A radar target detection probability threshold matching control method and system

Abstract

The present application relates to the technical field of radar detection, and discloses a radar target detection probability threshold matching control method and system, which comprises the following steps: acquiring radar data of a radar in real time; acquiring a radar beam azimuth pointing angle and a target azimuth angle relative to the radar in real time in each scanning period; determining the beam azimuth pointing angle, an antenna horizontal beam width in the radar data, and the azimuth angle; performing agglomeration processing on target point tracks generated in accordance with the determination; and obtaining effective point tracks after agglomeration. The detection probability calculated by the present application can reflect the influence of signal-to-noise ratio differences at different distance segments on detection performance, and is accurately matched with real radar detection performance, thereby avoiding deviations in probability statistics between simulated target point tracks and real radar detection results, and enabling accurate judgment of target point tracks.

Description

Technical Field

[0001] This invention relates to the field of radar detection technology, specifically to a radar target detection probability threshold matching control method and system. Background Technology

[0002] In existing radar target trajectory simulation technologies, a fixed detection probability threshold is usually used to simulate the process of radar detecting targets. That is, a uniform detection probability value is set in advance, a normalized random number is generated for each target to be detected, and the random number is compared with the fixed threshold. If the random number is less than or equal to the threshold, it is determined that the target has been detected and a target point is generated; otherwise, it is determined that the target has not been detected.

[0003] However, the above matching control has the following drawbacks: the detection probability of a real radar for an aerial target is not a constant value. The radar detection probability is affected by a variety of factors, such as the radar's equivalent radiated power, receiver sensitivity, and the target's radar cross-section. It exhibits differentiated distribution characteristics as the distance between the target and the radar changes. In particular, for targets at different distances, the signal-to-noise ratio is different, and the detection probability will change significantly. Existing simulation methods with fixed thresholds cannot reflect this dynamic distribution of detection probability with distance. This leads to a deviation between the simulated target traces and the real radar detection results in terms of probability statistics, resulting in inaccurate detection probabilities at different distances and making it impossible to accurately judge the target traces. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a radar target detection probability threshold matching control method and system, which solves the aforementioned problems.

[0005] The above-mentioned technical objective of the present invention is achieved through the following technical solution:

[0006] A radar target detection probability threshold matching control method includes:

[0007] Step S1: Real-time acquisition of radar data. In each scanning cycle, real-time acquisition of radar beam pointing angle and target azimuth angle relative to radar. Determination of beam pointing angle, antenna horizontal beamwidth and azimuth angle in radar data. Then, convergence processing of target points generated in accordance with the determination to obtain converged effective points.

[0008] Step S2: Obtain radar accuracy data, analyze the accuracy data, generate random errors that match the radar, and superimpose the random errors onto the valid point traces to obtain target point traces containing errors.

[0009] Step S3: Analyze the radar data, calculate the detection probability corresponding to different distance segments within the detection area, obtain the current distance of the target corresponding to the target point, determine the detection probability of the distance segment where the current distance is located, and generate random numbers according to the normal distribution random function;

[0010] Step S4: Judge the random number and the corresponding detection probability to obtain the matching point.

[0011] Furthermore, radar data and accuracy data include:

[0012] Radar data includes: equivalent radiated power, number of pulses accumulated, receiver sensitivity, antenna horizontal beamwidth, and the target's radar cross-section;

[0013] Accuracy data includes: root mean square error of distance, root mean square error of azimuth, and root mean square error of elevation.

[0014] Furthermore, the beam pointing angle, antenna horizontal beamwidth, and azimuth angle in the radar data are determined, and the target points generated according to the determination are aggregated to obtain the aggregated effective points, including:

[0015] For the beam pointing angle, the antenna horizontal beamwidth and azimuth angle in the radar data, the beam pointing angle, the antenna horizontal beamwidth and the target azimuth angle relative to the radar are calculated to obtain the output value;

[0016] When the output value is 1, it means that the target is within the current beam illumination range, and a target waypoint is generated. When the output value is 0, it means that the target is not within the current beam illumination range, and a target waypoint is not generated.

[0017] During radar beam scanning, the horizontal beam of the radar antenna has a certain width. When the beam sweeps across the target's azimuth area, multiple target track points will be generated to be processed. These target track points originate from the same illumination response of the same target. If they are directly output as multiple independent track points without processing, it will lead to redundancy and splitting of the target track points, and will not be able to truly reflect the radar's single detection result of a single target.

