The result is the data matrix to be processed. The data matrix to be processed can be represented as follows: Nv should satisfy the relationship Nv≥max(q1,q2) with q1 and q2. Nv can be set based on satisfying the above relationship, and is usually a power of 2.

[0070] For example, Figure 4 This is an example diagram illustrating an implementation of determining a data matrix to be processed, provided in an embodiment of this application. Figure 4 Taking two transmitting antennas, TX1 and TX2, as an example, for each transmitting antenna, analog-to-digital conversion, sampling, one-dimensional fast Fourier transform, antenna separation, and rearrangement are performed on the effective ramp to obtain a third data matrix. This third data matrix undergoes a two-dimensional fast Fourier transform in the Nv-point velocity dimension to obtain an Nr×Nv data matrix to be processed. The number of data matrices to be processed in this application is equal to the product of the number of transmitting antennas M and the number of receiving antennas N, i.e., M×N data matrices to be processed.

[0071] Optional, target information includes movement speed and distance.

[0072] S207. Perform incoherent integration on each data matrix to be processed to determine the matrix to be tested.

[0073] In this embodiment, the matrix to be tested can be specifically understood as the matrix used for detecting the target. Non-coherent integration (NCI) is performed on the data matrix to be processed to obtain the matrix to be tested S corresponding to each data matrix to be processed. NCI =∑|S2D | 2 In this embodiment of the application, the number of matrices to be tested is the same as the number of transmitting antennas, that is, the number of matrices to be tested obtained in this step is M.

[0074] S208. Select points on the matrix to be measured to determine the distance and velocity units of the target.

[0075] Based on the data in the matrix to be tested, detection points are selected to obtain the distance and velocity units where the target exists. Point selection in the matrix to be tested can be achieved using a pre-set algorithm, such as the Constant False Alarm Rate (CFAR) detection criterion. In this step, when detecting targets, values ​​that meet certain conditions can be used as targets. These conditions can be maxima, exceeding a preset threshold, etc. Taking a maxima as an example, the coordinates of the maxima in the matrix are the distance unit ri and the velocity unit vi. That is, the distance unit ri and the velocity unit vi obtained in this step are the coordinates of an element in the matrix, where the distance unit ri is the coordinate on Nr and the velocity unit vi is the coordinate on Nv.

[0076] In this embodiment of the application, when selecting points for the test matrix, points are selected sequentially for each test matrix to complete target detection and obtain the target's distance and velocity units. This step can yield 0, 1, or multiple targets, and target detection is performed sequentially for each test matrix. For each target, the motion speed and target distance can be determined based on its corresponding distance and velocity units, i.e., the motion speed and target distance are determined using the following method steps.

[0077] S209. For each target's range and velocity units, determine the maximum unambiguous velocity of each transmitting antenna based on the signal transmission interval of each transmitting antenna, and determine the first velocity of each transmitting antenna based on the velocity units.

[0078] In this embodiment, the signal transmission interval can be specifically understood as the time interval between two adjacent chips transmitted by the transmitting antenna, i.e., the time interval between two virtual ramps. The signal transmission interval is the reciprocal of the sampling rate of the velocity dimension. Taking the transmitting antenna TX1 as an example, its signal transmission interval is Ts1, the sampling rate of the velocity dimension is fs1, and Ts1 = 1 / fs1. The first velocity can be specifically understood as the velocity used to calculate the actual moving velocity of the target. There is a certain relationship between the first velocity and the moving velocity for different transmitting antennas.

[0079] For each transmitting antenna, the signal transmission interval of that antenna is determined. The maximum unambiguous velocity of the transmitting antenna is calculated based on the signal transmission interval and the formula for calculating the maximum unambiguous velocity. For example, this application provides a formula for calculating the maximum unambiguous velocity. Among them, v maxLet Ts be the maximum unambiguous velocity, λ be the signal transmission interval, and λ be the wavelength. The maximum unambiguous velocity of each transmitting antenna is determined based on its signal transmission interval. The Doppler frequency of the transmitting antenna is determined based on the velocity element, and the center frequency of the transmitting antenna is also determined. The first velocity is calculated based on the Doppler frequency and the center frequency.

