Target detection method and device, integrated circuit, radio device and sensor

Abstract

The embodiment of the invention relates to the technical field of radio, and discloses a target detection method and device, an integrated circuit, a radio device and a sensor. According to the method, target detection signals are transmitted through transmitting antennas of an MIMO antenna array, and the transmitting sequence of the transmitting antennas is random; receiving an echo signal through each receiving antenna of the MIMO antenna array, and recovering missing data in the received echo signal relative to a signal transmission mode of a single antenna by adopting a compressed sensing algorithm; and subsequently, target detection is carried out based on the recovered signals, and for a transmitting side, due to the fact that the transmitting sequence of each transmitting antenna is random, Doppler and angle coupling disappears; and for a receiving side, the maximum unambiguous velocity cannot be reduced due to a TDM mode, and finally, a target can be accurately estimated by the radar.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of radio technologies, and in particular, to a target detection method, apparatus, integrated circuit, radio device, and sensor. Background Art

[0002] For automotive radars, in order to improve the resolution of angle measurement, a multiple-in multiple-out (MIMO) mode is generally adopted. Among them, time-division multiplexing (TDM) is the most commonly used and simplest modulation mode. In the current related technologies, the signal processing method for the TDM mode is as follows: First, all chirp signals corresponding to each transmitting antenna (TX for short) are selected, and then windowing and fast Fourier transform (FFT for short) processing in fast time and slow time are respectively performed. Then, the data in two dimensions of all channels are combined and constant false alarm rate (CFAR) processing is performed. After CFAR processing, phase compensation is performed on the data of each range-Doppler cell, mainly to compensate for the phase difference between different TXs caused by target movement. After phase compensation, angle estimation is performed, and finally tracking processing is performed.

[0003] The inventors found that there are at least the following problems in the related technologies: When there are N TXs, since the N TXs in the TDM mode work alternately, at this time, due to the phase difference caused by target movement in the data in two dimensions of the same target for different TXs, the angle estimation is affected. And since different TXs transmit alternately in sequence, at this time, the maximum unambiguous speed becomes 1 / N of the maximum unambiguous speed when a single TX works, resulting in a decrease in estimation accuracy and even estimation errors. Summary of the Invention

[0004] Embodiments of the present invention aim to provide a target detection method, apparatus, integrated circuit, radio device, and sensor, so that in the TDM mode, the target can still be accurately estimated, and the maximum unambiguous speed will not become smaller due to the TDM mode.

[0005] To solve the above technical problems, an embodiment of the present invention provides a target detection method, including: in a time-division multiplexing (TDM) mode, transmitting a target detection signal through each transmitting antenna of a multiple-input multiple-output (MIMO) antenna array, where the transmission order of each transmitting antenna is random; receiving an echo signal through each receiving antenna of the MIMO antenna array; using a compressive sensing algorithm to recover the data missing in the received echo signal relative to the signal transmission mode of a single antenna; and performing target detection based on the recovered signal.

[0006] An embodiment of the present invention further provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above target detection method.

[0007] An embodiment of the present invention further provides an integrated circuit, including a radio frequency module, an analog signal processing module, and a digital signal processing module connected in sequence; the radio frequency module is used to generate a target detection signal and an echo signal; the analog signal processing module is used to perform down-conversion processing on the echo signal to obtain an intermediate frequency signal; and the digital signal processing module is used to perform analog-to-digital conversion on the intermediate frequency signal to obtain a digital signal, and perform target detection on the digital signal using the above target detection method.

[0008] An embodiment of the present invention further provides a target detection device, including: at least two transmitting channels for transmitting a target detection signal based on a time-division manner according to a preset rule, where, within one signal processing unit period, the signal units corresponding to any transmitting channel in the target detection signal are non-uniformly distributed in the time dimension; at least one receiving channel for receiving an echo signal; a data processor for respectively performing a recovery operation on the signals corresponding to each transmitting channel in the received echo signal to obtain a recovered echo signal equivalent to the single transmitting channel mode for each transmitting antenna; and performing target detection based on all the recovered echo signals.

[0009] An embodiment of the present invention further provides an integrated circuit, including: the target detection device described in any embodiment of the present application, for implementing the detection of a target.

[0010] An embodiment of the present invention further provides a radio device, including: a carrier; the above integrated circuit disposed on the carrier; an antenna disposed on the carrier, or the antenna and the integrated circuit are integrated into an integrated device disposed on the carrier; where the integrated circuit is connected to the antenna for transmitting the target detection signal and / or receiving the echo signal.

[0011] An embodiment of the present invention further provides a terminal device, including: a device body; and the above-mentioned radio device disposed on the device body; wherein, the radio device is used for target detection and / or communication to provide reference information for the operation of the device body.

[0012] An embodiment of the present invention further provides a millimeter-wave sensor, including at least two transmitting channels and at least one receiving channel. The at least two transmitting channels transmit target detection signals based on time-division multiplexing, and the at least one receiving channel is used to receive echo signals formed based on the target detection signals; wherein, for any transmitting channel, the time difference between two adjacent transmissions of the target detection signals is non-uniformly distributed in the time dimension.

[0013] In an embodiment of the present invention, in the time-division multiplexing (TDM) mode, target detection signals are transmitted through each transmitting antenna of the MIMO antenna array, wherein the transmission order of the respective transmitting antennas is random; echo signals are received through each receiving antenna of the MIMO antenna array, and then a compressive sensing algorithm is used to recover the data missing in the received echo signals relative to the signal transmission mode of a single antenna; and subsequent target detection based on the recovered signals. In this way, for the transmitting side, due to the random transmission order of the respective transmitting antennas in the proposed transmission scheme, the coupling of Doppler and angle disappears. For the receiving side, since the signals corresponding to each transmitting channel in the received echo signals are respectively recovered, the recovered echo signals equivalent to the single-transmitting-channel mode of each transmitting antenna can be obtained. Therefore, target detection based on the recovered echo signals can prevent the maximum unambiguous speed from decreasing due to the TDM mode, and finally enables the radar to accurately estimate the speed of the target. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] One or more embodiments are illustrated by way of example in the accompanying drawings, which do not constitute a limitation to the embodiments. Elements with the same reference numerals in the drawings are represented as similar elements, unless otherwise stated, and the drawings in the figures do not constitute a scale limitation.

