Parameter scheduling method, device, equipment, medium and product of radar

Abstract

The application discloses a radar parameter scheduling method and device, equipment, medium and product, and belongs to the technical field of radar signal processing. The method comprises the following steps: acquiring vehicle state information sent by multiple vehicles in a target area; determining at least one radar pair in the target area and the spatial interference weight of each radar pair according to the vehicle state information of the multiple vehicles, wherein each vehicle comprises at least one radar, and each radar pair comprises two radars which have the possibility of mutual interference; constructing a target model according to the spatial interference weight of each radar pair; determining target waveform parameters of each radar in the target area according to the target model; and sending the target waveform parameters corresponding to the radar on each vehicle to each vehicle in the target area, wherein the target waveform parameters are used for adjusting the transmission waveform of the radar of each vehicle. In this way, the same-frequency interference probability of the radar in the dense traffic scene can be effectively reduced.

Description

Technical Field

[0001] This application belongs to the field of radar signal processing technology, and in particular relates to a radar parameter scheduling method, device, equipment, medium and product. Background Technology

[0002] In the field of Advanced Driving Assistance Systems (ADAS), corner radars generally adopt the Frequency Modulated Continuous Wave (FMCW) system, with operating frequencies concentrated in the 76-81 GHz range. In densely populated areas such as parking lot entrances and exits, congested urban roads, and highway toll booths, a large number of vehicles' corner radars are operating simultaneously. Because the waveform parameters of each radar often use default values ​​or random settings, they are prone to co-channel interference. This interference generates "ghost" targets in the radar point cloud, leading to false alarms or missed detections in blind spot monitoring, lane change assist, and other functions, seriously affecting driving safety and user experience.

[0003] In related technologies, electronic devices 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, they cannot effectively reduce co-channel interference of radar in dense traffic scenarios. Summary of the Invention

[0004] This application provides a radar parameter scheduling method, apparatus, device, medium, and product that can effectively reduce the probability of co-channel interference between radars in dense traffic scenarios.

[0005] In a first aspect, embodiments of this application provide a radar parameter scheduling method, the method comprising: Obtain vehicle status information sent by multiple vehicles in the target area; At least one radar pair in the target area is determined based on the vehicle status information of the plurality of vehicles, and the spatial interference weight of each radar pair is determined, wherein each vehicle includes at least one radar, and each radar pair includes two radars that have the possibility of interfering with each other. A target model is constructed based on the spatial interference weights of each radar pair, wherein the target model aims to minimize the total interference conflict of all radar pairs in the target region. Based on the target model, determine the target waveform parameters of each radar in the target area; The target waveform parameters corresponding to the radar on each vehicle are sent to each vehicle in the target area, wherein the target waveform parameters are used by each vehicle to adjust the transmission waveform of its radar.

[0006] Secondly, embodiments of this application provide a radar parameter scheduling device, the device comprising: The first acquisition module is used to acquire vehicle status information sent by multiple vehicles in the target area; The first determining module is used to determine at least one radar pair in the target area and the spatial interference weight of each radar pair based on the vehicle status information of the plurality of vehicles, wherein each vehicle includes at least one radar and each radar pair includes two radars that have the possibility of interfering with each other. A construction module is used to construct a target model based on the spatial interference weights of each radar pair, wherein the target model aims to minimize the total interference conflict of all radar pairs in the target region. The second determining module is used to determine the target waveform parameters of each radar in the target area based on the target model. The transmitting module is used to transmit the target waveform parameters corresponding to the radar on each vehicle to each vehicle in the target area, wherein the target waveform parameters are used by each vehicle to adjust the transmission waveform of its radar.

[0007] Thirdly, embodiments of this application provide an electronic device, which includes: Processor and memory storing computer program instructions; When the processor executes the computer program instructions, it implements the radar parameter scheduling method 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 radar parameter scheduling 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 processor of an electronic device, cause the electronic device to perform the radar parameter scheduling method as described in the first aspect.

[0010] In this embodiment, vehicle status information of each vehicle within the target area can be obtained first. Based on the vehicle status information, radar pairs with potential interference and their spatial interference weights can be determined. Then, an optimization model is constructed with the goal of minimizing the total interference conflict of all radar pairs. By solving this target model, optimal target waveform parameters are assigned to each radar and distributed to each vehicle for execution. This application, through global collaborative scheduling, allocates low-interference waveform parameters to radars that are prone to spatial interference from the source, which can effectively reduce the probability of co-channel interference between radars in dense traffic scenarios. 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 radar parameter scheduling method provided in an embodiment of this application; Figure 2 This is a schematic diagram of the structure of the radar parameter scheduling device provided in the embodiments of this application; Figure 3 This is a schematic diagram of the hardware structure of the electronic device 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: In the field of Advanced Driving Assistance Systems (ADAS), corner radars generally adopt the Frequency Modulated Continuous Wave (FMCW) system, with operating frequencies concentrated in the 76-81 GHz range. In densely populated areas such as parking lot entrances and exits, congested urban roads, and highway toll booths, a large number of vehicles' corner radars are operating simultaneously. Because the waveform parameters of each radar often use default values ​​or random settings, they are prone to co-channel interference. This interference generates "ghost" targets in the radar point cloud, leading to false alarms or missed detections in blind spot monitoring, lane change assist, and other functions, seriously affecting driving safety and user experience.