[0080] As an optional embodiment of this example, this optional embodiment further optimizes the determination of the first velocity of each transmitting antenna based on the velocity unit as follows:

[0081] B1. For each transmitting antenna, determine the Doppler frequency based on the sampling rate and velocity element of the velocity dimension of the transmitting antenna, and determine the center frequency based on the frequency of the virtual ramp.

[0082] A formula for calculating the Doppler frequency is predetermined. For each transmitting antenna, the sampling rate and velocity element of the velocity dimension are substituted into the calculation formula to obtain the Doppler frequency. Exemplarily, this application provides a formula for calculating the Doppler frequency. Among them, f d1 Here, vi is the Doppler frequency, fs1 is the velocity element, and Nv is the second preset number used in step S206 when performing a two-dimensional fast Fourier transform on the third data matrix in the velocity dimension. For each transmitting antenna, the corresponding Doppler frequency is calculated based on its sampling rate in the velocity dimension, combined with vi and Nv. The center frequency is the center frequency of each virtual ramp, determined based on the frequency of the virtual ramp, which depends on the starting frequency and ramp bandwidth. The starting frequency and ramp bandwidth are preset before transmitting the waveform.

[0083] B2. Determine the first velocity of the transmitting antenna based on the Doppler frequency and the center frequency.

[0084] A formula for calculating the first velocity is predetermined. The Doppler frequency and center frequency are then substituted into the formula to obtain the first velocity. For example, the formula for calculating the first velocity is: Where v is the first velocity, f d1 Let f be the Doppler frequency, f0 be the center frequency, and c be the speed of light. Calculate the first velocity of each transmitting antenna using the above formula.

[0085] S210. Determine the motion speed based on each maximum unambiguous speed and each first speed.

[0086] Velocity deambiguity is performed based on the maximum unambiguous velocity and the first velocity corresponding to each transmitting antenna to determine the unambiguous motion velocity. Velocity deambiguity is further extended to the least common multiple of the maximum unambiguous velocities by utilizing the coprime nature of the maximum unambiguous velocities of each transmitting antenna.

[0087] As an optional embodiment of this example, this optional embodiment further optimizes the determination of the target's motion speed based on each maximum unambiguous velocity and the first velocity as follows:

[0088] C1. Control the first variable to traverse within a predetermined range of values. Based on the predetermined speed constraint relationship, the maximum unambiguous speed and the first speed, calculate the value of the second variable corresponding to each traversal. The first variable and the second variable are variables in the speed constraint relationship.

[0089] In this embodiment, the velocity constraint relationship can be specifically understood as the constraint relationship between the target's relative speed to the radar, the maximum unambiguous speed, and the first speed, which can be expressed by a formula or expression. The velocity constraint relationship includes a first variable and a second variable.

[0090] For example, an embodiment of this application provides an expression for a velocity constraint relationship: v′=v1+n1*V max =v² + n² * V max2 Where n1 is the first variable, n2 is the second variable, and V max1 V is the maximum unambiguous velocity of the transmitting antenna TX1. max2 Let v1 be the maximum unambiguous velocity of transmitting antenna TX2, v2 be the first velocity of transmitting antenna TX1, v' be the velocity of transmitting antenna TX2, and v' be the velocity of motion. The above velocity constraint relationship is based on two transmitting antennas as an example, and can be extended according to the number of transmitting antennas in practical applications.

[0091] Predetermine the ranges of the first and second variables, taking n1 as [-N1, N1] and n2 as [-N2, N2] as an example, where N1 and N2 are coprime. Using the remainder theorem, we iterate through the range of [-N1, N1] for n1, and there exists... The value of the second variable is obtained during the traversal. That is, the value of the second variable is calculated based on each value of n1.