[0015] Figure 1 It is a schematic diagram of a transmitted signal waveform provided according to the related art of the present invention;

[0016] Figure 2 It is a schematic diagram of a transmitted signal waveform of the TDM mode provided according to the related art of the present invention;

[0017] Figure 3 It is a flowchart of a target detection method provided according to an embodiment of the present invention;

[0018] Figure 4Schematic diagram of transmission waveforms periodically transmitted by multiple transmitting antennas according to an embodiment of the present invention;

[0019] Figure 5 Schematic diagram of the transmission waveform of a single transmitting antenna according to an embodiment of the present invention;

[0020] Figure 6 Schematic diagram of the signal sequence after data recovery of a single transmitting antenna according to an embodiment of the present invention;

[0021] Figure 7 Flowchart of a method for missing data recovery using IMAT according to an embodiment of the present invention;

[0022] Figure 8 Schematic of a target detection method according to an embodiment of the present invention Figure 1 ;

[0023] Figure 9 Schematic of a target detection method according to an embodiment of the present invention Figure 2 ;

[0024] Figure 10 Schematic of a target detection method according to an embodiment of the present invention Figure 3 ;

[0025] Figure 11 Schematic diagram of target detection results according to an embodiment of the present invention;

[0026] Figure 12 Schematic diagram of the structure of a radar chip according to another embodiment of the present invention;

[0027] Figure 13 Schematic diagram of the structure of an integrated circuit according to another embodiment of the present invention;

[0028] Figure 14 Schematic diagram of the structure of a target detection device according to another embodiment of the present invention. Detailed implementation manners

[0029] In the current related technologies, for a frequency-modulated continuous wave signal modulated by a sawtooth wave, the transmitted signal waveform is as Figure 1 shown. Usually, one sawtooth is called a chirp. The unambiguous velocity is determined by the interval between two consecutive chirps. Therefore, compared with the case of a single antenna working, for the transmitted waveform using the TDM modulation mode as Figure 2 shown, when N antennas work in the TMD mode, the unambiguous velocity becomes 1 / N of the unambiguous velocity of a single antenna working. Moreover, due to the movement of the target, a phase difference will also be caused, affecting the angle estimation.

[0030] In this case, there are currently two solutions to this problem:

[0031] (1) Through the velocity ambiguity resolution algorithm, the velocity is resolved. After the ambiguity resolution is completed, the phase difference caused by the movement of the target is compensated. However, this solution requires the introduction of additional hardware resources. Moreover, if the velocity is misjudged, the subsequent phase compensation will also be incorrect.

[0032] (2) Make multiple assumptions about the velocity of the target, use different assumptions for phase compensation, and the phase compensation value corresponding to the true velocity has the highest peak when performing angle estimation after compensation. By this method, the velocity measurement range can be extended to the non-ambiguous velocity range when a single TX is working. At the same time, for the problem of incorrect angle estimation caused by the coupling of Doppler and angle, the phase difference brought by the target velocity is compensated, and then angle estimation is performed. However, this method has a fatal flaw, that is, since different ambiguity multiples are assumed based on the target velocity and the ambiguity is resolved by the peak height of the angle estimation after phase compensation, this actually restricts that there is only 1 target in the same range-Doppler cell. But in fact, the situation where there are multiple targets in the same range-Doppler cell often exists, and therefore there is often a high probability of error.

[0033] The above introduction of related technologies is to make the purpose, technical solutions, and advantages of the embodiments of the present invention easier to understand. Next, each embodiment of the present invention will be described in detail with reference to the accompanying drawings. However, those of ordinary skill in the art can understand that in each embodiment of the present invention, many technical details are provided to help readers better understand the present application. However, even without these technical details and various changes and modifications based on the following embodiments, the technical solutions claimed in the present application can still be implemented. The division of the following embodiments is for convenience of description and should not constitute any limitation on the specific implementation manner of the present invention. The various embodiments can be combined and cross-referenced with each other on the premise of not being contradictory.

[0034] An embodiment of the present invention relates to a target detection method, which can be applied to a radar chip or a vehicle-mounted radar device. In the time-division multiplexing (TDM) mode, target detection signals are transmitted through each transmitting antenna of the MIMO antenna array, wherein the transmission order of each transmitting antenna is random; echo signals are received through each receiving antenna of the MIMO antenna array; a compressive sensing algorithm is used to recover the missing data in the received echo signals relative to the signal transmission mode of a single antenna; and target detection is performed based on the recovered signals. By the proposed transmission method, the transmission order of each transmitting antenna is random, breaking the angle resolution error caused by the coupling of Doppler and angle due to the sequential transmission of the transmitting antennas. And by using a compressive sensing algorithm on the receiving side to recover the missing data in the received echo signals relative to the signal transmission of a single antenna, the maximum unambiguous speed will not become smaller due to the TDM mode. Finally, the radar can correctly and accurately estimate the speed of the target, and the maximum unambiguous speed will not become smaller due to the TDM mode. The implementation details of the target detection method according to the embodiment of the present invention will be specifically described below. The following content is only the implementation details provided for easy understanding and is not necessary for implementing this solution.

[0035] As Figure 3 shown, in step 301, the radar chip transmits target detection signals through each transmitting antenna of the MIMO antenna array in the time-division multiplexing (TDM) mode, wherein the transmission order of each transmitting antenna is random.

[0036] In one example, the random transmission order of each transmitting antenna includes: between different transmission cycles, or within a frame of target detection signals, the transmission order of each transmitting antenna (abbreviated as Tx) can be randomly transmitted.

[0037] The above-mentioned transmission cycle can include the cycle in which all transmitting antennas transmit signals in turn, or can be a cycle including at least one group, that is, within a group, each transmitting antenna takes turns to complete at least one transmission; as Figure 4 shown, each Group includes signals transmitted by N Tx, and the transmission order of signals by each Tx within the group is random or in a predetermined order, so that the intervals between the transmission times of the same Tx are non-uniformly distributed within the group and / or between groups, thereby achieving the Doppler and angle coupling caused by the sequential transmission of Tx at equal time intervals and reducing the angle resolution error. It can be understood that the duration of each transmission cycle can be the same or different.

[0038] The transmission order of each transmitting antenna is random. It can be transmitted in a random order within a transmission period, or it can be transmitted in a random order within a frame of target detection signals. For example, a frame of signals has 256 chirps, and within these 256 chirps, 4 Tx antennas transmit randomly. At this time, as long as it is ensured that each Tx can complete 64 transmissions. Such a transmission method can further reduce the impact of the TDM mode on the calculation of the maximum unambiguous velocity.

