Radar ranging method and device, electronic equipment and computer readable storage medium

Abstract

The invention provides a radar ranging method and device, electronic equipment and a computer readable storage medium, and relates to the technical field of radars, and the method comprises the steps: obtaining a plurality of measurement data sets obtained through the sampling of at least one echo signal in a measurement period based on a plurality of threshold values; wherein the echo signal is sampled based on a threshold value to obtain a measurement data set, and each measurement data set comprises at least one piece of measurement data; determining M pieces of alternative data and N pieces of auxiliary data in the plurality of measurement data sets, and obtaining the total composite degree of each piece of alternative 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 pieces of alternative data according to the total composite degree; and determining a measurement distance according to the target data. According to the invention, the dynamic range of the laser radar system can be improved, so that the measurement precision of the laser radar is improved, and the close-range detection blind area of the laser radar is shortened.

Description

Technical Field

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

[0002] The system dynamic range of the LiDAR is a very important indicator for the measurement accuracy of the LiDAR. It should not only prevent the close-range from being oversaturated, but also ensure that the echo signal can be fully detected at a long distance. When the close-range echo is saturated, it will not only lead to poor ranging accuracy, but also make it impossible to detect the echo of effective targets at close range, making the close-range blind area of ​​the LiDAR larger. Therefore, how to improve the measurement accuracy of the LiDAR and shorten the blind area of ​​the LiDAR's close-range detection has become an urgent problem to be solved. Summary of the invention

[0003] The present application provides a radar ranging method, device, electronic device and computer-readable storage medium, which can improve the dynamic range of the laser radar system, thereby improving the measurement accuracy of the laser radar and shortening the close-range detection blind area of ​​the laser radar.

[0004] In a first aspect, a radar ranging method is provided, the radar ranging method comprising: acquiring multiple measurement data sets obtained by sampling at least one echo signal within a measurement period based on multiple threshold values; wherein the echo signal is sampled based on one of the threshold values ​​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 multiple measurement data sets, and acquiring a total compositeness of each of the 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 data sets; determining target data from the M candidate data according to the total compositeness; and determining a measurement distance according to the target data.

[0005] In combination with the first aspect, in some possible implementations, the radar ranging method further includes: 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; and determining M candidate data and N auxiliary data from the multiple measurement data sets, including: determining the M candidate data and N auxiliary data from the multiple measurement data sets based on the measurement data sampled according to a preset threshold value corresponding to the sub-range interval.

[0006] In combination with the first aspect and the above-mentioned implementation manner, in some possible implementation manners, when an outgoing signal is emitted within the measurement period, the measurement data sampled according to the preset threshold value corresponding to the sub-distance interval is used to determine M candidate data and N auxiliary data in multiple measurement data sets, including: using the measurement data sampled according to the preset threshold value corresponding to each sub-distance interval in the multiple measurement data sets as the candidate data to obtain M candidate data; using the measurement data other than the M candidate data in the multiple measurement data sets as the auxiliary data to obtain N auxiliary data.

[0007] In combination with the first aspect and the above-mentioned implementation methods, in some possible implementation methods, when multiple outgoing signals are emitted within the measurement period and the emission powers of the multiple outgoing signals are different, the measurement data obtained by sampling according to the preset threshold value corresponding to the sub-distance interval determines M of the candidate data and N of the auxiliary data in the multiple measurement data sets, including: taking the echo signal corresponding to the outgoing signal with a preset emission power in the multiple measurement data sets, and the measurement data obtained by sampling according to the preset threshold value corresponding to each sub-distance interval, as the candidate data, so as to obtain M of the candidate data; taking the measurement data other than the M of the candidate data in the multiple measurement data sets as the auxiliary data, so as to obtain N of the auxiliary data.

[0008] In combination with the first aspect and the above-mentioned implementation manner, in some possible implementation manners, obtaining the total complexity of each of the candidate data includes: determining the sub-distance interval in which each measurement data in the multiple measurement data sets is located to obtain the sub-distance interval corresponding to the candidate data and the auxiliary data; determining the main complexity, auxiliary complexity and cross-complexity of the candidate data Si according to 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 the M candidate data, 1≤i≤M and i is an integer; performing weighted calculation according to the main complexity, the auxiliary complexity and the cross-complexity of the candidate data Si to obtain the total complexity of the candidate data Si.

[0009] In combination with the first aspect and the above-mentioned implementation methods, in some possible implementation methods, determining the main recombination degree of the alternative data Si includes: obtaining at least one of the alternative data that meets the preset requirements in multiple adjacent measurement cycles as a comparison data set; wherein, meeting the preset requirements is that the output signal corresponding to the alternative data in the comparison data set and the output signal corresponding to the alternative data Si in the current measurement cycle have the same transmission power, and the threshold corresponding to the alternative data in the comparison data set is the same as the threshold corresponding to the alternative data Si in the current measurement cycle; comparing the alternative data Si in the current measurement cycle with each of the alternative data in the comparison data set in turn; and obtaining the main recombination degree of the alternative data Si according to the comparison result.

[0010] In combination with the first aspect and the above-mentioned implementation manner, in some possible implementation manners, obtaining the main complexity of the candidate data Si according to the comparison result includes: 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 complexity of the candidate data Si is increased by 1; traversing each of the candidate data in the comparison data set to obtain the main complexity of the candidate data Si; or, when the main complexity of the candidate data Si is greater than or equal to a main complexity threshold, obtaining the main complexity of the candidate data Si as the main complexity threshold.

[0011] In combination with the first aspect and the above implementations, in some possible implementations, determining the auxiliary complexity of the candidate data Si includes: comparing the candidate data Si with the N auxiliary data in sequence; and obtaining the auxiliary complexity of the candidate data Si according to the comparison result.

[0012] In combination with the first aspect and the above-mentioned implementation manner, in some possible implementation manners, the auxiliary complexity of the alternative data Si is obtained according to the comparison result, including: when the difference between one of the N auxiliary data and the alternative data Si is less than a second preset value, the auxiliary complexity of the alternative data is increased by 1; each of the N auxiliary data is traversed to obtain the auxiliary complexity of the alternative data Si; or, when the auxiliary complexity of the alternative data Si is greater than or equal to the auxiliary complexity threshold, the auxiliary complexity of the alternative data Si is obtained as the auxiliary compliance threshold.

[0013] In combination with the first aspect and the above-mentioned implementation methods, in some possible implementation methods, determining the cross-complexity of the candidate data Si includes: determining that the sub-distance interval in which the candidate data Si is located is an intermediate sub-distance interval, obtaining at least one of the K sub-distance intervals other than the intermediate sub-distance interval as a target sub-distance interval, where K≤X, and K is a positive integer; comparing the candidate data Si with the candidate data in at least one of the target sub-distance intervals in turn; and obtaining the cross-complexity of the candidate data Si according to the comparison result.

[0014] In combination with the first aspect and the above-mentioned implementation manner, in some possible implementation manners, the cross-complexity of the alternative data Si is obtained according to the comparison result, including: when the difference between an alternative data in at least one of the target sub-distance intervals and the alternative data Si is less than a third preset value, the cross-complexity of the alternative data is increased by 1; the alternative data in at least one of the target sub-distance intervals are traversed to obtain the cross-complexity of the alternative data Si; or, when the cross-complexity of the alternative data Si is greater than or equal to a cross-complexity threshold, the cross-complexity of the alternative data Si is obtained as the cross-complexity threshold.

[0015] In combination with the first aspect and the above implementation manner, in some possible implementation manners, determining the target data from the M candidate data according to the total complexity includes:

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

[0017] In combination with the first aspect and the above-mentioned implementation methods, in some possible implementation methods, determining the target data from the M candidate data with the highest total compositeness includes: when the candidate data with the highest total compositeness among the M candidate data is a single one, determining the candidate data with the highest total compositeness as the target data; when the candidate data with the highest total compositeness among the M candidate data is multiple, determining the candidate data with the highest total compositeness and the largest distance value as the target data.

[0018] In combination with the first aspect and the above-mentioned 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 with the endpoint values ​​as the center; judging whether the target data is located within the smooth interval; and determining the measurement distance based on the judgment result.

[0019] In combination with the first aspect and the above-mentioned implementation manner, in some possible implementation manners, determining the measured distance based on the judgment result includes: when the target data is not within the smooth interval, determining that the measured distance is equal to the target data; when the target data is within the smooth interval, determining that the sub-distance interval where the target data is located is a first sub-distance interval, and determining that the sub-distance interval adjacent to the first sub-distance interval in the smooth interval is 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 that the measured data in the measurement data set sampled based on the first threshold and located in the smooth interval is the first distance, and the measured data in the measurement data set sampled based on the second threshold and located in the smooth interval is the second distance; and performing weighted calculation on the first distance and the second distance to obtain the measured distance.

[0020] In a second aspect, a radar ranging device is provided, the radar ranging device comprising:

[0021] A data acquisition module, used to acquire multiple measurement data sets obtained by sampling at least one echo signal within a measurement period based on multiple threshold values; wherein the echo signal is sampled based on one of the threshold values ​​to obtain one measurement data set, and each of the measurement data sets contains at least one measurement data;

[0022] A complexity calculation module, used to determine M candidate data and N auxiliary data in the plurality of measurement data sets, and obtain a 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 included in the plurality of measurement data sets;

[0023] A data selection module, used for determining target data from the M candidate data according to the total complexity;

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

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

[0026] An interval division unit, used to divide 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;

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

[0028] In combination with the second aspect and the foregoing implementation manner, in some possible implementation manners, when an outgoing signal is emitted within the measurement period, the complexity calculation module determines, based on the measurement data sampled according to the preset threshold value corresponding to the sub-distance interval, the M candidate data and the N auxiliary data in the plurality of measurement data sets, including:

[0029] A first acquisition unit is configured to use measurement data obtained by sampling the preset threshold corresponding to each of the sub-distance intervals in the plurality of measurement data sets as the candidate data to obtain M candidate data;

[0030] The first screening unit is used to use the measurement data except the M candidate data in the plurality of measurement data sets as the auxiliary data to obtain N auxiliary data.