[0018] Multiple target track points are aggregated to merge multiple target track points belonging to the same target and the same illumination into one effective track point, resulting in the aggregated effective track point.

[0019] Furthermore, multiple target track points are aggregated, merging multiple target track points belonging to the same target and the same illumination into a single effective track, resulting in the aggregated effective track, including:

[0020] The centroid method is used to calculate the azimuth of the target track point by weighting the azimuth coefficients corresponding to each track point.

[0021] Subtracting the target's azimuth angle relative to the radar from the beam pointing angle yields the deviation angle between the beam pointing direction and the target's track direction.

[0022] The deviation angle is substituted into the mathematical model of the radiation pattern corresponding to the radar antenna to obtain the corresponding function value. The function value is divided by the maximum value of the radar beam center to obtain the normalized radiation pattern coefficient. The radiation pattern coefficient is used to reflect the relative degree of antenna gain reduction after the target deviates from the beam center.

[0023] Through agglomeration processing, multiple target track points generated by the same target under the same illumination are merged into a single effective track point with a timestamp.

[0024] The effective trace includes the slant range value, the azimuth and elevation angle of the current antenna beam.

[0025] Furthermore, the accuracy data is analyzed to generate random errors that match the radar. These random errors are then superimposed onto the valid target points to obtain target points containing the errors, including:

[0026] Based on the radar's root mean square error of range, root mean square error of azimuth, and root mean square error of elevation, the random errors of range, azimuth, and elevation are calculated respectively.

[0027] Among them, random distance error, random azimuth error, and random elevation error are collectively referred to as random error;

[0028] Random errors are superimposed on the distance, azimuth, and elevation angle of the valid points to obtain the target points containing errors.

[0029] Furthermore, the radar data is analyzed to calculate the detection probability for different distance segments within the detection area, including:

[0030] Step S31: For each range segment within the radar detection area, calculate the echo signal power and total noise power based on the equivalent radiated power, pulse accumulation number, receiver sensitivity, and radar cross section parameters in the radar data, and calculate the signal-to-noise ratio for that range segment.

[0031] Step S32: Obtain the noise figure, receiver bandwidth, and standard reference temperature of the radar receiver, and calculate the noise power based on the noise figure, receiver bandwidth, and standard reference temperature.

[0032] Step S33: Obtain the amplitude value of the fixed detection threshold from the radar's signal processing module, square the amplitude value to obtain the threshold power, and unify it with the noise power on the power scale.

[0033] Step S34: Calculate the ratio of the negative threshold power of the natural constant e to the noise power to obtain the false alarm probability. Then, substitute the signal-to-noise ratio and the false alarm probability into the first-order Markum Q function. The first parameter is the square root of twice the signal-to-noise ratio, and the second parameter is the square root of the natural logarithm of the negative twice false alarm probability. The output value of the Q function is the detection probability of this distance segment.

[0034] Furthermore, the distance segment includes:

[0035] Different range segments are multiple concentric ring-shaped areas centered on the radar and divided according to the detection range.

[0036] Furthermore, determine the detection probability of the current distance segment, and generate random numbers based on a normal distribution random function, including:

[0037] For the current distance of the target, compare the current distance with each distance segment, find the corresponding distance segment, and call the detection probability of that distance segment;

[0038] Generate a random number using a normal distribution random function.

[0039] Furthermore, the random number and its corresponding detection probability are analyzed to obtain matching points, including:

[0040] For the detection probability of a random number and its corresponding distance segment, if the random number is less than or equal to the detection probability, the output value is 1; if the random number is greater than or equal to the detection probability, the output value is 0.

[0041] An output value of 1 indicates that a target has been detected and a track point exists. In this case, the target track point containing the error is used as the matching track point. An output value of 0 indicates that no target has been detected. The target track point containing the error is discarded and no matching track point is generated.

[0042] Furthermore, a radar target detection probability threshold matching control system, applied to the above control method, includes:

[0043] The detection and analysis unit is used to acquire radar data from the radar in real time. In each scanning cycle, it acquires the radar beam pointing angle and the target's azimuth angle relative to the radar in real time. It determines the beam pointing angle, the antenna horizontal beamwidth and azimuth angle in the radar data, and performs agglomeration processing on the target points generated that meet the determination to obtain the agglomerated effective points.