[0039] In step 302, the radar chip receives the echo signals through each receiving antenna of the MIMO antenna array for subsequent analysis and processing on the receiving side.

[0040] On the receiving side, since the transmissions of the above different Tx antennas are random, the intervals between adjacent chirps for the same Tx are not equally spaced. That is, in other alternative embodiments, by presetting a specific transmission order, as long as the signals transmitted by the same Tx within a group are non-equally spaced in the time dimension, the angle resolution error caused by the coupling of Doppler and angle due to the equally spaced transmission order of Tx can be effectively suppressed.

[0041] For example, for an electromagnetic wave sensor such as a millimeter wave sensor, as long as it includes at least two transmitting channels and at least one receiving channel (such as a two-transmit-one-receive, multiple-transmit-one-receive, or multiple-transmit-multiple-receive sensor), when the transmitting channels transmit target detection signals based on time-division multiplexing, for any transmitting channel, by setting the time difference between two adjacent target detection signals it transmits, which is non-equally spaced in the time dimension (such as random distribution, the time difference increasing successively, the time difference decreasing successively, the time difference increasing first and then decreasing, or the time difference decreasing first and then increasing, etc.), the effect of inaccurate angle estimation caused by the coupled phase of Doppler and angle can be effectively suppressed. Subsequently, based on the echo signals received by the receiving channel, compression sensing methods such as IMAT can be used to recover the signals corresponding to each transmitting channel at the transmission time points of other transmitting channels within the transmission period, so as to achieve an effect similar to that of a single transmitting antenna for this transmitting channel. Subsequently, based on the recovered signals, signal processing is performed, and the maximum unambiguous velocity in the TDM mode can be effectively improved.

[0042] Taking one of the transmitting antennas, Tx0, as an example, its chirp transmission sequence in the entire frame is as Figure 5 shown. At this time, since the intervals between adjacent chirps of this transmitting antenna are not equally spaced, in the time dimension, the missing part of the signal can be recovered to simulate the scenario of the single transmitting antenna Tx0 transmitting signals.

[0043] In step 303, the radar chip uses a compressive sensing algorithm to recover the missing data in the received echo signal relative to the signal transmission mode of a single antenna.

[0044] Using the compressive sensing algorithm to recover the missing data, after recovery, the transmitted chirp of Tx is still equally spaced, which is equivalent to the chirp signal sequence of the signal transmission mode of a single antenna. Still taking the above Tx0 as an example, after using the compressive sensing algorithm to recover the missing data, the schematic diagram of its recovered chirp signal sequence is as Figure 6 shown.

[0045] There are many methods for recovery using compressive sensing. In one example, a compressive sensing algorithm is used to recover the missing data in the received echo signal relative to the signal transmission of a single antenna, including: using the iterative adaptive threshold algorithm IMAT to recover the missing data in the received echo signal relative to the signal transmission mode of a single antenna. Using IMAT to recover the missing data, compared with other compressive sensing algorithms, this algorithm does not require sparsity and other prior information required for sparse signal processing. It can be understood that using IMAT to recover the missing data is just one implementation method. In other examples, other compressive sensing algorithms can also be used, such as the OMP (orthogonal matching pursuit) algorithm, the greedy iterative algorithm, or the optimization approximation algorithm, etc., which will not be listed one by one here.

[0046] As Figure 7 shows the specific process of using the iterative adaptive threshold algorithm IMAT to recover the missing data in the received echo signal relative to the signal transmission mode of a single antenna.

[0047] Step 701: Transform the chirp signal of each transmitting antenna to the frequency domain through FFT. Among them, the transformed frequency domain signal includes the actually received chirp signal and the missing signal after FFT transformation; among them, it can be assumed that the input signal sequence is X(n), (n = 0,... N - 1), and at the same time, if X(n) is sparse at a certain threshold. Then transform the original sequence X(n) to the sparse threshold, and the transformation used here is FFT representing the Fourier transform.

[0048] Step 702: Filter the transformed frequency domain signal based on the threshold range to obtain the filtered frequency domain signal; among them, the threshold range is updated according to the number of iterations.

[0049] For example, the above X(n), (n = 0,... N - 1) is compared and output in the sparse domain according to a certain threshold. Among them, the frequency components greater than the threshold will be retained, and the frequency components less than the threshold will be discarded. In this way, the selected and effective frequency components can be obtained. Here, the threshold changes according to an exponential law during each iteration process, and the threshold used in the j-th iteration can be expressed as: Thr j = β·e -α·(j-1) ; where β and α respectively represent the initial threshold and the attenuation factor.

[0050] Step 703: Perform IFFT transformation on the filtered frequency signal to the time domain, and use the signal transformed to the time domain to replace the signal missing in the time domain.

[0051] Step 704: Determine whether the maximum number of iterations is reached or the convergence criterion is met. If the maximum number of iterations is not reached or the convergence criterion is not met, then transform the signal after replacement in Step 703 to the frequency domain through the FFT in Step 706 and return to Step 702; if the maximum number of iterations is reached or the convergence criterion is met, then enter Step 705 to complete the signal recovery.

[0052] There can be many convergence criteria here. In one example, the convergence criterion can be that the change in spectral energy between two adjacent iterations is less than a preset value. Taking the above X(n), (n = 0,... N - 1) as an example, the change in spectral energy between two adjacent iterations at this time is: Among them, P j represents the total spectral energy obtained after the j-th iteration. Here, it can be specified that if the change in spectral energy obtained between two adjacent iterations is less than a given threshold (such as 1%), it can be considered that the iteration has converged.

[0053] In Step 304, the radar chip performs target detection based on the restored signal.

[0054] The following gives the specific details of using IMAT to restore the missing chirp and perform target detection based on the restored signal.

[0055] In one example, in the received echo signal, all chirp signals corresponding to each transmitting antenna are obtained; all chirp signals corresponding to each transmitting antenna are respectively subjected to fast-time dimension FFT processing to obtain a range-slow time-channel dimension data matrix; based on the range-slow time-channel dimension data matrices corresponding to each transmitting antenna, missing recovery is performed to obtain a restored echo signal after data recovery. Since the method of performing missing recovery based on the range-slow time-channel dimension data matrices corresponding to each transmitting antenna and the corresponding method of performing target detection based on the restored echo signal are not unique, they will be introduced separately below.

