Method, device and equipment for inhibiting ultrasonic wave same-frequency interference and storage medium

Abstract

The application discloses an ultrasonic wave same-frequency interference suppression method and device, equipment and a storage medium, and relates to the technical field of intelligent driving. The method comprises the following steps: acquiring single echo data and parking distance control data of an ultrasonic wave radar; performing filtering processing on the parking distance control data and the single echo data according to a preset filtering strategy to obtain updated echo data; and performing parking control according to the updated echo data. The method solves the problem of how to suppress same-frequency interference without changing the position of an ultrasonic probe of a vehicle and the time sequence mechanism of transmitting and receiving waves, and improves the stability and reliability of parking.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent driving, and particularly relates to an ultrasonic wave same-frequency interference suppression method and device, equipment and a storage medium. BACKGROUND

[0002] In a parking scenario, an ultrasonic wave radar, as an important component of a vehicle automatic parking system, undertakes key functions such as detecting a surrounding environment and providing a collision warning. However, in actual application, when multiple vehicles simultaneously start the ultrasonic wave radar, due to the similarity of the radar working frequency, the ultrasonic wave signals between different vehicles are prone to same-frequency interference, which causes the received signals to be mixed, and further affects the accurate judgment of the surrounding environment by the parking system, increasing the safety hazard in the parking process. In the existing method, in order to solve this problem, measures such as adjusting the radar receiving and transmitting timing, changing the radar working frequency or increasing physical isolation are usually adopted. However, these methods often involve hardware modification or modification of the manufacturer's source code, which not only has high implementation cost, but also is difficult to be universal in different brands of ultrasonic wave radar products.

[0003] Therefore, how to suppress the same-frequency interference without changing the ultrasonic probe position of the vehicle and the receiving and transmitting timing mechanism is a problem to be solved at present. SUMMARY

[0004] The main purpose of the present application is to provide an ultrasonic wave same-frequency interference suppression method, device, equipment and storage medium, aiming at solving the technical problem of how to suppress the same-frequency interference without changing the ultrasonic probe position of the vehicle and the receiving and transmitting timing mechanism.

[0005] To achieve the above-mentioned purpose, the present application provides an ultrasonic wave same-frequency interference suppression method, which comprises the following steps:

[0006] Obtaining single echo data and parking distance control data of an ultrasonic wave radar;

[0007] According to a preset filtering strategy, the parking distance control data and the single echo data are filtered to obtain updated echo data;

[0008] According to the updated echo data, parking control is performed to suppress ultrasonic wave same-frequency interference.

[0009] In an embodiment, the step of filtering the parking distance control data and the single echo data according to the preset filtering strategy to obtain the updated echo data comprises:

[0010] Initializing a state transition matrix, a prediction system covariance, a process covariance noise, a measurement matrix and an observation noise covariance based on a preset filtering strategy;

[0011] obtaining predicted echo data and predicted error covariance from at least two of the state transition matrix, the predicted system covariance, the process covariance noise, and the single echo data;

[0012] performing filter update to obtain updated echo data from the measurement matrix, the observation noise covariance, the predicted echo data, the predicted error covariance, and the parking distance control data.

[0013] In an embodiment, after the step of initializing the state transition matrix, the predicted system covariance, the process covariance noise, the measurement matrix, and the observation noise covariance based on the preset filter strategy, the method further comprises:

[0014] obtaining an adjustment ratio;

[0015] adjusting the order of magnitude of the observation noise covariance and the process covariance noise according to the adjustment ratio.

[0016] In an embodiment, the step of obtaining predicted echo data and predicted error covariance from at least two of the state transition matrix, the predicted system covariance, the process covariance noise, and the single echo data comprises:

[0017] obtaining predicted echo data from the state transition matrix and the single echo data;

[0018] obtaining predicted error covariance from the state transition matrix, the predicted system covariance, and the process covariance noise.

[0019] In an embodiment, the step of performing filter update to obtain updated echo data from the measurement matrix, the observation noise covariance, the predicted echo data, the predicted error covariance, and the parking distance control data comprises:

[0020] obtaining historical parking distance control data;

[0021] obtaining filter gain from the measurement matrix, the observation noise covariance, and the predicted error covariance;

[0022] obtaining updated echo data from the predicted echo data, the filter gain, the parking distance control data, the historical parking distance control data, and the measurement matrix.

[0023] In an embodiment, the step of obtaining updated echo data from the predicted echo data, the filter gain, the parking distance control data, the historical parking distance control data, and the measurement matrix comprises:

[0024] when the parking distance control data is updated, calculating updated echo data according to the parking distance control data, the predicted echo data, the filter gain and the measurement matrix;

[0025] when the parking distance control data is not updated, replacing the parking distance control data in the updating process with the historical parking distance control data, and calculating the updated echo data according to the parking distance control data, the predicted echo data, the filter gain and the measurement matrix.

