Interference signal identification method and device, vehicle, medium and product

Abstract

The application discloses a kind of identification method, device, vehicle, medium and product of interference signal, belong to radar signal processing technical field.Method includes: obtaining the intermediate frequency signal of the target radar of target vehicle, intermediate frequency signal is the signal obtained by mixing the local signal of target radar and the received signal received by target radar;Obtain the target time-frequency feature of intermediate frequency signal in target frequency modulation period, target time-frequency feature includes the first time-frequency feature of frequency fluctuation state of characterizing intermediate frequency signal, and the second time-frequency feature of the bending degree of the phase of characterizing intermediate frequency signal;In the case where the first time-frequency feature is greater than the first feature threshold value and the second time-frequency feature is greater than the second feature threshold value, determine that the received signal corresponding to intermediate frequency signal is interference signal.Such, can improve the identification accuracy of interference signal.

Description

Technical Field

[0001] This application belongs to the field of radar signal processing technology, and in particular relates to a method, device, vehicle, medium and product for identifying interference signals. Background Technology

[0002] In densely populated areas such as parking lots, congested urban roads, toll stations, and logistics parks, a large number of vehicles equipped with vehicle corner radars are operating simultaneously. Due to the limited frequency band resources and highly homogeneous modulation parameters of vehicle corner radars, co-channel interference can easily occur between the corner radars of different vehicles.

[0003] In related technologies, vehicles mainly attempt to avoid co-channel interference in the frequency or time domains through methods such as random frequency hopping or frequency modulation slope agility. However, since random frequency hopping or frequency modulation slope agility are probabilistic avoidance strategies, the accuracy of vehicles in identifying interference signals in these technologies is relatively poor. Summary of the Invention

[0004] This application provides a method, apparatus, vehicle, medium, and product for identifying interference signals, which can improve the accuracy of vehicle identification of interference signals.

[0005] In a first aspect, embodiments of this application provide a method for identifying interference signals, the method comprising: The intermediate frequency signal of the target radar of the target vehicle is acquired. The intermediate frequency signal is a signal obtained by mixing the local oscillator signal of the target radar with the received signal received by the target radar. The target time-frequency characteristics of the intermediate frequency signal within the target frequency modulation period are obtained. The target time-frequency characteristics include a first time-frequency characteristic characterizing the frequency fluctuation state of the intermediate frequency signal and a second time-frequency characteristic characterizing the curvature of the phase of the intermediate frequency signal. If the first time-frequency feature is greater than a preset first feature threshold and the second time-frequency feature is greater than a preset second feature threshold, the received signal corresponding to the intermediate frequency signal is determined to be an interference signal.

[0006] Secondly, embodiments of this application provide an interference signal identification device, the device comprising: The first acquisition module is used to acquire the intermediate frequency signal of the target radar of the target vehicle. The intermediate frequency signal is a signal obtained by mixing the local oscillator signal of the target radar and the received signal received by the target radar. The second acquisition module is used to acquire the target time-frequency characteristics of the intermediate frequency signal within the target frequency modulation period. The target time-frequency characteristics include a first time-frequency characteristic characterizing the frequency fluctuation state of the intermediate frequency signal and a second time-frequency characteristic characterizing the curvature of the phase of the intermediate frequency signal. The first determining module is used to determine that the received signal corresponding to the intermediate frequency signal is an interference signal when the first time-frequency feature is greater than a preset first feature threshold and the second time-frequency feature is greater than a preset second feature threshold.

[0007] Thirdly, embodiments of this application provide a vehicle, the vehicle comprising: Processor and memory storing computer program instructions; When the processor executes the computer program instructions, it implements the method for identifying interference signals as described in the first aspect.

[0008] Fourthly, embodiments of this application provide a computer storage medium storing computer program instructions, which, when executed by a processor, implement the interference signal identification method as described in the first aspect.

[0009] Fifthly, embodiments of this application provide a computer program product in which instructions, when executed by a vehicle's processor, cause the vehicle to perform the interference signal identification method as described in the first aspect.

[0010] In this embodiment, the intermediate frequency (IF) signal after mixing by the radar on the target vehicle can be acquired first. Then, a first time-frequency feature characterizing the degree of frequency fluctuation and a second time-frequency feature characterizing the degree of phase curvature of the IF signal within a single frequency modulation cycle are extracted. Finally, when the first time-frequency feature exceeds a preset first feature threshold and the second time-frequency feature exceeds a preset second feature threshold, the received signal is determined to be an interference signal. Since this scheme does not rely on empirical judgment based on kinematic thresholds, but directly detects the unique frequency jump and phase nonlinearity characteristics of the interference signal, it can fundamentally distinguish between the real target echo signal and the co-frequency interference signal, thereby accurately identifying the received interference signal. Attached Figure Description