[0056] Method 1: For each of the distance - slow time - channel dimension data matrices, use a compressive sensing algorithm (in this example, the IMAT algorithm) to recover the missing data in the slow time dimension for each range bin, obtaining the distance - slow time - channel dimension data matrix corresponding to each transmit antenna after signal recovery, which is used as the recovered echo signal. That is, the signal of the missing chirp is recovered.

[0057] In this solution, the term "recovery" mentioned herein is similar to the meaning of filling or supplementing. In the MIMO mode, in the time dimension, the "missing" part of the chirp signal corresponding to each transmit antenna is supplemented to simulate the signal pattern corresponding to the single transmit antenna mode. That is, the "recovery" algorithm is used to fill the echo signals corresponding to each transmit antenna in the MIMO mode to simulate the signals of each transmit antenna in the single transmit mode, and subsequent data processing operations are performed based on the aggregated signals simulated by each transmit antenna.

[0058] After obtaining the recovered echo signal with data recovery using the above Method 1, perform FFT processing on the slow time dimension of the recovered echo signal and then perform target detection.

[0059] In one example, performing FFT processing on the slow time dimension of the recovered echo signal and then performing target detection includes: respectively performing slow time FFT processing based on the distance - slow time - channel dimension data matrix corresponding to each transmit antenna after signal recovery to obtain the distance - velocity - channel dimension data matrix corresponding to each transmit antenna; merging the distance - velocity - channel dimension data matrices corresponding to each transmit antenna, and performing target detection based on the merged data to obtain the distance, velocity, and angle information of all targets.

[0060] The overall processing flow of the above Method 1 is as Figure 8As shown, first, fast-time dimension FFT processing is performed on all chirp signals corresponding to each transmitting antenna such as Tx0, Tx1, …, TxN-1 in the figure to obtain a range-slow time-channel dimension data matrix; for each range-slow time-channel dimension data matrix, the IMAT algorithm is used to recover the missing data in the slow time dimension of each range bin, and for the range-slow time-channel dimension data matrix corresponding to each transmitting antenna after signal recovery, slow-time FFT processing is respectively performed to obtain a range-velocity-channel dimension data matrix corresponding to each transmitting antenna. The range-velocity-channel dimension data matrices corresponding to each transmitting antenna are combined, and target detection is performed based on the combined data to obtain the range, velocity, and angle information of all targets. For example, CFAR detection, phase compensation, angle estimation, etc. are performed based on the combined data to obtain the range, velocity, and angle information of all targets. Additionally, it can be understood that before performing fast-time dimension FFT processing, windowing processing in the fast-time dimension can also be performed first, and fast-time dimension FFT processing is performed on the sampled signals within the chirp after windowing processing. Similarly, before performing slow-time dimension FFT processing, windowing processing in the slow-time dimension can also be performed first, and slow-time dimension FFT processing is performed on the signals after windowing processing.

[0061] Method 2: Similarly, in the received echo signals, all chirp signals corresponding to each transmitting antenna are obtained; similarly, fast-time dimension FFT processing is respectively performed on all chirp signals corresponding to each transmitting antenna to obtain a range-slow time-channel dimension data matrix. The difference from Method 1 is that in Method 1, for each range-slow time-channel dimension data matrix, the IMAT algorithm is used to recover the missing data in the slow time dimension of each range bin, while in Method 2, for each range bin, the compressive sensing algorithm (such as the IMAT algorithm) is used to recover the missing data in the slow time and channel dimensions to obtain the data after signal recovery for each range bin as the recovered echo signal. After obtaining the recovered echo signal, target detection is performed after performing slow-time dimension FFT processing on the recovered echo signal.

[0062] In one example, after obtaining the recovered echo signal, that is, after obtaining the data after signal recovery for each range bin, two-dimensional FFT processing in the slow time and channel dimensions is respectively performed based on the data after signal recovery for each range bin to obtain a Doppler and angle matrix corresponding to each range bin; based on the Doppler and angle matrix corresponding to each range bin, target detection is performed to obtain the velocity and angle information of all targets for each range bin. The overall process is as Figure 9As shown, first, perform fast-time dimension FFT processing on all chirp signals corresponding to each transmitting antenna such as Tx0, Tx1... TxN-1 in the figure to obtain a range-slow time-channel dimension data matrix; for each range bin, use the compressive sensing algorithm to recover the missing data in the slow time and channel dimensions, and obtain the data after signal recovery for each range bin as the recovered echo signal. Based on the data after signal recovery for each range bin, perform 2D FFT processing in the slow time and channel dimensions respectively to obtain the Doppler and angle matrix corresponding to each range bin. Based on the Doppler and angle matrix corresponding to each range bin, perform target detection to obtain the speed and angle information of all targets in each range bin.

[0063] In another example, after obtaining the recovered echo signal, that is, after obtaining the data after signal recovery for each range bin, perform FFT processing in the slow time dimension based on the data after signal recovery for each range bin to obtain the range-Doppler dimension data of each virtual channel; merge the range-Doppler dimension data of all virtual channels, and perform target detection based on the merged data to obtain the range, speed, and angle information of all targets. The overall process is as Figure 10 As shown, first, perform fast-time dimension FFT processing on all chirp signals corresponding to each transmitting antenna such as Tx0, Tx1... TxN-1 in the figure to obtain a range-slow time-channel dimension data matrix; for each range bin, use the compressive sensing algorithm to recover the missing data in the slow time and channel dimensions, and obtain the data after signal recovery for each range bin as the recovered echo signal. Based on the data after signal recovery for each range bin, perform FFT processing in the slow time dimension to obtain the range-Doppler dimension data of each virtual channel; merge the range-Doppler dimension data of all virtual channels, and perform target detection based on the merged data to obtain the range, speed, and angle information of all targets.

[0064] It can be understood that, similar to Method 1, before performing fast-time dimension FFT processing, windowing processing in the fast time dimension can also be performed first, and then perform fast-time dimension FFT processing on the sampled signals in the chirp after windowing processing. Similarly, before performing slow-time dimension FFT processing, windowing processing in the slow time dimension can also be performed first, and then perform slow-time dimension FFT processing on the signal after windowing processing.