[0026] In an embodiment, after the step of obtaining the filter gain according to the measurement matrix, the observation noise covariance and the prediction error covariance, the method further comprises:

[0027] updating a prediction system covariance according to the filter gain, the measurement matrix and the prediction error covariance to obtain an updated prediction system covariance;

[0028] updating the filter gain according to the updated prediction system covariance and the observation noise covariance.

[0029] In addition, to achieve the above object, the application further provides an ultrasonic wave same-frequency interference suppression device, which comprises:

[0030] a data acquisition module, configured to acquire single-echo data and parking distance control data of an ultrasonic wave radar;

[0031] a filter updating module, configured to perform filter processing on the parking distance control data and the single-echo data according to a preset filter strategy to obtain updated echo data;

[0032] a parking control module, configured to perform parking control according to the updated echo data to suppress ultrasonic wave same-frequency interference.

[0033] In addition, to achieve the above object, the application further provides an ultrasonic wave same-frequency interference suppression device, which comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the computer program is configured to implement the steps of the ultrasonic wave same-frequency interference suppression method as described above.

[0034] In addition, to achieve the above object, the application further provides a storage medium, which is a computer readable storage medium, and the storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the ultrasonic wave same-frequency interference suppression method as described above.

[0035] In addition, to achieve the above object, the application further provides a computer program product, which comprises a computer program, and the computer program, when executed by a processor, implements the steps of the method for suppressing ultrasonic wave same-frequency interference as described above.

[0036] The application provides a method for suppressing ultrasonic wave same-frequency interference. The application obtains single echo data of an ultrasonic wave radar and parking distance control data, filters the parking distance control data and the single echo data according to a preset filtering strategy to obtain updated echo data, and performs parking control according to the updated echo data to suppress ultrasonic wave same-frequency interference. As can be seen, the application performs real-time processing on single echo data of an ultrasonic wave radar, filters and updates the data in combination with data of a parking distance control system as a true value, thereby accurately extracting effective signals, solving the problem of how to suppress same-frequency interference without changing the position of an ultrasonic probe of a vehicle and the mechanism of a wave transmitting and receiving timing sequence, and improving the stability and reliability of a parking system. BRIEF DESCRIPTION OF DRAWINGS

[0037] The accompanying drawings, which are incorporated into and form a part of the specification, illustrate an embodiment consistent with the application and, together with the description, serve to explain the principles of the application.

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, for those skilled in the art, other drawings can also be obtained based on these drawings without any creative work.

[0039] Figure 1 A flowchart is provided for the first embodiment of the method for suppressing ultrasonic wave same-frequency interference of the application.

[0040] Figure 2 A flowchart is provided for the second embodiment of the method for suppressing ultrasonic wave same-frequency interference of the application.

[0041] Figure 3 A flowchart is provided for the third embodiment of the method for suppressing ultrasonic wave same-frequency interference of the application.

[0042] Figure 4 A module structure diagram is provided for the device for suppressing ultrasonic wave same-frequency interference of the embodiments of the application.

[0043] Figure 5 A device structure diagram is provided for the hardware running environment involved in the method for suppressing ultrasonic wave same-frequency interference of the embodiments of the application.

[0044] The object implementation, functional features and advantages of the application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0045] It should be understood that the specific embodiments described herein are merely exemplary of the application and do not limit the application.

[0046] In order to better understand the technical solutions of the present application, the following will be described in detail in combination with the drawings of the specification and specific embodiments.

[0047] The main solution of the embodiment of the present application is: obtaining single echo data of an ultrasonic radar and parking distance control data; filtering the parking distance control data and the single echo data according to a preset filtering strategy to obtain updated echo data; and performing parking control according to the updated echo data to suppress ultrasonic wave same frequency interference.

[0048] In the parking scenario, the ultrasonic radar, as an important part of the vehicle automatic parking system, undertakes the key functions of detecting the surrounding environment and providing collision warning. However, in actual application, when multiple vehicles simultaneously start the ultrasonic radar, due to the similarity of the radar working frequency, the ultrasonic signals between different vehicles are prone to same frequency interference, resulting in mixed signals received, and further affecting the accurate judgment of the surrounding environment by the parking system, increasing the safety hazard in the parking process. In the existing method, in order to solve this problem, measures such as adjusting the radar transmission and reception timing, changing the radar working frequency or increasing physical isolation are usually adopted. However, these methods often involve hardware modification or modification of the manufacturer's source code, which not only has high implementation cost, but also is difficult to be universal in different brands of ultrasonic radar products. Therefore, how to suppress the same frequency interference without changing the position of the ultrasonic probe of the vehicle and the transmission and reception timing mechanism is a problem that needs to be solved at present.

[0049] The present application solves the problem of how to suppress the same frequency interference without changing the position of the ultrasonic probe of the vehicle and the transmission and reception timing mechanism by real-time processing of the single echo data of the ultrasonic radar and filtering update combined with the data of the parking distance control system as the true value, thereby accurately extracting the effective signal, improving the stability and reliability of the parking system.

