Radar ranging methods, devices, electronic equipment and computer-readable storage media

Abstract

The application provides a radar ranging method and device, electronic equipment and computer readable storage medium, relates to the technical field of radar, and the method comprises the following steps: obtaining a plurality of measurement data sets of at least one echo signal in a measurement period based on a plurality of threshold values; wherein the echo signal is based on a threshold value sampling to obtain a measurement data set, and each measurement data set contains at least one measurement data; determining M candidate data and N auxiliary data in the plurality of measurement data sets, obtaining the total complexity of each candidate data; wherein M and N are positive integers, and the sum of M and N is equal to the total number of measurement data contained in the plurality of measurement data sets; determining target data from the M candidate data according to the total complexity; and determining the measurement distance according to the target data. The application can improve the dynamic range of the laser radar system, thereby improving the measurement accuracy of the laser radar and shortening the near distance detection blind area of the laser radar.

Description

Technical Field

[0001] This application relates to the field of radar technology, and more specifically, to a radar ranging method, apparatus, electronic device, and computer-readable storage medium in the field of radar technology. Background Technology

[0002] The dynamic range of a lidar system is a crucial indicator of its measurement accuracy. It must prevent over-saturation at close range while ensuring sufficient detection of echo signals at long range. When echo saturation occurs at close range, it not only degrades ranging accuracy but also prevents the detection of effective target echoes at close range, thus increasing the lidar's close-range blind zone. Therefore, improving lidar measurement accuracy and reducing its close-range detection blind zone is a pressing issue that needs to be addressed. Summary of the Invention

[0003] This application provides a radar ranging method, apparatus, electronic device, and computer-readable storage medium. The method can improve the dynamic range of a lidar system, thereby improving the measurement accuracy of the lidar and shortening the close-range detection blind zone of the lidar.

[0004] In a first aspect, a radar ranging method is provided, comprising: acquiring multiple measurement datasets obtained by sampling at least one echo signal within a measurement period based on multiple thresholds; wherein the echo signal is sampled based on one of the thresholds to obtain one measurement dataset, and each measurement dataset contains at least one measurement data; determining M candidate data and N auxiliary data from the multiple measurement datasets, and obtaining the total composite degree of each candidate data; wherein M and N are positive integers, and the sum of M and N is equal to the total number of measurement data contained in the multiple measurement datasets; determining target data from the M candidate data according to the total composite degree; and determining the measurement distance according to the target data.

[0005] In conjunction with the first aspect, in some possible implementations, the radar ranging method further includes: dividing the radar ranging range into X sub-distance intervals according to a preset rule; where X is a positive integer greater than 1; the step of determining M candidate data and N auxiliary data from the plurality of measurement datasets includes: determining M candidate data and N auxiliary data from the plurality of measurement datasets based on the measurement data sampled according to a preset threshold corresponding to the sub-distance intervals.

[0006] In conjunction with the first aspect and the above implementation, in some possible implementations, when transmitting an outgoing signal within the measurement period, the step of determining M candidate data and N auxiliary data from a plurality of measurement datasets based on the measurement data sampled according to the preset threshold corresponding to the sub-distance interval includes: using the measurement data sampled from the plurality of measurement datasets by the preset threshold corresponding to each of the sub-distance intervals as the candidate data to obtain M candidate data; and using the measurement data from the plurality of measurement datasets other than the M candidate data as the auxiliary data to obtain N auxiliary data.

[0007] In conjunction with the first aspect and the above implementation, in some possible implementations, when multiple outgoing signals are transmitted within the measurement period, and the transmission power of the multiple outgoing signals is different, the step of determining M candidate data and N auxiliary data from the multiple measurement datasets based on the measurement data sampled according to the preset threshold corresponding to the sub-distance interval includes: taking the echo signal corresponding to the outgoing signal with the preset transmission power in the multiple measurement datasets, and taking the measurement data sampled from the preset threshold corresponding to each of the sub-distance intervals as the candidate data, to obtain M candidate data; and taking the measurement data in the multiple measurement datasets other than the M candidate data as the auxiliary data, to obtain N auxiliary data.

[0008] In conjunction with the first aspect and the above implementation methods, in some possible implementation methods, obtaining the total composite degree of each of the candidate data includes: determining the sub-distance interval where each measurement data in the plurality of measurement datasets is located, so as to obtain the sub-distance intervals corresponding to the candidate data and the auxiliary data; determining the principal composite degree, auxiliary composite degree and cross composite degree of the candidate data Si according to the candidate data, the auxiliary data and the sub-distance intervals corresponding to the candidate data; wherein, the candidate data Si is any one of the M candidate data, 1≤i≤M and i is an integer; and performing a weighted calculation based on the principal composite degree, the auxiliary composite degree and the cross composite degree of the candidate data Si to obtain the total composite degree of the candidate data Si.

[0009] In combination with the first aspect and the above implementation, in some possible implementations, determining the principal composite degree of the candidate data Si includes: acquiring at least one candidate data that meets a preset requirement within multiple adjacent measurement periods, as a comparison dataset; wherein, meeting the preset requirement means that the emitted signal corresponding to the candidate data in the comparison dataset and the emitted signal corresponding to the candidate data Si in the current measurement period have the same emission power, and the threshold corresponding to the candidate data in the comparison dataset and the threshold corresponding to the candidate data Si in the current measurement period are the same; sequentially comparing the candidate data Si in the current measurement period with each candidate data in the comparison dataset; and obtaining the principal composite degree of the candidate data Si based on the comparison result.

[0010] In combination with the first aspect and the above implementation methods, in some possible implementation methods, obtaining the principal composite degree of the candidate data Si based on the comparison result includes: when the difference between a candidate data in the comparison dataset and the candidate data Si in the current measurement period is less than a first preset value, the principal composite degree of the candidate data Si is incremented by 1; each candidate data in the comparison dataset is traversed to obtain the principal composite degree of the candidate data Si; or, when the principal composite degree of the candidate data Si is greater than or equal to the principal composite degree threshold, the principal composite degree of the candidate data Si is obtained as the principal composite degree threshold.

[0011] In combination with the first aspect and the above implementation methods, in some possible implementation methods, determining the auxiliary composite degree of the candidate data Si includes: sequentially comparing the candidate data Si with N auxiliary data; and obtaining the auxiliary composite degree of the candidate data Si based on the comparison results.

[0012] In combination with the first aspect and the above implementation methods, in some possible implementation methods, obtaining the auxiliary composite degree of the candidate data Si based on the comparison result includes: when the difference between one of the N auxiliary data and the candidate data Si is less than a second preset value, the auxiliary composite degree of the candidate data is incremented by 1; traversing each of the N auxiliary data to obtain the auxiliary composite degree of the candidate data Si; or, when the auxiliary composite degree of the candidate data Si is greater than or equal to the auxiliary composite degree threshold, the auxiliary composite degree of the candidate data Si is obtained as the auxiliary composite degree threshold.

[0013] In combination with the first aspect and the above implementation methods, in some possible implementation methods, determining the cross-compoundness of the candidate data Si includes: determining the sub-distance interval where the candidate data Si is located as the intermediate sub-distance interval, obtaining at least one of the K sub-distance intervals other than the intermediate sub-distance interval as the target sub-distance interval, where K≤X, and K is a positive integer; sequentially comparing the candidate data Si with the candidate data in at least one of the target sub-distance intervals; and obtaining the cross-compoundness of the candidate data Si based on the comparison results.

[0014] In combination with the first aspect and the above implementation methods, in some possible implementation methods, obtaining the cross-complexity of the candidate data Si based on the comparison result includes: when the difference between a candidate data in at least one of the target sub-distance intervals and the candidate data Si is less than a third preset value, the cross-complexity of the candidate data is incremented by 1; traversing the candidate data in at least one of the target sub-distance intervals to obtain the cross-complexity of the candidate data Si; or, when the cross-complexity of the candidate data Si is greater than or equal to the cross-complexity threshold, the cross-complexity of the candidate data Si is obtained as the cross-complexity threshold.

[0015] In combination with the first aspect and the above implementation methods, in some possible implementation methods, determining the target data from the M candidate data based on the total composite degree includes:

[0016] The target data is determined from the candidate data with the highest total composite degree among the M candidate data.

[0017] In conjunction with the first aspect and the above implementation methods, in some possible implementation methods, determining the target data from the candidate data with the highest total composite degree among the M candidate data includes: when the candidate data with the highest total composite degree among the M candidate data is a single data, the candidate data with the highest total composite degree is determined as the target data; when there are multiple candidate data with the highest total composite degree among the M candidate data, the candidate data with the highest total composite degree and the largest distance value is determined as the target data.

[0018] In combination with the first aspect and the above implementation methods, in some possible implementation methods, determining the measurement distance based on the target data includes: obtaining the endpoint values ​​of the sub-distance interval and constructing a smooth interval centered on the endpoint values; determining whether the target data is located within the smooth interval; and determining the measurement distance based on the determination result.

[0019] In combination with the first aspect and the above implementation methods, in some possible implementation methods, determining the measurement distance based on the judgment result includes: when the target data is not within the smoothing interval, determining that the measurement distance is equal to the target data; when the target data is within the smoothing interval, determining that the sub-distance interval where the target data is located is the first sub-distance interval, and determining that the sub-distance interval adjacent to the first sub-distance interval within the smoothing interval is the second sub-distance interval; obtaining a first threshold corresponding to the first sub-distance interval and a second threshold corresponding to the second sub-distance interval; determining that the measurement data in the measurement dataset obtained based on the first threshold and located within the smoothing interval is the first distance, and the measurement data in the measurement dataset obtained based on the second threshold and located within the smoothing interval is the second distance; and performing a weighted calculation on the first distance and the second distance to obtain the measurement distance.

[0020] Secondly, a radar ranging device is provided, the radar ranging device comprising:

[0021] The data acquisition module is used to acquire multiple measurement datasets obtained by sampling at least one echo signal within a measurement period based on multiple thresholds; wherein, the echo signal is sampled based on one of the thresholds to obtain one measurement dataset, and each measurement dataset contains at least one measurement data.

[0022] The composite degree calculation module is used to determine M candidate data and N auxiliary data in multiple measurement datasets, and obtain the total composite degree of each candidate data; wherein M and N are positive integers, and the sum of M and N is equal to the total number of measurement data contained in the multiple measurement datasets;

[0023] The data selection module is used to determine the target data from the M candidate data based on the total composite degree;

[0024] The distance calculation module is used to determine the measurement distance based on the target data.

[0025] In conjunction with the second aspect, in some possible implementations, the radar ranging device further includes:

[0026] The interval division unit is used to divide the radar ranging range into X sub-range intervals according to a preset rule; where X is a positive integer greater than 1.

[0027] The composite degree calculation module is specifically used to determine M candidate data and N auxiliary data in the multiple measurement datasets by: determining M candidate data and N auxiliary data in the multiple measurement datasets based on the measurement data sampled according to the preset threshold corresponding to the sub-distance interval.