[0016] Among related technologies, anti-co-channel interference techniques are mainly divided into two categories. One category is passive random strategies, such as frequency modulation slope agility, where the radar randomly switches its frequency modulation slope in each transmission cycle in an attempt to reduce the probability of "colliding" with the waveform parameters of neighboring radars. The advantage is that it is simple to implement and does not require external communication; the disadvantage is that it is essentially a probabilistic avoidance strategy, and the probability of interference will still increase sharply when vehicle density increases. Moreover, all radars operate independently without global coordination, resulting in the inefficient use of limited spectrum resources.

[0017] Another type is interference suppression based on signal processing, which involves identifying and filtering interference at the receiver by analyzing signal characteristics. This method can handle interference that has already occurred, but it cannot prevent interference from occurring at the source, and strong interference may suppress the echo of the real target.

[0018] In summary, in scenarios with extremely high vehicle density, such as large multi-level parking lots and tollbooth queuing areas, even with frequency modulation slope agility, mutual interference still occurs frequently. The "ghosting" effect from the corner radar output causes ADAS functions to be falsely triggered, resulting in a poor user experience. The root cause lies in the lack of a mechanism in the relevant technology for proactive negotiation and reasonable allocation of waveform resources between vehicles.

[0019] To address the aforementioned problems, this application proposes a radar parameter scheduling method. The following detailed description, in conjunction with the accompanying drawings, provides an embodiment of the radar parameter scheduling method provided by this application and its application scenarios.

[0020] See Figure 1 , Figure 1 This is a flowchart illustrating a radar parameter scheduling method provided in an embodiment of this application. The method is applied to an electronic device, which can be independently deployed on the roadside such as a parking lot server or a toll station control unit, or it can exist in each vehicle.

[0021] like Figure 1 As shown, the radar parameter scheduling method includes, but is not limited to, the following steps: S101, Obtain vehicle status information sent by multiple vehicles in the target area.

[0022] In this embodiment, the target area can be any geographical area requiring radar parameter scheduling, such as areas covered by densely populated traffic scenarios like parking lots, highway toll stations, and congested urban roads. The vehicle status information can include at least one of the following: vehicle identification information, location information, heading information, speed information, and antenna parameter information of the radar mounted on the vehicle. The vehicle can be any vehicle equipped with an angle radar located within the target area.

[0023] In the aforementioned vehicle status information, the vehicle's position information can be two-dimensional or three-dimensional coordinates in a global or local coordinate system, such as latitude and longitude coordinates obtained through a Global Navigation Satellite System (GNSS). This vehicle position information is used to subsequently calculate the relative distances between vehicles and determine the spatial distribution relationships of each radar.

[0024] The vehicle status information mentioned above may include the vehicle's heading angle, which is the angle between the vehicle's direction of travel and the reference direction. For example, the reference direction may be due north, and the heading angle may range from 0 to 360 degrees.

[0025] The vehicle status information mentioned above includes radar antenna parameters such as antenna mounting angle and antenna gain. The antenna mounting angle is the angle between the radar antenna's main lobe direction and the vehicle's front direction. For example, the mounting angle of the radar on the left side of the vehicle could be -45 degrees, and the mounting angle of the radar on the right side could be +45 degrees. The antenna gain parameter can be the gain value of the radar's transmitting and receiving antennas. For radars of the same model, this gain value can be set to the same constant, such as 1.

[0026] The vehicle status information may also include the waveform parameters currently used by the vehicle's radar, such as the current center frequency and current modulation slope. The vehicle status information may also include the vehicle's speed information, which can be used to determine the update cycle of the waveform parameters.

[0027] The aforementioned vehicles can send vehicle status information to the collaborative management unit via the onboard communication module. The collaborative management unit can be an electronic device deployed on the roadside, such as a parking lot server or a toll station control unit; or it can be an electronic device distributed among the vehicles. The onboard communication module can communicate via C-V2X (Cellular Vehicle to Everything) or DSRC (Dedicated Short-Range Communications).

[0028] S102, determine at least one radar pair in the target area and the spatial interference weight of each radar pair based on the vehicle status information of the plurality of vehicles, wherein each vehicle includes at least one radar and each radar pair includes two radars that have the possibility of interfering with each other.

[0029] In this embodiment, the aforementioned radar pair refers to a combination of two radars in the target area that have the potential to interfere with each other. Each vehicle is typically equipped with at least one corner radar.

[0030] The aforementioned spatial interference weight is used to quantify the likelihood of mutual interference between the two radars in a corresponding radar pair. This spatial interference weight only reflects the spatial geometric relationship between the radar pairs and does not include waveform parameter similarity information. A larger spatial interference weight indicates that the corresponding radar pairs are more likely to interfere with each other; a smaller spatial interference weight indicates that the corresponding radar pairs are less likely to interfere with each other and can tolerate the use of the same or similar waveform parameters.

[0031] S103, construct a target model based on the spatial interference weights of each radar pair, wherein the target model aims to minimize the total interference conflict of all radar pairs in the target area.

[0032] In this embodiment, the target model described above is a mathematical model with the optimization objective of minimizing the total interference conflict of all radar pairs in the target area. The total interference conflict is the sum of the interference conflicts of each radar pair, and the interference conflict of each radar pair is the product of the spatial interference weight between the two radars in each pair and the corresponding indication function. The indication function characterizes whether the waveform parameters of the two radars in each pair satisfy the conflict condition; when the conflict condition is satisfied, the indication function is set to 1, otherwise it is set to 0.

[0033] The aforementioned target model can be expressed as an objective function, which uses the waveform parameters of each radar to be assigned as optimization variables and minimizes the total interference conflict of all radar pairs as the optimization objective. By solving this objective function, the target waveform parameter allocation scheme for each radar that minimizes the total interference conflict can be obtained.