[0044] The target determination unit is used to acquire the accuracy data of the radar, analyze the accuracy data, generate random errors that match the radar, and superimpose the random errors onto the valid point traces to obtain the target point traces containing the errors.

[0045] The target calculation unit is used to analyze radar data, calculate the detection probability corresponding to different distance segments within the detection area, obtain the current distance of the target corresponding to the target point, determine the detection probability of the distance segment where the current distance is located, and generate random numbers according to the normal distribution random function.

[0046] The target matching unit is used to determine the random number and its corresponding detection probability to obtain the matching point.

[0047] In summary, the present invention has the following main beneficial effects:

[0048] In step S1, a coincidence decision is made based on the antenna horizontal beamwidth and beam azimuth pointing angle. The centroid method is used to aggregate multiple target track points as weights, effectively eliminating azimuth redundancy and track splitting caused by the same target being illuminated by the same person. This makes the aggregated effective track unique and accurate in the azimuth dimension, while retaining timestamp information. In step S2, three-dimensional random errors are independently generated based on the root mean square error of range, root mean square error of azimuth, and root mean square error of elevation angle, combined with a normal distribution random function. These errors are then superimposed on the slant range, azimuth, and elevation angle of the effective track, making the fluctuation characteristics of the target track containing errors highly consistent with the statistical distribution of the real radar output.

[0049] Simultaneously, step S3 calculates the echo signal power and signal-to-noise ratio (SNR) precisely for each range segment based on the equivalent radiated power, receiving antenna gain, radar operating wavelength, radar cross section, and range value, thereby determining the false alarm probability. The SNR and false alarm probability are then substituted into a first-order Markum Q-function to output a detection probability that dynamically changes with range. This processing ensures that the detection probability accurately reflects the impact of SNR differences on detection performance across different range segments, and the detection probability at each range segment precisely matches the radar. Step S4 completes the detection decision by comparing normally distributed random numbers with the dynamic detection probability, ensuring that the statistical characteristics of the simulated point's distribution in the range dimension match the radar detection results. This makes the final output matching point approximate the real radar system in three key dimensions: azimuth accuracy, measurement noise, and detection probability. Compared to the uniform fixed threshold in existing technologies, the detection probability calculated by this scheme accurately reflects the impact of SNR differences on detection performance across different range segments and precisely matches the real radar detection performance, avoiding deviations in the probability statistics of simulated target points from the real radar detection results, and enabling accurate judgment of target points. Attached Figure Description

[0050] Figure 1This is a flowchart illustrating the radar target detection probability threshold matching control method of the present invention;

[0051] Figure 2 This is a diagram illustrating the detection probability calculation steps of the present invention;

[0052] Figure 3 This is a block diagram of the radar target detection probability threshold matching control system of the present invention. Detailed Implementation

[0053] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] refer to Figure 1 , Figure 2 and Figure 3 A radar target detection probability threshold matching control method, comprising:

[0055] Step S1: Real-time acquisition of radar data. In each scanning cycle, the radar beam pointing angle and the target's azimuth angle relative to the radar are acquired in real time. The beam pointing angle, the antenna horizontal beamwidth and azimuth angle in the radar data are judged, and the target points generated that meet the judgment are aggregated to obtain the aggregated effective points. All radars in this application are analog radars.

[0056] Step S2: Obtain radar accuracy data, analyze the accuracy data, generate random errors that match the radar, and superimpose the random errors onto the valid point traces to obtain target point traces containing errors.

[0057] Step S3: Analyze the radar data, calculate the detection probability corresponding to different distance segments within the detection area, obtain the current distance of the target corresponding to the target point, determine the detection probability of the distance segment where the current distance is located, and generate random numbers according to the normal distribution random function;

[0058] Step S4: Judge the random number and the corresponding detection probability to obtain the matching point.

[0059] In one embodiment, the radar data and accuracy data include:

[0060] Radar data includes: equivalent radiated power, pulse accumulation number, receiver sensitivity, antenna horizontal beamwidth, and the target's radar cross-section, etc.

[0061] Accuracy data includes: root mean square error of distance, root mean square error of azimuth, root mean square error of elevation, etc.

[0062] The root mean square error of the range is the standard deviation of the random error in radar measurement of the slant range of a target;

[0063] The root mean square error of azimuth is the standard deviation of the random error in radar measurement of the azimuth angle of a target;

[0064] The root mean square error of elevation angle is the standard deviation of the random error in radar measurement of the elevation angle of a target.