[0028] In conjunction with the second aspect and the above implementation methods, in some possible implementation methods, when transmitting an outgoing signal within the measurement period, the composite degree calculation module determines M candidate data and N auxiliary data from multiple measurement datasets based on the measurement data sampled according to the preset threshold corresponding to the sub-distance interval, including:

[0029] The first acquisition unit is used to take the measurement data obtained by sampling the preset threshold corresponding to each of the sub-distance intervals from the multiple measurement datasets as the candidate data, so as to obtain M candidate data.

[0030] The first filtering unit is used to select the measurement data from the multiple measurement datasets, excluding the M candidate data, as the auxiliary data to obtain N auxiliary data.

[0031] In conjunction with the second aspect and the above implementation, in some possible implementations, when multiple outgoing signals are emitted during the measurement period, and the transmission power of the multiple outgoing signals is different, the composite degree calculation module determines M candidate data and N auxiliary data from the multiple measurement datasets based on the measurement data sampled according to the preset threshold corresponding to the sub-distance interval, including:

[0032] The second acquisition unit is used to take the echo signal corresponding to the emitted signal with a preset transmission power in the multiple measurement data sets, and sample the measurement data obtained by sampling the preset threshold corresponding to each of the sub-distance intervals, as the candidate data, so as to obtain M candidate data.

[0033] The second filtering unit is used to select the measurement data from the multiple measurement datasets, excluding the M candidate data, as the auxiliary data to obtain N auxiliary data.

[0034] In combination with the second aspect and the above implementation methods, in some possible implementation methods, the composite degree calculation module, in obtaining the total composite degree of each of the candidate data, includes:

[0035] An interval determination unit is used to determine the sub-distance interval where each measurement data in the multiple measurement datasets is located, so as to obtain the sub-distance intervals corresponding to the candidate data and the auxiliary data;

[0036] The first calculation unit is used to determine the principal composite degree, auxiliary composite degree, and cross composite degree of the candidate data Si based on the candidate data, the auxiliary data, and the sub-distance interval corresponding to the candidate data; wherein the candidate data Si is any one of M candidate data, 1≤i≤M and i is an integer;

[0037] The second calculation unit is used to perform a weighted calculation based on the principal composite degree, the auxiliary composite degree, and the cross composite degree of the candidate data Si to obtain the total composite degree of the candidate data Si.

[0038] In combination with the second aspect and the above implementation methods, in some possible implementations, the first computing unit includes:

[0039] A dataset determination subunit is used to acquire at least one candidate data that meets preset requirements within multiple adjacent measurement cycles, as a comparison dataset; wherein, meeting the preset requirements means that the emitted signal corresponding to the candidate data in the comparison dataset and the emitted signal corresponding to the candidate data Si in the current measurement cycle have the same emission power, and the threshold corresponding to the candidate data in the comparison dataset and the threshold corresponding to the candidate data Si in the current measurement cycle are the same;

[0040] The first comparison subunit is used to sequentially compare the candidate data Si in the current measurement cycle with each candidate data in the comparison dataset;

[0041] The first determining subunit is used to obtain the principal composite degree of the candidate data Si based on the comparison results.

[0042] In conjunction with the second aspect and the above implementation methods, in some possible implementation methods, the first determining subunit is specifically used for: when the difference between a candidate data in the comparison dataset and the candidate data Si in the current measurement period is less than a first preset value, incrementing the principal composite degree of the candidate data Si by 1; traversing each candidate data in the comparison dataset to obtain the principal composite degree of the candidate data Si; or, when the principal composite degree of the candidate data Si is greater than or equal to the principal composite degree threshold, obtaining the principal composite degree of the candidate data Si as the principal composite degree threshold.

[0043] In conjunction with the second aspect and the above implementation methods, in some possible implementations, the first computing unit further includes:

[0044] The second comparison subunit is used to sequentially compare the candidate data Si with N auxiliary data;

[0045] The second determining subunit is used to obtain the auxiliary composite degree of the candidate data Si based on the comparison results.

[0046] In conjunction with the second aspect and the above implementation methods, in some possible implementation methods, the second determining subunit is specifically used for: when the difference between one of the N auxiliary data and the candidate data Si is less than a second preset value, the auxiliary composite degree of the candidate data is incremented by 1; traversing each of the N auxiliary data to obtain the auxiliary composite degree of the candidate data Si; or, when the auxiliary composite degree of the candidate data Si is greater than or equal to the auxiliary composite degree threshold, obtaining the auxiliary composite degree of the candidate data Si as the auxiliary composite degree threshold.

[0047] In conjunction with the second aspect and the above implementation methods, in some possible implementations, the first computing unit further includes:

[0048] The target interval determination sub-unit is used to determine that the sub-distance interval in which the candidate data Si is located is the middle sub-distance interval, and to obtain at least one of the K sub-distance intervals other than the middle sub-distance interval as the target sub-distance interval; where K≤X, and K is a positive integer;

[0049] The third comparison subunit is used to sequentially compare the candidate data Si with at least one candidate data within the target sub-distance interval;

[0050] The third determining subunit is used to obtain the cross-complexity of the candidate data Si based on the comparison results.

[0051] In conjunction with the second aspect and the above implementation methods, in some possible implementation methods, the third determining sub-unit is specifically used for: when the difference between a candidate data in at least one of the target sub-distance intervals and the candidate data Si is less than a third preset value, the cross-complexity of the candidate data is incremented by 1; traversing the candidate data in at least one of the target sub-distance intervals to obtain the cross-complexity of the candidate data Si; or, when the cross-complexity of the candidate data Si is greater than or equal to the cross-complexity threshold, obtaining the cross-complexity of the candidate data Si as the cross-complexity threshold.

[0052] In combination with the second aspect and the above implementation methods, in some possible implementation methods, the data selection module is specifically used to: determine the target data from the candidate data with the highest total composite degree among the M candidate data.

[0053] In conjunction with the second aspect and the above implementation methods, in some possible implementation methods, the data selection module determines the target data from the candidate data with the highest total composite degree among the M candidate data, including:

[0054] The first selection unit is used to determine the candidate data with the highest total composite degree among the M candidate data as the target data when the candidate data with the highest total composite degree is a single data.

[0055] The second selection unit is used to determine the candidate data with the highest total composite degree and the largest distance value as the target data when there are multiple candidate data with the highest total composite degree among the M candidate data.

[0056] In combination with the second aspect and the above implementation methods, in some possible implementations, the distance calculation module includes:

[0057] An interval construction unit is used to obtain the endpoint values ​​of the sub-distance interval and construct a smooth interval centered on the endpoint values;

[0058] A data judgment unit is used to determine whether the target data is located within the smoothing interval;

[0059] A distance determination unit is used to determine the measured distance based on the judgment result.

[0060] In combination with the second aspect and the above implementation methods, in some possible implementations, the distance determination unit includes:

[0061] The first distance determination subunit is used to determine that the measured distance is equal to the target data when the target data is not within the smoothing interval;

[0062] The second distance determination subunit is configured to, when the target data is within the smoothing interval, determine the sub-distance interval where the target data is located as the first sub-distance interval, and determine the sub-distance interval adjacent to the first sub-distance interval within the smoothing interval as the second sub-distance interval; obtain a first threshold corresponding to the first sub-distance interval and a second threshold corresponding to the second sub-distance interval; determine the measurement data in the measurement dataset sampled based on the first threshold that is located within the smoothing interval as the first distance, and the measurement data in the measurement dataset sampled based on the second threshold that is located within the smoothing interval as the second distance; and perform a weighted calculation on the first distance and the second distance to obtain the measurement distance.

[0063] Thirdly, an electronic device is provided, including a memory and a processor. The memory is used to store executable program code, and the processor is used to call and run the executable program code from the memory, causing the electronic device to perform the radar ranging method of the first aspect or any possible implementation thereof.

[0064] Fourthly, a computer program product is provided, comprising: computer program code, which, when run on a computer, causes the computer to execute the radar ranging method in the first aspect or any possible implementation thereof.

[0065] Fifthly, a computer-readable storage medium is provided, which stores computer program code that, when executed on a computer, causes the computer to perform the radar ranging method described in the first aspect or any possible implementation thereof.

[0066] The radar ranging method, apparatus, electronic device, and computer-readable storage medium provided in the embodiments of this application have the following technical effects:

[0067] This application embodiment employs a technical solution that uses multiple measurement datasets obtained from at least one echo signal within a measurement period and multiple threshold samplings to determine M candidate data and N auxiliary data from these datasets. It then obtains the total composite degree of each candidate data, determines the target data from the M candidate data based on the total composite degree, and determines the measurement distance based on the target data. This solution enables the overall control of the lidar during ranging by using multiple transmissions of signals with different transmission powers, multiple gains, multiple threshold samplings, and multiple analysis zones. Furthermore, it fuses the measurement data obtained from multiple transmissions of signals with different transmission powers, multiple gains, and multiple threshold samplings to obtain accurate measurement data. This not only improves the measurement accuracy of the lidar but also enhances the dynamic range of the lidar system and shortens the close-range detection blind zone of the lidar. Attached Figure Description

[0068] Figure 1 A schematic flowchart of a radar ranging method provided in an embodiment of this application is shown;

[0069] Figure 2 An exemplary framework diagram of the transmitting circuit is shown;

[0070] Figure 3 An exemplary framework diagram of the receiving circuit is shown;

[0071] Figure 4 An exemplary framework diagram of the receiving circuit is shown;

[0072] Figure 5 A schematic diagram of the lidar system framework is shown;

[0073] Figure 6 A schematic diagram of the ranging control process of a lidar is shown;

[0074] Figure 7An exemplary schematic diagram of the transmit and receive signals and timing of one measurement cycle of a lidar is shown;

[0075] Figure 8 A schematic diagram showing the division of the radar's ranging range is shown;

[0076] Figure 9 Another exemplary schematic diagram of the transmit and receive signals and timing of one measurement cycle of a lidar is shown;

[0077] Figure 10 An exemplary schematic diagram of a smoothing interval is shown;

[0078] Figure 11 This paper shows a data fusion block diagram of a radar ranging method provided in an embodiment of the present application;

[0079] Figure 12 This paper shows a schematic diagram of the structure of a radar ranging device provided in an embodiment of this application;

[0080] Figure 13 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown. Detailed Implementation

[0081] The technical solutions in this application will be clearly and thoroughly described below with reference to the accompanying drawings. In the description of the embodiments of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B. "And / or" in the text is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Furthermore, in the description of the embodiments of this application, "multiple" refers to two or more than two.

[0082] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.

[0083] The following is an embodiment of a radar ranging method provided in this application.

[0084] Figure 1 A schematic flowchart of a radar ranging method provided in an embodiment of this application is shown, as follows: Figure 1 As shown, the radar ranging method provided in this application embodiment is applied to a lidar, which includes a transmitting circuit and a receiving circuit. Figure 2 As shown, Figure 2A schematic diagram of an exemplary transmission circuit is shown. The transmission circuit includes units such as a controllable transmission power device, a transmission driver, an emitter, and a transceiver timing controller. In the transmission circuit, the transceiver timing controller generates power control signals with different transmission powers and sends them to the controllable transmission power device. The controllable transmission power device adjusts its transmission power according to the control signals and transmits the laser energy to the transmission driver. The transceiver timing controller also generates a transmission control signal and transmits it to the transmission driver. Upon receiving the transmission control signal, the transmission driver releases the laser energy to the emitter, which then emits the laser. The transceiver timing controller can control the timing and power of multiple transmissions.