[0031] In combination with the second aspect and the foregoing implementation manner, in some possible implementation manners, when multiple outgoing signals are transmitted within the measurement period and the transmission powers of the multiple outgoing signals are different, the complexity calculation module determines the M candidate data and the N auxiliary data in the multiple measurement data sets based on the measurement data sampled according to the preset threshold value corresponding to the sub-range interval, including:

[0032] A second acquisition unit is configured to use the echo signal corresponding to the outgoing signal with the preset transmission power in the multiple measurement data sets, and the measurement data obtained by sampling the preset threshold value corresponding to each sub-range interval as the candidate data, so as to obtain M candidate data;

[0033] The second screening unit is used to use the measurement data except the M candidate data in the multiple measurement data sets as the auxiliary data to obtain N auxiliary data.

[0034] In combination with the second aspect and the above implementation manner, in some possible implementation manners, the complexity calculation module, in acquiring the total complexity of each candidate data, includes:

[0035] An interval determination unit, configured to determine a sub-distance interval in which each measurement data in the plurality of measurement data sets is located, so as to obtain a sub-distance interval corresponding to the candidate data and the auxiliary data;

[0036] A first calculation unit is used to determine the primary complexity, the auxiliary complexity and the cross complexity of the candidate data Si according to 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 weighted calculation according to the main complexity, the auxiliary complexity and the cross complexity of the candidate data Si to obtain the total complexity of the candidate data Si.

[0038] In combination with the second aspect and the foregoing implementation manner, in some possible implementation manners, the first computing unit includes:

[0039] a data set determination subunit, configured to obtain at least one candidate data meeting preset requirements in a plurality of adjacent measurement cycles as a comparison data set; wherein, meeting the preset requirements is that an outgoing signal corresponding to the candidate data in the comparison data set and an outgoing signal corresponding to the candidate data Si in the current measurement cycle have the same transmission power, and a threshold corresponding to the candidate data in the comparison data set and a threshold corresponding to the candidate data Si in the current measurement cycle are the same;

[0040] A first comparison subunit, used for sequentially comparing the candidate data Si of the current measurement cycle with each candidate data in the comparison data set;

[0041] The first determining subunit is configured to obtain the main complexity of the candidate data Si according to the comparison result.

[0042] In combination with the second aspect and the above-mentioned implementation methods, in some possible implementation methods, the first determination subunit is specifically used to: 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 complexity 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 complexity of the candidate data Si; or, when the main complexity of the candidate data Si is greater than or equal to a main complexity threshold, the main complexity of the candidate data Si is obtained as the main complexity threshold.

[0043] In combination with the second aspect and the foregoing implementation manner, in some possible implementation manners, the first computing unit further includes:

[0044] A second comparing subunit, used for comparing the candidate data Si with the N auxiliary data in sequence;

[0045] The second determining subunit is used to obtain the auxiliary complexity of the candidate data Si according to the comparison result.

[0046] In combination with the second aspect and the above-mentioned implementation methods, in some possible implementation methods, the second determination subunit is specifically used to: when the difference between one of the N auxiliary data and the alternative data Si is less than a second preset value, the auxiliary complexity of the alternative data Si is increased by 1; each of the N auxiliary data is traversed to obtain the auxiliary complexity of the alternative data Si; or, when the auxiliary complexity of the alternative data Si is greater than or equal to the auxiliary complexity threshold, the auxiliary complexity of the alternative data Si is obtained as the auxiliary compliance threshold.

[0047] In combination with the second aspect and the foregoing implementation manner, in some possible implementation manners, the first computing unit further includes:

[0048] a target interval determination subunit, configured to determine that the sub-distance interval where the candidate data Si is located is an intermediate sub-distance interval, and obtain at least one of the K sub-distance intervals other than the intermediate sub-distance interval as a target sub-distance interval; wherein K≤X, and K is a positive integer;

[0049] A third comparison subunit, used for sequentially comparing the candidate data Si with the candidate data within at least one of the target sub-distance intervals;

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

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

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

[0053] In combination with the second aspect and the above implementation manner, in some possible implementation manners, the data selection module determines the target data from the candidate data with the highest total complexity among the M candidate data, including:

[0054] A first selection unit is configured to, when there is only one candidate data with the highest total complexity among the M candidate data, determine the candidate data with the highest total complexity as the target data;

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

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

[0057] An interval construction unit, used for obtaining the endpoint value of the sub-distance interval and constructing a smooth interval with the endpoint value as the center;

[0058] A data judging unit, used for judging whether the target data is within the smoothing interval;

[0059] The distance determination unit is used to determine the measured distance according to the judgment result.

[0060] In combination with the second aspect and the foregoing implementation manner, in some possible implementation manners, the distance determination unit includes:

[0061] A first distance determination subunit, configured 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 used to, when the target data is within the smooth interval, determine that the sub-distance interval in which the target data is located is a first sub-distance interval, and determine that the sub-distance interval adjacent to the first sub-distance interval in the smooth interval is a 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 that the measurement data in the measurement data set sampled based on the first threshold and located in the smooth interval is used as a first distance, and the measurement data in the measurement data set sampled based on the second threshold and located in the smooth interval is used as a second distance; and perform weighted calculation on the first distance and the second distance to obtain the measurement distance.

[0063] In a third aspect, an electronic device is provided, comprising 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, so that the electronic device executes the radar ranging method in the first aspect or any possible implementation of the first aspect.

[0064] In a fourth aspect, a computer program product is provided, comprising: a computer program code, which, when executed on a computer, enables the computer to execute the radar ranging method in the first aspect or any possible implementation of the first aspect.

[0065] In a fifth aspect, a computer-readable storage medium is provided, which stores a computer program code. When the computer program code runs on a computer, the computer executes the radar ranging method in the above-mentioned first aspect or any possible implementation of the first aspect.

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

[0067] The embodiment of the present application adopts a technical solution of obtaining multiple measurement data sets based on multiple threshold sampling of at least one echo signal within a measurement period, determining M candidate data and N auxiliary data in the multiple measurement data sets, obtaining the total compositeness of each candidate data, determining the target data from the M candidate data according to the total compositeness, and determining the measurement distance according to the target data. When the laser radar is measuring distance, it can use multiple emission signals with different emission powers, multiple gains, multiple threshold sampling, and multiple analysis areas to perform overall control of the laser radar, and fuse the measurement data obtained by multiple emission signals with different emission powers, multiple gains, and multiple threshold sampling to obtain accurate measurement data, which not only improves the measurement accuracy of the laser radar, but also improves the dynamic range of the laser radar system and shortens the close-range detection blind spot of the laser radar. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 A schematic flow chart of a radar ranging method provided in an embodiment of the present application is shown;

[0069] Figure 2 An exemplary schematic diagram of a transmitting circuit is shown;

[0070] Figure 3 An exemplary schematic diagram of a receiving circuit is shown;

[0071] Figure 4 An exemplary schematic diagram of a receiving circuit is shown;

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

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

[0074] Figure 7An exemplary schematic diagram showing the receiving and transmitting signals and timing of a measurement cycle of a laser radar;

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

[0076] Fig. 9 Another exemplary schematic diagram showing the receiving and transmitting signals and timing of a measurement cycle of a laser radar;

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

[0078] Fig.11 A data fusion block diagram of a radar ranging method provided by an embodiment of the present application is shown;

[0079] Fig.12 A schematic diagram of the structure of a radar ranging device provided in an embodiment of the present application is shown;

[0080] Fig.13 A schematic structural diagram of an electronic device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0081] The technical solution in the present application will be described clearly and in detail below in conjunction with the accompanying drawings. In the description of the embodiments of the present application, unless otherwise specified, " / " means or, for example, A / B can mean A or B: "and / or" in the text is only a description of the association relationship of associated objects, indicating that there can be three relationships, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, "multiple" means two or more than two.

[0082] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying 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 the features.

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

[0084] Figure 1 A schematic flow chart of a radar ranging method provided in an embodiment of the present application is shown, such as Figure 1 As shown, the radar ranging method provided in the embodiment of the present application is applied to a laser radar, and the laser radar includes a transmitting circuit and a receiving circuit. Figure 2 As shown, Figure 2An exemplary framework schematic diagram of a transmitting circuit is shown, and the transmitting circuit includes a controllable transmitting power device, a transmitting driver, a light emitter, a transceiver timing controller and other units. In the transmitting circuit, a power control signal with different transmitting powers is generated by the transceiver timing controller to the controllable transmitting power device, and the controllable transmitting power device adjusts the transmitting power according to the control signal and transmits the energy of the laser to the transmitting driver; the transceiver timing controller also generates a transmitting control signal and transmits it to the transmitting driver. After receiving the transmitting control signal, the transmitting driver releases the energy of the laser to the light emitter, and the laser is emitted through the light emitter. The transceiver timing controller can control the timing and power of multiple transmissions.