[0011] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a flowchart illustrating the method for identifying interference signals provided in an embodiment of this application; Figure 2 This is a flowchart illustrating the method for identifying interference signals provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of the interference signal identification device provided in the embodiments of this application; Figure 4This is a schematic diagram of the vehicle structure provided in the embodiments of this application. Detailed Implementation

[0013] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0014] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0015] For ease of understanding, the following describes some aspects of the embodiments of this application: With the widespread adoption of Advanced Driver Assistance Systems (ADAS) and autonomous driving technologies, vehicle corner radar has become an indispensable environmental perception sensor for modern automobiles. Vehicle corner radar is primarily used for functions such as blind spot monitoring, lane change assistance, and rear traffic crossing alerts. Currently, vehicle corner radar generally employs a frequency-modulated continuous wave (FMCV) mechanism, operating in the 76-81 GHz frequency band.

[0016] However, in densely populated scenarios such as parking lots, congested urban roads, toll booths, and logistics parks, numerous vehicles equipped with vehicle corner radars operate simultaneously. Due to the limited frequency band resources and highly homogeneous modulation parameters of vehicle corner radars, it's easy for the radar signal emitted by the first corner radar in one vehicle to enter the radar receiver of the second corner radar in another vehicle. The radar signal emitted by the first corner radar then becomes interference for the second corner radar, resulting in co-channel interference between the corner radars. After receiving the interference signal, the second corner radar generates "ghost" targets that are unrealistic in both range and velocity dimensions. These "ghosts" typically appear randomly, move at high speeds, or have abrupt position changes, creating false point clouds that severely interfere with the radar's target detection and tracking algorithms. This can lead to false alarms or missed detections of real dangerous targets, posing a potential threat to driving safety.

[0017] To address the aforementioned issues, relevant technologies primarily employ random frequency hopping or frequency modulation slope agility to attempt to circumvent interference signals in the frequency or time domains. However, in ultra-high-density radar scenarios, the success rate of random avoidance strategies drops significantly, and signal interference still occurs frequently. Furthermore, simple interference removal algorithms are often based on a single threshold, lacking in-depth identification of the essential characteristics of interference signals, which can easily lead to false positives or residual interference.

[0018] Therefore, this application proposes a method for identifying interference signals. The following detailed description, in conjunction with the accompanying drawings, through some embodiments and application scenarios, illustrates the interference signal identification method provided by this application.

[0019] See Figure 1 , Figure 1 This is a flowchart illustrating the method for identifying interference signals provided in an embodiment of this application.

[0020] like Figure 1 As shown, the method for identifying interference signals includes, but is not limited to, the following steps: S101, acquire the intermediate frequency signal of the target radar of the target vehicle, wherein the intermediate frequency signal is a signal obtained by mixing the local oscillator signal of the target radar and the received signal received by the target radar.

[0021] In this embodiment, the target radar can be an angle radar mounted on the target vehicle, and the target radar can adopt a frequency-modulated continuous wave (FMCW) system. The target radar may include components such as a waveform generator, transceiver, mixer, analog-to-digital converter, and signal processor. The waveform generator generates a linearly frequency-modulated radio frequency transmission signal, which is amplified and radiated through the transmitting antenna. When the transmitted signal encounters an obstacle, it is reflected, and the reflected echo signal is received by the receiving antenna of the target radar, amplified with low noise, and then sent to the mixer. In the mixer, the received signal is mixed with the local oscillator signal, and the difference frequency signal after mixing is the intermediate frequency signal.

[0022] S102, obtain the target time-frequency characteristics of the intermediate frequency signal within the target frequency modulation period. The target time-frequency characteristics include a first time-frequency characteristic characterizing the frequency fluctuation state of the intermediate frequency signal and a second time-frequency characteristic characterizing the curvature of the phase of the intermediate frequency signal.

[0023] In this embodiment, the target frequency modulation period can be any frequency modulation period in the current processing frame of the target radar. The frequency modulation period is a complete frequency modulation unit in the target radar, and its frequency changes linearly with time. Time-frequency characteristics are parameters that describe the characteristics of the intermediate frequency (IF) signal from a time-frequency domain perspective. Specifically, the first time-frequency characteristic characterizes the frequency fluctuation state of the IF signal, reflecting the frequency stability of the IF signal within the frequency modulation period; the second time-frequency characteristic characterizes the curvature of the IF signal phase, reflecting the nonlinear characteristics of the IF signal phase.

[0024] Specifically, the intermediate frequency (IF) signal in digital form sampled by the analog-to-digital converter can be analyzed by performing a short-time Fourier transform (STFT) or a Hilbert transform to calculate the target time-frequency characteristics of the IF signal within the target frequency modulation period.