[0085] like Figure 3 As shown, Figure 3 An exemplary schematic diagram of a receiving circuit is shown, which includes an optical receiving sensing unit, multiple gain circuits, and multiple comparator circuits. Each gain circuit corresponds to a different receiving gain; the high-gain circuit amplifies the echo signal with high gain, while the low-gain circuit amplifies the echo signal with low gain. For the output of the same set of gain circuits, different comparator circuits can be used to sample and process the echo signal, and then different measurement data can be obtained through the measurement unit. Figure 3 Two sets of gain circuits are shown as examples. In actual applications, more gain circuits can be added according to actual design requirements. Theoretically, the more groups of gain circuits there are, the more amplification stages are performed on the echo signal, and the larger the dynamic range of the entire system can be achieved. Figure 3 The diagram also shows two sets of comparison circuits 323 as examples. More comparison circuits can be added according to actual design requirements. The sampling thresholds of each set of comparison circuits can be equal or unequal. Theoretically, the more comparison circuits there are, the more data can be obtained, and the larger the dynamic range of the whole system can be achieved.

[0086] like Figure 4 As shown, Figure 4 An exemplary framework diagram of the receiving circuit is shown. Figure 4 The receiving circuit shown is a multi-stage serial receiving system. Using this multi-stage serial receiving circuit can also achieve multi-stage gain and possess the same characteristics as described above. Figure 3 It serves the same function as the receiving circuit. The multi-stage serial receiving circuit includes optical receiving sensing units (e.g., PIN, APD, SPAD, SiPM), multi-stage gain circuits, signal sampling modules, and measurement modules. Figure 4 The circuit shown is a 4-stage amplifier; the signal sampling module can use a comparator circuit to implement its sampling function.

[0087] like Figure 5 As shown, Figure 5The diagram illustrates the system framework of a lidar system, which includes a controller, a multi-level power transmitting circuit, a multi-level gain receiving circuit, and a multi-level detection threshold unit. Not all three units—the transmitting circuit, the receiving circuit, and the detection threshold unit—are required to have multi-level functionality. For example, the transmitting circuit could have a multi-level power output while the detection threshold unit could have a single level, or the receiving circuit could have a single level of gain; conversely, the transmitting circuit could have a single-level power output while the detection threshold unit has multiple levels, or the receiving circuit could have multiple levels of gain. For instance, in one measurement cycle, the transmitting circuit emits laser light three times, corresponding to three different transmission powers. The receiving circuit's amplification circuit has four levels of gain. Therefore, each emission will result in four different gain outcomes, leading to a total of 3*4=12 possible measurement combinations. If each receiving gain has three detection thresholds, then there are a total of 3*4*3=36 possible measurement combinations. Since these 36 measurement results are obtained from different power values, different receiving gains, and different detection threshold combinations, they can be used to cover both high-energy and low-energy echo reception and processing, thereby achieving a very large dynamic range extension.

[0088] like Figure 6 As shown, Figure 6 A schematic diagram of the ranging control process of a lidar is shown. Figure 6 Taking three transmissions as an example, multiple sets of measurement data can be obtained for each transmission. For instance, if the receiver has three gain levels for each transmission and samples the data, three sets of data can be obtained. Three transmissions will yield nine sets of measurement data. These nine sets of measurement data are then fused to obtain the measurement result. The data fusion process can select the most suitable result as the output based on signals such as distance and whether the echo energy is within the effective range.

[0089] The aforementioned radar ranging methods include the following schemes:

[0090] S110: Acquire at least one echo signal within the measurement period based on multiple measurement datasets obtained from multiple threshold sampling.

[0091] In one exemplary embodiment, a measurement cycle of the lidar includes at least one emitted signal and a corresponding echo signal; one emitted signal and one corresponding echo signal complete one detection, and a measurement distance is obtained by calculation. One echo signal can correspond to multiple threshold samples, and each threshold sample yields a corresponding measurement dataset. Here, the threshold can be understood as a reception threshold or a detection threshold.

[0092] When a lidar is used for ranging, the transmitting circuit will emit at least one outgoing signal in a time sequence during the measurement period. The outgoing signal can be understood as a laser signal. The receiving circuit will sample the echo signal corresponding to each outgoing signal based on multiple thresholds during the measurement period to obtain multiple measurement datasets.

[0093] like Figure 7 As shown, Figure 7 An exemplary schematic diagram of the transmit and receive signals and timing of one measurement cycle of a lidar is shown. Figure 7 The diagram illustrates that within one measurement cycle of a lidar system, two outgoing signals are emitted sequentially, and the receiving circuit receives two corresponding echo signals. The receiving circuit includes four different thresholds: threshold 1, threshold 2, threshold 3, and threshold 4. The two echo signals are sampled using each of the four different thresholds, resulting in eight measurement datasets per measurement cycle. Specifically, the echo signals corresponding to the first outgoing signal are sampled using the four thresholds to obtain four measurement datasets: Data_tx1_thr1, Data_tx1_thr2, Data_tx1_thr3, and Data_tx1_thr4. Similarly, the echo signals corresponding to the second outgoing signal are sampled using the same four thresholds to obtain four measurement datasets: Data_tx2_thr1, Data_tx2_thr2, Data_tx2_thr3, and Data_tx2_thr4.

[0094] For each echo signal, a measurement dataset is obtained based on a threshold sampling. When there are multiple threshold samplings, multiple measurement datasets are obtained for each echo signal based on multiple threshold samplings. Each measurement dataset contains at least one measurement data. It can be understood that each measurement dataset includes not only the measurement data obtained from the echo signal corresponding to the emitted signal reflected by the target object, but may also include the measurement data of the noise signal received by the lidar.

[0095] S120: Determine M candidate data and N auxiliary data from the multiple measurement datasets, and obtain the total composite degree of each candidate data.

[0096] For the multiple measurement datasets obtained in S110 above, all measurement data included in the multiple measurement datasets are partitioned to obtain M candidate data and N auxiliary data, where M and N are positive integers, and the sum of M and N equals the total number of measurement data contained in the multiple measurement datasets. For example, if the total number of measurement data contained in the multiple measurement datasets is 10, after data partitioning, 2 candidate data and 8 auxiliary data are obtained. The candidate data and auxiliary data are used together to determine the measurement distance of the target object.

[0097] After obtaining M candidate data through data partitioning, for each candidate data, the total composite degree of each candidate data is calculated. The total composite degree is used to represent the similarity between the candidate data and other candidate data in the same measurement period, and measurement data in adjacent measurement periods.

[0098] In one possible implementation, the radar ranging method described above also includes the following scheme:

[0099] The radar ranging range is divided into X sub-range intervals according to a preset rule; where X is a positive integer greater than 1.

[0100] The preset rule can be to set multiple distance boundary points within the ranging range, dividing the ranging range into X sub-distance intervals according to these boundary points. The difference between any two adjacent boundary points can be the same or different; that is, the length of each sub-distance interval can be the same or different, or some sub-distance intervals may have the same length while others have different lengths. Since the amplitude of the echo signal is related to factors such as the measurement distance, the reflectivity of the measured object, and the position of the measured object, for echo signals with different amplitudes, the measurement result obtained from multiple threshold samplings will have higher accuracy. Based on these considerations, multiple distance boundary points are determined according to the multiple threshold settings configured in the LiDAR system.

[0101] like Figure 8 As shown, Figure 8 A schematic diagram showing the division of the radar's ranging range is provided. Figure 8 In the diagram, 0~DIS_X represents the ranging range, and D_1, D_2, ..., D_X-1 represent the distance boundary points. D_1, D_2, ..., D_X-1 divide the ranging range into X sub-distance intervals, namely S_1, S_2, ..., S_X.

[0102] In one possible implementation, the determination of M candidate data and N auxiliary data from the plurality of measurement datasets includes the following schemes:

[0103] Based on the measurement data obtained by sampling the preset threshold corresponding to the sub-distance interval, M candidate data and N auxiliary data are determined from multiple measurement datasets.

[0104] As mentioned above, a corresponding preset threshold sampling is set according to the location of each sub-distance interval (e.g., in the far-distance segment, medium-distance segment, and near-distance segment of the ranging range). The measurement data obtained by sampling the preset threshold corresponding to each sub-distance interval has the highest confidence. Therefore, according to the measurement data obtained by sampling the preset threshold corresponding to the sub-distance interval, all measurement data included in multiple measurement datasets are divided into M candidate data and N auxiliary data. The accuracy of the measurement distance can be improved by using the obtained M candidate data to determine the measurement distance.

[0105] In one possible implementation, when transmitting an outgoing signal within the measurement period, determining M candidate data and N auxiliary data from multiple measurement datasets based on the measurement data sampled according to a preset threshold corresponding to the sub-distance interval includes the following scheme:

[0106] The measurement data obtained by sampling from the multiple measurement datasets based on the preset threshold corresponding to each of the sub-distance intervals are used as the candidate data to obtain M candidate data.

[0107] The measurement data from the multiple measurement datasets, excluding the M candidate data, are used as the auxiliary data to obtain N auxiliary data.

[0108] For example, the transmitting circuit emits an output signal during the measurement period, resulting in multiple measurement datasets. The total number of measurement data obtained is 10, namely measurement data 1, measurement data 2, ..., measurement data 10. There are four sub-distance intervals: interval 1, interval 2, ..., interval 4. Measurement data obtained by sampling the preset threshold corresponding to interval 1 is measurement data 1, measurement data obtained by sampling the preset threshold corresponding to interval 2 is measurement data 3, measurement data obtained by sampling the preset threshold corresponding to interval 3 is measurement data 5, and measurement data obtained by sampling the preset threshold corresponding to interval 4 is measurement data 8. Then, measurement data 1, measurement data 3, measurement data 5, and measurement data 8 are candidate data, totaling 4 candidate data; measurement data 2, measurement data 4, measurement data 6, measurement data 7, measurement data 9, and measurement data 10 are auxiliary data, totaling 6 auxiliary data.

[0109] For example, Figure 9 Another exemplary schematic diagram of the transmit and receive signals and timing of one measurement cycle of a lidar is shown. Figure 9 The Sino-Israeli lidar emits two signals sequentially within one measurement cycle, namely the first and second emitted signals. The emission power P1 of the first emitted signal is greater than the emission power P2 of the second emitted signal. The ranging range corresponding to the first emitted signal is large and is divided into four sub-range intervals. The ranging range corresponding to the second emitted signal is small and is divided into three sub-range intervals. The thresholds for sampling the received echo signals are threshold 1 to threshold 4. The first receiving and analysis area is used to receive the echo signal corresponding to the first emitted signal, and the second analysis area is used to receive the echo signal corresponding to the second emitted signal.