[0050] It should be noted that the execution subject of the present embodiment can be an ultrasonic wave same frequency interference suppression system, or a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above-mentioned ultrasonic wave same frequency interference suppression function, etc. The present embodiment does not specifically limit this. The following will be described taking the ultrasonic wave same frequency interference suppression system as an example.

[0051] Based on this, the embodiment of the present application provides an ultrasonic wave same frequency interference suppression method, which is described with reference toFigure 1 Figure 1 Figure 1 is a flowchart of the method for suppressing ultrasonic wave same-frequency interference according to the first embodiment of the present application.

[0052] In this embodiment, the method for suppressing ultrasonic wave same-frequency interference comprises steps S10-S30:

[0053] Step S10: Obtain single-echo data and parking distance control data of the ultrasonic wave radar.

[0054] It should be noted that the single-echo data refers to the first reflection signal directly received by the ultrasonic wave radar, which can reflect the existence of the obstacle, but will have a large error when there is same-frequency interference. The parking distance control data is more accurate, but cannot be directly used for real-time obstacle detection due to its low update frequency.

[0055] In addition, it should be noted that in this step, the system first obtains the single-echo data in real time through the ultrasonic wave radar on the vehicle. The single-echo data is the first reflection signal received after the ultrasonic wave radar transmits a signal, and its period is generally 40 ms, containing the distance information between the obstacle and the vehicle. At the same time, the PDC (Parking Distance Control) system also provides data in real time, which is a more accurate parking distance calculated by other sensors of the vehicle, and its update period is generally 100-200 ms.

[0056] Step S20: Filter the parking distance control data and the single-echo data according to a preset filtering strategy to obtain updated echo data.

[0057] It should be noted that the updated echo data refers to the obstacle distance information after Kalman filtering, which combines the advantages of the single-echo data and the parking distance control data, and can more accurately reflect the actual situation of the vehicle and the surrounding environment. In this step, the system will use Kalman filter as the preset filtering strategy. Kalman filter is a kind of efficient recursive filter (autoregressive filter), which can estimate the state of a dynamic system from a series of measurements containing statistical noise. Specifically, the system will initialize the parameters required for Kalman filtering (such as state transition matrix, predicted system covariance, covariance noise, observation noise covariance, etc.), and predict the obstacle distance at the current time according to these parameters. Then, the parking distance control data is used as the true value to update the obstacle distance at the current time (i.e. the observation value) using Kalman gain and measurement matrix to obtain the updated echo data.

[0058] ​It can be understood that this process is cyclic, and each frame of data is filtered to obtain more accurate obstacle distance information. This method effectively fuses single echo data and parking distance control data, and updates the echo data in real time through the prediction and update mechanism of Kalman filtering to suppress the error caused by the same frequency interference.

[0059] Step S30: performing parking control according to the updated echo data to suppress ultrasonic wave same frequency interference.

[0060] It should be noted that in this step, the updated echo data processed in step S20 will be used for parking control. These data have been optimized by Kalman filtering and can more accurately reflect the distance between the vehicle and the surrounding obstacles. Therefore, during parking, the system can control the speed and direction of the vehicle according to these data to ensure safe parking.

[0061] The embodiment provides a method for suppressing ultrasonic wave same frequency interference. The embodiment obtains single echo data of an ultrasonic radar and parking distance control data; performs filtering processing on the parking distance control data and the single echo data according to a preset filtering strategy to obtain updated echo data; and performs parking control according to the updated echo data to suppress ultrasonic wave same frequency interference. As can be known from the above, the embodiment performs real-time processing on the single echo data of the ultrasonic radar, and combines the data of the parking distance control system as a true value to perform filtering update, so that effective signals are accurately extracted, the problem of how to suppress same frequency interference without changing the position of the ultrasonic probe of the vehicle and the mechanism of the transmission and reception wave timing is solved, and the stability and reliability of the parking system are improved.

[0062] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as the above-mentioned first embodiment can be referred to the above introduction, and the following will not be repeated. On this basis, please refer to Figure 2 , Figure 2 is a flowchart of the second embodiment of the method for suppressing ultrasonic wave same frequency interference of the present application. The step S20 further comprises:

[0063] Step S201: initializing a state transition matrix, a prediction system covariance, a process covariance noise, a measurement matrix and an observation noise covariance based on a preset filtering strategy.