[0025] S103, if the first time-frequency feature is greater than a preset first feature threshold and the second time-frequency feature is greater than a preset second feature threshold, the received signal corresponding to the intermediate frequency signal is determined to be an interference signal.

[0026] In this embodiment, the feature threshold is a pre-set threshold used to distinguish between real echo signals and interference signals. This feature threshold includes a first threshold corresponding to a first time-frequency feature and a second threshold corresponding to a second time-frequency feature. Only when the first time-frequency feature is greater than the first threshold and the second time-frequency feature is greater than the second threshold can the received signal corresponding to the intermediate frequency signal be determined to be an interference signal. Otherwise, the received signal corresponding to the intermediate frequency signal can be determined to be the echo signal of the target radar's transmitted signal.

[0027] In this embodiment, the intermediate frequency (IF) signal after mixing by the radar on the target vehicle can be acquired first. Then, a first time-frequency feature characterizing the degree of frequency fluctuation and a second time-frequency feature characterizing the degree of phase curvature of the IF signal within a single frequency modulation cycle are extracted. Finally, when both the first and second time-frequency features exceed a preset threshold, the received signal is determined to be an interference signal. Since this scheme does not rely on empirical judgment based on kinematic thresholds but directly detects the unique frequency jumps and phase nonlinear characteristics of the interference signal, it can fundamentally distinguish between the real target echo signal and the co-frequency interference signal, thereby accurately identifying the received interference signal.

[0028] In one embodiment, obtaining the target time-frequency characteristics of the intermediate frequency signal within the target frequency modulation period includes: The instantaneous characteristics of the intermediate frequency signal within the target frequency modulation period are obtained, wherein the instantaneous characteristics include an instantaneous frequency sequence for determining the first time-frequency characteristic and a phase for determining the second time-frequency characteristic, wherein the instantaneous frequency sequence includes multiple instantaneous frequency values ​​of the intermediate frequency signal; The target time-frequency characteristics are determined based on the instantaneous characteristics.

[0029] In this embodiment, instantaneous features refer to the set of parameters extracted from the intermediate frequency (IF) signal to characterize the instantaneous changes of the IF signal within the target frequency modulation (FM) period. These instantaneous features may include the instantaneous frequency sequence and phase of the IF signal. The instantaneous frequency sequence refers to multiple instantaneous frequency values ​​arranged in chronological order, obtained by dividing the IF signal into time windows and performing spectral analysis within the target FM period. These instantaneous frequency values ​​reflect the frequency variation of the IF signal over time. The phase refers to the instantaneous phase angle of the IF signal, and its variation over time reflects the frequency variation characteristics of the signal. The first time-frequency feature characterizes the frequency fluctuation state of the IF signal and can be obtained through statistical analysis of the instantaneous frequency sequence. The second time-frequency feature characterizes the phase curvature of the IF signal and can be obtained by differentiating the phase.

[0030] In this embodiment, the instantaneous characteristics of the intermediate frequency (IF) signal within the target frequency modulation (FM) period are first obtained. These instantaneous characteristics include an instantaneous frequency sequence for determining the first time-frequency characteristic and a phase for determining the second time-frequency characteristic. Specifically, the IF signal can be divided into segments using a sliding time window. Spectral analysis is performed on the signal segments within each time window to obtain the instantaneous frequency value corresponding to each time window. Arranging all instantaneous frequency values ​​in chronological order yields the instantaneous frequency sequence. Simultaneously, the phase information of the IF signal can be extracted using methods such as Hilbert transform. Then, the target time-frequency characteristic is determined based on the obtained instantaneous characteristics. For example, a first time-frequency characteristic characterizing the degree of frequency fluctuation can be calculated based on the instantaneous frequency sequence, and a second time-frequency characteristic characterizing the degree of phase curvature can be calculated based on the phase.

[0031] In this embodiment, by simultaneously extracting frequency fluctuation features and phase curvature features from the intermediate frequency signal within the same frequency modulation cycle, it is possible to detect the presence of interference signals from both the time-frequency domain and the phase domain. Compared with single-dimensional detection methods, this improves the accuracy of interference signal identification.

[0032] In one embodiment, determining the target time-frequency features based on the instantaneous features includes: In the case where the instantaneous feature includes an instantaneous frequency sequence of an intermediate frequency signal, the variance values ​​of P instantaneous frequency values ​​in the instantaneous frequency sequence within the target frequency modulation period are determined, wherein the first time-frequency feature is the variance value, the variance value is used to characterize the frequency fluctuation state of the intermediate frequency signal, and P is a positive integer.