[0085] like Figure 3 As shown, Figure 3 An exemplary framework schematic diagram of a receiving circuit is shown, and the receiving circuit includes a light receiving sensor unit, multiple groups of gain circuits, multiple groups of comparison circuits, etc. The receiving gain corresponding to each group of gain circuits is different, and the high gain circuit realizes high gain amplification of the echo signal, and the low gain circuit realizes low gain amplification of the echo signal. For the output results of the same group of gain circuits, different comparison 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 groups of gain circuits are shown as examples. In actual applications, more gain circuits can be added according to actual design requirements. In theory, the more gain circuit groups there are, the more amplification stages there are for the echo signal, and the larger the achievable receiving dynamic range of the entire system. Figure 3 Two groups of comparison circuits 323 are shown as examples. More comparison circuits can be added according to actual design requirements. The sampling thresholds of each group of comparison circuits can be equal or unequal. In theory, the more comparison circuits there are, the more data can be obtained, and the larger the achievable dynamic range of the entire system reception can be.

[0086] like Figure 4 As shown, Figure 4 FIG. 1 shows an exemplary schematic diagram of a receiving circuit. Figure 4 The receiving circuit shown is a multi-stage serial receiving system. The multi-stage serial receiving circuit can also achieve multi-stage gain and has the same advantages as the above-mentioned Figure 3 The multi-stage serial receiving circuit includes an optical receiving sensor unit (such as PIN, APD, SPAD, SiPM), a multi-stage gain circuit, a signal sampling module and a measurement module. Figure 4 What is shown is a 4-stage amplification circuit; wherein the signal sampling module can use a comparison circuit to realize its sampling function.

[0087] like Figure 5 As shown, Figure 5The schematic diagram of the system framework of the laser radar is shown. The laser radar system includes a controller, a transmitting circuit with multi-level power, a receiving circuit with multi-level gain, and a multi-level detection threshold unit. Among them, the transmitting circuit, the receiving circuit and the detection threshold unit do not require each unit to have a multi-level function at the same time. For example, it can be a transmitting circuit with multi-level power, and the detection threshold unit can be one level, or the receiving circuit has one level of gain; it can also be a one-level power transmitting circuit, and the detection threshold unit has multiple levels, or the receiving circuit has multiple levels of gain. For example, in one measurement cycle, the transmitting circuit transmits lasers three times, corresponding to three different transmitting powers. The amplifier circuit in the receiving circuit has four levels of gain. Then each transmission will have four different gain results, so there are a total of 3*4=12 combinations of measurement results. If there are three detection thresholds in each receiving gain, there are a total of 3*4*3=36 combinations of measurement results. Since these 36 measurement results are obtained by combining different power values, different receiving gains, and different detection thresholds, they can be used to cover high-energy and low-energy echo reception and processing, thereby achieving a very large dynamic range expansion.

[0088] like Figure 6 As shown, Figure 6 The figure shows the schematic diagram of the ranging control process of the laser radar. Figure 6 Taking three transmissions as an example, multiple sets of measurement data can be obtained for each transmission. For example, the corresponding receiving device for each transmission has three levels of gain and then samples to obtain three sets of data. After three transmissions, nine sets of measurement data can be obtained. The nine sets of measurement data are fused to obtain the measurement result. Among them, data fusion can select the most appropriate result as output based on signals such as distance and whether the echo energy is within the effective range.

[0089] The above radar ranging method includes the following schemes:

[0090] S110: Acquire multiple measurement data sets obtained by sampling at least one echo signal within a measurement period based on multiple threshold values.

[0091] In an exemplary embodiment, a laser radar measurement cycle includes at least one emission signal and a corresponding echo signal; an emission signal and a corresponding echo signal complete a detection, and a measurement distance is obtained by solving. An echo signal can correspond to multiple threshold samples, and a corresponding measurement data set is obtained after each threshold sample. Among them, the threshold can be understood as a receiving threshold, and can also be understood as a detection threshold.

[0092] When the lidar is measuring distance, the transmitting circuit will emit at least one output signal in a time sequence within the measurement period. The output signal can be understood as a laser signal. The receiving circuit will sample the echo signal corresponding to each output signal based on multiple thresholds within the measurement period to obtain multiple measurement data sets.

[0093] like Figure 7 As shown, Figure 7 An exemplary schematic diagram showing the transmit and receive signals and timing of a measurement cycle of a lidar. Figure 7 It is shown in the figure that the laser radar transmits the outgoing signal twice in sequence within one measurement cycle, and the receiving circuit can receive two corresponding echo signals; the receiving circuit includes four different thresholds, namely threshold 1, threshold 2, threshold 3 and threshold 4, and the two echo signals are sampled using four different thresholds respectively, and eight measurement data sets can be obtained in one measurement cycle; the echo signal corresponding to the outgoing signal transmitted for the first time is sampled by the above four threshold samplings to obtain four measurement data sets, namely Data_tx1_thr1, Data_tx1_thr2, Data_tx1_thr3 and Data_tx1_thr4, and the echo signal corresponding to the outgoing signal transmitted for the second time is sampled by the above four threshold samplings to obtain four measurement data sets, namely Data_tx2_thr1, Data_tx2_thr2, Data_tx2_thr3 and Data_tx2_thr4.

[0094] For each echo signal, a measurement data set is obtained based on a threshold sampling. When the threshold sampling is multiple, multiple measurement data sets will be obtained based on multiple threshold samplings for each echo signal. Each measurement data set contains at least one measurement data. It can be understood that each measurement data set includes not only the measurement data obtained by the echo signal corresponding to the outgoing signal reflected by the target object, but also the measurement data of the noise signal received by the laser radar.

[0095] S120: Determine M candidate data and N auxiliary data in a plurality of the measurement data sets, and obtain a total complexity of each candidate data.

[0096] For the multiple measurement data sets obtained in S110, all the measurement data included in the multiple measurement data sets are divided to obtain M candidate data and N auxiliary data, where 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 multiple measurement data sets. For example, the total number of measurement data included in the multiple measurement data sets is 10, and after data division, 2 candidate data and 8 auxiliary data are obtained. The candidate data and the auxiliary data cooperate to participate in the determination of the measurement distance of the target to be measured.

[0097] After M candidate data are obtained through data division, for each candidate data, the total compositeness of each candidate data is calculated, and the total compositeness is used to represent the similarity between the candidate data and other candidate data in the same measurement cycle and the measurement data in the adjacent measurement cycle.

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

[0099] The ranging range of the radar 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 may be to set multiple distance dividing points within the ranging range, and divide the ranging range into X sub-range intervals according to these distance dividing points, wherein the difference between two adjacent dividing points may be the same or different, that is, the length of each sub-range interval may be the same or different, or the length of some sub-range intervals may be the same, and the length of other sub-range intervals may be different. Since the amplitude of the echo signal is related to factors such as the measurement distance, the reflectivity of the object being measured, and the position of the object being measured, among the measurement data obtained by sampling multiple threshold values, the measurement result of a certain threshold sampling is more accurate. Based on these considerations, multiple distance dividing points are determined according to the settings of multiple thresholds set in the laser radar system.

[0101] like Figure 8 As shown, Figure 8 The diagram shows the division of the radar's ranging range. Figure 8 Where 0 to DIS_X represent the ranging range, D_1, D_2, ..., D_X-1 represent distance dividing points, and D_1, D_2, ..., D_X-1 divide the ranging range into X sub-distance intervals, which are S_1, S_2, ..., S_X.

[0102] In a possible implementation manner, the determining of the M candidate data and the N auxiliary data in the plurality of measurement data sets includes the following scheme:

[0103] According to the measurement data sampled according to the preset threshold value corresponding to the sub-distance interval, the M candidate data and the N auxiliary data in the plurality of measurement data sets are determined.

[0104] As mentioned above, a corresponding preset threshold sampling is set according to the position of each sub-distance interval (for example, in the long-distance segment, medium-distance segment, short-distance segment, etc. of the ranging range), and the confidence of the measurement data obtained by the preset threshold sampling corresponding to each sub-distance interval is the highest. Therefore, according to the measurement data obtained by the preset threshold sampling corresponding to the sub-distance interval, all measurement data included in the multiple measurement data sets are divided into data, thereby obtaining M candidate data and N auxiliary data, and the measurement distance is determined by the obtained M candidate data, which can improve the accuracy of the measurement distance.

[0105] In a possible implementation, when an outgoing signal is transmitted within the measurement period, the measurement data sampled according to the preset threshold value corresponding to the sub-range interval is used to determine the M candidate data and the N auxiliary data in the plurality of measurement data sets, including the following scheme:

[0106] Taking the measurement data obtained by sampling the preset threshold value corresponding to each of the sub-distance intervals in the plurality of measurement data sets as the candidate data, so as to obtain M candidate data;

[0107] The measurement data except the M candidate data in the plurality of measurement data sets are used as the auxiliary data to obtain N auxiliary data.

[0108] For example, the transmitting circuit transmits an outgoing signal within the measurement period, and the total number of all measurement data included in the obtained multiple measurement data sets is 10, namely measurement data 1, measurement data 2, ..., measurement data 10, and the sub-range intervals include 4, namely interval 1, interval 2, ..., interval 4, wherein the measurement data obtained by sampling the preset threshold corresponding to interval 1 is measurement data 1, the measurement data obtained by sampling the preset threshold corresponding to interval 2 is measurement data 3, the measurement data obtained by sampling the preset threshold corresponding to interval 3 is measurement data 5, and the 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 alternative data, a total of 4 alternative data; measurement data 2, measurement data 4, measurement data 6, measurement data 7, measurement data 9 and measurement data 10 are auxiliary data, a total of 6 auxiliary data.