[0064] It should be noted that the system will initialize the parameters required for Kalman filtering. Specifically, the state transition matrix (F) is initialized, that is, the state transition matrix is set to 1, indicating that the distance prediction value in the current filtering period is directly equal to the observed value after the last filtering (i.e. the single echo distance observed by the sensor). The prediction system covariance (P) is initialized, that is, the prediction system covariance is set to a small positive number (such as 1), representing the uncertainty or error at the initial time of the system. This value will gradually adjust during the filtering process to more accurately reflect the current confidence of the system. The process covariance noise (Q) is initialized, which reflects the random noise in the state transition process. In this step, it can be assumed that the single echo data is relatively stable, and the initial value of Q needs to be set according to the characteristics of the system and experience, which is usually a small positive number, but in actual application it may need to be adjusted according to the specific situation to reduce the influence of uncertainty on the prediction of system state. The measurement matrix (H) is also initialized to 1 in this step, indicating that the observed value (parking distance control data) is directly used to update the system state. This is because the parking distance control data as the true value of the parking distance control has high precision and reliability. The observation noise covariance (R) is initialized, which reflects the difference between the observed value (parking distance control data PDC) and the true value. Similarly, its initial value also needs to be set according to the characteristics of the system and experience to balance the degree of trust in the observed value and the prediction of the system state.

[0065] In a possible implementation, after the step S201, the method further includes:

[0066] Step A10: Obtain an adjustment ratio.

[0067] It should be noted that the adjustment ratio is a dynamic adjustment parameter, which can be adjusted in real time according to the actual application scene and the specific situation in the parking process. In this step, the system will determine a suitable adjustment ratio by analyzing historical data or experimental tests, which is used to adjust the order of magnitude relationship between the observation noise covariance R and the process covariance noise Q. The choice of the adjustment ratio depends on the requirements of the system for real-time performance and accuracy, as well as the understanding of the noise characteristics. It can be understood that the setting of the adjustment ratio is crucial to the performance of the Kalman filtering algorithm. A proper adjustment ratio can make the algorithm maintain high real-time performance while reducing errors caused by noise interference, thereby improving the accuracy of parking distance control.

[0068] Step A20: Adjust the order of magnitude of the observation noise covariance and the process covariance noise according to the adjustment ratio.

[0069] It should be noted that after the adjustment ratio is determined, the system will adjust the order of magnitude of R and Q according to the adjustment ratio. Specifically, the initial values of R and Q are scaled according to the preset adjustment ratio to ensure the balance between the accuracy and smoothness of the output result in the filtering process. For example, in the automatic parking process, when the system detects that the co-channel interference is relatively serious, the order of magnitude of R can be appropriately reduced (i.e., the value is increased), while the order of magnitude of Q is maintained or appropriately reduced (i.e., the value is not increased or is slightly increased), so as to increase the trust degree of the algorithm to the PDC true value, thereby effectively restraining the co-channel interference.

[0070] It can be understood that by adjusting the order of magnitude of R and Q, the trust degree of the Kalman filtering algorithm to the PDC true value and the DE observation value can be affected. When R is small, the algorithm tends to believe the PDC true value; and when Q is small, the algorithm tends to rely on the DE observation value. Through reasonable adjustment, the algorithm can still maintain high accuracy and real-time performance when facing co-channel interference.

[0071] Step S202: obtaining predicted echo data and predicted error covariance according to at least two of the state transition matrix, the predicted system covariance, the process covariance noise, and the single-echo data.

[0072] It should be noted that the predicted echo data is obtained based on the current state of the system and the prediction of the future, and the predicted error covariance is a quantitative evaluation of the accuracy of such prediction. Specifically, in each filtering period, the system will first predict the DE distance of the current period (i.e., the predicted echo data) according to the single-echo data DE and the state transition matrix F, and calculate the uncertainty of the prediction (i.e., the predicted error covariance P_predict).

[0073] In a feasible implementation, the step S202 specifically includes:

[0074] Step B10: obtaining predicted echo data according to the state transition matrix and the single-echo data.

[0075] It should be noted that in this step, the single-echo data DE refers to the obstacle distance directly measured by the ultrasonic radar, which is the original data without filtering processing. The predicted echo data DE_predict refers to the predicted value of the obstacle distance at the current time calculated by the state transition matrix F and the optimal estimation value of the last time.

[0076] Additionally, it is noted that in this step, the system will use the pre-set state transition matrix F (1 in this example, indicating that the distance prediction value is equal to the last sensor observation value) and the single-echo data (i.e. DE data, representing the result of the first direct reflection of the obstacle distance measured by the ultrasonic radar) to calculate the predicted echo data (denoted as DE_predict), as shown in Equation 1:

[0077] DE predict = F * DE (Equation 1)

[0078] It can be understood that the purpose of this step is to use the last optimal estimation result and the state transition matrix to predict the obstacle distance at the current time.

[0079] Step B20: obtaining a prediction error covariance according to the state transition matrix, the prediction system covariance, and the process covariance noise.

[0080] It is noted that in this step, the system will use the state transition matrix F, the prediction system covariance P (representing the uncertainty of the system state prediction), and the process covariance noise Q (representing the error of the process model itself) to calculate the prediction error covariance P_predict, as shown in Equation 2:

[0081] P predict = F * P * F T + Q (Equation 2)

[0082] It can be understood that the purpose of this step is to evaluate the uncertainty of the current prediction, providing a basis for subsequent filtering update using new observation data (PDC value).