[0033] In this embodiment, the instantaneous frequency sequence is a sequence of discrete instantaneous frequency values ​​obtained through time-frequency analysis of the intermediate frequency (IF) signal. After obtaining the instantaneous frequency sequence, any frequency modulation (FM) period can be selected as the target FM period. Then, P instantaneous frequency values ​​within the target FM period are obtained, and the variance of these P instantaneous frequency values ​​is calculated. This variance can then be used to describe the frequency fluctuation state of the IF signal; a larger variance indicates more severe frequency fluctuations of the IF signal within the target FM period. For example, the instantaneous frequency sequence can be obtained through Hilbert transform or short-time Fourier transform.

[0034] Since the instantaneous frequency of a true echo signal is constant or linearly changes within a single frequency modulation cycle, its variance is very small. However, interference signals experience frequency jumps at the boundaries of the frequency modulation cycle, causing drastic fluctuations in the instantaneous frequency sequence and significantly increasing the variance. Therefore, by calculating the variance of P instantaneous frequency values, the degree of this jump can be quantified, thereby identifying interference signals.

[0035] Therefore, the aforementioned variance value can be defined as the first time-frequency characteristic. If the variance value is greater than a preset variance threshold, it can be considered that there is an abnormal frequency jump in the received signal, and the received signal may be an interference signal. If the variance value is less than or equal to the preset variance threshold, the received signal is considered a normal echo signal.

[0036] For example, the formula for calculating the variance can be:

[0037] Where V represents the variance value, and Var(·) represents the variance function. This represents the nth instantaneous frequency value, where t is the start time.

[0038] In this embodiment, the variance of the instantaneous frequency sequence within a single frequency modulation cycle can be used as the first time-frequency feature. This variance can quantify the frequency fluctuation of the intermediate frequency signal, thereby effectively identifying frequency instability caused by interference signals and improving the accuracy of interference signal identification.

[0039] In one embodiment, acquiring the instantaneous frequency sequence of the intermediate frequency signal within the target frequency modulation period includes: The intermediate frequency signal is divided by a sliding time window to obtain multiple continuous signal segments; Spectral analysis is performed on multiple consecutive signal segments to obtain the instantaneous frequency values ​​corresponding to each signal segment; The instantaneous frequency values ​​are arranged in chronological order to obtain the instantaneous frequency sequence.

[0040] In this embodiment, a sliding time window is an analysis method that divides a continuous time signal into multiple short time segments. Each signal segment is a portion of the intermediate frequency signal within a single time window.

[0041] Specifically, parameters such as the window width and sliding step size of the sliding time window can be set first. Then, starting from the beginning of the frequency modulation cycle, the time window is moved sequentially by the sliding step size to divide the intermediate frequency signal into P consecutive signal segments. Spectral analysis is then performed on each signal segment, specifically using a Fast Fourier Transform (FFT) to extract the frequency corresponding to the spectral peak of each signal segment as the instantaneous frequency value of that segment. The obtained instantaneous frequency values ​​can then be sorted in chronological order to obtain the instantaneous frequency sequence.

[0042] Since the instantaneous frequency sequence can finely characterize the frequency change process of the intermediate frequency signal in the time-frequency domain, it can provide high-resolution time-frequency information for subsequent interference signal identification.

[0043] In one embodiment, determining the target time-frequency features based on the instantaneous features includes: When the instantaneous feature includes the phase of the intermediate frequency signal within the target frequency modulation period, the second derivative of the phase is obtained by taking the second derivative of the phase, wherein the second time-frequency feature is the second derivative of the phase, and the second derivative is used to characterize the curvature of the phase of the intermediate frequency signal.

[0044] In this embodiment, the phase is the phase angle of the intermediate frequency (IF) signal at a certain moment, reflecting the time delay information of the IF signal. The second derivative of the phase is a physical quantity describing the degree of phase nonlinearity. Specifically, the analytic signal can be extracted using the Hilbert transform, and then the phase of the IF signal can be obtained.

[0045] For an ideal intermediate frequency signal obtained by mixing the local oscillator signal and the true echo signal, its phase can be modeled as follows:

[0046] in, Let k be the phase of an ideal intermediate frequency signal, which varies with time t, and k be the frequency modulation slope. Here, c is the center frequency of the radar, R is the speed of light, and v is the radial velocity of the target object. This is the initial phase. Its instantaneous angular frequency... It can be a constant or a linear change.

[0047] For interference signals generated by another interfering radar that directly enter or are reflected into the receiver of this radar, the intermediate frequency signal obtained after mixing the interference signal and the local oscillator signal can be expressed as:

[0048] Taking the derivative with respect to time yields the instantaneous frequency:

[0049] in, Indicates the start time of the jamming radar signal. This represents the phase of the intermediate frequency signal as a function of time t. This indicates the frequency modulation slope of this radar. This indicates the frequency modulation slope of the jamming radar. Indicates the start time of the jamming radar's transmitted signal. This indicates the initial phase of the signal used by the jamming radar. Indicates the start time of the radar signal. Indicates the starting frequency of this radar. Indicates the starting frequency of the jamming radar. This represents the initial phase difference.