[0109] For example, Fig. 9 Another exemplary schematic diagram showing the transmit and receive signals and timing of a measurement cycle of a lidar. Fig. 9 The Chinese-Israeli laser radar transmits two outgoing signals in succession within one measurement cycle, namely the first outgoing signal and the second outgoing signal. The transmission power P1 of the first outgoing signal is greater than the transmission power P2 of the second outgoing signal. The ranging range corresponding to the first outgoing signal is large and is divided into four sub-range intervals. The ranging range corresponding to the second outgoing signal is small and is divided into three sub-range intervals. At the same time, the thresholds for sampling the received echo signals are threshold 1-threshold 4 respectively. The first receiving and analyzing area is used to receive the echo signal corresponding to the first outgoing signal, and the second analysis area is used to receive the echo signal corresponding to the second outgoing signal.

[0110] Taking the echo signal corresponding to the first outgoing signal as an example, when the threshold 1 samples the echo signal, two measurement data can be obtained, namely H11 and H21. When the threshold 2, threshold 3, and threshold 4 sample the echo signal, one measurement data can be obtained, namely H12, H13, and H14. The measurement data H11, H12, H13, and H14 are all located in interval 2, and the preset threshold corresponding to interval 2 is threshold 3. Then the measurement data H13 is the candidate data, and the remaining measurement data H11, H12, and H14 are auxiliary data; the measurement data H21 is located in interval 4, and the preset threshold corresponding to interval 4 is threshold 1. Then the measurement data H21 is the candidate data. In actual applications, the measurement data obtained by sampling the preset threshold corresponding to each sub-range interval may include one, may include multiple, or may not include any.

[0111] The echo signal includes the detection echo returned after the outgoing signal is reflected by the object within the detection range, and also includes the interference echo caused by the ambient light entering the laser radar. In practice, when the detection echo in the echo signal is sampled using different thresholds, a measurement data can be obtained within a certain sub-distance interval. The measurement data obtained by sampling the detection echo with different thresholds are relatively similar. The measurement data obtained by sampling the preset threshold corresponding to the sub-distance interval is used as the candidate data, and the test data obtained by sampling other thresholds is used as auxiliary data. The total compositeness of the candidate data is relatively large and is finally determined as the result output. On the contrary, the measurement data obtained by sampling the interference echo with different thresholds is relatively discrete and evenly distributed in each sub-distance interval. Even if it is confirmed as the candidate data, the calculated total compositeness is also small and will not be determined as the result output.

[0112] In a possible implementation manner, when multiple outgoing signals are transmitted within the measurement period and the transmission powers of the multiple outgoing signals are different, the measurement data obtained by sampling according to the preset threshold corresponding to the sub-range interval, determining the M candidate data and the N auxiliary data in the multiple measurement data sets includes the following scheme:

[0113] The echo signal corresponding to the outgoing signal with the preset transmission power in the plurality of measurement data sets is sampled by the preset threshold value corresponding to each of the sub-range intervals as the candidate data to obtain M candidate data;

[0114] The measurement data except the M candidate data in the plurality of measurement data sets are used as the auxiliary data to obtain N auxiliary data.

[0115] In the aforementioned Fig. 9Based on the embodiment of , the echo signal corresponding to the first transmission signal is sampled using 4 thresholds to obtain 5 measurement data F11, F12, F13, F14, and F21, and the echo signal corresponding to the second transmission signal is sampled using 4 thresholds to obtain 4 measurement data F31, F32, F33, and F41. Among them, the measurement data F11, F12, F13, and F14 are located in the interval 2 corresponding to the first transmission signal, the measurement data F21 is located in the interval 4 corresponding to the first transmission signal, and the measurement data F31, F32, F33, and F41 are located in the interval 2 corresponding to the second transmission signal. The preset threshold corresponding to interval 2 of the first transmitted signal is threshold 3, the measurement data F13 is alternative data, and the 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 the measurement data F21 is alternative data; the preset threshold corresponding to interval 2 of the second transmitted signal is threshold 3, the measurement data F33 is alternative data, and the measurement data F31, F32, and F41 are auxiliary data.

[0116] For the case where multiple outgoing signals with different powers are emitted during a measurement period, the following description applies:

[0117] First, for the same sub-range interval, when sampling echo signals corresponding to different transmission powers to obtain candidate data, the corresponding preset thresholds may be different. Fig. 9 Based on the embodiment, in the ranging range of the first transmission signal with a transmission power of P1, the preset threshold corresponding to interval 2 is threshold 3; and in the ranging range of the second transmission 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 transmission signal and the second transmission signal detect the same object to be measured within the ranging range, the amplitude of the detection echo H1 is greater than the amplitude of the detection echo H3, and the measurement data obtained by sampling the detection echo H3 using the relatively low threshold 2 is more accurate.

[0118] Second, when the same threshold is used to sample echo signals corresponding to different transmission powers in the same sub-range interval, the measurement data obtained may be different. The detection echo H1 and the detection echo H3 are obtained by detecting the same object to be measured, and the measurement data obtained after sampling are theoretically the same. For example, if the amplitude of the detection echo H1 is greater than the amplitude of the detection echo H3, the two detection echoes are sampled using threshold 2 and the measurement data obtained are different. The detection echo H1 is more saturated, and the measurement data obtained by sampling is smaller.

[0119] In a possible implementation, the obtaining of the total complexity of each candidate data includes the following scheme:

[0120] Determine a sub-distance interval where each measurement data in the plurality of measurement data sets is located, so as to obtain a sub-distance interval corresponding to the candidate data and the auxiliary data;

[0121] Determine the primary complexity, the auxiliary complexity and the cross complexity 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;

[0122] The total complexity of the candidate data Si is obtained by performing weighted calculation according to the main complexity, the auxiliary complexity and the cross complexity of the candidate data Si.

[0123] Since the measurement range is divided into multiple sub-distance intervals in advance, and the distance value of each measurement data is known through calculation, each measurement data in the multiple measurement data sets is divided into intervals according to the distance value to determine the sub-distance interval where each measurement data is located. After the sub-distance interval where each measurement data is located is determined, the sub-distance interval corresponding to each candidate data and each auxiliary data is naturally determined. Assume that the ranging range is 0-200 meters, and the sub-distance intervals after division are [0,2), [2,5), [5,20), [20,200]. For example, 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 where measurement data 1 is located is [2,5), and the sub-distance interval where measurement data 2 is located is [20,200]. If measurement data 1 is candidate data, the sub-distance interval of the candidate data is [2,5), and if measurement data 2 is auxiliary data, the sub-distance interval of the auxiliary data is [20,200].

[0124] After obtaining the sub-distance intervals corresponding to each candidate data and each auxiliary data, the main complexity, auxiliary complexity and cross complexity of the candidate data Si are calculated according to the sub-distance intervals corresponding to the candidate data, the auxiliary data, and the alternative data and the auxiliary data, that is, the main complexity, auxiliary complexity and cross complexity of each candidate data are calculated.

[0125] According to the actual application requirements, the corresponding weight values ​​are set for the main complexity, auxiliary complexity and cross complexity respectively. After obtaining the main complexity, auxiliary complexity and cross complexity of the candidate data Si, the main complexity, auxiliary complexity and cross complexity of the candidate data Si are weightedly calculated to obtain the total complexity of the candidate data Si. The total complexity of the candidate data Si is expressed as 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 Indicates the main composite degree, F2 Si Indicates auxiliary compound degree, F3 Si represents the cross-complexity, w1 represents the weight value of the main complexity, w2 represents the weight value of the auxiliary complexity, and w3 represents the weight value of the cross-complexity.

[0128] The above data weighted calculation is to make the data equivalent return to one data unit. Of course, the total compositeness of the candidate data Si can also be calculated without weighted calculation. The total compositeness of the candidate data Si is obtained by adding the main compositeness, auxiliary compositeness and cross compositeness of the candidate data Si, that is:

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

[0130] In a possible implementation, determining the primary complexity of the candidate data Si includes the following schemes:

[0131] Acquire at least one candidate data meeting preset requirements in a plurality of adjacent measurement cycles as a comparison data set;

[0132] Comparing the candidate data Si of the current measurement cycle with each candidate data in the comparison data set in sequence;

[0133] The main complexity of the candidate data Si is obtained according to the comparison result.

[0134] Regarding the calculation of the main composite degree: the preset requirement is that the emission signal corresponding to the candidate data in the comparison data set and the emission signal corresponding to the candidate data Si in the current measurement cycle have the same transmission power, and the threshold corresponding to the candidate data in the comparison data set is the same as the threshold corresponding to the candidate data Si in the current measurement cycle. For example, the emission power of the emission 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 the threshold C1. Assuming that the emission power of the emission 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 the threshold C1, and the emission power of the emission 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 the threshold C1, therefore, the candidate data 11 and the candidate data 31 are used as the comparison data set, that is, the emission power and threshold corresponding to the candidate data 11 and the candidate data 31 are respectively the same as the emission power and threshold corresponding to the candidate data Si.

[0135] In the aforementioned Fig. 9 Based on the embodiment of the present invention, the measurement data F13, F21, and F33 are all candidate data. Taking the comparison data set for obtaining the measurement data F13 as an example, the echo signal corresponding to the first outgoing signal is obtained from an adjacent measurement cycle of the current measurement cycle shown in the figure, and the measurement data obtained by sampling with a threshold value 3 is used. If there is, it is the candidate data in the comparison data set, and if not, another adjacent measurement cycle is confirmed; the comparison data set can be obtained only from a measurement cycle adjacent to the current measurement cycle, or from multiple measurement cycles adjacent to the current measurement cycle, such as 5 adjacent measurement cycles. The methods for obtaining the comparison data sets of the measurement data F21 and F33 are similar, and will not be repeated here.