[0083] Step S203: performing filtering update to obtain updated echo data according to the measurement matrix, the observation noise covariance, the predicted echo data, the prediction error covariance, and the parking distance control data.

[0084] It is noted that specifically, the system will use the measurement matrix H, the observation noise covariance R, and the prediction error covariance P_predict to calculate the Kalman gain K at the current time. The size of the K value reflects the influence degree of the observation value (parking distance control data, i.e. PDC data) on the system state update. At the same time, the system will use the PDC data as the true value, use the Kalman gain K and the measurement matrix H to update the predicted echo data, and obtain the optimal estimation value at the current time (i.e. the updated echo data DE).

[0085] In this embodiment, the Kalman filtering method is used to effectively suppress the same frequency interference of ultrasonic radar in the parking scene, reduce the possibility of misjudgment and false report, and improve the accuracy and reliability of the parking system. At the same time, since this method only needs to deploy the algorithm at the software level without modifying the hardware and manufacturer's source code, it has low implementation cost and high universality.

[0086] Based on the first and second embodiments of the present application, in the third embodiment of the present application, the same or similar contents as the above embodiments one and two can be referred to the above introduction, and will not be described in detail. On this basis, please refer to Figure 3 , Figure 3 The flowchart of the third embodiment of the method for suppressing ultrasonic wave same frequency interference of the present application is shown in the figure. The step S203 specifically comprises:

[0087] Step C10: Obtain historical parking distance control data.

[0088] It should be noted that the historical parking distance control data refers to the actual distance information between the vehicle and the obstacle recorded by the PDC system in a certain period of time in the past. These data are dynamically changing and constantly updated with the movement of the vehicle position and the change of the obstacle. It can be understood that obtaining the historical parking distance control data (PDC value) is an important step in the filtering update process. These data are usually provided by the vehicle's parking assistance system in real time, which are used as a reference for the true distance to calibrate and correct the one-way echo (DE) data of the ultrasonic radar. Specifically, the system reads the PDC value of the last time or multiple times from the database of the parking assistance system. These values represent the distance of the obstacle perceived by the vehicle at a specific time point, which have high accuracy and reliability.

[0089] Step C20: Obtain the filtering gain according to the measurement matrix, the observation noise covariance and the prediction error covariance.

[0090] It should be noted that the filtering gain, i.e. Kalman filtering gain, can be adaptively adjusted according to the dynamic changes of the observation noise and the prediction error to achieve the optimal filtering effect.

[0091] In addition, it should be noted that in this step, the system will calculate the filtering gain, i.e. Kalman filtering gain K, at the current time according to the set measurement matrix H (usually set to 1 here, indicating that the DE value is directly related to the PDC value) and the current observation noise covariance R (indicating the noise level in the PDC value measurement), combined with the prediction error covariance P_predict (indicating the uncertainty of DE value prediction), through the Kalman filtering formula, as shown in formula 3:

[0092] K=P predict *HT (H predict H T +R) -1 (Formula 3)

[0093] It can be understood that the Kalman filter gain K is used to determine the relative weight between the observation value (PDC value) and the predicted value (DE value) in the updating process, so as to optimize the updated DE value.

[0094] In a possible implementation, after the step C20, the method further includes:

[0095] Step D10: updating the predicted system covariance according to the filter gain, the measurement matrix and the predicted error covariance to obtain an updated predicted system covariance.

[0096] It should be noted that in this step, the system updates the predicted system covariance by comprehensively considering the previous predicted error covariance (P_predict), the current observation noise covariance (R) and the filter gain (K), so as to reflect the uncertainty of the current system state. Specifically, the system calculates the updated predicted system covariance P according to the updating equation of the Kalman filter, using the filter gain K, the measurement matrix H and the observation noise covariance R, as shown in Formula 4:

[0097] P = (1-K*H)*P predict (Formula 4)

[0098] Step D20: updating the filter gain according to the updated predicted system covariance and the observation noise covariance.

[0099] It should be noted that in this step, the system updates the filter gain according to the updated predicted system covariance P and the observation noise covariance R, and the updating process is consistent with Formula 3 in step C20, which will not be repeated here. It can be understood that the updated predicted system covariance can affect the calculation of the filter gain and the updating of the state estimation in the subsequent steps.

[0100] Step C30: obtaining updated echo data according to the predicted echo data, the filter gain, the parking distance control data, the historical parking distance control data and the measurement matrix.