[0050] In summary, for echoes from real targets, since the target range and velocity can be considered constant within a single frequency modulation cycle, the instantaneous frequency should revolve around a constant value or exhibit linear variation with minimal frequency fluctuations, linear phase variation, and a second derivative close to zero. However, for co-channel interference, due to the asynchronous frequency modulation parameters between the interfering radar and the main radar, frequency jumps or phase discontinuities occur at the boundaries of the frequency modulation cycle, resulting in a significant increase in the variance of the instantaneous frequency and a non-zero second derivative of the phase.

[0051] Therefore, the second derivative of the aforementioned phase can be determined as the second time-frequency characteristic. If the second derivative of this phase is greater than a preset second threshold, the phase of the received signal can be considered discontinuous, and the received signal may be an interference signal. If the second derivative of this phase is less than or equal to the preset second threshold, the received signal is considered a normal echo signal. The second threshold can be a constant greater than 0.

[0052] For example, the formula for calculating the second derivative of the phase can be:

[0053] Where C represents the second derivative of the phase, The t represents the phase of the intermediate frequency signal, and the t represents time. This represents the signal sample value at the current moment. Indicates the delay interval.

[0054] In this embodiment, the second derivative of the phase can be used as a time-frequency feature to effectively detect nonlinear modulation components in the intermediate frequency signal, accurately identify co-frequency interference caused by asynchronous frequency modulation parameters, and improve the accuracy of interference identification.

[0055] like Figure 2 As shown, in one embodiment, after determining that the received signal corresponding to the intermediate frequency signal is an interference signal, the method further includes: S201, determine the direction of the interference source corresponding to the interference signal; S202, acquire the target point cloud detected by the target radar, wherein the target point cloud includes multiple target points; S203, determine abnormal target points from the plurality of target points based on the motion characteristics of each target point; S204, if the vertical distance from the abnormal target point to the interference source direction line is less than the distance threshold, the abnormal target point is removed from the target point cloud, wherein the interference source direction line is a ray extending along the interference source direction with the target radar as the starting point; S205, determine the obstacle position of the target vehicle based on the remaining target points in the target point cloud after removing the abnormal target points.

[0056] In this embodiment, the direction of the interference source refers to the direction from which the interference signal originates, i.e., the azimuth angle from the target radar to the interfering radar. The direction of the interference source can be determined using various methods, such as phase difference direction finding based on multiple receiving channels, energy detection based on beam scanning, or algorithms based on angle of arrival estimation.

[0057] For example, for a given interference signal, the covariance matrix of the received data from the radar's receiving antenna array can be used. Perform DOA estimation:

[0058] in, E{} is used to represent the interference signal vector, while E{} represents the mathematical expectation.

[0059] After determining the data covariance matrix, eigenvalue decomposition can be performed, and spatial spectrum estimation algorithms such as MUSIC or Capon can be used to find the direction of arrival angle of the interference signal. .

[0060] in, It is the array guide vector. It is a noise subspace. Represents the spatial spectrum of MUSIC. This indicates the scanning angle. The MUSIC algorithm calculates the guide vector. With noise subspace The orthogonality of the scanning angle is used to determine the direction of the interference source, and the peak value is the direction of the interference source.

[0061] A target point cloud is a collection of detection results obtained by target radar through signal processing. Each target point in the target point cloud represents a detected potential target and includes its position and velocity information. The motion characteristics of each target point include its position and velocity information. Based on these motion characteristics, it can be determined whether each target point is an anomalous target point. An anomalous target point is defined as a false target point that may be caused by co-channel interference. The interference source direction line is a ray extending from the target radar along the direction of the interference source; it is used to determine whether a target point is associated with the direction of the interference source.

[0062] Specifically, the direction of the interference source is first determined, and then a target point cloud near that direction is acquired by the target radar. For each target point in the target point cloud, its motion characteristics, such as velocity, position changes, and fluctuation states, are analyzed to determine whether it is an anomalous target point. If a target point meets the anomalous condition, the vertical distance from the anomalous target point to the interference source direction line is further calculated. If the vertical distance is less than a pre-set distance threshold, it indicates that the anomalous target point is located near the interference source direction and is likely a "ghost" target generated by that interference source. In this case, anomalous target points with vertical distances less than the distance threshold can be clustered to form a ghost group, which is then removed from the target point cloud. The aforementioned distance threshold can be dynamically adjusted based on the uncertainty of the interference source distance estimation.