[0136] After obtaining the comparison data set, each candidate data in the comparison data set is compared with the candidate data Si, and then the main compositeness of the candidate data Si is set based on the comparison result. Among them, the main compositeness is used to represent the similarity between the candidate data Si obtained in the current measurement cycle and the candidate data obtained in the adjacent measurement cycle and meeting the preset requirements. Since the probability of detecting the same object under test in adjacent measurement cycles is relatively high, the similarity of the obtained detection echoes is large. Based on this premise, when the candidate data of the current detection cycle is compared with the candidate data of the adjacent detection cycle, the greater the main compositeness is when the real detection echo is used as the candidate data for the main compositeness calculation; the smaller the main compositeness is when the noise echo caused by ambient light is used as the candidate data for the main compositeness calculation.

[0137] In a possible implementation, obtaining the main complexity of the candidate data Si according to the comparison result includes the following scheme:

[0138] When the difference between one of the candidate data in the comparison data set and the candidate data Si in the current measurement cycle is less than a first preset value, the main complexity of the candidate data Si is increased by 1;

[0139] Traversing each candidate data in the comparison data set to obtain the main complexity of the candidate data Si; or,

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

[0141] For each candidate data traversed in the comparison data set, the difference between the candidate data Si and the candidate data traversed in the comparison data set is calculated, and the difference is recorded as the first difference. Then, the first difference obtained in each traversal is compared with the first preset value. If the first difference is less than the first preset value, the main composite degree of the candidate data Si is increased by 1, and so on, the main composite degree of the candidate data Si is obtained. For example, the initial main composite degree of the candidate data Si is 0, the first difference obtained in the first traversal is less than the first preset value, and the main composite degree of the candidate data Si is 1. The first difference obtained in the second traversal is less than the first preset value, and the main composite degree of the candidate data Si is 1+1=2. The first difference obtained in the third traversal is less than the first preset value, and the main composite degree of the candidate data Si is 1+1+1=3. And so on, the final main composite degree of the candidate data Si is obtained.

[0142] After the principal complexity of the candidate data Si is calculated in the above manner, in order to reduce the amount of calculation, after each traversal to obtain the principal complexity of the candidate data Si, the principal complexity of the candidate data Si is compared with the principal complexity threshold. If the principal complexity of the candidate data Si is greater than or equal to the principal complexity threshold, the principal complexity threshold is used as the principal complexity of the candidate data Si, and the traversal is stopped.

[0143] In a possible implementation, determining the auxiliary complexity of the candidate data Si includes the following schemes:

[0144] Comparing the candidate data Si with the N auxiliary data in sequence;

[0145] The auxiliary complexity of the candidate data Si is obtained according to the comparison result.

[0146] Regarding the calculation of auxiliary complexity: compare the candidate data Si with each of the N auxiliary data, and set the auxiliary complexity of the candidate data Si according to the comparison result. Among them, the auxiliary complexity is used to indicate the similarity between the candidate data Si and the auxiliary data. The detected echo signal is detected by the output signals of different transmission powers and sampled with different thresholds within a detection cycle, and the probability of obtaining similar measurement data is high. Based on this premise, the auxiliary complexity of the candidate data of the detected echo signal is higher. When the noise or interference is high and is used as the candidate data, the calculated auxiliary complexity is lower.

[0147] In the aforementioned Fig. 9 On the basis of the embodiment, the measurement data F13, F21, and F33 are all candidate data, and the measurement data F11, F12, F14, F31, F32, and F41 are all auxiliary data.

[0148] Taking the auxiliary complex degree of the measurement data F13 as an example, the candidate data F13 is compared with the auxiliary data F11, F12, F14, F31, F32, and F41 to obtain the comparison results. Among them, the auxiliary data F11, F12, F14 and the candidate data F13 are the measurement data obtained by sampling the same detection echo H1 by different thresholds, so the similarity is relatively high; the detection echo H3 corresponding to the auxiliary data F31 and F32 and the detection echo H1 corresponding to the candidate data F13 are obtained by detecting the same object to be measured, so the similarity is relatively high; the auxiliary data F41 is the measurement data obtained by sampling the noise echo H4 by the threshold 1, and the similarity with the candidate data F13 is low. Therefore, 5 of the 6 auxiliary data have a high similarity with the candidate data F13.

[0149] Taking the auxiliary complex degree of the measurement data F21 as an example, the candidate data F21 is compared with the auxiliary data F11, F12, F14, F31, F32, and F41 to obtain the comparison result. Among them, the candidate data F21 is the measurement data obtained by sampling the noise echo H2 by the threshold 1, and F11, F12, F14, F31, F32, and F41 are not in the same subinterval with it, and the similarity is low. Therefore, the similarity between the 6 auxiliary data and the candidate data F21 is low.

[0150] In a possible implementation, obtaining the auxiliary complexity of the candidate data Si according to 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 complexity of the candidate data is increased by 1;

[0152] Traverse each of the N auxiliary data to obtain the auxiliary complexity of the candidate data Si; or,

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

[0154] For each auxiliary data traversed in each of the N auxiliary data, the difference between the candidate data Si and the candidate data traversed each time is calculated, and the difference is recorded as the second difference. Then, the second difference obtained in each traversal is compared with the second preset value. If the second difference is less than the second preset value, the auxiliary complexity of the candidate data Si is increased by 1, and so on, the auxiliary complexity of the candidate data Si is obtained. For example, the initial auxiliary complexity of the candidate data Si is 0, the second difference obtained in the first traversal is less than the second preset value, and the auxiliary complexity of the candidate data Si is 1. The second difference obtained in the second traversal is less than the second preset value, and the auxiliary complexity of the candidate data Si is 1+1=2. The second difference obtained in the third traversal is less than the second preset value, and the auxiliary complexity of the candidate data Si is 1+1+1=3. And so on, the final auxiliary complexity of the candidate data Si is obtained.

[0155] After the auxiliary complexity of the candidate data Si is calculated in the above manner, in order to reduce the amount of calculation, after each traversal to obtain the auxiliary complexity of the candidate data Si, the auxiliary complexity of the candidate data Si is compared with the auxiliary complexity threshold. If the auxiliary complexity of the candidate data Si is greater than or equal to the auxiliary complexity threshold, the auxiliary complexity threshold is used as the auxiliary complexity of the candidate data Si, and the traversal is stopped.

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

[0157] Determine that the sub-distance interval where the candidate data Si is located is an intermediate sub-distance interval, and obtain at least one of the K sub-distance intervals except the intermediate sub-distance interval as a target sub-distance interval; wherein K≤X, and K is a positive integer;

[0158] Comparing the candidate data Si with the candidate data within at least one of the target sub-distance intervals in sequence;

[0159] The cross-complexity of the candidate data Si is obtained according to the comparison result.

[0160] Since not every sub-distance interval has candidate data, only the sub-distance interval where the candidate data is located actually participates in the calculation of the cross-complexity. Among the X sub-distance intervals, K sub-distance intervals contain candidate data. The candidate data of the target sub-distance interval determined from the K sub-distance intervals participate in the calculation of the cross-complexity. Among them, the target sub-distance interval can be a sub-distance interval adjacent to the middle sub-distance interval, a sub-distance interval that is adjacent to or not adjacent to the middle sub-distance interval, or the other K-1 sub-distance intervals except the middle sub-distance interval.

[0161] Regarding the calculation of cross-complexity: the sub-distance interval where the candidate data Si is located 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 the candidate data Si is located is the middle sub-distance interval, and at least one is determined as the target sub-distance interval from intervals 2, 4, and 6; a 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 not adjacent to interval 3 can be determined as the target sub-distance interval, and other sub-distance intervals except 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 in the target sub-distance interval can be confirmed, and then the candidate data Si is compared with each candidate data in the target sub-distance interval to obtain a comparison result. For example, the candidate data Si is S1, and the target sub-distance interval includes 3, namely interval 2, interval 3 and interval 4. Interval 2 and interval 4 each correspond to an alternative data, and there is no alternative data in interval 3. S1 is compared with the alternative data corresponding to interval 2 and the alternative number corresponding to interval 4 in turn. A comparison result is obtained each time, and the cross-complexity of the candidate data Si is set based on the comparison result obtained each time. Among them, the cross-complexity is used to represent the similarity between the candidate data Si and the candidate data corresponding to the target sub-distance interval.

[0163] In the aforementioned Fig. 9 On the basis of the embodiment, the measurement data F13, F21, and F33 are all candidate data, and the measurement data F11, F12, F14, F31, F32, and F41 are all auxiliary data.

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

[0165] For echo signals falling near the distance dividing point of the sub-range interval, after sampling with different thresholds, their measurement data may fall within two adjacent sub-range intervals and be determined as candidate data for the two sub-range intervals respectively. Since the measurement is for the same detection echo or interference echo, the measurement data obtained are similar, and the cross-complexity of the calculated candidate data is relatively large.

[0166] In a possible implementation, obtaining the cross-complexity of the candidate data Si according to the comparison result 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] Traversing the candidate data within at least one of the target sub-distance intervals to obtain the cross-complexity of the candidate data Si; 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, one candidate data in a target sub-distance interval is traversed each time, and the difference between the candidate data obtained in each traversal and the candidate data Si is calculated, and the difference is recorded as the third difference, and then the third difference obtained in each traversal is compared with the third preset value. If the third difference is less than the third preset value, the cross-complexity threshold of the candidate data Si is increased by 1, and so on, to obtain the cross-complexity threshold of the candidate data Si. For example, the initial cross-complexity threshold of the candidate data Si is 0, the third difference obtained by traversing the first target sub-distance interval for the first time is less than the third preset value, and the cross-complexity threshold of the candidate data Si is 1, the third difference obtained by traversing the first target sub-distance interval for the second time is less than the third preset value, and the cross-complexity threshold of the candidate data Si is 1+1=2, the third difference obtained by traversing the third target sub-distance interval for the third time is less than the third preset value, and the cross-complexity threshold of the candidate data Si is 1+1+1=3, and so on, to obtain the final cross-complexity threshold of the candidate data Si.