[0101] It should be noted that in this step, the updated echo data (i.e. the updated DE value, DE_update) is obtained by applying the Kalman filter gain K to the predicted echo data and the parking distance control data. Specifically, the system will use the Kalman filter gain K to adjust the predicted echo data (i.e. the DE value predicted value at the previous time, DE_predict) and then update it in combination with the current parking distance control data (PDC value). In the updating process, the historical parking distance control data is also referred to to ensure the continuity and stability of the data, as shown in formula 5:

[0102] DE updata = DE predict + K * (PDC - H * DE predict ) (Formula 5)

[0103] In one possible implementation, the step C30 specifically comprises:

[0104] Step C301: When the parking distance control data is updated, the updated echo data is calculated according to the parking distance control data, the predicted echo data, the filter gain and the measurement matrix.

[0105] It should be noted that in this step, when the parking distance control (PDC) data is updated, it indicates that the system has obtained new and more accurate obstacle distance information. At this time, the system will update the single echo (DE) data of the ultrasonic radar using the Kalman filter algorithm to ensure that in the presence of same frequency interference, the actual distance of the obstacle can still be accurately reflected. For example, in the automatic parking process, the vehicle is gradually approaching an obstacle. At a certain time, the PDC system detects that the obstacle is 1.5 meters away from the vehicle and updates the PDC data. At this time, the system obtains the DE data of the previous frame after filtering processing as 1.6 meters (there is a slight same frequency interference). By calculating the Kalman gain K and combining the PDC true value, the system obtains the updated DE distance as 1.51 meters, which is closer to the actual distance, effectively suppressing the same frequency interference.

[0106] It can be understood that this step fuses the PDC true value and the DE predicted value through the Kalman filter algorithm, effectively reduces the influence of the same frequency interference on the DE data, and improves the accuracy and real-time performance of the obstacle distance measurement.

[0107] Step C302: When the parking distance control data is not updated, the parking distance control data in the updating process is replaced according to the historical parking distance control data, and the updated echo data is calculated according to the parking distance control data, the predicted echo data, the filter gain and the measurement matrix.

[0108] It should be noted that when the PDC data is not updated, it indicates that there is no new obstacle distance information available at present. In order to ensure the continuity and stability of the system, the system will use the PDC data of the last frame (i.e. historical parking distance control data) as a substitute to continue the update processing of the DE data (echo data).

[0109] In the embodiment, by collecting the past parking distance control data (i.e. PDC data) as a reference and performing real-time processing and updating of the DE data according to the Kalman filtering algorithm, the interference of other same-frequency ultrasonic wave radar signals is effectively suppressed, and the environmental perception ability of the parking system is improved. At the same time, the PDC data is used as the true value for filtering and updating, which ensures the accuracy and real-time performance of the DE data, thereby improving the overall accuracy of the parking system.

[0110] The application also provides an ultrasonic wave same-frequency interference suppression device, which refers to Figure 4 , and the ultrasonic wave same-frequency interference suppression device comprises:

[0111] A data acquisition module 10 is configured to acquire single-echo data and parking distance control data of an ultrasonic wave radar.

[0112] A filtering and updating module 20 is configured to perform filtering processing on the parking distance control data and the single-echo data according to a preset filtering strategy to obtain updated echo data.

[0113] A parking control module 30 is configured to perform parking control according to the updated echo data to suppress ultrasonic wave same-frequency interference.

[0114] The ultrasonic wave same-frequency interference suppression device provided by the application adopts the ultrasonic wave same-frequency interference suppression method in the above embodiment, and can solve the technical problem of how to suppress same-frequency interference without changing the ultrasonic probe position of the vehicle and the transmission-reception wave timing mechanism. Compared with the prior art, the ultrasonic wave same-frequency interference suppression device provided by the application has the same beneficial effects as the ultrasonic wave same-frequency interference suppression method provided by the above embodiment, and other technical features in the ultrasonic wave same-frequency interference suppression device are the same as the features disclosed in the above embodiment method, which will not be described here.

[0115] In an embodiment, the filtering and updating module 20 is further configured to initialize a state transition matrix, a predicted system covariance, a process covariance noise, a measurement matrix and an observation noise covariance based on a preset filtering strategy; obtain predicted echo data and a predicted error covariance based on at least two of the state transition matrix, the predicted system covariance, the process covariance noise and the single-echo data; and perform filtering and updating based on the measurement matrix, the observation noise covariance, the predicted echo data, the predicted error covariance and the parking distance control data to obtain updated echo data.

[0116] In an embodiment, the data obtaining module 10 is further configured to obtain an adjustment ratio.

[0117] In an embodiment, the filter updating module 20 is further configured to adjust an order of magnitude of an observation noise covariance and a process covariance noise according to the adjustment ratio.

[0118] In an embodiment, the filter updating module 20 is further configured to obtain predicted echo data according to the state transition matrix and the single-echo data; and obtain a predicted error covariance according to the state transition matrix, the predicted system covariance and the process covariance noise.

[0119] In an embodiment, the data obtaining module 10 is further configured to obtain historical parking distance control data.