[0065] To make the technical effects of the present application more clear and obvious, as an example, some simple simulations will be presented below, and the simulation parameter configurations are as follows:

[0066] In the received echo signal, obtain all chirp signals corresponding to each transmitting antenna in the manner mentioned above; perform windowing in the fast time dimension and FFT processing on all chirp signals corresponding to each transmitting antenna respectively to obtain a range-slow time-channel dimension data matrix; for each range bin, use the compressive sensing algorithm to recover the missing data in the slow time and channel dimensions to obtain the data after signal recovery for each range bin as the recovered echo signal, and perform FFT processing in the slow time and channel dimensions respectively based on the data after signal recovery for each range bin to obtain the Doppler and angle matrices corresponding to each range bin; the specific parameters involved are shown in the following table:

[0067] Variable Name Explanation Parameter Fc (GHz) Tuning Frequency 77 BandWidth (MHz) Bandwidth 300 T_ramp_up (us) Ramp-up Time 12.8 Tr (us) Chirp Repetition Period 15 Fs (MHz) Resonant Frequency 40 Rng_fft Number of Points in Range Dimension Fourier Transform 512 Nchirp Number of Chirps 256 Tx_number Number of Transmitting Antennas 4 Rx_number Number of Receiving Antennas 4

[0068] The parameter settings of the target are shown in the following table:

[0069] Target 1 Target 2 Target 3 Range (Distance) / m 40 40 40 Velocity / m / s -30 20 45 Angle / ° 30 -30 20 Signal-to-Interference-plus-Noise Ratio (SNR) / dB 20 0 -10

[0070] At this time, at the normal distance R = 40 m of the target, the calculation results obtained are as Figure 11 shown. It can be seen that there are 3 obvious peaks at the set target. By simply performing target detection, the speed and angle information of all targets in each range bin can be obtained. It should be noted that at this time, the length of a single chirp we set is 15 μs, and the maximum unambiguous speed it determines is 65 m / s. If in the TDM mode, then the chirp repetition period at this time is 60 μs, and the corresponding maximum unambiguous speed is only 16.25 m / s. However, according to the processing flow of this embodiment, even if the speed is 45 m / s, that is, exceeding 16.25 m / s, the speed of the target can still be correctly estimated without ambiguity.

[0071] In the embodiment of the present invention, in the time division multiplexing (TDM) mode, target detection signals are transmitted by each transmitting antenna of the MIMO antenna array, wherein the transmission order of the respective transmitting antennas is random; echo signals are received by each receiving antenna of the MIMO antenna array, and then the compressive sensing algorithm is used to recover the data missing in the received echo signal relative to the signal transmission mode of a single antenna; and subsequent target detection is performed based on the recovered signal. For the transmitting side, due to the random transmission order of the respective transmitting antennas in the proposed transmission scheme, the coupling of Doppler and angle disappears. For the receiving side, this method can prevent the maximum unambiguous speed from decreasing due to the TDM mode. Finally, it is realized that the radar can correctly and accurately estimate the speed of the target without ambiguity.

[0072] The step division of the above method is only for clear description. In implementation, it can be combined into one step or some steps can be split into multiple steps. As long as the same logical relationship is included, it is within the protection scope of this patent. Adding insignificant modifications to the algorithm or process or introducing insignificant designs without changing the core design of the algorithm and process are within the protection scope of this patent.

[0073] In addition, the examples mentioned in the above embodiments can be freely combined, and any combination method can be understood as an embodiment. The "embodiments" or "examples" that appear at various positions in the specification do not necessarily refer to the same embodiments, nor are they independent or alternative embodiments that are mutually exclusive with other embodiments. Those skilled in the art can understand that the embodiments described herein can be combined with other embodiments.

[0074] Another embodiment of the present invention relates to a radar chip, as Figure 12 shown, including: a processor 1201 and a memory 1202; the memory 1202 is used to store instructions executable by at least one processor 1201, and the instructions are executed by at least one processor 1201 so that at least one processor 1201 can execute the embodiments of the target detection method as described above.

[0075] Among them, the memory 1202 and the processor 1201 are connected by a bus. The bus can include any number of interconnected buses and bridges. The bus connects various circuits of one or more processors 1201 and the memory 1202 together. The bus can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits together, which are well known in the art. Therefore, they will not be further described herein. The data processed by the processor 1201 is transmitted on a wireless medium through an antenna. Further, the antenna also receives data and transmits the data to the processor 1201.

[0076] The processor 1201 is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interface, voltage regulation, power management, and other control functions. The memory 1202 can be used to store the data used by the processor 1201 when performing operations.

[0077] Another embodiment of the present invention relates to a computer-readable storage medium storing a computer program. Through the description of the above embodiments, those skilled in the art can easily understand that the example embodiments described herein can be implemented by software or by a combination of software and necessary hardware. The technical solution according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes a number of instructions to enable a computing device (such as a personal computer, a server, or a network device, etc.) to execute the above method according to the embodiments of the present application.

[0078] The software product may employ any combination of one or more readable media. The readable media may be a readable signal medium or a readable storage medium. The readable storage medium may, for example, but not be limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the readable storage medium (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0079] The computer-readable storage medium may include a data signal propagated in a baseband or as a part of a carrier wave, in which the readable program code is carried. Such a propagated data signal may take various forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The readable storage medium may also be any readable medium other than the readable storage medium, which can send, propagate, or transmit a program used by or in combination with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted by any appropriate medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination of the above.

[0080] The program code for performing the operations of the present application can be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C++, etc., and also including conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, executed as a stand-alone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., by connecting through the Internet using an Internet service provider).

[0081] The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by a device, the computer-readable medium realizes the foregoing functions.

[0082] Those skilled in the art can understand that the above-mentioned modules can be distributed in the device according to the description of the embodiments, or can be correspondingly changed and distributed in one or more devices that are only different from this embodiment. The modules of the above embodiments can be combined into one module, or can be further split into multiple sub-modules.

[0083] According to an embodiment of the present application, a computer program is provided, including a computer program or instruction, which when executed by a processor, can perform the methods described above. In an optional embodiment, the above integrated circuit can be a millimeter-wave radar chip. The types of digital functional modules in the integrated circuit can be determined according to actual needs. For example, in a millimeter-wave radar chip, a data processing module can be used for operations such as range-Doppler transform, velocity-Doppler transform, constant false alarm rate detection, direction of arrival detection, point cloud processing, etc., to obtain information such as the distance, angle, velocity, shape, size, surface roughness, and dielectric properties of a target.