[0110] Taking the echo signal corresponding to the first emitted signal as an example, when sampling the echo signal at threshold 1, two measurement data points, H11 and H21, are obtained. When sampling the echo signal at thresholds 2, 3, and 4, one measurement data point, H12, H13, and H14, are obtained respectively. Measurement data points H11, H12, H13, and H14 are all located in interval 2, and the preset threshold corresponding to interval 2 is threshold 3. Therefore, measurement data point H13 is a candidate data point, and the remaining measurement data points H11, H12, and H14 are auxiliary data points. Measurement data point H21 is located in interval 4, and the preset threshold corresponding to interval 4 is threshold 1. Therefore, measurement data point H21 is also a candidate data point. In practical applications, the measurement data points obtained by sampling the preset thresholds corresponding to each sub-distance interval may include one, multiple, or none.

[0111] The echo signal includes the detection echo returned after the emitted signal is reflected by an object within the detection range, as well as interference echoes caused by ambient light entering the lidar. In practice, when the detection echo in the echo signal is sampled using different thresholds, a measurement data point can be obtained within a certain sub-distance interval. The measurement data obtained from the detection echo sampled with different thresholds are relatively similar. The measurement data obtained from the preset threshold sampled for the sub-distance interval is used as candidate data, and the test data obtained from other threshold sampled are used as auxiliary data. The resulting candidate data has a relatively large total composite degree value and is ultimately determined as the output result. Conversely, the measurement data obtained from the interference echo sampled with different thresholds are more discrete and uniformly distributed within each sub-distance interval. Even if it is identified as candidate data, the calculated total composite degree value is relatively small and will not be determined as the output result.

[0112] In one possible implementation, when multiple outgoing signals are transmitted within the measurement period, and the transmission power of the multiple outgoing signals is different, the step of determining M candidate data and N auxiliary data from the multiple measurement datasets based on the measurement data sampled according to the preset threshold corresponding to the sub-distance interval includes the following scheme:

[0113] The echo signal corresponding to the emitted signal with a preset transmission power in the multiple measurement data sets is sampled by the preset threshold corresponding to each of the sub-distance intervals to obtain the measurement data as the candidate data, so as to obtain M candidate data.

[0114] The measurement data from the multiple measurement datasets, excluding the M candidate data, are used as the auxiliary data to obtain N auxiliary data.

[0115] In the foregoing Figure 9Based on the previous embodiment, the echo signal corresponding to the first transmitted signal is sampled using four thresholds to obtain five measurement data points: F11, F12, F13, F14, and F21. The echo signal corresponding to the second transmitted signal is sampled using four thresholds to obtain four measurement data points: F31, F32, F33, and F41. Measurement data points F11, F12, F13, and F14 are located in interval 2 corresponding to the first transmitted signal; measurement data point F21 is located in interval 4 corresponding to the first transmitted signal; and measurement data points F31, F32, F33, and F41 are located in interval 2 corresponding to the second transmitted signal. The preset threshold corresponding to interval 2 of the first transmitted signal is threshold 3, measurement data F13 is the alternative data, and measurement data F11, F12, and F14 are auxiliary data; the preset threshold corresponding to interval 4 of the first transmitted signal is threshold 1, and measurement data F21 is the alternative data; the preset threshold corresponding to interval 2 of the second transmitted signal is threshold 3, measurement data F33 is the alternative data, and measurement data F31, F32, and F41 are auxiliary data.

[0116] For the case of transmitting multiple outgoing signals with different powers within the measurement period, the following explanation is provided:

[0117] First, for the same sub-distance interval, when sampling echo signals corresponding to different transmit powers to obtain candidate data, the corresponding preset thresholds can be different. (As mentioned above...) Figure 9 Based on the previous embodiment, in the ranging range of the first transmitted signal with a transmission power of P1, the preset threshold corresponding to interval 2 is threshold 3; while in the ranging range of the second transmitted signal with a transmission power of P2, the preset threshold corresponding to interval 2 can be changed to threshold 2. Since P1 is greater than P2, when the first and second transmitted signals detect the same object within the ranging range, the amplitude of the detected echo H1 is greater than the amplitude of the detected echo H3. Therefore, the measurement data obtained by sampling the detected echo H3 using the relatively low threshold 2 is more accurate.

[0118] Secondly, when the same threshold is used to sample echo signals corresponding to different transmission powers within the same sub-distance interval, the resulting measurement data can be different. Since both probe echo H1 and probe echo H3 are obtained from detecting the same object, the sampled measurement data should theoretically be the same. However, as mentioned above, the amplitude of probe echo H1 is greater than that of probe echo H3. Even though both probe echoes are sampled using threshold 2, the resulting measurement data differ; probe echo H1 is more saturated, resulting in smaller sampled measurement data.

[0119] In one possible implementation, obtaining the total composite degree of each of the candidate data includes the following scheme:

[0120] Determine the sub-distance interval where each measurement data in the multiple measurement datasets is located, so as to obtain the sub-distance intervals corresponding to the candidate data and the auxiliary data;

[0121] Based on the candidate data, the auxiliary data, and the sub-distance intervals corresponding to the candidate data, determine the principal composite degree, auxiliary composite degree, and cross composite degree of the candidate data Si; wherein, the candidate data Si is any one of the M candidate data, 1≤i≤M and i is an integer;

[0122] The total complexity of the candidate data Si is obtained by weighting the primary complexity, the secondary complexity, and the cross complexity of the candidate data Si.

[0123] Since the measurement range is pre-divided into multiple sub-distance intervals, and the distance value of each measurement data point is known through calculation, the intervals for each measurement data point in the multiple measurement datasets are divided according to the distance value to determine the sub-distance interval in which each measurement data point belongs. Once the sub-distance interval in which each measurement data point belongs is determined, the sub-distance intervals corresponding to each candidate data point and each auxiliary data point are also determined. For example, assuming the distance range is 0-200 meters, the divided sub-distance intervals are [0,2), [2,5), [5,20), [20,200]. For instance, if the distance value of measurement data 1 is 4 meters and the distance value of measurement data 2 is 50 meters, then the sub-distance interval in which measurement data 1 belongs is [2,5), and the sub-distance interval in which measurement data 2 belongs is [20,200]. If measurement data 1 is candidate data, then the sub-distance interval for candidate data is [2,5); if measurement data 2 is auxiliary data, then the sub-distance interval for auxiliary data is [20,200].

[0124] After obtaining the sub-distance intervals corresponding to each candidate data and each auxiliary data, the principal composite degree, auxiliary composite degree, and cross composite degree of candidate data Si are calculated based on the sub-distance intervals corresponding to the candidate data, auxiliary data, and each candidate data and auxiliary data. In other words, the principal composite degree, auxiliary composite degree, and cross composite degree of each candidate data are calculated.

[0125] Based on actual application requirements, corresponding weight values ​​were pre-set for the primary composite degree, secondary composite degree, and cross composite degree. After obtaining the primary composite degree, secondary composite degree, and cross composite degree of candidate data Si, a weighted calculation was performed on the primary composite degree, secondary composite degree, and cross composite degree of candidate data Si to obtain the total composite degree of candidate data Si. The total composite degree of candidate data Si is represented by Z. Si Z Si The calculation formula is as follows:

[0126] Z Si =F1Si ×w1+F2 Si ×w2+F3 Si ×w3;

[0127] Among them, F1 Si F2 represents the principal complexity. Si Indicates the degree of auxiliary complexity, F3 Si The values ​​represent the cross-complexity, where w1 represents the weight of the primary complexity, w2 represents the weight of the secondary complexity, and w3 represents the weight of the cross-complexity.

[0128] The above data weighting calculation is to bring the data equivalent back to one data unit. Of course, the total complexity of the candidate data Si can also be calculated without weighting. The total complexity of the candidate data Si can be obtained by summing the principal complexity, auxiliary complexity, and cross complexity of the candidate data Si, that is:

[0129] Z Si =F1 Si +F2 Si +F3 Si .

[0130] In one possible implementation, determining the principal composite degree of the candidate data Si includes the following schemes:

[0131] At least one of the candidate data that meets the preset requirements within multiple adjacent measurement periods is obtained as a comparison dataset;

[0132] The candidate data Si of the current measurement cycle is compared sequentially with each candidate data in the comparison dataset;

[0133] The principal composite degree of the candidate data Si is obtained based on the comparison results.

[0134] Regarding the calculation of principal composite degree: The preset requirement is that the emitted signal corresponding to the candidate data in the comparison dataset and the emitted signal corresponding to the candidate data Si in the current measurement cycle have the same emission power, and the threshold corresponding to the candidate data in the comparison dataset and the threshold corresponding to the candidate data Si in the current measurement cycle are the same. For example, the emission power of the emitted signal corresponding to the candidate data Si obtained in the current measurement cycle is P1, and the candidate data Si is obtained by sampling through threshold C1. Assuming that the emission power of the emitted signal corresponding to the candidate data 11 obtained in the previous measurement cycle is P1, and the candidate data 11 is obtained by sampling through threshold C1, and the emission power of the emitted signal corresponding to the candidate data 31 obtained in the next measurement cycle is P1, and the candidate data 31 is obtained by sampling through threshold C1, therefore, candidate data 11 and candidate data 31 are used as the comparison dataset. That is, the emission power and threshold corresponding to candidate data 11 and candidate data 31 are the same as the emission power and threshold corresponding to candidate data Si.

[0135] In the foregoing Figure 9 Based on the previous embodiment, measurement data F13, F21, and F33 are all candidate data. Taking the acquisition of the comparison dataset for measurement data F13 as an example, from an adjacent measurement cycle of the current measurement cycle shown in the figure, the echo signal corresponding to the first emitted signal is obtained and sampled using a threshold of 3. If it exists, it is candidate data in the comparison dataset; otherwise, another adjacent measurement cycle is confirmed. The comparison dataset can be obtained from only one adjacent measurement cycle of the current measurement cycle, or it can be obtained from multiple adjacent measurement cycles of the current measurement cycle, such as five adjacent measurement cycles. The acquisition methods for the comparison datasets of measurement data F21 and F33 are similar and will not be described again here.

[0136] After obtaining the comparison dataset, each candidate data in the comparison dataset is compared with candidate data Si, and then the principal composite degree of candidate data Si is set based on the comparison results. The principal composite degree represents the similarity between candidate data Si obtained in the current measurement cycle and candidate data obtained in adjacent measurement cycles that meet preset requirements. Since the probability of detecting the same object in adjacent measurement cycles is relatively high, the similarity of the detected echoes is high. Based on this premise, when comparing candidate data in the current detection cycle with candidate data in adjacent detection cycles, the principal composite degree is larger when the actual detected echo is used as candidate data for principal composite degree calculation; the principal composite degree is smaller when noise echoes caused by ambient light are used as candidate data for principal composite degree calculation.

[0137] In one possible implementation, obtaining the principal composite degree of the candidate data Si based on the comparison result includes the following schemes:

[0138] When the difference between a candidate data in the comparison dataset and the candidate data Si in the current measurement period is less than a first preset value, the principal composite degree of the candidate data Si is increased by 1.

[0139] By iterating through each candidate data in the comparison dataset, the principal composite degree of the candidate data Si is obtained; or...

[0140] When the principal complexity of the candidate data Si is greater than or equal to the principal complexity threshold, the principal complexity of the candidate data Si is obtained as the principal complexity threshold.