[0034] S104, Based on the target model, determine the target waveform parameters of each radar in the target area; In this embodiment, the target model described above can be solved using an optimization algorithm to obtain the target waveform parameters for each radar. These target waveform parameters may include parameters such as center frequency and frequency modulation slope. During the solution process, a set of available waveform parameter combinations can be predefined as a candidate parameter set. Then, through traversal search, heuristic algorithms, or numerical optimization methods, a set of target waveform parameters is selected for each radar from the candidate parameter set to minimize the value of the objective function.

[0035] S105, send the target waveform parameters corresponding to the radar on each vehicle to each vehicle in the target area, wherein the target waveform parameters are used by each vehicle to adjust the transmission waveform of its radar.

[0036] In this embodiment, the electronic device can transmit the solved target waveform parameters to each vehicle via vehicle-to-everything (V2X) or DSRC communication methods. After receiving the target waveform parameters, each vehicle adjusts its radar's transmitted waveform to match the assigned target waveform parameters, thereby reducing co-channel interference between its radar and other vehicles' radars in actual operation.

[0037] In this embodiment, vehicle status information of each vehicle within the target area can be obtained first. Based on the vehicle status information, radar pairs with potential interference and their spatial interference weights can be determined. Then, an optimization model is constructed with the goal of minimizing the total interference conflict of all radar pairs. By solving this target model, optimal target waveform parameters are assigned to each radar and distributed to each vehicle for execution. This application, through global collaborative scheduling, allocates low-interference waveform parameters to radars that are prone to spatial interference from the source, which can effectively reduce the probability of radar co-channel interference in dense traffic scenarios.

[0038] In one embodiment, determining at least one radar pair in the target area based on the vehicle status information of the plurality of vehicles includes: An interference conflict graph is constructed based on the vehicle status information of the multiple vehicles. Each node in the interference conflict graph represents a radar, and the radars corresponding to the two endpoints of each edge in the interference conflict graph constitute a radar pair.

[0039] In this embodiment, the aforementioned interference conflict graph is an undirected graph consisting of vehicle radars as nodes and edges formed by radars satisfying interference conditions. It is used to characterize the spatial interference potential between radars in the region.

[0040] For example, the above-mentioned interference and conflict graph can be represented as an undirected graph G=(V, E). The set of nodes in the interference and conflict graph can be represented as V={v1, v2, ..., v...} N}, where N is the total number of radars to be scheduled in the target area. Each vertex v i This corresponds to one radar. The set of edges in the interference conflict graph can be represented as E = {e...} ij | i≠j, d ij <D max}, where d ij Let D be the straight-line distance between radar i and radar j. max The preset effective interference range can be 100 meters. An edge connecting the two corresponding nodes is added to the interference conflict graph only when the straight-line distance between two radars is less than the effective interference range. Radars that are more than this effective interference range are not involved in scheduling because the spatial distance between them is too great and the possibility of mutual interference is extremely low.

[0041] The effective interference distance can be set according to the actual application scenario. For example, in relatively enclosed and densely populated scenarios such as parking lots, the effective interference distance can be set to a smaller value, such as 50 meters, because the distance between vehicles is generally small and the reflection environment in parking lots is complex, making close-range interference more significant. In scenarios such as highway toll stations where the distance between vehicles may be large, the effective interference distance can be set to a larger value, such as 150 meters or 200 meters.

[0042] The construction process of the aforementioned interference conflict graph can be as follows: First, the collaborative management unit obtains the vehicle status information reported by all vehicles in the target area and extracts the identification information and location information of each radar from it; then, it calculates the straight-line distance between every two radars; finally, it determines whether the straight-line distance between two radars is less than the effective interference distance. If the straight-line distance between two radars is less than the effective interference distance, an edge is added between the nodes corresponding to the two radars in the interference conflict graph to indicate that the radar pair may interfere with each other.

[0043] In this embodiment, radar pairs are determined by using an interference conflict graph, which can abstract the complex interference relationships between radars into a graph structure, making it easier to analyze and optimize using graph theory algorithms and reducing computational complexity.

[0044] In one embodiment, determining the spatial interference weight of each radar pair in the target area based on the vehicle status information of the plurality of vehicles includes: For any given radar pair, the spatial interference weight of the radar pair is determined based on the distance between the two radars in the radar pair, the angle between the main lobe directions of the two radar antennas, and the antenna gain parameters of the two radars.

[0045] In this embodiment, the aforementioned spatial interference weight is positively correlated with the antenna gain of the two radars, because the greater the antenna gain, the stronger the transmitted signal and the higher the receiving sensitivity, making mutual interference more likely. The spatial interference weight is negatively correlated with the square of the distance between the two radars, because electromagnetic waves experience free space path loss during propagation in space, and the greater the distance, the more severe the signal attenuation, and the lower the possibility of mutual interference. The spatial interference weight is negatively correlated with the angle between the main lobe directions of the two radar antennas, because when the main lobes of the two radar antennas are directly opposite each other, the signal transmitted by one is most easily received by the receiving antenna of the other, resulting in the strongest interference; while when the main lobe directions of the two radar antennas are orthogonal, the transmitted signal of one is located in the sidelobe region of the receiving antenna of the other, resulting in a lower receiving gain and the weakest interference.