[0084] Another embodiment of the present application relates to a target detection device, such as Figure 14As shown in the figure, it includes: at least two transmitting channels 1401, which are used to transmit target detection signals based on a time-division manner according to a preset rule. Among them, within one signal processing unit cycle, the signal units corresponding to any transmitting channel in the target detection signal are unevenly distributed in the time dimension; at least one receiving channel 1402, which is used to receive echo signals; a data processor 1403, which is used to perform restoration operations on the signals corresponding to each transmitting channel in the received echo signals respectively, so as to obtain the restored echo signals equivalent to single-transmitting-channel mode transmission of each transmitting antenna. That is, within one signal processing unit cycle, the signal units corresponding to any transmitting channel in the restored echo signals are evenly distributed in the time dimension. For example, a compressive sensing algorithm, such as the IMAT algorithm, can be used to perform restoration operations on the signals corresponding to each transmitting channel in the received echo signals respectively; and target detection is performed based on all the restored echo signals.

[0085] It can be understood that the preset rule can be to transmit target detection signals according to a random transmission rule, or to transmit target detection signals in the order of other preset rules, as long as it satisfies the condition that within one signal processing unit cycle, the signal units corresponding to any transmitting channel in the target detection signal are unevenly distributed in the time dimension, so as to reduce the coupling effect of speed and / or angle. In addition, only one receiving channel 1402 and two transmitting channels 1401 are shown in this embodiment, but in practical applications, there can be multiple receiving channels 1402 and more transmitting channels 1401. That is, the number of transmitting channels and receiving channels is not limited. Figure 14 Only one receiving channel 1402 and two transmitting channels 1401 are shown, but in practical applications, there can be multiple receiving channels 1402 and more transmitting channels 1401. That is, the number of transmitting channels and receiving channels is not limited.

[0086] In one example, the target detection signal is a frequency-modulated continuous wave (FMCW) signal;

[0087] In one example, the waveform of the frequency-modulated continuous wave (FMCW) signal is a frequency-hopping FMCW wave, such as an MBC FMCW wave.

[0088] In one example, performing restoration operations on the signals corresponding to each transmitting channel in the received echo signals respectively includes: in the received echo signals, obtaining all chirp signals corresponding to each transmitting antenna; respectively performing fast-time dimension FFT processing on all chirp signals corresponding to each transmitting antenna to obtain a range-slow time-channel dimension data matrix; performing restoration of missing data based on the range-slow time-channel dimension data matrix to obtain the restored echo signals after data restoration; performing target detection based on all the restored echo signals includes: performing target detection after performing slow-time dimension FFT processing on the restored echo signals.

[0089] In one example, the recovery of missing data based on the range-slow time-channel dimension data matrix includes: for each range-slow time-channel dimension data matrix, a compressive sensing algorithm is used to recover the missing data in the slow time dimension of each range bin, and the range-slow time-channel dimension data matrix corresponding to each transmit antenna after signal recovery is obtained as the recovered echo signal.

[0090] In one example, after performing FFT processing on the slow time dimension of the recovered echo signal, target detection is performed, including: based on the range-slow time-channel dimension data matrix corresponding to each transmit antenna after signal recovery, slow time and FFT processing are respectively performed to obtain the range-velocity-channel dimension data matrix corresponding to each transmit antenna; the range-velocity-channel dimension data matrices corresponding to each transmit antenna are combined, and target detection is performed based on the combined data to obtain the range, velocity, and angle information of all targets.

[0091] In one example, the recovery of missing data based on the range-slow time-channel dimension data matrix includes: for each range bin, a compressive sensing algorithm is used to recover the missing data in the two dimensions of slow time and channel, and the data of the signal after recovery of each range bin is obtained as the recovered echo signal.

[0092] In one example, after performing FFT processing on the slow time dimension of the recovered echo signal, target detection is performed, including: based on the data of the signal after recovery of each range bin, slow time and channel dimension FFT processing are respectively performed to obtain the Doppler and angle matrix corresponding to each range bin; based on the Doppler and angle matrix corresponding to each range bin, target detection is performed to obtain the velocity and angle information of all targets in each range bin; or, based on the data of the signal after recovery of each range bin, slow time dimension FFT processing is performed to obtain the range-Doppler dimension data of each virtual channel; the range-Doppler dimension data of all virtual channels are combined, and target detection is performed based on the combined data to obtain the range, velocity, and angle information of all targets.

[0093] It can be understood that before performing FFT processing on the fast time dimension, windowing processing on the fast time dimension can also be performed first, and FFT processing on the fast time dimension is performed on the sampled signal within the chirp after windowing processing. Similarly, before performing FFT processing on the slow time dimension, windowing processing on the slow time dimension can also be performed first, and FFT processing on the slow time dimension is performed on the signal after windowing processing.

[0094] Another embodiment of the present invention relates to an integrated circuit, such as Figure 13As shown, it includes a radio frequency module 1301, an analog signal processing module 1302, and a digital signal processing module 1303 connected in sequence; the radio frequency module 1301 is used to generate a target detection signal and an echo signal; the analog signal processing module 1302 is used to down-convert the echo signal to obtain an intermediate frequency signal; and the digital signal processing module 1303 is used to perform analog-to-digital conversion on the intermediate frequency signal to obtain a digital signal, and adopt the target detection method described in the above method embodiment to perform target detection on the digital signal. In one example, the integrated circuit may be a millimeter-wave chip.

[0095] In another alternative embodiment, an integrated circuit may also be a target detection device described in any embodiment of the present application, such as Figure 14 As shown, for realizing the detection of a target.

[0096] Another embodiment of the present invention relates to a radio device, including: a carrier; an integrated circuit as shown in Figure 13 Or 14, disposed on the carrier; an antenna, disposed on the carrier, or the antenna and the integrated circuit are integrated into a single device and disposed on the carrier; wherein, the integrated circuit is connected to the antenna and is used to transmit the target detection signal and / or receive the echo signal.

[0097] Another embodiment of the present invention relates to a terminal device, including: a device body; and the radio device as described above disposed on the device body; wherein, the radio device is used for target detection and / or communication to provide reference information for the operation of the device body.

[0098] Specifically, based on the above embodiments, in an alternative embodiment of the present application, the radio device may be disposed outside the device body, or disposed inside the device body, and in other alternative embodiments of the present application, a part of the radio device may also be disposed inside the device body and a part of it may be disposed outside the device body. The embodiments of the present application do not limit this, and it can be determined according to specific circumstances.