[0141] For each candidate data point encountered in the comparison dataset, the difference between the candidate data Si and each candidate data point encountered in the comparison dataset is calculated and recorded as the first difference. Then, the first difference obtained in each iteration is compared with a first preset value. If the first difference is less than the first preset value, the principal composite degree of the candidate data Si is incremented by 1. This process is repeated to obtain the principal composite degree of the candidate data Si. For example, if the initial principal composite degree of the candidate data Si is 0, and the first difference obtained in the first iteration is less than the first preset value, the principal composite degree of the candidate data Si is 1. If the first difference obtained in the second iteration is less than the first preset value, the principal composite degree of the candidate data Si is 1+1=2. If the first difference obtained in the third iteration is less than the first preset value, the principal composite degree of the candidate data Si is 1+1+1=3. This process is repeated to obtain the final principal composite degree of the candidate data Si.

[0142] After calculating the principal composite degree of the candidate data Si in the above manner, in order to reduce the amount of computation, after each traversal to calculate the principal composite degree of the candidate data Si, the principal composite degree of the candidate data Si is compared with the principal composite degree threshold. If the principal composite degree of the candidate data Si is greater than or equal to the principal composite degree threshold, then the principal composite degree threshold is used as the principal composite degree of the candidate data Si, and the traversal stops.

[0143] In one possible implementation, determining the auxiliary composite degree of the candidate data Si includes the following schemes:

[0144] The candidate data Si is compared sequentially with the N auxiliary data.

[0145] The auxiliary composite degree of the candidate data Si is obtained based on the comparison results.

[0146] Regarding the calculation of auxiliary composite degree: The candidate data Si is compared with each of the N auxiliary data sets, and the auxiliary composite degree of the candidate data Si is set based on the comparison results. The auxiliary composite degree represents the similarity between the candidate data Si and the auxiliary data. Within a detection period, the detected echo signal is detected by outgoing signals with different transmission powers and sampled with different thresholds, resulting in a higher probability of obtaining similar measurement data. Based on this premise, the auxiliary composite degree of the candidate data for the detected echo signal is higher; conversely, when high noise or interference is used as candidate data, the calculated auxiliary composite degree is lower.

[0147] In the foregoing Figure 9 Based on the embodiments, measurement data F13, F21, and F33 are all alternative data, and measurement data F11, F12, F14, F31, F32, and F41 are all auxiliary data.

[0148] Taking the acquisition of the auxiliary composite degree of measurement data F13 as an example, the candidate data F13 is compared with the auxiliary data F11, F12, F14, F31, F32, and F41 respectively to obtain the comparison results. Among them, auxiliary data F11, F12, F14 and candidate data F13 are measurement data obtained from the same probe echo H1 sampled from different thresholds, therefore the similarity is high; the probe echo H3 corresponding to auxiliary data F31 and F32 and the probe echo H1 corresponding to candidate data F13 are obtained from the same object being measured, therefore the similarity is high; auxiliary data F41 is measurement data obtained from noise echo H4 sampled from threshold 1, and its similarity with candidate data F13 is low. Therefore, 5 out of the 6 auxiliary data have a high similarity to candidate data F13.

[0149] Taking the acquisition of auxiliary composite degree of measurement data F21 as an example, the candidate data F21 is compared with auxiliary data F11, F12, F14, F31, F32, and F41 respectively to obtain the comparison results. Among them, candidate data F21 is the measurement data obtained by sampling noise echo H2 from threshold 1. F11, F12, F14, F31, F32, and F41 are not in the same sub-interval as it, and therefore have low similarity. Thus, the similarity between the six auxiliary data and candidate data F21 is low.

[0150] In one possible implementation, obtaining the auxiliary composite degree of the candidate data Si based on the comparison result includes the following schemes:

[0151] When the difference between one of the N auxiliary data and the candidate data Si is less than a second preset value, the auxiliary composite degree of the candidate data is increased by 1;

[0152] By iterating through each of the N auxiliary data, the auxiliary composite degree of the candidate data Si is obtained; or,

[0153] When the auxiliary composite degree of the candidate data Si is greater than or equal to the auxiliary composite degree threshold, the auxiliary composite degree of the candidate data Si is obtained as the auxiliary composite degree threshold.

[0154] For each auxiliary data point encountered in each iteration of the N auxiliary data sets, calculate the difference between the candidate data Si and the candidate data encountered in each iteration. This difference is denoted as the second difference. Then, compare the second difference obtained in each iteration with a second preset value. If the second difference is less than the second preset value, increment the auxiliary composite degree of the candidate data Si by 1. Continue this process to obtain the auxiliary composite degree of the candidate data Si. For example, if the initial auxiliary composite degree of the candidate data Si is 0, and the second difference obtained in the first iteration is less than the second preset value, the auxiliary composite degree of the candidate data Si is 1. If the second difference obtained in the second iteration is less than the second preset value, the auxiliary composite degree of the candidate data Si is 1+1=2. If the second difference obtained in the third iteration is less than the second preset value, the auxiliary composite degree of the candidate data Si is 1+1+1=3. Continue this process to obtain the final auxiliary composite degree of the candidate data Si.

[0155] After calculating the auxiliary composite degree of the candidate data Si in the above manner, in order to reduce the amount of computation, after each iteration of calculating the auxiliary composite degree of the candidate data Si, the auxiliary composite degree of the candidate data Si is compared with the auxiliary composite degree threshold. If the auxiliary composite degree of the candidate data Si is greater than or equal to the auxiliary composite degree threshold, then the auxiliary composite degree threshold is used as the auxiliary composite degree of the candidate data Si, and the iteration stops.

[0156] In one possible implementation, determining the cross-complexity of the candidate data Si includes the following schemes:

[0157] The sub-distance interval where the candidate data Si is located is determined to be the middle sub-distance interval, and at least one of the K sub-distance intervals other than the middle sub-distance interval is obtained as the target sub-distance interval; where K≤X, and K is a positive integer;

[0158] The candidate data Si is sequentially compared with at least one candidate data within the target sub-distance interval;

[0159] The cross-complexity of the candidate data Si is obtained based on the comparison results.

[0160] Since not every sub-distance interval has candidate data, only the sub-distance interval containing the candidate data participates in the cross-complexity calculation. Of the X sub-distance intervals, K sub-distance intervals contain candidate data. The candidate data for the target sub-distance interval, determined from the K sub-distance intervals, participates in the cross-complexity calculation. The target sub-distance interval can be a sub-distance interval adjacent to the middle sub-distance interval, a sub-distance interval that is second to or not adjacent to the middle sub-distance interval, or any of the other K-1 sub-distance intervals besides the middle sub-distance interval.

[0161] Regarding the calculation of cross-compoundness: The sub-distance interval containing candidate data Si is determined as the middle sub-distance interval, and at least one sub-distance interval other than the middle sub-distance interval among the K sub-distance intervals is determined as the target sub-distance interval. For example, intervals 2, 3, 4, and 6 among the 6 sub-distance intervals contain candidate data (i.e., X=6, K=4); interval 3, where candidate data Si is located, is the middle sub-distance interval, and at least one of intervals 2, 4, and 6 is determined as the target sub-distance interval; one sub-distance interval adjacent to interval 3 can be determined, i.e., interval 3 is the target sub-distance interval; two sub-distance intervals adjacent to interval 3 can be determined, i.e., intervals 2 and 4 are the target sub-distance intervals; interval 6, which is not adjacent to interval 3, can be determined as the target sub-distance interval; other sub-distance intervals besides interval 3 can also be determined, i.e., intervals 2, 4, and 6 are the target sub-distance intervals; and so on.

[0162] After obtaining the target sub-distance interval, the candidate data within the target sub-distance interval can be identified. Then, the candidate data Si is compared with each candidate data within the target sub-distance interval to obtain the comparison result. For example, if the candidate data Si is S1, and the target sub-distance interval includes three intervals (interval 2, interval 3, and interval 4), with each interval 2 and interval 4 corresponding to one candidate data, and interval 3 containing no candidate data, S1 is compared sequentially with the candidate data corresponding to interval 2 and the candidate data corresponding to interval 4. Each comparison yields a comparison result, and the cross-compoundness of the candidate data Si is set based on each comparison result. The cross-compoundness represents the similarity between the candidate data Si and the candidate data corresponding to the target sub-distance interval.

[0163] In the foregoing Figure 9 Based on the embodiments, measurement data F13, F21, and F33 are all alternative data, and measurement data F11, F12, F14, F31, F32, and F41 are all auxiliary data.

[0164] Taking the cross-complexity of measurement data F13 as an example, measurement data F13 is located in interval 2. Intervals 2 and 4 contain measurement data. Interval 4 is determined as the target sub-distance interval. The cross-complexity of measurement data F13 and F21 is calculated.

[0165] For echo signals falling near the distance boundary of a sub-distance interval, after sampling with different thresholds, the measurement data may fall within two adjacent sub-distance intervals and be identified as candidate data for each sub-distance interval. Since the measurement is of the same probe echo or interference echo, the obtained measurement data are similar, and the cross-complexity of the calculated candidate data is relatively large.

[0166] In one possible implementation, obtaining the cross-complexity of the candidate data Si based on the comparison results includes the following schemes:

[0167] When the difference between a candidate data in at least one of the target sub-distance intervals and the candidate data Si is less than a third preset value, the cross-complexity of the candidate data is increased by 1.

[0168] By traversing at least one of the candidate data within the target sub-distance interval, the cross-complexity of the candidate data Si is obtained; or,

[0169] When the cross-complexity of the candidate data Si is greater than or equal to the cross-complexity threshold, the cross-complexity of the candidate data Si is obtained as the cross-complexity threshold.

[0170] For at least one target sub-distance interval obtained, each time a candidate data point in a target sub-distance interval is traversed, the difference between the candidate data obtained in each traversal and the candidate data Si is calculated. This difference is recorded as the third difference. Then, the third difference obtained in each traversal is compared with a third preset value. If the third difference is less than the third preset value, the cross-complexity threshold of the candidate data Si is incremented by 1. This process is repeated to obtain the cross-complexity threshold of the candidate data Si. For example, if the initial cross-complexity threshold of the candidate data Si is 0, and the third difference obtained in the first traversal of the first target sub-distance interval is less than the third preset value, the cross-complexity threshold of the candidate data Si is 1. If the third difference obtained in the second traversal of the first target sub-distance interval is less than the third preset value, the cross-complexity threshold of the candidate data Si is 1+1=2. If the third difference obtained in the third traversal of the third target sub-distance interval is less than the third preset value, the cross-complexity threshold of the candidate data Si is 1+1+1=3. This process is repeated to obtain the final cross-complexity threshold of the candidate data Si.

[0171] After calculating the cross-complexity threshold of the candidate data Si in the above manner, in order to reduce the amount of computation, after each traversal to calculate the cross-complexity threshold of the candidate data Si, the cross-complexity threshold of the candidate data Si is compared with the cross-complexity threshold. If the cross-complexity threshold of the candidate data Si is greater than or equal to the cross-complexity threshold, then the cross-complexity threshold is used as the cross-complexity threshold of the candidate data Si, and the traversal stops.