[0046] Therefore, in some embodiments, the spatial interference weight between radar i and radar j can be calculated using the following formula:

[0047] Where wij is the spatial interference weight of the radar pair consisting of radar i and radar j; Gt and Gr are the transmitting antenna gain and receiving antenna gain of the radar, respectively. For radars of the same model, they can be set to the same constant, such as 0 dBi or simplified to 1; dij is the straight-line distance between radar i and radar j, in meters, calculated from the position coordinates of the vehicles to which the two radars belong; Δθij is the angle between the main lobe directions of the two radar antennas. This angle is acute and ranges from 0 to π / 2; ε is a preset value, which is a small positive number, such as 0.01 radians, about 0.57 degrees. The setting of ε is used to avoid the case where the denominator is 0.

[0048] In this embodiment, spatial interference weights are determined by comprehensively considering the distance between radar pairs, the angle between the antenna main lobe directions, and the antenna gain parameters. This allows for precise quantification of the potential interference intensity between two radars under their spatial geometric relationship. This enables subsequent waveform parameter scheduling to prioritize radar pairs with high interference potential, thereby effectively reducing the probability of co-channel interference.

[0049] In one embodiment, before determining the spatial interference weight of the radar pair based on the distance between the two radars in the radar pair, the angle between the main lobe directions of the two radar antennas, and the antenna gain parameters of the two radars, the method further includes: For any radar pair, determine the antenna direction angle of each radar in the radar pair in the global coordinate system. The antenna direction angle is the sum of the heading angle of the vehicle to which the radar belongs and the antenna installation angle of the radar. Determine the first absolute value of the difference in the antenna azimuth angles of the two radars in the radar pair; Determine the second value obtained by subtracting the first absolute value from the first value; The smaller of the first absolute value and the second value is determined as the angle between the main lobe directions of the two radar antennas in the radar alignment.

[0050] In this embodiment, the antenna heading angle of each radar in the global coordinate system can be determined by the sum of the heading angle of the vehicle to which the radar belongs and the antenna mounting angle of the radar. The first value is π.

[0051] For example, taking radar i and radar j as examples, the antenna azimuth angle of the main lobe of radar i in the global coordinate system. It can be represented as:

[0052] in, α is the heading angle of the vehicle to which radar i belongs, i.e., the angle between the vehicle's direction of travel and the reference direction, with a value ranging from 0 to 360 degrees; i The antenna mounting angle of radar i is the angle between the main lobe direction of the radar antenna and the direction of the vehicle's front. The left-side radar of the vehicle usually has a negative value, while the right-side radar of the vehicle usually has a positive value.

[0053] Similarly, the azimuth angle of the main lobe of radar j in the global coordinate system It can be represented as:

[0054] After obtaining the antenna direction angles of radar j and radar i, the absolute value of the difference between the two antenna direction angles can be calculated, i.e., the first absolute value. Then, determine the second value obtained by subtracting the first absolute value from the first value π. Finally, the smaller of the first absolute value and the second numerical value is determined as the angle between the main lobe directions of the two radar antennas. ,Right now:

[0055] The above operation of taking the minimum value is to ensure that the resulting included angle is acute. This is because the antenna direction is bidirectional, and two antennas with a direction angle difference of 180 degrees are actually firing at each other. Therefore, it is necessary to map the angle to the range of 0 to π / 2 using the above method.

[0056] In this embodiment, the antenna direction angle of each radar is obtained by adding the heading angle and the antenna installation angle, and the smaller of the absolute value of the difference between the antenna direction angles of two radars and the second value obtained by subtracting the first absolute value from the first value is taken as the included angle. This allows for the accurate calculation of the minimum included angle between the main lobes of the two antennas, thereby improving the accuracy of spatial interference weight assessment.

[0057] In one embodiment, the target model is a target function, and the total interference conflict of all radar pairs in the target area is the sum of the products of the spatial interference weights of each radar pair and the corresponding indication functions; The indicator function is used to characterize whether the waveform parameters of the two radars in each radar pair meet the conflict conditions.

[0058] In this embodiment, the optimization objective of the above objective function is to minimize the total interference conflict of all radar pairs. The total interference conflict is the sum of the products of the spatial interference weight and the indication function of each radar pair. That is, the radar pair with a larger spatial interference weight will contribute more to the total interference conflict if its waveform parameters meet the conflict condition; the radar pair with a smaller spatial interference weight will contribute relatively less to the total interference conflict even if its waveform parameters meet the conflict condition, and this can be tolerated during the optimization process.

[0059] The aforementioned indication function is used to indicate whether the waveform parameters of two radars in a radar pair meet the collision condition. For example, the indication function between radar j and radar i can be expressed as I(p i , p j ), where I(p i , p j The value of ) depends on whether the waveform parameters assigned to the two radars will cause waveform conflict. In this embodiment, the indication function I(p) i , p j )=1 indicates that when radar i and radar j simultaneously use waveform parameter p i and p j When transmitting FMCW signals, the signals from both will interfere with each other at the receiving end; I(p i , p j )=0 indicates that the signals of the two will not interfere with each other.

[0060] Therefore, in some embodiments, the above objective function can be expressed as:

[0061] Among them, w ij I(p) represents the spatial interference weights for radar at (i,j); i , p j ) is an indicator function used to indicate the waveform parameters p assigned to radar i. iWith the waveform parameters p assigned to radar j j Does the conflict condition satisfy? If the conflict condition is satisfied, I(p) i , p j )=1, otherwise I(p) i , p j )=0. This indicates that all radar pairs in the target area are traversed.

[0062] In this embodiment, the objective function uses the waveform parameters of each radar to be assigned as optimization variables. These waveform parameters may include center frequency and modulation slope, etc. The optimization objective is to find a set of waveform parameter assignment schemes that minimizes the value of the objective function.