[0092] C2. Determine the target second variable value based on the values ​​of each second variable.

[0093] In this embodiment, the target second variable value can be specifically understood as the final value of the second variable. By comparing the values ​​of each second variable, the value closest to an integer is determined. That is, satisfy The final result obtained at this time This is the value of the second target variable. Simultaneously, the final value of n1, which is the value of the first target variable, can be obtained. The value of n1 when the second target variable is calculated is the value of the first target variable.

[0094] C3. Determine the target's speed based on the value of the second variable.

[0095] By substituting the target's second variable value into the expression for the velocity constraint relationship, and combining it with the corresponding maximum unambiguous velocity and the first velocity, the target's velocity v′ is calculated. The velocity v′ can also be determined using the target's first variable value.

[0096] S211. Determine the target's distance based on the movement speed and distance unit.

[0097] The distance is calculated based on the motion speed and distance unit. The beat frequency is calculated through the distance unit, and the Doppler frequency is updated by the motion speed. According to the predetermined distance calculation formula, the beat frequency and the updated Doppler frequency are substituted into the distance calculation formula to calculate the distance of the target.

[0098] As an optional embodiment of this example, this optional embodiment further optimizes the determination of the target distance based on the motion speed and distance unit as follows:

[0099] D1. Determine the beat frequency based on the distance unit, the first preset quantity, and the sampling rate of the distance dimension.

[0100] A formula for calculating the beat frequency is predetermined. The number of range cells, the number of sampling points in the range dimension, and the sampling rate of the range dimension are then substituted into the formula to calculate the beat frequency. For example, this embodiment provides a formula for calculating the beat frequency: Where, f b Let ri be the beat frequency, fs2 be the range cell, and fs2 be the sampling rate of the range dimension. fs2 is determined by the radar range performance. When all data on the slope are valid data, there is typically... Nr is the first preset quantity, and ts is the slope duration.

[0101] D2. Determine the target Doppler frequency based on the motion speed and center frequency.

[0102] In this embodiment, the target Doppler frequency can be specifically understood as the frequency obtained by updating the Doppler frequency based on the actual motion speed of the target. A calculation formula for the target Doppler frequency is predetermined, and the motion speed and center frequency are substituted into the calculation formula to obtain the target Doppler frequency. For example, this application embodiment provides a calculation formula for the target Doppler frequency: Where, f d2 Let v' be the target Doppler frequency, v′ be the velocity, f0 be the center frequency, and c be the speed of light.

[0103] D3. Determine the target distance based on the beat frequency, target Doppler frequency, ramp duration, signal bandwidth, and a predetermined distance calculation formula.

[0104] For a linear frequency modulated continuous wave signal with bandwidth B and ramp duration ts, the following relationship exists: With modifications, one distance calculation formula in this application embodiment can be the following formula: Where R is the target distance, ts is the ramp duration, and B is the signal bandwidth. The target distance can be obtained by substituting the beat frequency, target Doppler frequency, ramp duration, and signal bandwidth into the distance calculation formula.

[0105] For example, Figure 5 An example diagram displaying target information is provided. Using the method described in this application, four targets are identified. The horizontal axis represents distance in meters (m), and the vertical axis represents velocity in meters per second (m / s). Target 1 has a distance R1 = 30 meters and a velocity V1 = -33 m / s; Target 2 has a distance R2 = 30 meters and a velocity V1 = 1 m / s; Target 3 has a distance R3 = 30.2 meters and a velocity V3 = 15 m / s; and Target 4 has a distance R4 = 70 meters and a velocity V4 = -75 m / s. After velocity unambiguity resolution and distance calculation, the obtained distance and velocity results are R1 = 29.97 m / s, V1 = -33.1 m / s, R2 = 30 m / s, V2 = 0.99 m / s, R3 = 30.2 m / s, V3 = 15.06 m / s, and R4 = 70.4 m / s, V4 = -75.16 m / s, respectively. This method can accurately solve for the target's velocity and distance, and has a large maximum unambiguous velocity range. The velocity in the figure represents the target's moving speed.