[0063] After identifying and clustering "ghost" targets, the remaining target point cloud will no longer contain false targets generated by co-channel interference. Based on this purified target point cloud, the system can accurately calculate the specific position and motion state of real obstacles relative to the vehicle, thereby providing reliable environmental perception information for higher-level driver assistance functions such as blind spot monitoring and lane change assist.

[0064] In this embodiment, by determining the direction of the interference source, acquiring the target point cloud, identifying abnormal target points based on the motion characteristics of each target point in the target point cloud, and removing the abnormal target points from the target point cloud, the "ghost" targets generated by co-channel interference can be effectively eliminated, thereby improving the accuracy of radar target detection.

[0065] In one embodiment, determining abnormal target points from the plurality of target points based on the motion characteristics of each target point includes: Target points that meet the abnormal conditions are identified as abnormal target points, wherein the motion characteristics include the radial velocity, position, and fluctuation state of the target point, and the abnormal conditions include at least one of the following: The absolute value of the radial velocity at the target point is greater than the velocity threshold; The distance of the target point's position change is greater than the position change threshold; The fluctuation state of the target point is abnormal.

[0066] In this embodiment, the velocity threshold is used to determine whether the velocity of the target point is abnormal, and can be set according to the maximum possible velocity of the real target in the application scenario. The position change threshold is used to determine whether the position of the target point has undergone abnormal jumps. This position change threshold can reflect the maximum reasonable displacement of the target object between consecutive moments. Abnormal fluctuation state refers to the position and velocity changes of the target point exceeding the normal range within a preset time period, exhibiting violent fluctuations.

[0067] Specifically, in determining whether the speed of a target point is abnormal, for each target point in the target point cloud, its radial velocity can be obtained. If the radial velocity is greater than a set speed threshold, the target point is determined to meet the abnormal condition. The speed threshold can be set according to the specific application scenario. For example, in a parking lot, the maximum speed of a vehicle is usually no more than 10 m / s, and the speed of a pedestrian is no more than 2 m / s, so the speed threshold can be set to 15 m / s.

[0068] In determining whether a target point's position jump is abnormal, for each target point in the target point cloud, its current first position coordinates and its previous second position coordinates (x_{t-1}, y_{t-1}) can be obtained. The position jump value is then calculated based on these first and second position coordinates. If the position jump distance is greater than a position change threshold, the target point is also considered to meet the abnormal position jump condition. The position change threshold can be set according to the target's maximum speed and the radar's frame period in the application scenario.

[0069] In determining whether the fluctuation state of a target point is abnormal, for each target point in the target point cloud, its position and velocity changes can be tracked within a first preset time period, and the position variance and velocity variance of the target point within the first preset time period can be calculated. If the position variance is greater than a preset third threshold and the velocity variance is greater than a preset fourth threshold, then the target point is determined to meet the abnormal fluctuation state condition. The fluctuation condition is set based on the motion stability of the real target. The position and velocity of the real target should be relatively stable in a short period of time, while the position and velocity of the interfering "ghost" exhibit violent fluctuations due to the randomness of the interference signal.

[0070] In this embodiment, by setting multiple anomaly judgment conditions, the abnormal characteristics of interfering "ghost" targets can be identified from different dimensions, thereby improving the comprehensiveness and accuracy of interference suppression.

[0071] It should be noted that the various embodiments described in this application can be combined with each other or implemented individually without conflict, and this application does not limit this.

[0072] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0073] Based on the interference signal identification method provided in the above embodiments, this application also provides specific implementations of the interference signal identification device. Please refer to the following embodiments.

[0074] See Figure 3 The interference signal identification device 300 provided in this application embodiment may include: The first acquisition module 301 is used to acquire the intermediate frequency signal of the target radar of the target vehicle. The intermediate frequency signal is a signal obtained by mixing the local oscillator signal of the target radar and the received signal received by the target radar. The second acquisition module 302 is used to acquire the target time-frequency characteristics of the intermediate frequency signal within the target frequency modulation period. The target time-frequency characteristics include a first time-frequency characteristic characterizing the frequency fluctuation state of the intermediate frequency signal and a second time-frequency characteristic characterizing the curvature of the phase of the intermediate frequency signal. The first determining module 303 is used to determine that the received signal corresponding to the intermediate frequency signal is an interference signal when the first time-frequency feature is greater than a preset first feature threshold and the second time-frequency feature is greater than a preset second feature threshold.

[0075] In one embodiment, the second acquisition module 302 includes: The first acquisition unit is configured to acquire the instantaneous characteristics of the intermediate frequency signal within the target frequency modulation period, wherein the instantaneous characteristics include an instantaneous frequency sequence for determining the first time-frequency characteristic and a phase for determining the second time-frequency characteristic, wherein the instantaneous frequency sequence includes multiple instantaneous frequency values ​​of the intermediate frequency signal; The first determining unit is used to determine the target time-frequency characteristics based on the instantaneous characteristics.