[0120] In an embodiment, the filter updating module 20 is further configured to obtain a filter gain according to the measurement matrix, the observation noise covariance and the predicted error covariance; and obtain updated echo data according to the predicted echo data, the filter gain, the parking distance control data, the historical parking distance control data and the measurement matrix.

[0121] In an embodiment, the filter updating module 20 is further configured to calculate the updated echo data according to the parking distance control data, the predicted echo data, the filter gain and the measurement matrix when the parking distance control data is updated; and replace the parking distance control data in the updating process with the historical parking distance control data when the parking distance control data is not updated, and calculate the updated echo data according to the parking distance control data, the predicted echo data, the filter gain and the measurement matrix.

[0122] In an embodiment, the filter updating module 20 is further configured to update the predicted system covariance according to the filter gain, the measurement matrix and the predicted error covariance to obtain updated predicted system covariance; and update the filter gain according to the updated predicted system covariance and the observation noise covariance.

[0123] The application provides an ultrasonic wave same-frequency interference suppression device, which comprises at least one processor and a memory connected with the at least one processor in communication; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the ultrasonic wave same-frequency interference suppression method in the above embodiment one.

[0124] Reference will be made to the following description of embodiments Figure 5This document illustrates a structural schematic diagram of a device suitable for implementing the embodiments of this application to suppress ultrasonic co-channel interference. The device for suppressing ultrasonic co-channel interference in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The illustrated device for suppressing ultrasonic co-frequency interference is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this application.

[0125] like Figure 5 As shown, the device for suppressing ultrasonic co-channel interference may include a processing unit 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the device for suppressing ultrasonic co-channel interference. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the ultrasonic co-channel interference suppression device to communicate wirelessly or wiredly with other devices to exchange data. Although ultrasonic co-channel interference suppression devices with various systems are shown in the figures, it should be understood that it is not required to implement or possess all of the systems shown. More or fewer systems can be implemented alternatively.

[0126] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer readable medium, the computer program containing program code for executing the method shown in the flowchart. In such embodiments, the computer program can be downloaded and installed from a network through a communication device, or installed from the storage device 1003, or installed from the ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiments disclosed in the present application are executed.

[0127] The ultrasonic wave same frequency interference suppression device provided by the present application adopts the ultrasonic wave same frequency interference suppression method in the above embodiments, and can solve the technical problem of how to suppress same frequency interference without changing the position of the vehicle ultrasonic probe and the transceiving wave timing mechanism. Compared with the prior art, the ultrasonic wave same frequency interference suppression device provided by the present application has the same beneficial effects as the ultrasonic wave same frequency interference suppression method provided by the above embodiments, and other technical features in the ultrasonic wave same frequency interference suppression device are the same as the features disclosed in the previous embodiment method, which will not be repeated here.

[0128] It should be understood that parts of the present application can be realized by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0129] The above is merely specific implementation of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0130] The present application provides a computer readable storage medium having stored thereon computer readable program instructions (i.e. computer program) for executing the ultrasonic wave same frequency interference suppression method in the above embodiments.

[0131] The computer readable storage medium provided in the application may, for example, be a U disk, but is not limited to an electric, magnetic, optical, electromagnetic, infrared, or semiconductor system, system, or device, or any combination of the above. More specific examples of the computer readable storage medium may include, but are not limited to, an electric connection with one or more conductive wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the embodiment, the computer readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system, or device. The program code contained on the computer readable storage medium can be transmitted by any suitable medium, including but not limited to an electric wire, an optical cable, an RF (Radio Frequency), and the like, or any suitable combination of the above.

[0132] The computer readable storage medium described above may be contained in the ultrasonic wave same frequency interference suppression device, or may exist independently without being assembled into the ultrasonic wave same frequency interference suppression device.

[0133] The computer readable storage medium described above carries one or more programs, and when the one or more programs are executed by the ultrasonic wave same frequency interference suppression device, the ultrasonic wave same frequency interference suppression device is caused to: acquire single echo data of an ultrasonic wave radar and parking distance control data; perform filtering processing on the parking distance control data and the single echo data according to a preset filtering strategy to obtain updated echo data; and perform parking control according to the updated echo data to suppress ultrasonic wave same frequency interference.

[0134] Computer program code for carrying out operations of the present application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0135] The flow diagrams and the block diagrams in the drawings are meant as methodological and functional description of implementations of systems, methods, and computer program products according to various embodiments of the present application. In this regard, each block in the flow diagrams or block diagrams can represent a module, a segment, or a portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently or the blocks may

[0136] The modules involved in the embodiments of the present application can be implemented in software or hardware. In some cases, the names of the modules do not limit the modules themselves.

[0137] The readable storage medium provided by the present application is a computer readable storage medium, which stores computer readable program instructions (i.e. computer program) for executing the above-mentioned method for suppressing ultrasonic wave same frequency interference. The computer readable program instructions can solve the technical problem of how to suppress same frequency interference without changing the position of the ultrasonic probe of the vehicle and the mechanism of the transmission and reception wave timing. Compared with the prior art, the computer readable storage medium provided by the present application has the same beneficial effects as the method for suppressing ultrasonic wave same frequency interference provided by the above-mentioned embodiments, and will not be described here.