[0106] Optionally, the receiving channels of each transmitting antenna may spatially overlap with the receiving channel of the next transmitting antenna.

[0107] The difference in sampling rate of the waveforms transmitted by different transmitting antennas in the velocity dimension introduces an additional phase difference. To obtain this phase difference, MIMO radar arrays employ an equivalent virtual array single-channel overlapping layout design. This involves adjusting the spacing between the transmitting and receiving channels so that, in the equivalent virtual array of the MIMO array, a channel located at one of the different transmitting antennas spatially overlaps. The antenna array arrangement is predetermined, and the array is designed to ensure that the receiving channels of each transmitting antenna are spatially equivalent to the receiving channels of the next transmitting antenna.

[0108] In this application embodiment, the spatial equivalent overlap between the receiving channel of the transmitting antenna and the receiving channel of the next transmitting antenna can be achieved by equivalently overlapping the last receiving channel of the previous transmitting antenna with the first receiving channel of the next transmitting antenna, resulting in the largest equivalent aperture. This application determines the azimuth angle of each target by the phase difference of the transmitted waveforms corresponding to the overlapping array elements.

[0109] For example, Figure 6 A schematic diagram of a MIMO array is provided, with two transmitting antennas, TX1 and TX2, and receiving antennas from RX1 to RX2. N For example, there are N in total. Figure 7 A schematic diagram of a virtual equivalent array is provided, wherein the Nth receiving channel RX' of the transmitting antenna TX1 is... N It overlaps with the first receiving channel RX"1 of the transmitting antenna TX2, forming an overlapping array element.

[0110] Optionally, the target information also includes azimuth angle; as an optional embodiment of this example, this optional embodiment is further optimized by including the following steps:

[0111] E1. Determine the target received data based on the velocity unit and the range unit.

[0112] In this embodiment, the target received data can be specifically understood as the data corresponding to the target when the presence of the target is determined based on the received signal. The target received data is obtained from the data matrix to be processed.

[0113] Data at corresponding positions are obtained from the data matrix to be processed based on velocity and range elements, serving as target received data. Since there are M×N data matrices to be processed, M×N target received data can be obtained. For each transmitting antenna, its transmitted signal is received by all receiving antennas. Therefore, for each transmitted signal, there are N receiving antennas, resulting in N corresponding data matrices to be processed. The target received data selected from these N data matrices is determined. For each transmitted signal, the data received by its corresponding N receiving antennas is extracted from the data matrix to be processed, thus obtaining its corresponding N target received data. The target received data is classified according to the transmitting and receiving antennas, and then step E2 is performed to determine the phase difference of the overlapping array elements.

[0114] E2. For each overlapping array element formed after equivalent overlap, determine the phase difference based on the phase of the two receiving channels corresponding to the overlapping array element.

[0115] Since the overlapping array element is generated by overlapping the equivalent receiving channels of two transmitting antennas, the two receiving channels corresponding to the overlapping array element are the receiving channels of the corresponding two transmitting antennas. Taking the first transmitting antenna TX1 and the second transmitting antenna TX2 as an example, the receiving channels corresponding to the overlapping array element are one receiving channel of TX1 and one receiving channel of TX2, preferably the last receiving channel of TX1 and the first receiving channel of TX2. The phase of the received signal received by the receiving channel is determined; this phase is the phase of the receiving channel. The phase difference between the two receiving channels is obtained. This phase difference is the phase difference of the transmitted waveform.

[0116] For example, assuming there are N receiving array elements, the Nth receiving channel of the first transmitting antenna TX1 and the first receiving channel of the second transmitting antenna TX2 are spatially overlapped after being equivalent. Calculate the phase difference between the two transmitted waveforms on this overlapping array element. The calculation formula is as follows:

[0117]

[0118] in, For the phase of the Nth receiving channel of the first transmitting antenna TX1, This refers to the phase of the first receiving channel of the second transmitting antenna TX2.