[0172] S130: Determine the target data from the M candidate data based on the total composite degree.

[0173] After calculating the total composite degree of each candidate data, the candidate data with the highest confidence level is selected from the M candidate data according to the total composite degree as the target data for determining the measurement distance.

[0174] In one possible implementation, determining the target data from the M candidate data based on the total composite degree includes the following schemes:

[0175] The target data is determined from the candidate data with the highest total composite degree among the M candidate data.

[0176] After obtaining the total composite degree of each of the M candidate data, the total composite degrees are sorted, and the candidate data with the highest total composite degree is obtained. Then, the target data is determined based on the candidate data with the highest total composite degree.

[0177] In one possible implementation, determining the target data from the candidate data with the highest total composite degree among the M candidate data includes the following schemes:

[0178] When the candidate data with the highest total composite degree among the M candidate data is a single data, the candidate data with the highest total composite degree is determined as the target data;

[0179] When there are multiple candidate data with the highest total composite degree among the M candidate data, the candidate data with the highest total composite degree and the largest distance value is determined as the target data.

[0180] Considering that there may be one or more candidate data points with the highest total composite degree among the M candidate data points, there are two scenarios. The method for determining the target data differs depending on the scenario: If there is only one candidate data point with the highest total composite degree, then that candidate data point with the highest total composite degree is determined as the target data. Specifically, if there are multiple candidate data points with the highest total composite degree, then the candidate data point with the highest total composite degree is determined as the target data. The distance values ​​among these candidate data points are then sorted to obtain the candidate data point with the largest distance value. This candidate data point with the largest distance value is then determined as the target data. In other words, the target data is the candidate data point with the highest total composite degree and the largest distance value among the M candidate data points. Since the interference signal at a distance is relatively weaker than that at a close distance, the echo signal at a distance is less affected by the interference signal, and the confidence of the obtained measurement data is higher. Therefore, when there are multiple candidate data with the highest total composite degree, the candidate data with the highest total composite degree and the largest distance value among the M candidate data is determined as the target data.

[0181] S140: Determine the measurement distance based on the target data.

[0182] In one possible implementation, determining the measurement distance based on the target data includes the following schemes:

[0183] Obtain the endpoint values ​​of the sub-distance interval, and construct a smooth interval centered on the endpoint values;

[0184] Determine whether the target data is located within the smoothing interval;

[0185] The measured distance is determined based on the judgment result.

[0186] The endpoints of each sub-distance interval are the aforementioned distance boundaries, including the maximum and minimum values ​​of the sub-distance interval. A smoothing interval is constructed using the endpoints of each sub-distance interval as the center and preset distance smoothing adjustment values. These distance smoothing adjustment values ​​are D_smooth1 and D_smooth2. Assuming the endpoint value is D_X, then the smoothing interval is [D_X - D_smooth1, D_X + D_smooth2]. Multiple smoothing intervals are constructed in this way. Figure 8As shown, if the endpoint value is D_1, then the smoothing interval is [D_1-D_smooth1, D_1+D_smooth2]; if the endpoint value is D_2, then the smoothing interval is [D_2-D_smooth1, D_2+D_smooth2], and so on. Here, |D_smooth1| and |D_smooth2| can be the same or different. That is, the construction of the smoothing interval can be symmetrical or asymmetrical with respect to the endpoint values. Specifically, when |D_smooth1|=|D_smooth2|, the smoothing interval is symmetrical with respect to the endpoint values; when |D_smooth1| is not equal to |D_smooth2|, the smoothing interval is asymmetrical with respect to the endpoint values.

[0187] After constructing the smoothing interval, it is determined whether the target data is within the smoothing interval. The distance is then determined based on the determination result, and the method for determining the distance varies depending on the determination result.

[0188] In one possible implementation, determining the measurement distance based on the judgment result includes the following schemes:

[0189] When the target data is not within the smoothing interval, the measured distance is determined to be equal to the target data;

[0190] When the target data is within the smoothing interval, the sub-distance interval where the target data is located is determined as the first sub-distance interval, and the sub-distance interval adjacent to the first sub-distance interval within the smoothing interval is determined as the second sub-distance interval;

[0191] Obtain the first threshold corresponding to the first sub-distance interval and the second threshold corresponding to the second sub-distance interval;

[0192] The measurement data in the measurement dataset obtained based on the first threshold sampling that falls within the smooth interval is determined as the first distance, and the measurement data in the measurement dataset obtained based on the second threshold sampling that falls within the smooth interval is determined as the second distance;

[0193] The measured distance is obtained by weighting the first distance and the second distance.

[0194] If the judgment result is that the target data is not within the smoothing interval, it means that the target data is not at the intersection of two adjacent sub-distance intervals. The target data is accurate as measurement data and does not need to be spliced ​​and smoothed. In this case, the target data is determined as measurement data, that is, the measurement distance is equal to the target data.

[0195] If the judgment result indicates that the target data is within the smoothing interval, it means that the target data may have biases as measurement data. For example, as mentioned above, probe echoes or noise echoes (a signal peak in the echo signal) near the distance boundary point may result in measurement results within different distance sub-intervals when sampled with different thresholds. To reduce such measurement fluctuations at boundary locations, a stitching smoothing process is needed. Therefore, the sub-distance interval containing the target data is designated as the first sub-distance interval, and the sub-distance interval within the smoothing interval and adjacent to the first sub-distance interval is determined as the second sub-distance interval. For example... Figure 10 As shown, Figure 10 An exemplary schematic diagram of a smoothing interval is shown. For example, the smoothing interval where the target data is located is [Q1, Q2], and D_X is within [Q1, Q2], where Q1 = D_X - D_smooth1 and Q2 = D_X + D_smooth2. If the target data is within the sub-distance interval S_X, then S_X is the first sub-distance interval. The sub-distance interval S_X+1 intersects with [Q1, Q2], indicating that S_X+1 is within [Q1, Q2], and S_X+1 is adjacent to S_X. Therefore, S_X+1 is the second sub-distance interval.

[0196] Furthermore, a first threshold corresponding to the first sub-distance interval and a second threshold corresponding to the second sub-distance interval are obtained. The first threshold is a preset threshold corresponding to the first sub-distance interval, and the second threshold is a preset threshold corresponding to the second sub-distance interval. The first threshold corresponding to the first sub-distance interval represents the echo signal within the first sub-distance interval. Compared to measurement data obtained by sampling with other thresholds, the measurement data obtained by sampling with the first threshold has the highest confidence level. Taking the first threshold corresponding to the first sub-distance interval as an example, for a lidar using a fixed transmission power signal, the measurement data obtained by sampling the echo signal with the first threshold within the first sub-distance interval is the candidate data; for a lidar using multiple transmission powers, the measurement data obtained by sampling the echo signal corresponding to the preset transmission power with the first threshold within the first sub-distance interval is the candidate data. The function of the second threshold corresponding to the second sub-distance interval is the same as that of the first threshold corresponding to the first sub-distance interval, and will not be elaborated further.

[0197] After obtaining the first threshold and the second threshold, the measurement data in the measurement dataset sampled with the first threshold that falls within the smooth interval is taken as the first distance, and the measurement data in the measurement dataset sampled with the second threshold that falls within the smooth interval is taken as the second distance. For example, if the measurement dataset sampled with the first threshold includes measurement data 11-15, and measurement data 15 falls within the smooth interval, then measurement data 15 is the first distance; if the measurement dataset sampled with the second threshold includes measurement data 21-25, and measurement data 22 falls within the smooth interval, then measurement data 22 is the second distance.

[0198] After obtaining the first and second distances, a weighted calculation is performed on the first and second distances to obtain the measured distance. For example... Figure 10 As shown, the first distance is denoted as J1, and the second distance is denoted as J2. The weight values ​​corresponding to the first and second distances are determined according to the weight functions corresponding to the sub-distance intervals of the first and second distances, respectively. The weight function corresponding to the sub-distance interval of the first distance is L1, and the weight function corresponding to the sub-distance interval of the second distance is L2. Substituting J1 as the independent variable into the weight function L1 yields the weight value corresponding to J1, denoted as Coef_x. Similarly, substituting J2 as the independent variable into the weight function L2 yields the weight value corresponding to J2, denoted as Coef_x+1. That is, the measured distance = (J1×Coef_x+J2×Coef_x+1). The measured distance obtained in this way avoids the result being an echo signal near the endpoint value, and the result deviation caused by sampling with different thresholds. By using the values ​​on both sides of the endpoint value for weighted calculation, a smoothing effect is achieved.

[0199] This application embodiment employs a technical solution that uses multiple measurement datasets obtained from at least one echo signal within a measurement period and multiple threshold samplings to determine M candidate data and N auxiliary data from these datasets. It then obtains the total composite degree of each candidate data, determines the target data from the M candidate data based on the total composite degree, and determines the measurement distance based on the target data. This solution enables the overall control of the lidar during ranging by using multiple transmissions of signals with different transmission powers, multiple gains, multiple threshold samplings, and multiple analysis zones. Furthermore, it fuses the measurement data obtained from multiple transmissions of signals with different transmission powers, multiple gains, and multiple threshold samplings to obtain accurate measurement data. This not only improves the measurement accuracy of the lidar but also enhances the dynamic range of the lidar system and shortens the close-range detection blind zone of the lidar.

[0200] The following is another embodiment of a radar ranging method provided in the embodiments of this application.

[0201] Figure 11 This paper illustrates a data fusion block diagram of a radar ranging method provided in an embodiment of this application, as shown below. Figure 11As shown, during ranging, the lidar emits multiple signals within a measurement period according to a time sequence. Each emitted signal has a different transmission power. Since the lidar's threshold sampling includes multiple parameters, multiple measurement data points can be acquired. These measurement data points correspond to different transmission powers and threshold sampling values, resulting in multiple measurement data points with different transmission powers and different threshold sampling values. After acquiring multiple measurement data points, data allocation, data fusion, data judgment and selection, and distance stitching smoothing are performed to obtain the measured distance. The data allocation, data fusion, data judgment and selection, and distance stitching smoothing are all controlled by parameters.

[0202] Regarding data allocation: The ranging range of the LiDAR is pre-divided into X sub-range intervals according to preset rules. Then, the sub-range interval corresponding to each measurement data point is determined, thus allocating a sub-range interval for each measurement data point. After allocating a sub-range interval for each measurement data point, the measurement data sampled based on the preset threshold corresponding to each sub-range interval is further divided into M candidate data points and N auxiliary data points. Since each measurement data point has already been assigned a corresponding sub-range interval, the sub-range intervals corresponding to the candidate and auxiliary data points are also known.

[0203] Regarding data fusion: For each of the M candidate data points, denoted as Si, i=1,2,...,M. Based on the candidate data, auxiliary data, and their respective sub-distance intervals, calculate the principal composite degree, auxiliary composite degree, and cross composite degree of Si. Then, weight the principal composite degree, auxiliary composite degree, and cross composite degree to obtain the total composite degree of Si. This yields the total composite degree of each of the M candidate data points.