[0063] In this embodiment, the objective function described above considers not only the interference of individual radar pairs but also seeks the optimal waveform parameter allocation scheme from the perspective of the entire radar network in the target area. Compared to the method of each radar independently and randomly selecting waveform parameters, this global optimization method can make full use of spectrum resources and allocate non-conflicting waveform parameters to as many radar pairs as possible from a limited set of candidate waveform parameters, thereby effectively reducing the overall interference level in densely trafficked scenarios.

[0064] In one embodiment, the waveform parameters include the center frequency and the frequency modulation slope. For any two radars in a radar pair, the conflict conditions include: The absolute value of the difference between the center frequencies of the two radars in the radar pair is less than a first threshold. The product of the frequency modulation slopes of the two radars in the radar pair is greater than 0. The ratio of the smaller of the second difference in the radar pair and the absolute values ​​of the frequency modulation slopes of the two radars is less than a second threshold, wherein the second difference is the absolute value of the difference in the frequency modulation slopes of the two radars in the radar pair.

[0065] In this embodiment, the waveform parameters include the center frequency and the frequency modulation slope. For any two radars in a radar pair, the collision condition includes the simultaneous satisfaction of the following three sub-conditions: Sub-condition 1: The absolute value of the difference between the center frequencies of the two radars in the radar pair is less than the first threshold.

[0066] Specifically, the first sub-condition mentioned above is used to determine whether the center frequencies of the two radars are sufficiently close. When the center frequencies of the two radars are very close, their FMCW signals will overlap significantly in the frequency domain, making them prone to mutual interference at the receiver. The first threshold mentioned above is a preset protection bandwidth. For example, the first threshold can be set to 50MHz.

[0067] Subcondition 2: The product of the frequency modulation slopes of the two radars in the radar pair is greater than 0.

[0068] Specifically, sub-condition two above is used to determine whether the frequency modulation directions of the two radars are the same. The frequency modulation slope of the FMCW signal can be positive or negative. When the product of the frequency modulation slopes of the two radars is greater than 0, it indicates that the frequency modulation directions of the two radars are the same. At this time, their signals have the same trend in the time-frequency domain, making it easier for them to overlap in the intermediate frequency output at the receiving end, thus causing interference. When the product of the frequency modulation slopes of the two radars is less than 0, it indicates that the frequency modulation directions of the two radars are opposite. Although there may still be some interference, the degree of interference is usually lighter, and it can be considered that the conflict condition is not met.

[0069] Sub-condition 3: The ratio of the smaller of the second difference between the radar alignment and the absolute values ​​of the frequency modulation slopes of the two radars is less than the second threshold.

[0070] Specifically, sub-condition three above is used to further determine whether the frequency modulation slopes of the two radars are sufficiently close. The second difference is the absolute value of the difference between the frequency modulation slopes of the two radars. The second threshold is a preset slope similarity threshold, which can be set to, for example, 0.1, 0.2, or other values. When the ratio of the smaller of the second difference and the absolute values ​​of the frequency modulation slopes of the two radars is less than the second threshold, the frequency modulation slopes of the two radars are considered too close.

[0071] In this embodiment, the indicator function takes a value of 1 only when all three sub-conditions mentioned above are met simultaneously, indicating a conflict in the waveform parameters of the two radars; otherwise, the indicator function takes a value of 0. Specifically, this can be expressed by the following formula:

[0072] Among them, f i f represents the center frequency of radar i. j This represents the center frequency of radar j. The first threshold, which can be determined by the protection bandwidth, can be set to 50 MHz. The first threshold must be greater than half the bandwidth of the radar intermediate frequency filter. i k represents the frequency modulation slope of radar i. j This represents the frequency modulation slope of radar j. The second threshold can be the relative difference threshold of the frequency modulation slope, and the second threshold can be 0.1.

[0073] In this embodiment, three conditions are simultaneously defined: the center frequencies are close, the frequency modulation slopes have the same sign, and the slopes are similar in magnitude, before it is determined that the radars meet the conflict conditions. This allows more radars to operate simultaneously without interference under limited spectrum resources, thereby improving spectrum utilization.

[0074] In one embodiment, determining the target waveform parameters of each radar in the target region based on the target model includes: Obtain a waveform parameter set, wherein the waveform parameter set includes multiple candidate waveform parameters; Based on the objective function, candidate waveform parameters corresponding to each radar are determined from the set of waveform parameters; The candidate waveform parameters that satisfy the optimization objective are determined as the target waveform parameters for each radar.

[0075] In this embodiment, the aforementioned waveform parameter set can be predefined, including all available candidate waveform parameters. Each candidate waveform parameter can include a combination of a center frequency and a frequency modulation slope. For example, F candidate center frequency values ​​and K candidate frequency modulation slope values ​​can be defined, resulting in a total of M = F × K candidate waveform parameters in the waveform parameter set. For instance, the candidate center frequency values ​​can be selected from the range of 76-81 GHz with a fixed step size, resulting in F = 101 candidate frequency values.

[0076] In this embodiment, the above-mentioned determination of candidate waveform parameters corresponding to each radar from the waveform parameter set according to the objective function can be achieved by traversal search or heuristic algorithm.