[0065] In one embodiment, the beam azimuth pointing angle, the antenna horizontal beamwidth and azimuth angle in the radar data are determined, and the target points generated according to the determination are aggregated to obtain aggregated effective points, including:

[0066] For the beam pointing angle, the antenna horizontal beamwidth and azimuth angle in the radar data, the beam pointing angle, the antenna horizontal beamwidth, and the target's azimuth angle relative to the radar are calculated to obtain the output value. The specific calculation formula is as follows:

[0067] ;

[0068] In the formula, To satisfy the output value of the decision function, when When =1, it indicates that the target is within the current beam illumination range, and a target waypoint is generated. When =0, it means the target is not within the current beam's illumination range, and therefore no target waypoint is generated. Indicates the beam azimuth pointing angle. Indicates the horizontal beamwidth of the antenna. Indicates the target's azimuth angle relative to the radar;

[0069] During radar beam scanning, the horizontal beam of the radar antenna has a certain width. When the beam sweeps across the target's azimuth area, the same target may meet the judgment conditions in multiple consecutive scanning cycles or adjacent beam positions, resulting in multiple target track points to be processed. These target track points are essentially derived from the same illumination response of the same target. If they are directly output as multiple independent track points without processing, it will lead to redundancy and splitting of the target track, and will not be able to truly reflect the radar's single detection result of a single target.

[0070] Multiple target track points are aggregated to merge multiple target track points belonging to the same target and the same illumination into a single valid track point, resulting in the aggregated valid track point.

[0071] In one embodiment, multiple target trackpoints are aggregated to merge multiple target trackpoints belonging to the same target and the same illumination into a single valid trackpoint, resulting in an aggregated valid trackpoint, including:

[0072] The centroid method is used to calculate the azimuth of the target track point, using the azimuth coefficients corresponding to each track point as weights. The specific calculation formula is as follows:

[0073]

[0074] In the formula, The bearing value of the target waypoint. This represents the orientation pattern coefficients of each target point trace before agglomeration. This indicates the angle of deviation between the beam direction and the target point's direction. This indicates the total number of target track points participating in the aggregation process, i.e., the number of newly added target track points generated after the criteria are met (the number of track points before aggregation). Indicates the index of the waypoint;

[0075] Subtracting the target's azimuth angle relative to the radar from the beam pointing angle yields the deviation angle between the beam pointing direction and the target's trajectory. ;

[0076] Among them, Substituting this into the mathematical model of the radar antenna's radiation pattern, such as a Gaussian pattern, we can use an exponential decay function to describe the gain as a function of frequency. Changes, The smaller the value, the closer the function value is to 1. The larger the value, the more drastic the function value decays, thus yielding the corresponding function value.

[0077] Dividing the function value by the maximum value at the center of the radar beam yields the pattern coefficients normalized to the 0-1 interval. These pattern coefficients reflect the relative degree of antenna gain reduction after the target deviates from the beam center.

[0078] Through agglomeration processing, multiple target track points generated by the same target under the same illumination are merged into a single effective track point with a timestamp.

[0079] The effective point trace includes the slant range value, the azimuth and elevation angles of the current antenna beam. This is because after the simulator generates the target echo signal and it undergoes matched filtering and envelope detection, the radar will collect the echo envelope at a fixed distance. At this moment, the main lobe beam of the radar antenna is pointing towards a specific set of azimuth and elevation angles in the airspace. If a target is detected, a single detection will bind three coordinates to this point trace: the slant range value obtained from the time-delay conversion corresponding to the distance sampling, and the azimuth code disk reading and elevation code disk reading of the current antenna beam. The azimuth code disk reading and elevation code disk reading correspond to the azimuth and elevation angles. These three values—slant range, azimuth, and elevation angle—are the original measurement values ​​of the effective point trace.

[0080] By dynamically changing the detection probability with the target slant range value, the impact of signal-to-noise ratio differences on the detection probability at different range segments is accurately reflected. This ensures that the statistical characteristics accurately match the actual radar detection performance, improving the physical reliability of target track generation. Simultaneously, based on the radiation pattern coefficient, the beam azimuth pointing angle, antenna horizontal beamwidth, and target azimuth angle relative to the radar are determined. The centroid method is used to weighted aggregate the target track points that satisfy the determination across multiple consecutive beams, eliminating redundant tracks generated by the same target under the same illumination. This ensures that only a unique and effective track carrying the slant range value, azimuth, and elevation angle is output. This processing method preserves the amplitude modulation characteristics of the radar antenna radiation pattern and avoids target track splitting and false alarm accumulation, thus improving the estimation accuracy of track positions.