[0119] The phase difference corresponding to each overlapping array element is determined using the method described above, resulting in...

[0120] E3. Determine the target phase difference between the first transmitting antenna and the remaining transmitting antennas based on each phase difference.

[0121] In this embodiment, the target phase difference can be specifically understood as the phase difference between the first transmitting antenna and the remaining transmitting antennas.

[0122] The phase difference determined in step E1 can be used to determine the phase difference between the first transmitting antenna TX1 and the third transmitting antenna TX3, the fourth transmitting antenna TX4, and so on. For example, Therefore, the phase difference between the first transmitting antenna TX1 and other transmitting antennas can be obtained. Since this has already been determined in step E2... so It can be directly used as the target phase difference between the first transmitting antenna TX1 and the second transmitting antenna TX2.

[0123] E4. Perform phase compensation based on the phase difference of each target to determine the compensated received data.

[0124] For each transmitting antenna, phase compensation is performed to determine the compensated received data. This application directly uses the N target received data points corresponding to the first transmitting antenna as the compensated received data. For the second and subsequent transmitting antennas, the corresponding N target received data points are multiplied by their respective target phase differences to obtain the compensated received data. This process yields M×N compensated received data points.

[0125] For example, this application provides a formula for phase compensation:

[0126]

[0127] Where, x TX1 (k), x TX2 (k), x TX3 (k)…represents the target received data for TX1, TX2, TX3…, x' TX1 (k), x' TX2 (k), x'TX3(k)... are the compensated received data, and x′ TX1 (N)=x′ TX2 (1).

[0128] The above formula can be used to sequentially determine the phase-compensated target data received by the first to Nth receiving channels of the first transmitting antenna, the phase-compensated target data received by the first to Nth receiving channels of the second transmitting antenna, and so on, until the phase-compensated target data received by the first to Nth receiving channels of all transmitting antennas is determined.

[0129] E5. Determine the received data matrix based on the compensated received data.

[0130] In this embodiment, the received data matrix can be specifically understood as a matrix formed by the compensated received data. The compensated received data are arranged according to the order of the transmitting antennas to form the received data matrix. This step sorts the compensated received data of the equivalent virtual array based on the array layout.

[0131] For example, this application provides an example of receiving a data matrix x:

[0132] x=[x′ TX1 (1),x′ TX1 (2)…x′ TX1 (N),x′ TX2 (2),…x′ TX2 (N)...]

[0133] E6. Perform a fast Fourier transform on the received data matrix to obtain the power spectrum.

[0134] E7. The power spectrum is detected by a constant false alarm rate (CFAR) detection algorithm to determine the extreme values, and the azimuth angle is determined based on the extreme values.

[0135] In this embodiment, the azimuth angle is the angle between the target and the radar (or vehicle, etc.) and the target. A constant false alarm rate (CFAR) detection algorithm is used to detect the power spectrum and obtain the extreme values ​​of the CFAR. There can be one or more extreme values, which are used as the target's azimuth angle. If multiple extreme values ​​exist, then multiple targets at the same distance and speed, but at different angles, are identified.

[0136] The target detection method of this invention solves the problems of long detection cycles and low refresh rates. It designs sparse coding using an intelligent optimization algorithm, controlling each transmitting antenna to send signals according to the sparse coding. This ensures that, under the same transmission duration, time-division multiplexing does not reduce the maximum unambiguous speed of a single transmitting antenna, and eliminates the need for multi-frame cascading. It only utilizes the different transmitted and received signals within a single frame for speed deambiguation to determine target information and achieve target identification. The speed measurement range is large without increasing the system cycle or reducing the system refresh rate. The signal processing algorithm is simple and easy to implement in hardware. The different sampling rates of the waveforms transmitted by the transmitting antennas in the speed dimension eliminate the need for multi-frame detection pairing, reducing the risk of deambiguation failure due to information differences between multiple frames.