[0077] In some embodiments, a greedy allocation algorithm can be used to solve the objective function. Specifically, waveform parameters can be allocated to radars in each radar pair in descending order of spatial interference weight. For each radar pair corresponding to an edge, priority is given to allocating waveform parameters that do not conflict with those of radars that have already been allocated waveform parameters to radars that have not yet been allocated waveform parameters. Furthermore, during the allocation process for each radar, if there is at least one candidate waveform parameter in the waveform parameter set that does not meet the conflict condition with the waveform parameters currently allocated to all its neighboring radars with allocated waveform parameters, then one of the at least one candidate waveform parameter is selected as the target waveform parameter for that radar, where the neighboring radars are other radars that have an edge with that radar; if all candidate waveform parameters in the waveform parameter set meet a preset conflict condition with the waveform parameters of at least one neighboring radar with allocated waveform parameters, then the candidate waveform parameter with the smallest sum of conflict weights with all neighboring radars is selected as the target waveform parameter for that radar. This strategy has low computational complexity and is suitable for scenarios with high real-time requirements.

[0078] In some embodiments, a distributed negotiation algorithm can be used to solve the objective function. Specifically, when the electronic device is a cooperative management unit, and the cooperative management units exist in a distributed manner among the vehicles, each radar broadcasts its own bidding information through vehicle-to-vehicle communication. This bidding information includes the candidate waveform parameters and corresponding costs of each radar, and each vehicle receives the bidding information from its neighboring radars. Each radar recalculates the cost of each candidate waveform parameter based on its own spatial interference weight with its neighboring radars and the received bidding information, and selects the waveform parameter with the lowest cost as the new round of bidding. The above broadcasting and updating steps are repeated until the bids of all radars no longer change and a consensus is reached, resulting in the final waveform parameter allocation scheme. The waveform parameters corresponding to each radar in the final waveform allocation scheme are the target waveform parameters. This solution method does not rely on a centralized cooperative management unit and is suitable for scenarios without infrastructure deployment such as roadside units.

[0079] In some embodiments, a hybrid hierarchical scheduling algorithm can also be used to solve the objective function. Specifically, the target area can be divided into multiple sub-regions according to geographical location, and a centralized or distributed scheduling algorithm can be run independently within each sub-region. For radars located near the boundaries of sub-regions, their waveform parameter allocation can be adjusted through boundary negotiation to reduce cross-sub-region interference. This method for solving the objective function is suitable for large-scale scenarios with a large number of vehicles.

[0080] In this embodiment, determining the candidate waveform parameters corresponding to the optimization target as the target waveform parameters of each radar can be achieved by using the allocation scheme that minimizes the target function value obtained by the solution algorithm as the final result.

[0081] In this embodiment, by selecting candidate parameters that meet the optimization objective from a predefined set of waveform parameters for each radar, the limited center frequency and modulation slope resources can be optimally allocated, thereby minimizing global interference.

[0082] In one embodiment, after sending the target waveform parameters corresponding to the radar on each vehicle to each vehicle in the target area, the method further includes: The update period of the target waveform parameters is determined based on the average speed of vehicles in the target area and / or the scene type corresponding to the target area. The update cycle is sent to each vehicle in the target area, wherein the update cycle is used to instruct each vehicle to report vehicle status information to trigger the re-determination of the target waveform parameters.

[0083] In this embodiment, the dynamic update mechanism of the waveform parameters is related to the vehicle's motion state. Since the vehicle in the target area may be constantly moving, the originally optimized target waveform parameters may become no longer optimal over time, so it is necessary to periodically re-determine the target waveform parameters.

[0084] In some embodiments, the update period for the target waveform parameters can be determined based on the average speed of vehicles in the target area. When the average speed of vehicles is high, the relative positions between vehicles change rapidly, and the effective time of the waveform parameter allocation scheme is short, thus requiring a shorter update period. When the average speed of vehicles is low, the relative positions between vehicles change slowly, and the effective time of the waveform parameter allocation scheme is long, thus allowing for a longer update period. The aforementioned average speed can be the average of the speeds of all vehicles in the target area.

[0085] In some embodiments, the update period of the target waveform parameters can be determined based on the scene type corresponding to the target area. Different application scenarios have different vehicle motion characteristics and interference modes, and different update periods can be pre-configured for different scenarios. For example, the update period for a parking lot scenario can be set to a longer period because vehicles in a parking lot are usually moving at low speeds or stationary, and the parking lot environment is relatively stable; the update period for a highway toll station scenario can be set to a medium period because vehicles at toll stations will experience deceleration, queuing, and acceleration; the update period for a congested urban road scenario can be set to a shorter period because vehicles in congested road sections frequently start, stop, and change lanes.

[0086] As some alternative embodiments, in low-speed or stationary scenarios such as parking lots and toll stations, the update period of the target waveform parameters can be determined to be 5-10 seconds; in slow-moving scenarios such as congested road sections, the update period of the target waveform parameters can be determined to be 1-2 seconds. Alternatively, the update period can be determined based on the average speed of vehicles in the target area using the following formula. To calculate the update period of the target waveform parameters:

[0087] in, Indicates the update cycle. This is a preset fixed value, which can be set to 100m. This represents the average speed of vehicles in the target area. For a pre-set fixed value, it can be .

[0088] It should be noted that, when calculating the update cycle based on the above formula, the smaller of the value calculated according to the formula and the preset cycle upper limit can be determined as the final update cycle. For example, the cycle upper limit can be 10 seconds to avoid the calculated update cycle being too long when the average speed of vehicles in the target area is low or even zero.

[0089] In this embodiment, the aforementioned update cycle can be calculated by the collaborative management unit and then distributed to each vehicle. Each vehicle periodically reports its vehicle status information according to the received update cycle. After receiving the vehicle status information, the collaborative management unit re-executes the aforementioned parameter scheduling process and distributes the updated target waveform parameters to each vehicle.