[0099] In some alternative embodiments, the above device body may be components and products applied to fields such as smart cities, smart homes, transportation, smart home appliances, consumer electronics, security monitoring, industrial automation, in-cabin detection (such as smart cockpits), medical devices, and healthcare. For example, the device body may be intelligent transportation devices (such as cars, bicycles, motorcycles, ships, subways, trains, etc.), security devices (such as cameras), liquid level / flow rate detection devices, smart wearable devices (such as bracelets, glasses, etc.), smart home appliances (such as floor-sweeping robots, door locks, TVs, air conditioners, smart lights, etc.), various communication devices (such as mobile phones, tablets, etc.), as well as road gates, intelligent traffic lights, intelligent signs, traffic cameras, and various industrial robotic arms (or robots), and may also be various instruments for detecting vital sign parameters and various devices equipped with such instruments, such as in-cabin vital sign detection in cars, indoor personnel monitoring, intelligent medical devices, consumer electronic devices, etc.

[0100] The radio device may be the radio device described in any embodiment of the present application. The structure and working principle of the radio device have been described in detail in the above embodiments and will not be elaborated here one by one.

[0101] It should be noted that the radio device can achieve functions such as target detection and / or communication by transmitting and receiving radio signals, so as to provide detection target information and / or communication information to the device body, thereby assisting or even controlling the operation of the device body.

[0102] For example, when the above device body is applied to an Advanced Driving Assistance System (ADAS), the radio device as a vehicle-mounted sensor (such as millimeter-wave radar, lidar, etc.) can assist the ADAS system to achieve application scenarios such as adaptive cruise control, autonomous emergency braking (AEB), blind spot detection (BSD), lane change assist (LCA), rear cross traffic alert (RCTA), parking assistance, warning of rear vehicles, anti-collision, pedestrian detection, etc. At the same time, it can also be applied to application scenarios such as anti-collision when opening the door of a car.

[0103] In this embodiment, in the time-division multiplexing (TDM) mode, a target detection signal is transmitted through each transmitting antenna of the MIMO antenna array, where the transmission order of each transmitting antenna is random; the echo signal is received through each receiving antenna of the MIMO antenna array, and then the compressed sensing algorithm is used to recover the data missing in the received echo signal relative to the signal transmission mode of a single antenna; and subsequent target detection is performed based on the recovered signal. For the transmitting side, due to the random transmission order of each transmitting antenna in the proposed transmission scheme, the coupling of Doppler and angle disappears. For the receiving side, this method can prevent the maximum unambiguous speed from decreasing due to the TDM mode. Finally, the radar can correctly and accurately estimate the target speed without ambiguity.

[0104] It is not difficult to find that this embodiment is a device embodiment corresponding to the above method embodiment, and this embodiment can be implemented in cooperation with the above method embodiment. The relevant technical details mentioned in the above method embodiment are still valid in this embodiment. To avoid repetition, they will not be elaborated here. Correspondingly, the relevant technical details mentioned in this embodiment can also be applied to the above method embodiment.

[0105] It is worth mentioning that each module involved in this embodiment is a logical module. In actual applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. In addition, to highlight the innovative part of the present invention, units not closely related to solving the technical problems proposed by the present invention are not introduced in this embodiment, but this does not mean that there are no other units in this embodiment.

[0106] Those of ordinary skill in the art can understand that the above embodiments are specific embodiments for implementing the present invention, and in actual applications, various changes can be made in form and details without departing from the spirit and scope of the present invention.

Claims

1. A target detection method, characterized in that, Including: In the time-division multiplexing (TDM) mode, target detection signals are transmitted through each transmitting antenna of the MIMO antenna array, where the transmission order of each transmitting antenna is random. Echo signals are received through each receiving antenna of the MIMO antenna array. A compressed sensing algorithm is used to recover the data missing in the received echo signals relative to the signal transmission mode of a single antenna. Target detection is performed based on the recovered signals.

2. The object detection method according to claim 1, wherein The random transmission order of each transmitting antenna includes: Within one transmission period or within one frame of the target detection signals, the transmission order of each transmitting antenna is random.

3. The object detection method according to claim 1, wherein The recovery of the missing data in the received echo signals relative to the signal transmission of a single antenna includes: In the received echo signals, all chirp signals corresponding to each transmitting antenna are acquired. All chirp signals corresponding to each transmitting antenna are respectively subjected to fast-time dimension FFT processing to obtain a range-slow time-channel dimension data matrix. Missing data recovery is performed based on the range-slow time-channel dimension data matrices corresponding to each transmitting antenna to obtain a recovered echo signal after data recovery. The target detection based on the recovered signals includes: Target detection is performed after performing FFT processing on the recovered echo signals in the slow-time dimension.

4. The object detection method according to claim 3, wherein The recovery of the missing data based on the range-slow time-channel dimension data matrix includes: For each range-slow time-channel dimension data matrix, a compressed sensing algorithm is used to recover the missing data in the slow-time dimension of each range bin, and the range-slow time-channel dimension data matrix corresponding to each transmitting antenna after signal recovery is obtained as the recovered echo signal.

5. The object detection method according to claim 4, wherein The target detection after performing FFT processing on the recovered echo signals in the slow-time dimension includes: Based on the range-slow time-channel dimension data matrices corresponding to each transmitting antenna after signal recovery, slow-time FFT processing is respectively performed to obtain a range-velocity-channel dimension data matrix corresponding to each transmitting antenna. The range-velocity-channel dimension data matrices corresponding to each transmitting antenna are combined, and target detection is performed based on the combined data to obtain the range, velocity, and angle information of all targets.

6. The object detection method according to claim 3, wherein The recovery of the missing data based on the range-slow time-channel dimension data matrix to obtain a recovered echo signal after data recovery includes: For each range bin, a compressed sensing algorithm is used to recover the missing data in the slow-time and channel dimensions to obtain the data after signal recovery for each range bin as the recovered echo signal.

7. The object detection method according to claim 6, characterized in that, The target detection after performing FFT processing on the recovered echo signals in the slow-time dimension includes: Based on the data after signal recovery for each range bin, slow-time and channel dimension FFT processing is respectively performed to obtain a Doppler and angle matrix corresponding to each range bin. Based on the Doppler and angle matrix corresponding to each range bin, target detection is performed to obtain the velocity and angle information of all targets in each range bin.

8. The object detection method according to claim 6, wherein Performing target detection after performing FFT processing on the restored echo signal in the slow time dimension includes: Based on the data after signal restoration for each range bin, performing FFT processing in the slow time dimension to obtain range-Doppler dimension data for each virtual channel; Merging the range-Doppler dimension data of all the virtual channels, and performing target detection based on the merged data to obtain the range, velocity, and angle information of all targets.