[0076] In one embodiment, the first determining unit includes: The first determining subunit is configured to determine the variance of P instantaneous frequency values ​​in the instantaneous frequency sequence within the target frequency modulation period, when the instantaneous feature includes an instantaneous frequency sequence of an intermediate frequency signal, wherein the first time-frequency feature is the variance value, the variance value is used to characterize the frequency fluctuation state of the intermediate frequency signal, and P is a positive integer.

[0077] In one embodiment, the first acquisition unit includes: The sub-unit is used to divide the intermediate frequency signal by a sliding time window to obtain multiple continuous signal segments; The analysis subunit is used to perform spectral analysis on multiple consecutive signal segments to obtain the instantaneous frequency value corresponding to each signal segment; The arrangement subunit is used to arrange the instantaneous frequency values ​​in chronological order to obtain the instantaneous frequency sequence.

[0078] In one embodiment, the first determining unit further includes: The second determining subunit is used to perform a second derivative on the phase when the instantaneous feature includes the phase of the intermediate frequency signal within the target frequency modulation period, to obtain the second derivative of the phase, wherein the second time-frequency feature is the second derivative of the phase, and the second derivative is used to characterize the curvature of the phase of the intermediate frequency signal.

[0079] In one embodiment, the interference signal identification device further includes: The second determining module is used to determine the direction of the interference source corresponding to the interference signal; The detection module is used to acquire the target point cloud detected by the target radar, wherein the target point cloud includes multiple target points; The third determining module is used to determine abnormal target points from the plurality of target points based on the motion characteristics of each target point; The elimination module is used to remove the abnormal target point from the target point cloud when the vertical distance from the abnormal target point to the interference source direction line is less than a distance threshold. The interference source direction line is a ray that extends along the interference source direction starting from the target radar. The fourth determining module is used to determine the obstacle position of the target vehicle based on the remaining target points in the target point cloud after removing the abnormal target points.

[0080] In one embodiment, the third determining module includes: The second determining unit is used to determine target points that meet abnormal conditions as abnormal target points, wherein the motion characteristics include the radial velocity, position, and fluctuation state of the target point, and the abnormal conditions include at least one of the following: The absolute value of the radial velocity at the target point is greater than the velocity threshold; The distance of the target point's position change is greater than the position change threshold; The fluctuation state of the target point is abnormal.

[0081] The interference signal identification device provided in this application embodiment can achieve Figure 1 The various processes implemented by the interference signal identification device in the method embodiment will not be described again here to avoid repetition.

[0082] Figure 4 A schematic diagram of the hardware structure of the vehicle provided in an embodiment of this application is shown.

[0083] The vehicle may include a processor 401 and a memory 402 storing computer program instructions.

[0084] Specifically, the processor 401 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0085] Memory 402 may include mass storage for data or instructions. For example, and not limitingly, memory 402 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 402 may include removable or non-removable (or fixed) media. Where appropriate, memory 402 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 402 is non-volatile solid-state memory.

[0086] Memory may include read-only memory (ROM), random access memory (RAM), disk storage media devices, optical storage media devices, flash memory devices, and electrical, optical, or other physical / tangible memory storage devices. Therefore, typically, memory includes one or more tangible (non-transitory) computer storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to one aspect of this disclosure.

[0087] The processor 401 reads and executes computer program instructions stored in the memory 402 to implement any of the interference signal identification methods in the above embodiments.

[0088] In one example, the vehicle may also include a communication interface 403 and a bus 410. Wherein, as... Figure 3 As shown, the processor 401, memory 402, and communication interface 403 are connected through bus 410 and complete communication with each other.

[0089] The communication interface 403 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0090] Bus 410 includes hardware, software, or both, that couples vehicle components together. This is an example, not a limitation. The bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, bus 410 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, this application contemplates any suitable bus or interconnect.

[0091] Furthermore, in conjunction with the interference signal identification methods in the above embodiments, this application embodiment can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the interference signal identification methods in the above embodiments.

[0092] This application embodiment may also provide a computer program product, wherein when the instructions in the computer program product are executed by the vehicle's processor, the vehicle performs any of the interference signal identification methods described in the above embodiments.

[0093] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0094] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. The programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM, floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0095] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0096] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0097] The above description is merely a specific embodiment of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A method for identifying interference signals, characterized in that, include: The intermediate frequency signal of the target radar of the target vehicle is acquired. The intermediate frequency signal is a signal obtained by mixing the local oscillator signal of the target radar with the received signal received by the target radar. The target time-frequency characteristics of the intermediate frequency signal within the target frequency modulation period are obtained. The target time-frequency characteristics include a first time-frequency characteristic characterizing the frequency fluctuation state of the intermediate frequency signal and a second time-frequency characteristic characterizing the curvature of the phase of the intermediate frequency signal. If the first time-frequency feature is greater than a preset first feature threshold and the second time-frequency feature is greater than a preset second feature threshold, the received signal corresponding to the intermediate frequency signal is determined to be an interference signal.