[0090] In this embodiment, through a dynamic update mechanism, the waveform parameter allocation scheme can be adjusted in a timely manner when the vehicle's motion state changes, ensuring the continuity and stability of the interference suppression effect. This is particularly important for scenarios where the vehicle is moving at high speed or frequently enters and exits the target area, effectively avoiding the problem of parameter allocation scheme failure caused by changes in vehicle position.

[0091] Furthermore, for non-cooperative vehicles that are not connected to the network or participate in this method, electronic devices cannot actively schedule their waveform parameters using the aforementioned method. To address this, a small portion of the available waveform parameter set can be reserved as a common frequency band for non-cooperative vehicles to randomly occupy. Cooperative vehicles participating in the scheduling will actively avoid these frequency bands during scheduling, thereby reducing the probability of interference. When interference from non-cooperative vehicles exceeds a threshold, a temporary switch to random frequency hopping mode can be implemented, resuming cooperative scheduling once the interference disappears.

[0092] 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.

[0093] See Figure 2 The radar parameter scheduling device 200 provided in this application embodiment may include: The first acquisition module 201 is used to acquire vehicle status information sent by multiple vehicles in the target area; The first determining module 202 is used to determine at least one radar pair in the target area and the spatial interference weight of each radar pair based on the vehicle status information of the plurality of vehicles, wherein each vehicle includes at least one radar and each radar pair includes two radars that have the possibility of interfering with each other. The construction module 203 is used to construct a target model based on the spatial interference weights of each radar pair, wherein the target model aims to minimize the total interference conflict of all radar pairs in the target area. The second determining module 204 is used to determine the target waveform parameters of each radar in the target area based on the target model; The transmitting module 205 is used to transmit the target waveform parameters corresponding to the radar on each vehicle to each vehicle in the target area, wherein the target waveform parameters are used by each vehicle to adjust the transmission waveform of its radar.

[0094] In this embodiment, vehicle status information of each vehicle within the target area can be obtained first. Based on the vehicle status information, radar pairs with potential interference and their spatial interference weights can be determined. Then, an optimization model is constructed with the goal of minimizing the total interference conflict of all radar pairs. By solving this target model, optimal target waveform parameters are assigned to each radar and distributed to each vehicle for execution. This application, through global collaborative scheduling, allocates low-interference waveform parameters to radars that are prone to spatial interference from the source, which can effectively reduce the probability of co-channel interference between radars in dense traffic scenarios.

[0095] In one embodiment, the first determining module 202 includes: The construction unit is used to construct an interference conflict graph based on the vehicle status information of the multiple vehicles. Each node in the interference conflict graph represents a radar, and the radars corresponding to the two endpoints of each edge in the interference conflict graph constitute a radar pair.

[0096] In one embodiment, the first determining module 202 further includes: The first determining unit is used to determine the spatial interference weight of the radar pair for any given radar pair based on the distance between the two radars in the radar pair, the angle between the main lobe directions of the two radar antennas, and the antenna gain parameters of the two radars.

[0097] In one embodiment, the radar parameter scheduling device 200 further includes: The third determining module is used to determine the antenna direction angle of each radar in the radar pair in the global coordinate system for any radar pair. The antenna direction angle is the sum of the heading angle of the vehicle to which the radar belongs and the antenna installation angle of the radar. The fourth determining module is used to determine the first absolute value of the difference in the antenna azimuth angles of the two radars in the radar pair; The fifth determining module is used to determine the second value obtained by subtracting the first absolute value from the first value; The sixth determining module is used to determine the smaller of the first absolute value and the second value as the angle between the main lobe directions of the two radar antennas in the radar alignment.

[0098] In one embodiment, the second determining module 204 further includes: The first acquisition unit is used to acquire a waveform parameter set, wherein the waveform parameter set includes multiple candidate waveform parameters; The second determining unit is used to determine the candidate waveform parameters corresponding to each radar from the waveform parameter set according to the objective function; The third determining unit is used to determine the candidate waveform parameters that satisfy the optimization objective as the target waveform parameters of each radar.

[0099] In one embodiment, the radar parameter scheduling device 200 further includes: The seventh determining module is used to determine the update period of the target waveform parameters based on the average speed of vehicles in the target area and / or the scene type corresponding to the target area; The sending module 205 is used to send the update cycle to each vehicle in the target area, wherein the update cycle is used to instruct each vehicle to report vehicle status information to trigger the re-determination of the target waveform parameters.

[0100] The radar parameter scheduling device provided in this application embodiment can achieve Figure 1 The various processes of the radar parameter scheduling method in the method embodiment will not be described again here to avoid repetition.

[0101] Figure 3 A schematic diagram of the hardware structure of the electronic device provided in an embodiment of this application is shown.

[0102] The electronic device may include a processor 401 and a memory 402 storing computer program instructions.

[0103] 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.

[0104] 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 an electronic device. In a particular embodiment, memory 402 is a non-volatile solid-state memory.

[0105] 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-readable 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 methods according to one aspect of this disclosure.

[0106] The processor 401 reads and executes computer program instructions stored in the memory 402 to implement any of the radar parameter scheduling methods in the above embodiments.

[0107] In one example, the electronic device may also include a communication interface 403 and a bus 410. For example, Figure 3 As shown, the processor 401, memory 402, and communication interface 403 are connected through bus 410 and complete communication with each other.

[0108] The communication interface 403 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0109] Bus 410 includes hardware, software, or both, that couples components of an electronic device 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.