[0137] Example 3

[0138] Figure 8 A schematic diagram of an electronic device 30 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of radar sensors, such as automotive radar sensors and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0139] like Figure 8As shown, the electronic device 30 includes at least one set of transmitting antenna arrays 31 and at least one set of receiving antenna arrays 32, wherein the transmitting antenna array includes a plurality of transmitting antenna elements and the receiving antenna array includes a plurality of receiving antenna elements. The transmitting antennas and receiving antennas are respectively connected to radio frequency components, which include couplers 33, mixers 34, analog-to-digital converters 35, and voltage-controlled oscillators 36. The signal processing module 38 performs signal modulation, controls the generation of waveforms through waveform generators 37, generates frequency-modulated continuous wave signals by voltage-controlled oscillators 36, and splits the transmitted signal into two paths by mixers 33. One path is radiated through the transmitting antennas 31, and the other path is used as a local oscillator signal, which is mixed with the signal received by the receiving array 32 in mixers 34 to generate intermediate frequency signals. The analog signal is converted into a digital signal in the analog-to-digital converter 35 and sent to the signal processing module 38 for target detection. The result of the signal processing is exchanged with other devices through component 39.

[0140] The signal processing module 38 reads the digital received signal sent by the analog-to-digital conversion module 35, stores it in the internal storage unit of the signal processing module 38, and loads a pre-designed computer program to perform various appropriate actions and processes.

[0141] The signal processing module 38 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 31 include, but are not limited to, central processing unit (CPU), digital signal processor (DSP), field-programmable gate array (FPGA), microcontroller unit (MCU), etc. The signal processing module 38 performs the various methods and processes described above, such as 2D-FFT, target selection, ranging, velocity measurement, and angle measurement.

[0142] Interaction interface 39 enables the radar to interact with the outside world, such as interacting with a computer to read target test information through a host computer; or interacting with an in-vehicle system to enable the car to make warnings, avoidance and other responses.

[0143] In some embodiments, the target detection method may be implemented as a computer program tangibly contained in a computer-readable storage medium. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 30 via the signal processing module 38. When the computer program is loaded into the signal processing module 38 and executed, one or more steps of the target detection method described above may be performed. Alternatively, in other embodiments, the signal processing module 38 may be configured to perform the target detection method by any other suitable means (e.g., by means of firmware).

[0144] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), microcontroller units (MCUs), system-on-a-chip (SoCs), central processing units (CPUs), computed programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the signal processing system, the at least one input device, and the at least one output device.

[0145] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0146] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0147] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., an LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input).

[0148] The systems and technologies described herein can be implemented in computing systems that include back-end components (e.g., as information for vehicle actions), or computing systems that include middleware components (e.g., as raw information for data fusion), or computing systems that include front-end components (e.g., a user computer with a graphical user interface through which a user can interact with implementations of the systems and technologies described herein), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., Ethernet communication, CAN communication).

[0149] A computing system may include clients and servers. Clients and servers are generally located far apart and typically interact through a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other.

[0150] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0151] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A target detection method, characterized in that, include: Control at least two transmitting antennas to transmit signals according to corresponding sparse codes, wherein each transmitting antenna has a different sampling rate in the velocity dimension; Acquire at least two received signals received by the receiving antenna; The received signals are mixed and transformed with the corresponding transmitted signals to obtain at least two data matrices to be processed; Determine the target information based on each of the data matrices to be processed; The number of virtual ramps included in the sparse coding corresponding to each of the transmitting antennas is different. The virtual ramps include effective ramps and static regions. The number of effective ramps in the sparse coding of each of the transmitting antennas is the same. The sparse coding is obtained by optimizing the coding using a genetic algorithm. The genetic algorithm uses the coding of the transmitting antenna and the start time difference as variables to be optimized, uses whether the waveforms of the transmitting antenna overlap in the time domain as a penalty function, and takes the optimal signal-to-noise ratio as the optimization objective to optimize the coding.