[0204] Regarding data selection: From the M candidate data, determine the target data from the candidate data with the highest total composite degree. If the candidate data with the highest total composite degree is a single data, then the candidate data with the highest total composite degree is determined as the target data. If the M candidate data has multiple data, then the candidate data with the highest total composite degree and the largest distance value is determined as the target data.

[0205] Regarding distance stitching smoothing: A smoothing interval is constructed centered on the endpoint value of each sub-distance interval. It is determined whether the target data is located within the smoothing interval. If the target data is not located within the smoothing interval, the target data is identified as the measurement data. If the target data is located within the smoothing interval, the sub-distance interval containing the target data is identified as the first sub-distance interval, and the sub-distance interval adjacent to the first sub-distance interval within the smoothing interval is identified as the second sub-distance interval. Then, the first threshold corresponding to the first sub-distance interval and the second threshold corresponding to the second sub-distance interval are obtained. The measurement data in the measurement dataset sampled based on the first threshold that are located within the smoothing interval are taken as the first distance, and the measurement data in the measurement dataset sampled based on the second threshold that are located within the smoothing interval are taken as the second distance. The first distance and the second distance are then weighted and calculated to obtain the accurate measurement distance. The technical solution provided in this application example enables the overall control of the lidar during lidar ranging by using multiple transmission signals with different transmission powers, multiple gains, multiple threshold sampling, and multiple analysis zones. It also fuses the measurement data obtained from multiple transmission signals with different transmission powers, multiple gains, and multiple threshold sampling to obtain accurate measurement data. This not only improves the measurement accuracy of the lidar but also enhances the dynamic range of the lidar system and shortens the close-range detection blind zone of the lidar.

[0206] The following are embodiments of the apparatus described in this application, which can be used to execute the embodiments of the method described in this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method described in this application.

[0207] Figure 12 A schematic diagram of a radar ranging device according to an embodiment of this application is shown. For example, as shown... Figure 12 As shown, the radar ranging device 1200 includes:

[0208] The data acquisition module 1210 is used to acquire multiple measurement datasets obtained by sampling at least one echo signal within a measurement period based on multiple thresholds; wherein, the echo signal is sampled based on one of the thresholds to obtain one measurement dataset, and each measurement dataset contains at least one measurement data.

[0209] The composite degree calculation module 1220 is used to determine M candidate data and N auxiliary data in multiple measurement datasets, and obtain the total composite degree of each candidate data; wherein M and N are positive integers, and the sum of M and N is equal to the total number of measurement data contained in the multiple measurement datasets;

[0210] The data selection module 1230 is used to determine the target data from the M candidate data according to the total composite degree;

[0211] The distance calculation module 1240 is used to determine the measurement distance based on the target data.

[0212] In one possible implementation, the radar ranging device 1200 further includes:

[0213] The interval division unit is used to divide the radar ranging range into X sub-range intervals according to a preset rule; where X is a positive integer greater than 1.

[0214] The composite degree calculation module 1220 is specifically used to determine M candidate data and N auxiliary data in the plurality of measurement datasets by: determining M candidate data and N auxiliary data in the plurality of measurement datasets based on the measurement data obtained by sampling the measurement data corresponding to the preset threshold of the sub-distance interval.

[0215] In one possible implementation, when transmitting an outgoing signal during the measurement period, the composite degree calculation module 1220 determines M candidate data and N auxiliary data from a plurality of measurement datasets based on the measurement data sampled according to a preset threshold corresponding to the sub-distance interval, including:

[0216] The first acquisition unit is used to take the measurement data obtained by sampling the preset threshold corresponding to each of the sub-distance intervals from the multiple measurement datasets as the candidate data, so as to obtain M candidate data.

[0217] The first filtering unit is used to select the measurement data from the multiple measurement datasets, excluding the M candidate data, as the auxiliary data to obtain N auxiliary data.

[0218] In one possible implementation, when multiple outgoing signals are emitted during the measurement period, and the transmission power of the multiple outgoing signals is different, the composite degree calculation module 1220 determines M candidate data and N auxiliary data from the multiple measurement datasets based on the measurement data sampled according to the preset threshold corresponding to the sub-distance interval, including:

[0219] The second acquisition unit is used to take the echo signal corresponding to the emitted signal with a preset transmission power in the multiple measurement data sets, and sample the measurement data obtained by sampling the preset threshold corresponding to each of the sub-distance intervals, as the candidate data, so as to obtain M candidate data.

[0220] The second filtering unit is used to select the measurement data from the multiple measurement datasets, excluding the M candidate data, as the auxiliary data to obtain N auxiliary data.

[0221] In one possible implementation, the compositeness calculation module 1220, in obtaining the total compositeness of each of the candidate data, includes:

[0222] An interval determination unit is used to determine the sub-distance interval where each measurement data in the multiple measurement datasets is located, so as to obtain the sub-distance intervals corresponding to the candidate data and the auxiliary data;

[0223] The first calculation unit is used to determine the principal composite degree, auxiliary composite degree, and cross composite degree of the candidate data Si based on the candidate data, the auxiliary data, and the sub-distance interval corresponding to the candidate data; wherein the candidate data Si is any one of M candidate data, 1≤i≤M and i is an integer;

[0224] The second calculation unit is used to perform a weighted calculation based on the principal composite degree, the auxiliary composite degree, and the cross composite degree of the candidate data Si to obtain the total composite degree of the candidate data Si.

[0225] In one possible implementation, the first computing unit includes:

[0226] A dataset determination subunit is used to acquire at least one candidate data that meets preset requirements within multiple adjacent measurement cycles, as a comparison dataset; wherein, meeting the preset requirements means that the emitted signal corresponding to the candidate data in the comparison dataset and the emitted signal corresponding to the candidate data Si in the current measurement cycle have the same emission power, and the threshold corresponding to the candidate data in the comparison dataset and the threshold corresponding to the candidate data Si in the current measurement cycle are the same;

[0227] The first comparison subunit is used to sequentially compare the candidate data Si in the current measurement cycle with each candidate data in the comparison dataset;

[0228] The first determining subunit is used to obtain the principal composite degree of the candidate data Si based on the comparison results.

[0229] In one possible implementation, the first determining subunit is specifically used to: increment the principal composite degree of the candidate data Si by 1 when the difference between a candidate data in the comparison dataset and the candidate data Si in the current measurement period is less than a first preset value; traverse each candidate data in the comparison dataset to obtain the principal composite degree of the candidate data Si; or, when the principal composite degree of the candidate data Si is greater than or equal to the principal composite degree threshold, obtain the principal composite degree of the candidate data Si as the principal composite degree threshold.

[0230] In one possible implementation, the first computing unit further includes:

[0231] The second comparison subunit is used to sequentially compare the candidate data Si with N auxiliary data;

[0232] The second determining subunit is used to obtain the auxiliary composite degree of the candidate data Si based on the comparison results.

[0233] In conjunction with the second aspect and the above implementation methods, in some possible implementation methods, the second determining subunit is specifically used for: when the difference between one of the N auxiliary data and the candidate data Si is less than a second preset value, the auxiliary composite degree of the candidate data is incremented by 1; traversing each of the N auxiliary data to obtain the auxiliary composite degree of the candidate data Si; or, when the auxiliary composite degree of the candidate data Si is greater than or equal to the auxiliary composite degree threshold, obtaining the auxiliary composite degree of the candidate data Si as the auxiliary composite degree threshold.

[0234] In one possible implementation, the first computing unit further includes:

[0235] The target interval determination sub-unit is used to determine that the sub-distance interval in which the candidate data Si is located is the middle sub-distance interval, and to obtain at least one of the K sub-distance intervals other than the middle sub-distance interval as the target sub-distance interval; where K≤X, and K is a positive integer;

[0236] The third comparison subunit is used to sequentially compare the candidate data Si with at least one candidate data within the target sub-distance interval;

[0237] The third determining subunit is used to obtain the cross-complexity of the candidate data Si based on the comparison results.

[0238] In one possible implementation, the third determining sub-unit is specifically used to: increment the cross-complexity of the candidate data by 1 when the difference between a candidate data in at least one of the target sub-distance intervals and the candidate data Si is less than a third preset value; traverse the candidate data in at least one of the target sub-distance intervals to obtain the cross-complexity of the candidate data Si; or, when the cross-complexity of the candidate data Si is greater than or equal to the cross-complexity threshold, obtain the cross-complexity of the candidate data Si as the cross-complexity threshold.

[0239] In one possible implementation, the data selection module 1230 is specifically used to: determine the target data from the candidate data with the highest total composite degree among the M candidate data.

[0240] In one possible implementation, the data selection module 1230 determines the target data from the candidate data with the highest total composite degree among the M candidate data, including:

[0241] The first selection unit is used to determine the candidate data with the highest total composite degree among the M candidate data as the target data when the candidate data with the highest total composite degree is a single data.

[0242] The second selection unit is used to determine the candidate data with the highest total composite degree and the largest distance value as the target data when there are multiple candidate data with the highest total composite degree among the M candidate data.

[0243] In one possible implementation, the distance calculation module 1240 includes:

[0244] An interval construction unit is used to obtain the endpoint values ​​of the sub-distance interval and construct a smooth interval centered on the endpoint values;

[0245] A data judgment unit is used to determine whether the target data is located within the smoothing interval;

[0246] A distance determination unit is used to determine the measured distance based on the judgment result.

[0247] In one possible implementation, the distance determination unit includes:

[0248] The first distance determination subunit is used to determine that the measured distance is equal to the target data when the target data is not within the smoothing interval;

[0249] The second distance determination subunit is configured to, when the target data is within the smoothing interval, determine the sub-distance interval where the target data is located as the first sub-distance interval, and determine the sub-distance interval adjacent to the first sub-distance interval within the smoothing interval as the second sub-distance interval; obtain a first threshold corresponding to the first sub-distance interval and a second threshold corresponding to the second sub-distance interval; determine the measurement data in the measurement dataset sampled based on the first threshold that is located within the smoothing interval as the first distance, and the measurement data in the measurement dataset sampled based on the second threshold that is located within the smoothing interval as the second distance; and perform a weighted calculation on the first distance and the second distance to obtain the measurement distance.

[0250] It should be noted that the radar ranging device provided in the above embodiments is only illustrated by the division of the above functional modules when performing the radar ranging method. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the radar ranging device and the radar ranging method embodiments provided in the above embodiments belong to the same concept. Therefore, for details not disclosed in the device embodiments of this application, please refer to the radar ranging method embodiments of this application, which will not be repeated here.

[0251] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0252] Figure 13 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown.

[0253] For example, such as Figure 13 As shown, the electronic device 1300 includes a memory 1301 and a processor 1302. The memory 1301 stores executable program code 13011, and the processor 1302 is used to call and execute the executable program code 13011 to perform a radar ranging method.

[0254] This embodiment can divide the electronic device into functional modules according to the above method example. For example, each module can correspond to a separate functional module, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware. It should be noted that the module division in this embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0255] When each functional module is divided according to its corresponding function, the electronic device may include: a data acquisition module, a composite degree calculation module, a data selection module, a distance calculation module, etc. It should be noted that all relevant content of each step involved in the above method embodiments can be referenced from the functional description of the corresponding functional module, and will not be repeated here.