[0171] After the cross-complexity threshold of the candidate data Si is calculated in the above manner, in order to reduce the amount of calculation, after each traversal to obtain 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 threshold. If the cross-complexity threshold of the candidate data Si is greater than or equal to the cross-complexity threshold threshold, the cross-complexity threshold threshold is used as the cross-complexity threshold of the candidate data Si, and the traversal is stopped.

[0172] S130: Determine target data from the M candidate data according to the total complexity.

[0173] After the total compositeness of each candidate data is calculated, a candidate data with the highest confidence is determined from the M candidate data according to the total compositeness as the target data for determining the measurement distance.

[0174] In a possible implementation, the determining the target data from the M candidate data according to the total complexity includes the following scheme:

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

[0176] After obtaining the total compositeness of each of the M candidate data, all the total compositeness are sorted, and then the candidate data with the highest total compositeness is obtained, and then the target data is determined according to the candidate data with the highest total compositeness.

[0177] In a possible implementation, determining the target data from the candidate data with the highest total complexity among the M candidate data includes the following scheme:

[0178] When there is only one candidate data with the highest total complexity among the M candidate data, determining the candidate data with the highest total complexity as the target data;

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

[0180] Considering that there may be one or more candidate data with the highest total compositeness among the M candidate data, that is, there are two situations, and the methods of determining the target data are different in different situations, that is, if the candidate data with the highest total compositeness is single, the candidate data with the highest total compositeness is determined as the target data; wherein, when the candidate data with the highest total compositeness is one, the candidate data with the highest total compositeness is determined as the target data, indicating that the candidate data with the highest total compositeness has the highest confidence among all the candidate data. If there are multiple candidate data with the highest total compositeness, the candidate data with the highest total compositeness is determined as the target data, and the distance values ​​among the candidate data with the highest total compositeness are sorted to obtain the candidate data with the largest distance value among the candidate data with the highest total compositeness, and the candidate data with the largest distance value among the candidate data with the highest total compositeness is determined as the target data, that is, the target data is the candidate data with the highest total compositeness and the largest distance value among the M candidate data. Among them, since the interference signal at a long distance is weaker than that at a short distance, the echo signal at a long 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 complexity, the candidate data with the highest total complexity and the largest distance value among the M candidate data is determined as the target data.

[0181] S140: Determine a measurement distance according to the target data.

[0182] In a possible implementation, determining the measurement distance according to the target data includes the following scheme:

[0183] Obtaining the endpoint value of the sub-distance interval, and constructing a smooth interval with the endpoint value as the center;

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

[0185] The measuring distance is determined according to the judgment result.

[0186] The endpoint values ​​of the sub-distance intervals are the above-mentioned distance demarcation points, including the maximum and limit values ​​of the sub-distance intervals. The smoothing intervals are constructed with the endpoint values ​​of each sub-distance interval as the center and the preset distance smoothing adjustment values. Among them, the distance smoothing adjustment values ​​are D_smooth1 and D_smooth2. Assuming that the endpoint value is D_X, then the smoothing interval is [D_X-D_smooth1, D_X+D_smooth2]. The smoothing intervals constructed in this way include multiple ones. Figure 8As shown, when the endpoint value is D_1, the smoothing interval is [D_1-D_smooth1, D_1+D_smooth2], and when the endpoint value is D_2, the smoothing interval is [D_2-D_smooth1, D_2+D_smooth2], etc. Among them, |D_smooth1| and |D_smooth2| can be the same or different, that is, the construction of the smoothing interval can be symmetrical or asymmetrical relative to the endpoint value, that is, when |D_smooth1|=|D_smooth2|, the smoothing interval is symmetrical relative to the endpoint value; when |D_smooth1| is not equal to |D_smooth2|, the smoothing interval is asymmetrical relative to the endpoint value.

[0187] After the smoothing interval is constructed, it is determined whether the target data is within the smoothing interval, and the measurement distance is determined based on the judgment result. Different judgment results lead to different ways of determining the measurement distance.

[0188] In a possible implementation, determining the measurement distance according to the judgment result includes the following scheme:

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

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

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

[0192] Determine the measurement data in the measurement data set obtained by sampling based on the first threshold and located within the smooth interval as the first distance, and determine the measurement data in the measurement data set obtained by sampling based on the second threshold and located within the smooth interval as the second distance;

[0193] The measured distance is obtained by performing weighted calculation on 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 junction 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 is that the target data is within the smoothing interval, it means that the target data as measured data may have deviations, such as the detection echo or noise echo (a signal peak in the echo signal) located near the distance boundary point mentioned above. The measurement results obtained when sampling with different thresholds may be in different distance sub-intervals. In order to reduce the measurement fluctuations at this boundary position, splicing and smoothing processing is required. In this case, the sub-distance interval where the target data is located is 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. Fig.10 As shown, Fig.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], D_X is in [Q1, Q2], Q1 = D_X-D_smooth1, Q2 = D_X+D_smooth2. Among them, the target data is in the sub-distance interval S_X, then S_X is the first sub-distance interval, and the sub-distance interval S_X+1 intersects with [Q1, Q2], indicating that S_X+1 is in [Q1, Q2], and S_X+1 is adjacent to S_X, then S_X+1 is the second sub-distance interval.

[0196] Then, obtain a first threshold corresponding to the first sub-distance interval and a second threshold corresponding to the second sub-distance interval, wherein 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. Among them, the first threshold corresponding to the first sub-distance interval represents the echo signal within the range of the first sub-distance interval. Compared with the measurement data obtained by sampling with other thresholds, the measurement data obtained by sampling with the first threshold has the highest confidence. Taking the first threshold corresponding to the first sub-distance interval as an example, for a laser radar using a transmission signal with a fixed transmission power, within the first sub-distance interval, the measurement data obtained by sampling the echo signal with the first threshold is the alternative data; for a laser radar using a transmission signal with multiple transmission powers, within the first sub-distance interval, the measurement data obtained by sampling the echo signal corresponding to the preset transmission power with the first threshold is the alternative data. The second threshold corresponding to the second sub-distance interval has the same function as the first threshold corresponding to the first sub-distance interval, and will not be repeated.

[0197] After obtaining the first threshold and the second threshold, the measurement data in the measurement data set obtained by sampling the first threshold and located in the smooth interval are used as the first distance, and the measurement data in the measurement data set obtained by sampling the second threshold and located in the smooth interval are used as the second distance. For example, the measurement data set obtained by sampling the first threshold includes measurement data 11-measurement data 15, wherein measurement data 15 is within the smooth interval, and measurement data 15 is the first distance; the measurement data set obtained by sampling the second threshold includes measurement data 21-measurement data 25, wherein measurement data 22 is within the smooth interval, and measurement data 22 is the second distance.

[0198] After obtaining the first distance and the second distance, a weighted calculation operation is performed on the first distance and the second distance to obtain the measured distance. Fig.10 As shown, the first distance is represented as J1, the second distance is represented as J2, and the weight values ​​corresponding to the first distance and the second distance are determined according to the weight functions corresponding to the sub-distance intervals where the first distance and the second distance are located. The weight function corresponding to the sub-distance interval where the first distance is located is L1, and the weight function corresponding to the sub-distance interval where the second distance is located is L2. The value obtained by substituting J1 as an independent variable into the weight function L1 is the weight value corresponding to J1, which is represented as Coef_x. Similarly, the value obtained by substituting J2 as an independent variable into the weight function L2 is the weight value corresponding to J2, which is represented as Coef_x+1, that is, the measured distance = (J1×Coef_x+J2×Coef_x+1). The measured distance obtained in the above manner avoids the result deviation caused by sampling at different thresholds when the measurement result is an echo signal near the endpoint value, and the weighted calculation is performed using the values ​​on both sides of the endpoint value to play a smoothing role.

[0199] The embodiment of the present application adopts a technical solution of obtaining multiple measurement data sets based on multiple threshold sampling of at least one echo signal within a measurement period, determining M candidate data and N auxiliary data in the multiple measurement data sets, obtaining the total compositeness of each candidate data, determining the target data from the M candidate data according to the total compositeness, and determining the measurement distance according to the target data. When the laser radar is measuring distance, it can use multiple emission signals with different emission powers, multiple gains, multiple threshold sampling, and multiple analysis areas to perform overall control of the laser radar, and fuse the measurement data obtained by multiple emission signals with different emission powers, multiple gains, and multiple threshold sampling to obtain accurate measurement data, which not only improves the measurement accuracy of the laser radar, but also improves the dynamic range of the laser radar system and shortens the close-range detection blind spot of the laser radar.

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

[0201] Fig.11 A data fusion block diagram of a radar ranging method provided in an embodiment of the present application is shown. Fig.11As shown, when the laser radar is measuring distance, it transmits multiple outgoing signals in the measurement cycle according to the time sequence. The transmission power of each outgoing signal is different. Since the laser radar includes multiple threshold sampling, multiple measurement data can be obtained. The transmission power and threshold sampling corresponding to these measurement data are different, that is, multiple measurement data with different transmission powers and different threshold sampling are obtained. After obtaining multiple measurement data, these measurement data are subjected to data allocation, data fusion, data judgment and selection, and distance splicing and smoothing to obtain the measured distance. Among them, data allocation, data fusion, data judgment and selection, and distance splicing and smoothing are all controlled by parameters.