2. The method according to claim 1, characterized in that, The process of mixing and transforming each received signal with its corresponding transmitted signal to obtain at least two data matrices to be processed includes: The received signals are mixed with the corresponding transmitted signals to obtain the first data matrix; The first data matrix is ​​separated according to each of the transmitting antennas to determine the second data matrix corresponding to each of the transmitting antennas; The second data matrices are rearranged according to their corresponding sparse codes to obtain the third data matrix; Perform a two-dimensional fast Fourier transform on each of the third data matrices in the velocity dimension to obtain the data matrix to be processed.

3. The method according to claim 2, characterized in that, The step of mixing each of the received signals with its corresponding transmitted signal to obtain a first data matrix includes: The received signals are mixed with the corresponding transmitted signals to obtain the corresponding intermediate frequency signals; The intermediate frequency signals are converted from analog to digital, and the converted signals are sampled to determine a first preset number of digital signals; Perform a one-dimensional fast Fourier transform on each of the digital signals in the distance dimension to form a first data matrix.

4. The method according to claim 1, characterized in that, The target information includes movement speed and distance, and the step of determining the target information based on each of the data matrices to be processed includes: Perform incoherent integration on each of the data matrices to be processed to determine the matrix to be tested; Points are selected on the matrix to be measured to determine the distance and velocity units of the target; For each target's range and velocity units, the maximum unambiguous velocity of each transmitting antenna is determined based on the signal transmission interval of each transmitting antenna, and the first velocity of each transmitting antenna is determined based on the velocity units; The target's speed is determined based on each of the maximum unambiguous speeds and each of the first speeds; The distance to the target is determined based on the movement speed and the distance unit.

5. The method according to claim 4, characterized in that, Determining the first velocity of each of the transmitting antennas based on the velocity unit includes: For each transmitting antenna, the Doppler frequency is determined based on the sampling rate and velocity element of the transmitting antenna in the velocity dimension, and the center frequency is determined based on the frequency of the virtual ramp. The first velocity of the transmitting antenna is determined based on the Doppler frequency and the center frequency.

6. The method according to claim 4, characterized in that, Determining the target's velocity based on the maximum unambiguous velocity and the first velocity includes: The first variable is controlled to traverse within a predetermined range of values. Based on the predetermined speed constraint relationship, the maximum unambiguous speed and the first speed, the value of the second variable corresponding to each traversal is calculated. The first variable and the second variable are variables in the speed constraint relationship. The target second variable value is determined based on the values ​​of each of the second variables; The speed of movement is determined based on the target second variable value.

7. The method according to claim 4, characterized in that, Determining the distance to the target based on the movement speed and the distance unit includes: The beat frequency is determined based on the distance unit, the first preset number, and the sampling rate of the distance dimension; The target Doppler frequency is determined based on the motion speed and center frequency. The distance to the target is determined based on the beat frequency, target Doppler frequency, ramp duration, signal bandwidth, and a predetermined distance calculation formula.

8. The method according to claim 4, characterized in that, The target information includes azimuth angle, and the receiving channels of each transmitting antenna are spatially equivalent to the receiving channels of the next transmitting antenna. The method further includes: The target received data is determined based on the velocity and range units; For each overlapping array element formed after equivalent overlap, the phase difference is determined based on the phase of the two receiving channels corresponding to the overlapping array element. The target phase difference between the first transmitting antenna and the remaining transmitting antennas is determined based on the phase differences described above; Phase compensation is performed based on the phase difference of each target to determine the compensated received data. Determine the received data matrix based on the compensated received data; Perform a fast Fourier transform on the received data matrix to obtain the power spectrum; The power spectrum is detected by a constant false alarm rate (CFAR) detection algorithm to determine the extreme values, and the azimuth angle is determined based on the extreme values.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the target detection method according to any one of claims 1-8.

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