9. The object detection method according to any one of claims 1 to 8, characterized in that, The method for restoring the missing data in the received echo signal relative to the signal transmission of a single antenna by using the compressive sensing algorithm includes: Using the iterative adaptive threshold algorithm IMAT to restore the missing data in the received echo signal relative to the signal transmission mode of a single antenna.

10. The object detection method according to claim 9, wherein The method for restoring the missing data in the received echo signal relative to the signal transmission mode of a single antenna by using the iterative adaptive threshold algorithm IMAT includes: S1. Transforming the chirp signal of each transmitting antenna to a frequency-domain signal through FFT; S2. Filtering the transformed frequency-domain signal based on a threshold range to obtain a filtered frequency-domain signal; wherein, the threshold range is updated according to the number of iterations; S3. Performing IFFT transformation on the filtered frequency signal to the time domain, and using the signal transformed to the time domain to replace the signal missing in the time domain; S4. Judging whether the maximum number of iterations is reached or the convergence criterion is reached. If the maximum number of iterations is not reached or the convergence criterion is not reached, then transforming the signal after replacement in S3 to the frequency domain through FFT and returning to S2; if the maximum number of iterations is reached or the convergence criterion is reached, then the signal restoration is completed.

11. The object detection method according to claim 10, wherein The convergence criterion includes: the change in spectral energy between two adjacent iterations is less than a preset value.

12. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the target detection method according to any one of claims 1 to 11.

13. An integrated circuit, characterized in that, It includes a radio frequency module, an analog signal processing module, and a digital signal processing module connected in sequence; The radio frequency module is used to generate a target detection signal and an echo signal; The analog signal processing module is used to perform down-conversion processing on the echo signal to obtain an intermediate-frequency signal; and The digital signal processing module is used to perform analog-to-digital conversion on the intermediate-frequency signal to obtain a digital signal, and adopt the target detection method according to any one of claims 1 to 11 to perform target detection on the digital signal.

14. An object detection device, characterized in that, It includes: At least two transmitting channels, which are used to transmit target detection signals based on a time-division manner according to a preset rule. Among them, within one signal processing unit cycle, the signal units corresponding to any transmitting channel in the target detection signal are non-uniformly distributed in the time dimension; At least one receiving channel, which is used to receive echo signals; A data processor, which is used to perform restoration operations on the signals corresponding to each transmitting channel in the received echo signal respectively to obtain restored echo signals equivalent to the single transmitting channel mode for each transmitting antenna; and Performing target detection based on all the restored echo signals.

15. The object detection device according to claim 14, wherein The target detection signal is a frequency-modulated continuous wave FMCW signal.

16. The target detection device according to claim 15, characterized in that, The waveform of the frequency-modulated continuous wave (FMCW) signal is a frequency-hopping FMCW wave.

17. The object detection device according to claim 14, wherein The respective restoration operations performed on the signals corresponding to each transmitting channel in the received echo signal include: In the received echo signal, obtaining all chirp signals corresponding to each transmitting antenna; Separately performing fast-time dimension FFT processing on all chirp signals corresponding to each transmitting antenna to obtain a range-slow-time-channel dimension data matrix; Restoring missing data based on the range-slow-time-channel dimension data matrix corresponding to each transmitting antenna to obtain a restored echo signal after data restoration; The target detection based on all the restored echo signals includes: Performing target detection after performing FFT processing on the restored echo signal in the slow-time dimension.

18. The target detection device according to claim 17, wherein The restoration of missing data based on the range-slow-time-channel dimension data matrix includes: For each range-slow-time-channel dimension data matrix, using a compressive sensing algorithm to restore the missing data in the slow-time dimension of each range bin to obtain a range-slow-time-channel dimension data matrix corresponding to each transmitting antenna after signal restoration, as the restored echo signal; The performing target detection after performing FFT processing on the restored echo signal in the slow-time dimension includes: Based on the range-slow-time-channel dimension data matrix corresponding to each transmitting antenna after signal restoration, separately performing slow-time FFT processing to obtain a range-velocity-channel dimension data matrix corresponding to each transmitting antenna; Combining the range-velocity-channel dimension data matrix corresponding to each transmitting antenna and performing target detection based on the combined data to obtain the range, velocity, and angle information of all targets.

19. The object detection device according to claim 17, characterized in that The restoration of missing data based on the range-slow-time-channel dimension data matrix includes: For each range bin, using a compressive sensing algorithm to restore the missing data in the two dimensions of slow-time and channel to obtain the data after signal restoration of each range bin, as the restored echo signal; The performing target detection after performing FFT processing on the restored echo signal in the slow-time dimension includes: Based on the data after signal restoration of each range bin, separately performing FFT processing in the slow-time and channel dimensions to obtain a Doppler and angle matrix corresponding to each range bin; Based on the Doppler and angle matrix corresponding to each range bin, performing target detection to obtain the velocity and angle information of all targets in each range bin; Or, Based on the data after signal restoration of each range bin, performing slow-time dimension FFT processing to obtain range-Doppler dimension data of each virtual channel; Combining the range-Doppler dimension data of all the virtual channels and performing target detection based on the combined data to obtain the range, velocity, and angle information of all targets.

20. An integrated circuit, characterized in that, Including: The target detection device according to any one of claims 14-19, for realizing the detection of a target.

21. A radio device, characterized in that, Including: A carrier; The integrated circuit according to claim 13 or 20, disposed on the carrier; An antenna, which is disposed on the carrier, or the antenna and the integrated circuit are integrated into an integrated device and disposed on the carrier; Wherein, the integrated circuit is connected to the antenna and is configured to transmit the target detection signal and / or receive the echo signal.

22. A terminal device, characterized in that, Comprising: A device body; And A radio device as described in claim 21 disposed on the device body; Wherein, the radio device is used for target detection and / or communication to provide reference information for the operation of the device body.

23. A millimeter-wave sensor, characterized in that, Comprising at least two transmitting channels and at least one receiving channel, the at least two transmitting channels transmit the target detection signal based on time-division multiplexing, and the at least one receiving channel is configured to receive the echo signal formed based on the target detection signal; Wherein, for any one of the transmitting channels, the time difference between two adjacent transmissions of the target detection signal is non-uniformly distributed in the time dimension.

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