[0202] Regarding data allocation: the ranging range of the laser radar is divided into X sub-range intervals according to preset rules in advance, and then the sub-range interval where each measurement data is located is determined to obtain the sub-range interval corresponding to each measurement data, thereby allocating a sub-range interval to each measurement data. After allocating a sub-range interval to each measurement data, the measurement data obtained by sampling according to the preset threshold corresponding to each sub-range interval is divided into multiple measurement data to obtain M candidate data and N auxiliary data. Since each measurement data has been assigned a corresponding sub-range interval, the sub-range intervals corresponding to the candidate data and the auxiliary data are also known.

[0203] Regarding data fusion: For each candidate data in the M candidate data, it is represented as Si, i = 1, 2, .., M. Based on the candidate data, auxiliary data, and the sub-distance intervals corresponding to the candidate data and the auxiliary data, the main complexity, auxiliary complexity and cross complexity of Si are calculated respectively, and then the main complexity, auxiliary complexity and cross complexity are weighted to obtain the total complexity of Si, so as to obtain the total complexity of each of the M candidate data.

[0204] Regarding data judgment and selection: determine the target number from the candidate data with the highest total complexity among the M candidate data. If there is only one candidate data with the highest total complexity among the M candidate data, the candidate data with the highest total complexity will be determined as the target data; if there are multiple candidate data with the highest total complexity among the M candidate data, the candidate data with the highest total complexity and the largest distance value will be determined as the target data.

[0205] Regarding distance splicing smoothing: construct a smoothing interval with the endpoint value of each sub-distance interval as the center, determine whether the target data is located in the smoothing interval, and if the target data is not in the smoothing interval, determine the target data as the measurement data; if the target data is in 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 in the smoothing interval as the second sub-distance interval; then, obtain the first threshold corresponding to the first sub-distance interval and the second threshold corresponding to the second sub-distance interval; take the measurement data located in the smoothing interval in the measurement data set sampled based on the first threshold as the first distance, and take the measurement data located in the smoothing interval in the measurement data set sampled based on the second threshold as the second distance, and then perform weighted calculation on the first distance and the second distance to obtain an accurate measurement distance. The technical solution provided in the example of this application can use multiple transmission signals with different transmission powers, multiple gains, multiple threshold sampling, and multiple analysis areas to perform overall control of the laser radar when measuring laser radar distance, and fuse the measurement data obtained by multiple transmission signals with different transmission powers, multiple gains and multiple threshold sampling to obtain accurate measurement data, which not only improves the measurement accuracy of the laser radar, but also improves the dynamic range of the laser radar system and shortens the close-range detection blind spot of the laser radar.

[0206] The following is an embodiment of the device of the present application, which can be used to execute the embodiment of the method of the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the method of the present application.

[0207] Fig.12 FIG. 1 shows a schematic diagram of the structure of a radar ranging device provided in an embodiment of the present application. Fig.12 As shown, the radar ranging device 1200 includes:

[0208] The data acquisition module 1210 is used to acquire multiple measurement data sets obtained by sampling at least one echo signal in a measurement period based on multiple threshold values; wherein the echo signal is sampled based on one of the threshold values ​​to obtain one measurement data set, and each of the measurement data sets contains at least one measurement data;

[0209] The complexity calculation module 1220 is used to determine M candidate data and N auxiliary data in the multiple measurement data sets, and obtain 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 included in the multiple measurement data sets;

[0210] A data selection module 1230 is used to determine target data from the M candidate data according to the total complexity;

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

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

[0213] An interval division unit, used to divide 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;

[0214] The complexity calculation module 1220 is specifically used to determine the M candidate data and N auxiliary data in the multiple measurement data sets: determine the M candidate data and N auxiliary data in the multiple measurement data sets according to the measurement data sampled according to the preset threshold value corresponding to the sub-distance interval.

[0215] In a possible implementation, when an outgoing signal is transmitted within the measurement period, the complexity calculation module 1220 determines the M candidate data and the N auxiliary data in the plurality of measurement data sets based on the measurement data sampled according to the preset threshold value corresponding to the sub-range interval, including:

[0216] A first acquisition unit is configured to use measurement data obtained by sampling the preset threshold corresponding to each of the sub-distance intervals in the plurality of measurement data sets as the candidate data to obtain M candidate data;

[0217] The first screening unit is used to use the measurement data except the M candidate data in the plurality of measurement data sets as the auxiliary data to obtain N auxiliary data.

[0218] In a possible implementation, when multiple outgoing signals are transmitted within the measurement period and the transmission powers of the multiple outgoing signals are different, the complexity calculation module 1220 determines the M candidate data and the N auxiliary data in the multiple measurement data sets based on the measurement data sampled according to the preset threshold value corresponding to the sub-range interval, including:

[0219] A second acquisition unit is configured to use the echo signal corresponding to the outgoing signal with the preset transmission power in the multiple measurement data sets, and the measurement data obtained by sampling the preset threshold value corresponding to each sub-range interval as the candidate data, so as to obtain M candidate data;

[0220] The second screening unit is used to use the measurement data except the M candidate data in the plurality of measurement data sets as the auxiliary data to obtain N auxiliary data.

[0221] In a possible implementation, the complexity calculation module 1220, in acquiring the total complexity of each candidate data, includes:

[0222] An interval determination unit, configured to determine a sub-distance interval in which each measurement data in the plurality of measurement data sets is located, so as to obtain a sub-distance interval corresponding to the candidate data and the auxiliary data;

[0223] A first calculation unit is used to determine the primary complexity, the auxiliary complexity and the cross complexity of the candidate data Si according to 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 weighted calculation according to the main complexity, the auxiliary complexity and the cross complexity of the candidate data Si to obtain the total complexity of the candidate data Si.

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

[0226] a data set determination subunit, configured to obtain at least one candidate data meeting preset requirements in a plurality of adjacent measurement cycles as a comparison data set; wherein, meeting the preset requirements is that an outgoing signal corresponding to the candidate data in the comparison data set and an outgoing signal corresponding to the candidate data Si in the current measurement cycle have the same transmission power, and a threshold corresponding to the candidate data in the comparison data set and a threshold corresponding to the candidate data Si in the current measurement cycle are the same;

[0227] A first comparison subunit, used for sequentially comparing the candidate data Si of the current measurement cycle with each candidate data in the comparison data set;

[0228] The first determining subunit is configured to obtain the main complexity of the candidate data Si according to the comparison result.

[0229] In a possible implementation, the first determination subunit is specifically used to: 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 complexity of the candidate data Si is increased by 1; traverse each of the candidate data in the comparison data set to obtain the main complexity of the candidate data Si; or, when the main complexity of the candidate data Si is greater than or equal to a main complexity threshold, obtain the main complexity of the candidate data Si as the main complexity threshold.

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

[0231] A second comparing subunit, used for comparing the candidate data Si with the N auxiliary data in sequence;

[0232] The second determining subunit is used to obtain the auxiliary complexity of the candidate data Si according to the comparison result.

[0233] In combination with the second aspect and the above-mentioned implementation methods, in some possible implementation methods, the second determination subunit is specifically used to: when the difference between one of the N auxiliary data and the alternative data Si is less than a second preset value, the auxiliary complexity of the alternative data Si is increased by 1; each of the N auxiliary data is traversed to obtain the auxiliary complexity of the alternative data Si; or, when the auxiliary complexity of the alternative data Si is greater than or equal to the auxiliary complexity threshold, the auxiliary complexity of the alternative data Si is obtained as the auxiliary compliance threshold.

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

[0235] a target interval determination subunit, configured to determine that the sub-distance interval where the candidate data Si is located is an intermediate sub-distance interval, and obtain at least one of the K sub-distance intervals other than the intermediate sub-distance interval as a target sub-distance interval; wherein K≤X, and K is a positive integer;

[0236] A third comparison subunit, used for sequentially comparing the candidate data Si with the candidate data within at least one of the target sub-distance intervals;

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

[0238] In a possible implementation, the third determination subunit is specifically used to: when the difference between an alternative data within at least one of the target sub-distance intervals and the alternative data Si is less than a third preset value, the cross-complexity of the alternative data is increased by 1; traversing the alternative data within at least one of the target sub-distance intervals to obtain the cross-complexity of the alternative data Si; or, when the cross-complexity of the alternative data Si is greater than or equal to a cross-complexity threshold, obtaining the cross-complexity of the alternative data Si as the cross-complexity threshold.

[0239] In a possible implementation, the data selection module 1230 is specifically configured to determine the target data from the candidate data with the highest total complexity among the M candidate data.

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

[0241] A first selection unit is configured to, when there is only one candidate data with the highest total complexity among the M candidate data, determine the candidate data with the highest total complexity as the target data;

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

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

[0244] An interval construction unit, used for obtaining the endpoint value of the sub-distance interval and constructing a smooth interval with the endpoint value as the center;

[0245] A data judging unit, used for judging whether the target data is within the smoothing interval;

[0246] The distance determination unit is used to determine the measured distance according to the judgment result.