[0081] In one embodiment, the accuracy data is analyzed to generate random errors that match the radar. These random errors are then superimposed onto the valid point traces to obtain target point traces containing the errors. This process includes:

[0082] Based on the radar's root mean square error of range, root mean square error of azimuth, and root mean square error of elevation, the random errors of range, azimuth, and elevation are calculated respectively.

[0083] Among them, distance random error, azimuth random error, and elevation random error are collectively referred to as random error, and the formula for calculating random error is as follows:

[0084] ;

[0085] In the formula, , and These are the random errors for distance, azimuth, and elevation angle, respectively. , and These are the root mean square errors of the radar's range, azimuth, and elevation, respectively. These are normally distributed random numbers.

[0086] The random error is superimposed on the distance, azimuth, and elevation angle of the valid point to obtain the target point containing the error;

[0087] By independently generating random range, random azimuth, and random elevation errors, and based on the actual root mean square errors of range, azimuth, and elevation of the radar, a normal distribution random function is used to accurately simulate measurement noise in each dimension. This ensures that the superimposed target point traces with errors are highly consistent with the actual radar measurements in terms of statistical characteristics. This overcomes the shortcomings of traditional simulations that ignore measurement randomness or only add fixed deviations, ensuring that the point trace position distribution conforms to the Gaussian noise assumption and improving the physical authenticity and credibility of the simulated target point traces.

[0088] In one embodiment, the radar data is analyzed to calculate the detection probability for different distance segments within the detection area, including:

[0089] Step S31: For each range segment within the radar detection area, calculate the echo signal power and total noise power based on the equivalent radiated power, pulse accumulation number, receiver sensitivity, and radar cross section parameters in the radar data. Calculate the signal-to-noise ratio for that range segment by multiplying the equivalent radiated power, receiver antenna gain, squared radar operating wavelength, and radar cross section for each range segment's range value by multiplying the product of 4π cubed and the fourth power of the range value to obtain the echo signal power.

[0090] The equivalent radiated power already includes the transmitting antenna gain, the receiving antenna gain, and the radar operating wavelength, all of which are known radar parameters.

[0091] To convert receiver sensitivity to total noise power, the following steps are taken: Receiver sensitivity is usually given in decibels and milliwatts in the specifications, representing the minimum signal level that the radar can detect. To convert it, calculate 10 to the power of "receiver sensitivity divided by 10" to get the total noise power in milliwatts. For example, if the sensitivity is -90, divide -90 by 10 to get -9, then calculate 10 to the power of -9 to get the corresponding total noise power.

[0092] Divide the echo signal power by the total noise power to obtain the signal-to-noise ratio under a single pulse, and multiply the number of pulse accumulations by the single pulse signal-to-noise ratio to obtain the signal-to-noise ratio of that distance segment after accumulation enhancement;

[0093] Step S32: Obtain the noise figure, receiver bandwidth and standard reference temperature of the radar receiver, and multiply the Boltzmann constant by the standard reference temperature, then by the receiver bandwidth and then by the noise figure to obtain the noise power, which is the receiver's noise floor power.

[0094] Step S33: Obtain the amplitude value of the fixed detection threshold from the radar's signal processing module, square the amplitude value to obtain the threshold power, and unify it with the noise power on the power scale.

[0095] Step S34: Calculate the ratio of the negative threshold power of the natural constant e to the noise power to obtain the false alarm probability. Then, substitute the signal-to-noise ratio and the false alarm probability into the first-order Markum Q function, where M=1 represents the single-pulse case. The first parameter is the square root of twice the signal-to-noise ratio, and the second parameter is the square root of the natural logarithm of the negative twice the false alarm probability. The output value of the Q function is the detection probability of this distance segment.

[0096] In one embodiment, the distance segment includes:

[0097] Different range segments are multiple concentric ring-shaped areas centered on the radar and divided according to the detection range.