2. The method for identifying interference signals according to claim 1, characterized in that, The step of obtaining the target time-frequency characteristics of the intermediate frequency signal within the target frequency modulation period includes: The instantaneous characteristics of the intermediate frequency signal within the target frequency modulation period are obtained, wherein the instantaneous characteristics include an instantaneous frequency sequence for determining the first time-frequency characteristic and a phase for determining the second time-frequency characteristic, wherein the instantaneous frequency sequence includes multiple instantaneous frequency values ​​of the intermediate frequency signal; The target time-frequency characteristics are determined based on the instantaneous characteristics.

3. The method for identifying interference signals according to claim 2, characterized in that, Determining the target time-frequency features based on the instantaneous features includes: When the instantaneous feature includes an instantaneous frequency sequence of an intermediate frequency signal, the variance of P instantaneous frequency values ​​in the instantaneous frequency sequence within the target frequency modulation period is determined, wherein the first time-frequency feature is the variance value, the variance value is used to characterize the frequency fluctuation state of the intermediate frequency signal, and P is a positive integer.

4. The method for identifying interference signals according to claim 2, characterized in that, The step of obtaining the instantaneous frequency sequence of the intermediate frequency signal within the target frequency modulation period includes: The intermediate frequency signal is divided by a sliding time window to obtain multiple continuous signal segments; Spectral analysis is performed on multiple consecutive signal segments to obtain the instantaneous frequency values ​​corresponding to each signal segment; The instantaneous frequency values ​​are arranged in chronological order to obtain the instantaneous frequency sequence.

5. The method for identifying interference signals according to claim 2, characterized in that, Determining the target time-frequency features based on the instantaneous features includes: When the instantaneous feature includes the phase of the intermediate frequency signal within the target frequency modulation period, the second derivative of the phase is obtained by taking the second derivative of the phase, wherein the second time-frequency feature is the second derivative of the phase, and the second derivative is used to characterize the curvature of the phase of the intermediate frequency signal.

6. The method for identifying interference signals according to claim 1, characterized in that, After determining that the received signal corresponding to the intermediate frequency signal is an interference signal, the method further includes: Determine the direction of the interference source corresponding to the interference signal; The target point cloud, detected by the target radar, is obtained, and the target point cloud includes multiple target points; Based on the motion characteristics of each target point, abnormal target points are determined from the plurality of target points; If the vertical distance from the abnormal target point to the interference source direction line is less than a distance threshold, the abnormal target point is removed from the target point cloud. The interference source direction line is a ray extending along the interference source direction, starting from the target radar. The obstacle location of the target vehicle is determined based on the remaining target points in the target point cloud after removing the abnormal target points.

7. The method for identifying interference signals according to claim 6, characterized in that, The step of determining abnormal target points from the plurality of target points based on the motion characteristics of each target point includes: Target points that meet the abnormal conditions are identified as abnormal target points, wherein the motion characteristics include the radial velocity, position, and fluctuation state of the target point, and the abnormal conditions include at least one of the following: The absolute value of the radial velocity at the target point is greater than the velocity threshold; The distance of the target point's position change is greater than the position change threshold; The fluctuation state of the target point is abnormal.

8. A device for identifying interference signals, characterized in that, The device includes: The first acquisition module is used to acquire the intermediate frequency signal of the target radar of the target vehicle. The intermediate frequency signal is a signal obtained by mixing the local oscillator signal of the target radar and the received signal received by the target radar. The second acquisition module is used to acquire the target time-frequency characteristics of the intermediate frequency signal within the target frequency modulation period. The target time-frequency characteristics include a first time-frequency characteristic characterizing the frequency fluctuation state of the intermediate frequency signal and a second time-frequency characteristic characterizing the curvature of the phase of the intermediate frequency signal. The first determining module is used to determine that the received signal corresponding to the intermediate frequency signal is an interference signal when the first time-frequency feature is greater than a preset first feature threshold and the second time-frequency feature is greater than a preset second feature threshold.

9. A vehicle, characterized in that, The vehicle includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, it implements the method for identifying interference signals as described in any one of claims 1-7.

10. A computer storage medium, characterized in that, The computer storage medium stores computer program instructions, which, when executed by a processor, implement the method for identifying interference signals as described in any one of claims 1-7.

11. A computer program product, characterized in that, When the instructions in the computer program product are executed by the vehicle's processor, the vehicle performs the interference signal identification method as described in any one of claims 1-7.

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