[0138] The application also provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of the method for suppressing ultrasonic wave co-frequency interference as described above.

[0139] The computer program product provided by the application can solve the technical problem of how to suppress co-frequency interference without changing the position of the ultrasonic probe of the vehicle and the mechanism of the transmission and reception wave timing. Compared with the prior art, the beneficial effects of the computer program product provided by the application are the same as those of the method for suppressing ultrasonic wave co-frequency interference provided by the above-mentioned embodiments, and will not be repeated here.

[0140] The above only describes some embodiments of the application, and does not limit the patent scope of the application. Any equivalent structural transformation, direct / indirect application in other related technical fields, or direct / indirect application in other related technical fields based on the technical concept of the application and the content of the specification and drawings are included in the patent protection scope of the application.

Claims

1. A method of suppressing ultrasonic co-channel interference, comprising: The method comprises: acquiring single echo data and parking distance control data of an ultrasonic radar; initializing a state transition matrix, a predicted system covariance, a process covariance noise, a measurement matrix and an observation noise covariance based on a preset filtering strategy; obtaining predicted echo data and a predicted error covariance from at least two of the state transition matrix, the predicted system covariance, the process covariance noise and the single echo data; performing filtering update based on the measurement matrix, the observation noise covariance, the predicted echo data, the predicted error covariance and the parking distance control data to obtain updated echo data; performing parking control based on the updated echo data to suppress ultrasonic same-frequency interference.

2. The method of claim 1, wherein, After the step of initializing the state transition matrix, the predicted system covariance, the process covariance noise, the measurement matrix and the observation noise covariance based on the preset filtering strategy, the method further comprises: acquiring an adjustment ratio; adjusting the order of magnitude of the observation noise covariance and the process covariance noise according to the adjustment ratio.

3. The method of claim 1, wherein, The step of obtaining the predicted echo data and the predicted error covariance from at least two of the state transition matrix, the predicted system covariance, the process covariance noise and the single echo data comprises: obtaining the predicted echo data from the state transition matrix and the single echo data; obtaining the predicted error covariance from the state transition matrix, the predicted system covariance and the process covariance noise.

4. The method of claim 1, wherein, The step of performing filtering update based on the measurement matrix, the observation noise covariance, the predicted echo data, the predicted error covariance and the parking distance control data to obtain the updated echo data comprises: acquiring historical parking distance control data; obtaining a filtering gain based on the measurement matrix, the observation noise covariance and the predicted error covariance; obtaining the updated echo data based on the predicted echo data, the filtering gain, the parking distance control data, the historical parking distance control data and the measurement matrix.

5. The method of claim 4, wherein, The step of obtaining the updated echo data based on the predicted echo data, the filtering gain, the parking distance control data, the historical parking distance control data and the measurement matrix comprises: when the parking distance control data is updated, calculating the updated echo data based on the parking distance control data, the predicted echo data, the filtering gain and the measurement matrix; when the parking distance control data is not updated, replacing the parking distance control data in the updating process with the historical parking distance control data, and calculating the updated echo data based on the parking distance control data, the predicted echo data, the filtering gain and the measurement matrix.

6. The method of claim 4, wherein, After the step of obtaining the filtering gain based on the measurement matrix, the observation noise covariance and the predicted error covariance, the method further comprises: updating the predicted system covariance based on the filtering gain, the measurement matrix and the predicted error covariance to obtain updated predicted system covariance; updating the filtering gain based on the updated predicted system covariance and the observation noise covariance.

7. A device for suppressing ultrasonic co-frequency interference, characterized in that, The device comprises: The data acquisition module is configured to acquire single echo data and parking distance control data of the ultrasonic radar. The filter updating module is configured to initialize a state transition matrix, a predicted system covariance, a process covariance noise, a measurement matrix and an observation noise covariance based on a preset filter strategy; obtain predicted echo data and a predicted error covariance based on at least two of the state transition matrix, the predicted system covariance, the process covariance noise and the single echo data; and perform filter updating based on the measurement matrix, the observation noise covariance, the predicted echo data, the predicted error covariance and the parking distance control data to obtain updated echo data. The parking control module is configured to perform parking control based on the updated echo data to suppress ultrasonic same-frequency interference.

8. A device for suppressing ultrasonic co-frequency interference, characterized in that, The device comprises a memory, a processor and a computer program stored on the memory and executable on the processor, and the computer program is configured to implement the steps of the method for suppressing ultrasonic same-frequency interference according to any one of claims 1 to 6.

9. A storage medium, characterized by The storage medium is a computer readable storage medium, and the storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the method for suppressing ultrasonic same-frequency interference according to any one of claims 1 to 6.

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