[0256] The electronic device provided in this embodiment is used to execute the radar ranging method described above, and therefore can achieve the same effect as the above implementation method.

[0257] When using integrated units, the electronic device may include a processing module and a storage module. The processing module is used to control and manage the operation of the electronic device. The storage module is used to support the execution of program code and data by the electronic device.

[0258] The processing module may be a processor or a controller, which can implement or execute various exemplary logic blocks, modules, and circuits as disclosed in this application. The processor may also be a combination of computing functions, such as a combination of one or more microprocessors, a combination of digital signal processing (DSP) and a microprocessor, etc., and the storage module may be a memory.

[0259] This embodiment also provides a computer-readable storage medium storing computer program code. When the computer program code is run on a computer, the computer executes the above-described related method steps to implement a radar ranging method in the above embodiment.

[0260] This embodiment also provides a computer program product that, when run on a computer, causes the computer to perform the aforementioned related steps to implement a radar ranging method as described in the above embodiment.

[0261] In addition, the electronic device provided in the embodiments of this application may specifically be a chip, component or module. The electronic device may include a connected processor and a memory. The memory is used to store instructions. When the electronic device is running, the processor can call and execute the instructions to make the chip execute a radar ranging method in the above embodiments.

[0262] In this embodiment, the electronic device, computer-readable storage medium, computer program product or chip are all used to execute the corresponding radar ranging method provided above. Therefore, the beneficial effects that can be achieved can be referred to the beneficial effects of the corresponding radar ranging method provided above, and will not be repeated here.

[0263] Through the above description of the embodiments, those skilled in the art will understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0264] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0265] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A radar ranging method, characterized by, The radar ranging method comprises: Obtaining a plurality of measurement data sets of at least one echo signal in a measurement period based on a plurality of threshold samplings; wherein the echo signal is based on one of the threshold samplings to obtain one of the measurement data sets, and each of the measurement data sets contains at least one measurement data; Determining M candidate data and N auxiliary data in the plurality of measurement data sets; wherein M and N are positive integers, and the sum of M and N is equal to the total number of measurement data contained in the plurality of measurement data sets; Determining the sub-range interval of each measurement data in the plurality of measurement data sets to obtain the sub-range interval corresponding to the candidate data and the auxiliary data; According to the candidate data, the auxiliary data and the sub-range interval corresponding to the candidate data, determining the main composite degree, the auxiliary composite degree and the cross composite degree of the candidate data Si; wherein the candidate data Si is any one of the M candidate data, 1≤i≤M and i is an integer, and the main composite degree of the candidate data Si is used to represent the similarity between the candidate data Si obtained in the current measurement period and the candidate data obtained in the adjacent measurement period and meeting the preset requirements; According to the main composite degree, the auxiliary composite degree and the cross composite degree of the candidate data Si, performing weighted calculation to obtain the total composite degree of the candidate data Si, and the total composite degree of the candidate data Si is used to represent the similarity between the candidate data Si and other candidate data in the same measurement period, and the measurement data in the adjacent measurement period; According to the total composite degree, determining the target data from the M candidate data; According to the target data, determining the measurement distance.

2. The radar ranging method of claim 1, wherein, The radar ranging method further comprises: Dividing the ranging range of the radar into X sub-range intervals according to a preset rule; wherein X is a positive integer greater than 1; The determination of the M candidate data and the N auxiliary data in the plurality of measurement data sets comprises: According to the measurement data obtained by the preset threshold sampling corresponding to the sub-range interval, determining the M candidate data and the N auxiliary data in the plurality of measurement data sets.

3. The radar ranging method of claim 2, wherein, When transmitting one outgoing signal in the measurement period, the determination of the M candidate data and the N auxiliary data in the plurality of measurement data sets according to the measurement data obtained by the preset threshold sampling corresponding to the sub-range interval comprises: Taking the measurement data obtained by the preset threshold sampling corresponding to each of the sub-range intervals in the plurality of measurement data sets as the candidate data to obtain the M candidate data; Taking the measurement data in the plurality of measurement data sets except the M candidate data as the auxiliary data to obtain the N auxiliary data.

4. The radar ranging method of claim 2, wherein, When transmitting a plurality of outgoing signals in the measurement period, and the transmission powers of the plurality of outgoing signals are different, the determination of the M candidate data and the N auxiliary data in the plurality of measurement data sets according to the measurement data obtained by the preset threshold sampling corresponding to the sub-range interval comprises: The echo signals corresponding to the exit signals of the preset transmission power in the multiple measurement data sets are sampled by the preset threshold values corresponding to each of the sub-distance intervals to obtain measurement data as the candidate data, so as to obtain M candidate data; Measurement data in the multiple measurement data sets except for the M candidate data is taken as the auxiliary data, so as to obtain N auxiliary data.

5. The radar ranging method of claim 1, wherein, The determination of the main composite degree of the candidate data Si includes: At least one candidate data meeting preset requirements in multiple adjacent measurement periods is taken as a comparison data set; wherein, the candidate data in the comparison data set and the candidate data Si in the current measurement period correspond to exit signals with the same transmission power, and the candidate data in the comparison data set and the candidate data Si in the current measurement period correspond to the same threshold value; The candidate data Si in the current measurement period is compared with each of the candidate data in the comparison data set in sequence; The main composite degree of the candidate data Si is obtained according to the comparison result.

6. The radar ranging method of claim 5, wherein, The main composite degree of the candidate data Si is obtained according to the comparison result, including: When the difference between one of the candidate data in the comparison data set and the candidate data Si in the current measurement period is less than a first preset value, the main composite degree of the candidate data Si is increased by 1; Each of the candidate data in the comparison data set is traversed to obtain the main composite degree of the candidate data Si; or, When the main composite degree of the candidate data Si is greater than or equal to a main composite degree threshold value, the main composite degree of the candidate data Si is obtained as the main composite degree threshold value.

7. The radar ranging method of claim 1, wherein, The determination of the auxiliary composite degree of the candidate data Si includes: The candidate data Si is compared with N auxiliary data in sequence; The auxiliary composite degree of the candidate data Si is obtained according to the comparison result.

8. The radar ranging method of claim 7, wherein, The auxiliary composite degree of the candidate data Si is obtained according to the comparison result, including: When the difference between one of the auxiliary data in the N auxiliary data and the candidate data Si is less than a second preset value, the auxiliary composite degree of the candidate data is increased by 1; Each of the auxiliary data in the N auxiliary data is traversed to obtain the auxiliary composite degree of the candidate data Si; or, When the auxiliary composite degree of the candidate data Si is greater than or equal to an auxiliary composite degree threshold value, the auxiliary composite degree of the candidate data Si is obtained as the auxiliary composite degree threshold value.

9. The radar ranging method of claim 1, wherein, The determination of the cross composite degree of the candidate data Si includes: The sub-distance interval where the candidate data Si is located is determined as an intermediate sub-distance interval, and at least one of the sub-distance intervals other than the intermediate sub-distance interval in K sub-distance intervals is taken as a target sub-distance interval; wherein, K≤X, and K is a positive integer; The candidate data Si is compared with the candidate data in at least one target sub-distance interval in sequence; The cross composite degree of the candidate data Si is obtained according to the comparison result.

10. The radar ranging method of claim 9, wherein, The cross composite degree of the candidate data Si is obtained according to the comparison result, including: when the difference between the candidate data in at least one of the target sub-distance intervals and the candidate data Si is less than a third preset value, the cross-complexity of the candidate data is increased by 1; traversing the candidate data in at least one of the target sub-distance intervals to obtain the cross-complexity of the candidate data Si; or when the cross-complexity of the candidate data Si is greater than or equal to a cross-complexity threshold, the cross-complexity of the candidate data Si is obtained as the cross-complexity threshold.

11. The radar ranging method of claim 1, wherein, The method further includes: determining the target data from the candidate data with the highest total complexity among the M candidate data.

12. The radar ranging method of claim 11, wherein, The method further includes: when the candidate data with the highest total complexity among the M candidate data is a single data, determining the candidate data with the highest total complexity as the target data; when the candidate data with the highest total complexity among the M candidate data is multiple data, determining the candidate data with the highest total complexity and the largest distance value as the target data.

13. The radar ranging method of claim 2, wherein, The method further includes: obtaining endpoint values of the sub-distance intervals and constructing a smoothing interval centered on the endpoint values; determining whether the target data is located in the smoothing interval; determining the measurement distance according to the determination result.

14. The radar ranging method of claim 13, wherein, The method further includes: when the target data is not located in the smoothing interval, determining the measurement distance to be equal to the target data; when the target data is located in the smoothing interval, determining the sub-distance interval where the target data is located as a first sub-distance interval, determining a sub-distance interval adjacent to the first sub-distance interval in the smoothing interval as a second sub-distance interval; obtaining a first threshold corresponding to the first sub-distance interval and a second threshold corresponding to the second sub-distance interval; determining measurement data located in the smoothing interval in a measurement data set sampled based on the first threshold as a first distance and measurement data located in the smoothing interval in a measurement data set sampled based on the second threshold as a second distance; performing weighted calculation on the first distance and the second distance to obtain the measurement distance.

15. A radar ranging device, characterized by The radar distance measuring device includes: a data acquisition module configured to obtain a plurality of measurement data sets sampled based on a plurality of thresholds from at least one echo signal in a measurement period; wherein one measurement data set is obtained from the echo signal based on one threshold, and each measurement data set includes at least one measurement data. The composite degree calculation module is configured to determine M candidate data and N auxiliary data in the plurality of measurement data sets, determine a sub-distance interval in which each measurement data in the plurality of measurement data sets is located, to obtain a sub-distance interval corresponding to the candidate data and the auxiliary data, determine a main composite degree, an auxiliary composite degree and a cross composite degree of the candidate data Si according to the candidate data, the auxiliary data and the sub-distance interval corresponding to the candidate data, and perform weighted calculation according to the main composite degree, the auxiliary composite degree and the cross composite degree of the candidate data Si to obtain a total composite degree of the candidate data Si; wherein M and N are positive integers, and the sum of M and N is equal to the total number of measurement data included in the plurality of measurement data sets, the candidate data Si is any one of the M candidate data, 1≤i≤M and i is an integer, the main composite degree of the candidate data Si is used to represent the similarity between the candidate data Si obtained in the current measurement period and the candidate data obtained in the adjacent measurement period and meeting the preset requirement, and the total composite degree of the candidate data Si is used to represent the similarity between the candidate data Si and other candidate data in the same measurement period and the measurement data in the adjacent measurement period; The data selection module is configured to determine target data from the M candidate data according to the total composite degree. The distance calculation module is configured to determine a measurement distance according to the target data.

16. An electronic device, comprising: The electronic device comprises: a memory configured to store executable program code; a processor configured to call and run the executable program code from the memory, so that the electronic device performs the radar ranging method according to any one of claims 1 to 14.

17. A computer readable storage medium characterized in that, The computer readable storage medium stores a computer program, when the computer program is executed, the radar ranging method according to any one of claims 1 to 14 is realized.

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