[0247] In a possible implementation, the distance determining unit includes:

[0248] A first distance determination subunit, configured 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 used to, when the target data is within the smooth interval, determine that the sub-distance interval in which the target data is located is a first sub-distance interval, and determine that the sub-distance interval adjacent to the first sub-distance interval in the smooth interval is a 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 that the measurement data in the measurement data set sampled based on the first threshold and located in the smooth interval is used as a first distance, and the measurement data in the measurement data set sampled based on the second threshold and located in the smooth interval is used as a second distance; and perform 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 embodiment only uses the division of the above functional modules as an example when executing the radar ranging method. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the radar ranging device provided in the above embodiment and the radar ranging method embodiment belong to the same concept. Therefore, for details not disclosed in the device embodiment of the present application, please refer to the above-mentioned radar ranging method embodiment of the present application, which will not be repeated here.

[0251] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0252] Fig.13 A schematic structural diagram of an electronic device provided in an embodiment of the present application is shown.

[0253] For example, Fig.13 As shown, the electronic device 1300 includes: a memory 1301 and a processor 1302, wherein the memory 1301 stores an 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] In this embodiment, the electronic device can be divided into functional modules according to the above method example. For example, each functional module can be corresponded to, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware. It should be noted that the division of modules in this embodiment is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0255] In the case of dividing each functional module according to each function, the electronic device may include: a data acquisition module, a compositeness calculation module, a data selection module, a distance calculation module, etc. It should be noted that all relevant contents of each step involved in the above method embodiment can be referred to the functional description of the corresponding functional module, which will not be repeated here.

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

[0257] In the case of an integrated unit, the electronic device may include a processing module and a storage module. The processing module may be used to control and manage the actions of the electronic device. The storage module may be used to support the electronic device in executing mutual program codes and data.

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

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

[0260] This embodiment further provides a computer program product. When the computer program product is run on a computer, the computer is enabled to execute the above-mentioned related steps to implement a radar ranging method in the above-mentioned embodiment.

[0261] In addition, the electronic device provided in the embodiments of the present application may specifically be a chip, a component or a module, and the electronic device may include a connected processor and a memory; wherein the memory is used to store instructions, and when the electronic device is running, the processor may call and execute instructions so that the chip executes a radar ranging method in the above-mentioned embodiments.

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

[0263] Through the description of the above implementation methods, technical personnel in the relevant field can understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned 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 the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0265] The above contents are only specific implementation methods of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A radar ranging method, characterized in that: The radar ranging method comprises: Acquire multiple measurement data sets obtained by sampling at least one echo signal within a measurement period based on multiple threshold values; wherein the echo signal is sampled based on one of the threshold values ​​to obtain one measurement data set, and each of the measurement data sets contains at least one measurement data; Determine M candidate data and N auxiliary data in the plurality of measurement data sets, and obtain a 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 included in the plurality of measurement data sets; Determine the target data from the M candidate data according to the total complexity; A measurement distance is determined according to the target data.

2. The radar ranging method according to claim 1, characterized in that: The radar ranging method also includes: Divide the ranging range of the radar into X sub-range intervals according to a preset rule; where X is a positive integer greater than 1; The determining of the M candidate data and the N auxiliary data from the plurality of measurement data sets comprises: According to the measurement data sampled according to the preset threshold value corresponding to the sub-distance interval, the M candidate data and the N auxiliary data in the plurality of measurement data sets are determined.

3. The radar ranging method according to claim 2, characterized in that: When an outgoing signal is transmitted within the measurement period, the measurement data sampled according to the preset threshold value corresponding to the sub-range interval is used to determine the M candidate data and the N auxiliary data in the plurality of measurement data sets, including: Taking the measurement data obtained by sampling the preset threshold value corresponding to each of the sub-distance intervals in the plurality of measurement data sets as the candidate data, so as to obtain M candidate data; The measurement data except the M candidate data in the plurality of measurement data sets are used as the auxiliary data to obtain N auxiliary data.

4. The radar ranging method according to claim 2, characterized in that: When multiple outgoing signals are transmitted within the measurement period and the transmission powers of the multiple outgoing signals are different, the measurement data obtained by sampling according to the preset threshold value corresponding to the sub-range interval, determining M candidate data and N auxiliary data in multiple measurement data sets, includes: The echo signal corresponding to the outgoing signal with the preset transmission power in the plurality of measurement data sets is sampled by the preset threshold value corresponding to each of the sub-range intervals as the candidate data to obtain M candidate data; The measurement data except the M candidate data in the plurality of measurement data sets are used as the auxiliary data to obtain N auxiliary data.

5. The radar ranging method according to claim 2, characterized in that: The obtaining of the total complexity of each candidate data comprises: Determine a sub-distance interval where each measurement data in the plurality of measurement data sets is located, so as to obtain a sub-distance interval corresponding to the candidate data and the auxiliary data; Determine the primary complexity, the auxiliary complexity and the cross complexity 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; The total complexity of the candidate data Si is obtained by performing weighted calculation according to the main complexity, the auxiliary complexity and the cross complexity of the candidate data Si.

6. The radar ranging method according to claim 5, characterized in that: Determining the main complexity of the candidate data Si includes: Acquire at least one candidate data that meets the preset requirements in a plurality of adjacent measurement cycles as a comparison data set; wherein, the preset requirements are met in that an outgoing signal corresponding to the candidate data in the comparison data set and an outgoing signal corresponding to the candidate data Si in the current measurement cycle have the same transmission power, and a threshold corresponding to the candidate data in the comparison data set and a threshold corresponding to the candidate data Si in the current measurement cycle are the same; Comparing the candidate data Si of the current measurement cycle with each candidate data in the comparison data set in sequence; The main complexity of the candidate data Si is obtained according to the comparison result.

7. The radar ranging method according to claim 6, characterized in that: The step of obtaining the main complexity of the candidate data Si according to the comparison result includes: When the difference between one of the candidate data in the comparison data set and the candidate data Si in the current measurement cycle is less than a first preset value, the main complexity of the candidate data Si is increased by 1; Traversing each candidate data in the comparison data set to obtain the main complexity of the candidate data Si; or, When the main complexity of the candidate data Si is greater than or equal to the main complexity threshold, the main complexity of the candidate data Si is obtained to be the main complexity threshold.

8. The radar ranging method according to claim 5, characterized in that: Determining the auxiliary complexity of the candidate data Si includes: Comparing the candidate data Si with the N auxiliary data in sequence; The auxiliary complexity of the candidate data Si is obtained according to the comparison result.

9. The radar ranging method according to claim 8, characterized in that: The step of obtaining the auxiliary complexity of the candidate data Si according to 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 complexity of the candidate data is increased by 1; Traverse each of the N auxiliary data to obtain the auxiliary complexity of the candidate data Si; or, When the auxiliary complexity of the candidate data Si is greater than or equal to the auxiliary complexity threshold, the auxiliary complexity of the candidate data Si is obtained as the auxiliary compliance threshold.

10. The radar ranging method according to claim 5, characterized in that: Determining the cross-complexity of the candidate data Si includes: Determine that the sub-distance interval where the candidate data Si is located is an intermediate sub-distance interval, and obtain at least one of the K sub-distance intervals except the intermediate sub-distance interval as a target sub-distance interval; wherein K≤X, and K is a positive integer; Comparing the candidate data Si with the candidate data within at least one of the target sub-distance intervals in sequence; The cross-complexity of the candidate data Si is obtained according to the comparison result.

11. The radar ranging method according to claim 10, characterized in that: The step of obtaining the cross-complexity of the candidate data Si according to 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 increased by 1; Traversing the candidate data within 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.

12. The radar ranging method according to claim 1, characterized in that: Determining the target data from the M candidate data according to the total complexity includes: The target data is determined from the candidate data with the highest total complexity among the M candidate data.

13. The radar ranging method according to claim 12, characterized in that: The step of determining the target data from the candidate data with the highest total complexity among the M candidate data includes: When there is only one candidate data with the highest total complexity among the M candidate data, determining the candidate data with the highest total complexity as the target data; When there are multiple candidate data with the highest total complexity among the M candidate data, the candidate data with the highest total complexity and the largest distance value is determined as the target data.

14. The radar ranging method according to claim 2, characterized in that: Determining the measurement distance according to the target data includes: Obtaining the endpoint value of the sub-distance interval, and constructing a smooth interval with the endpoint value as the center; Determining whether the target data is within the smoothing interval; The measuring distance is determined according to the judgment result.

15. The radar ranging method according to claim 14, characterized in that: The determining the measurement distance according to the judgment result includes: When the target data is not within the smoothing interval, determining that the measured distance is equal to the target data; When the target data is within the smoothing interval, determining the sub-distance interval where the target data is located as a first sub-distance interval, and determining the sub-distance interval adjacent to the first sub-distance interval in the smoothing interval as a 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 data set obtained by sampling based on the first threshold and located within the smooth interval as the first distance, and determine the measurement data in the measurement data set obtained by sampling based on the second threshold and located within the smooth interval as the second distance; The measured distance is obtained by performing weighted calculation on the first distance and the second distance.

16. A radar ranging device, characterized in that: The radar ranging device comprises: A data acquisition module, used to acquire multiple measurement data sets obtained by sampling at least one echo signal within a measurement period based on multiple threshold values; wherein the echo signal is sampled based on one of the threshold values ​​to obtain one measurement data set, and each of the measurement data sets contains at least one measurement data; A complexity calculation module, used to determine M candidate data and N auxiliary data in the plurality of measurement data sets, and obtain a 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 included in the plurality of measurement data sets; A data selection module, used for determining target data from the M candidate data according to the total complexity; The distance calculation module is used to determine the measurement distance according to the target data.

17. An electronic device, characterized in that: The electronic device comprises: A memory for storing executable program codes; A processor, configured to call and run the executable program code from the memory, so that the electronic device executes the radar ranging method as described in any one of claims 1 to 15.

18. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed, the radar ranging method according to any one of claims 1 to 15 is implemented.

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