[0110] Furthermore, in conjunction with the radar parameter scheduling 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 radar parameter scheduling methods in the above embodiments.

[0111] This application embodiment may also provide a computer program product, wherein when the instructions in the computer program product are executed by the processor of an electronic device, the electronic device performs any of the radar parameter scheduling methods described in the above embodiments.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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 parameter scheduling method for radar, characterized in that, include: Obtain vehicle status information sent by multiple vehicles in the target area; At least one radar pair in the target area is determined based on the vehicle status information of the plurality of vehicles, and the spatial interference weight of each radar pair is determined, wherein each vehicle includes at least one radar, and each radar pair includes two radars that have the possibility of interfering with each other. A target model is constructed based on the spatial interference weights of each radar pair, wherein the target model aims to minimize the total interference conflict of all radar pairs in the target region. Based on the target model, determine the target waveform parameters of each radar in the target area; The target waveform parameters corresponding to the radar on each vehicle are sent to each vehicle in the target area, wherein the target waveform parameters are used by each vehicle to adjust the transmission waveform of its radar.

2. The radar parameter scheduling method according to claim 1, characterized in that, Determining at least one radar pair in the target area based on the vehicle status information of the plurality of vehicles includes: An interference conflict graph is constructed based on the vehicle status information of the multiple vehicles. Each node in the interference conflict graph represents a radar, and the radars corresponding to the two endpoints of each edge in the interference conflict graph constitute a radar pair.

3. The radar parameter scheduling method according to claim 1, characterized in that, The step of determining the spatial interference weight of each radar pair in the target area based on the vehicle status information of the plurality of vehicles includes: For any given radar pair, the spatial interference weight of the radar pair is determined based on the distance between the two radars in the radar pair, the angle between the main lobe directions of the two radar antennas, and the antenna gain parameters of the two radars.

4. The radar parameter scheduling method according to claim 3, characterized in that, Before determining the spatial interference weight of the radar pair based on the distance between the two radars in the radar pair, the angle between the main lobe directions of the two radar antennas, and the antenna gain parameters of the two radars, the method further includes: For any radar pair, determine the antenna direction angle of each radar in the radar pair in the global coordinate system. The antenna direction angle is the sum of the heading angle of the vehicle to which the radar belongs and the antenna installation angle of the radar. Determine the first absolute value of the difference in the antenna azimuth angles of the two radars in the radar pair; Determine the second value obtained by subtracting the first absolute value from the first value; The smaller of the first absolute value and the second value is determined as the angle between the main lobe directions of the two radar antennas in the radar alignment.

5. The radar parameter scheduling method according to claim 1, characterized in that, The target model is a target function, and the total interference conflict of all radar pairs in the target area is the sum of the products of the spatial interference weights of each radar pair and the corresponding indication functions. The indicator function is used to characterize whether the waveform parameters of the two radars in each radar pair meet the conflict conditions.

6. The radar parameter scheduling method according to claim 5, characterized in that, The waveform parameters include the center frequency and the frequency modulation slope. For any two radars in a radar pair, the conflict conditions include: The absolute value of the difference between the center frequencies of the two radars in the radar pair is less than a first threshold. The product of the frequency modulation slopes of the two radars in the radar pair is greater than 0. The ratio of the smaller of the second difference in the radar pair and the absolute values ​​of the frequency modulation slopes of the two radars is less than a second threshold, wherein the second difference is the absolute value of the difference in the frequency modulation slopes of the two radars in the radar pair.

7. The radar parameter scheduling method according to claim 5, characterized in that, The step of determining the target waveform parameters of each radar in the target area based on the target model includes: Obtain a waveform parameter set, wherein the waveform parameter set includes multiple candidate waveform parameters; Based on the objective function, candidate waveform parameters corresponding to each radar are determined from the set of waveform parameters; The candidate waveform parameters that satisfy the optimization objective are determined as the target waveform parameters for each radar.

8. The radar parameter scheduling method according to claim 1, characterized in that, After sending the target waveform parameters corresponding to the radar on each vehicle to each vehicle in the target area, the method further includes: The update period of the target waveform parameters is determined based on the average speed of vehicles in the target area and / or the scene type corresponding to the target area. The update cycle is sent to each vehicle in the target area, wherein the update cycle is used to instruct each vehicle to report vehicle status information to trigger the re-determination of the target waveform parameters.

9. A parameter scheduling device for radar, characterized in that, The device includes: The first acquisition module is used to acquire vehicle status information sent by multiple vehicles in the target area; The first determining module is used to determine at least one radar pair in the target area and the spatial interference weight of each radar pair based on the vehicle status information of the plurality of vehicles, wherein each vehicle includes at least one radar and each radar pair includes two radars that have the possibility of interfering with each other. A construction module is used to construct a target model based on the spatial interference weights of each radar pair, wherein the target model aims to minimize the total interference conflict of all radar pairs in the target region. The second determining module is used to determine the target waveform parameters of each radar in the target area based on the target model. The transmitting module is used to transmit the target waveform parameters corresponding to the radar on each vehicle to each vehicle in the target area, wherein the target waveform parameters are used by each vehicle to adjust the transmission waveform of its radar.

10. An electronic device, characterized in that, The electronic device includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, it implements the radar parameter scheduling method as described in any one of claims 1-8.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions, which, when executed by a processor, implement the radar parameter scheduling method as described in any one of claims 1-8.

12. A computer program product, characterized in that, When the instructions in the computer program product are executed by the processor of the electronic device, the electronic device causes the electronic device to perform the radar parameter scheduling method as described in any one of claims 1-8.

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