[0098] In one embodiment, the detection probability of the distance segment containing the current distance is determined, and random numbers are generated according to a normal distribution random function, including:

[0099] For the current distance of the target, compare the current distance with each distance segment, find the corresponding distance segment, and call the detection probability of that distance segment;

[0100] Generate a random number with a value between 0 and 1 using a normal distribution random function.

[0101] The echo signal power is accurately calculated using equivalent radiated power, receiving antenna gain, radar operating wavelength, radar cross section, and range. The total noise power is converted based on the receiver sensitivity, and the signal-to-noise ratio is improved by combining the number of pulse accumulations. The noise power is obtained by analyzing the receiver noise figure, receiver bandwidth, and standard reference temperature. Finally, the false alarm probability is calculated. Then, the range-specific signal-to-noise ratio and false alarm probability are substituted into the first-order Markum Q function to obtain the detection probability for that range segment. This allows the detection probability to change dynamically and continuously with the range, truly reflecting the impact of signal-to-noise ratio differences on detection performance at different range segments. This ensures that the simulated track is highly consistent with the radar in terms of probability statistics, improving the range resolution realism of the track simulation.

[0102] In one embodiment, the matching points are obtained by judging the random number and the corresponding detection probability, including:

[0103] For the detection probability of a random number and its corresponding distance segment, if the random number is less than or equal to the detection probability, the output value is 1; if the random number is greater than or equal to the detection probability, the output value is 0.

[0104] An output value of 1 indicates that a target has been detected and a track point exists. In this case, the target track point containing the error is used as the matching track point. An output value of 0 indicates that no target has been detected. The target track point containing the error is discarded and no matching track point is generated.

[0105] In one embodiment, a radar target detection probability threshold matching control system, applied in the above control method, includes:

[0106] The detection and analysis unit is used to acquire radar data from the radar in real time. In each scanning cycle, it acquires the radar beam pointing angle and the target's azimuth angle relative to the radar in real time. It determines the beam pointing angle, the antenna horizontal beamwidth and azimuth angle in the radar data, and performs agglomeration processing on the target points generated that meet the determination to obtain the agglomerated effective points.

[0107] The target determination unit is used to acquire the accuracy data of the radar, analyze the accuracy data, generate random errors that match the radar, and superimpose the random errors onto the valid point traces to obtain the target point traces containing the errors.

[0108] The target calculation unit is used to analyze radar data, calculate the detection probability corresponding to different distance segments within the detection area, obtain the current distance of the target corresponding to the target point, determine the detection probability of the distance segment where the current distance is located, and generate random numbers according to the normal distribution random function.

[0109] The target matching unit is used to determine the random number and its corresponding detection probability to obtain the matching point.

[0110] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A radar target detection probability threshold matching control method, characterized in that, include: Step S1: Real-time acquisition of radar data. In each scanning cycle, real-time acquisition of radar beam pointing angle and target azimuth angle relative to radar. Determination of beam pointing angle, antenna horizontal beamwidth and azimuth angle in radar data. Then, convergence processing of target points generated in accordance with the determination to obtain converged effective points. Step S2: Obtain radar accuracy data, analyze the accuracy data, generate random errors that match the radar, and superimpose the random errors onto the valid point traces to obtain target point traces containing errors. Step S3: Analyze the radar data, calculate the detection probability corresponding to different distance segments within the detection area, obtain the current distance of the target corresponding to the target point, determine the detection probability of the distance segment where the current distance is located, and generate random numbers according to the normal distribution random function; Step S4: Judge the random number and the corresponding detection probability to obtain the matching point.

2. The radar target detection probability threshold matching control method according to claim 1, characterized in that, Radar data and accuracy data, including: Radar data includes: equivalent radiated power, number of pulses accumulated, receiver sensitivity, antenna horizontal beamwidth, and the target's radar cross-section; Accuracy data includes: root mean square error of distance, root mean square error of azimuth, and root mean square error of elevation.

3. The radar target detection probability threshold matching control method according to claim 1, characterized in that, The beam pointing angle, antenna horizontal beamwidth, and azimuth angle in the radar data are determined, and the target points generated according to the determination are aggregated to obtain the aggregated effective points, including: For the beam pointing angle, the antenna horizontal beamwidth and azimuth angle in the radar data, the beam pointing angle, the antenna horizontal beamwidth and the target azimuth angle relative to the radar are calculated to obtain the output value; When the output value is 1, it means that the target is within the current beam illumination range, and a target track point is generated. When the output value is 0, it means that the target is not within the current beam illumination range, and a target track point is not generated. During radar beam scanning, the horizontal beam of the radar antenna has a certain width. When the beam sweeps across the target's azimuth area, multiple target track points will be generated to be processed. These target track points originate from the same illumination response of the same target. If they are directly output as multiple independent track points without processing, it will lead to redundancy and splitting of the target track points, and will not be able to truly reflect the radar's single detection result of a single target. Multiple target track points are aggregated to merge multiple target track points belonging to the same target and the same illumination into one effective track point, resulting in the aggregated effective track point.

4. The radar target detection probability threshold matching control method according to claim 3, characterized in that, Multiple target track points are aggregated, merging multiple target track points belonging to the same target and the same illumination into a single effective track, resulting in the aggregated effective track, including: The centroid method is used to calculate the azimuth of the target track point by weighting the azimuth coefficients corresponding to each track point. Subtracting the target's azimuth angle relative to the radar from the beam pointing angle yields the deviation angle between the beam pointing direction and the target's track direction. The deviation angle is substituted into the mathematical model of the radiation pattern corresponding to the radar antenna to obtain the corresponding function value. The function value is divided by the maximum value of the radar beam center to obtain the normalized radiation pattern coefficient. The radiation pattern coefficient is used to reflect the relative degree of antenna gain reduction after the target deviates from the beam center. Through agglomeration processing, multiple target track points generated by the same target under the same illumination are merged into a single effective track point with a timestamp. The effective trace includes the slant range value, the azimuth and elevation angle of the current antenna beam.

5. The radar target detection probability threshold matching control method according to claim 4, characterized in that, The accuracy data is analyzed to generate random errors that match the radar. These random errors are then superimposed onto the valid target points to obtain the target points containing the errors, including: Based on the radar's root mean square error of range, root mean square error of azimuth, and root mean square error of elevation, the random errors of range, azimuth, and elevation are calculated respectively. Among them, random distance error, random azimuth error, and random elevation error are collectively referred to as random error; Random errors are superimposed on the distance, azimuth, and elevation angle of the valid points to obtain the target points containing errors.

6. The radar target detection probability threshold matching control method according to claim 5, characterized in that, Analyze radar data to calculate the detection probability for different distance segments within the detection area, including: For each range segment within the radar detection area, the echo signal power and total noise power are calculated based on the equivalent radiated power, pulse accumulation number, receiver sensitivity, and radar cross section parameters in the radar data. The signal-to-noise ratio for that range segment is then calculated based on the echo signal power and total noise power. Obtain the noise figure, receiver bandwidth, and standard reference temperature of the radar receiver, and calculate the noise power based on the noise figure, receiver bandwidth, and standard reference temperature. The amplitude value of a fixed detection threshold is obtained from the radar's signal processing module. The amplitude value is squared to obtain the threshold power, which is then aligned with the noise power on the power scale. The false alarm probability is obtained by raising the ratio of the negative threshold power of the natural constant e to the noise power. Then, the signal-to-noise ratio and the false alarm probability are substituted into the first-order Markum Q function. The first parameter is the square root of twice the signal-to-noise ratio, and the second parameter is the square root of the natural logarithm of the negative twice false alarm probability. The output value of the Q function is the detection probability of that distance segment.

7. The radar target detection probability threshold matching control method according to claim 6, characterized in that, Distance segments, including: Different range segments are multiple concentric ring-shaped areas centered on the radar and divided according to the detection range.

8. The radar target detection probability threshold matching control method according to claim 7, characterized in that, Determine the detection probability of the current distance segment, and generate random numbers based on a normal distribution random function, including: For the current distance of the target, compare the current distance with each distance segment, find the corresponding distance segment, and call the detection probability of that distance segment; Generate a random number using a normal distribution random function.

9. The radar target detection probability threshold matching control method according to claim 6, characterized in that, The matching points are obtained by judging the random numbers and their corresponding detection probabilities, including: For the detection probability of a random number and its corresponding distance segment, if the random number is less than or equal to the detection probability, the output value is 1; if the random number is greater than or equal to the detection probability, the output value is 0. An output value of 1 indicates that a target has been detected and a track point exists. In this case, the target track point containing the error is used as the matching track point. An output value of 0 indicates that no target has been detected. The target track point containing the error is discarded and no matching track point is generated.

10. A radar target detection probability threshold matching control system, applied in the control method as described in any one of claims 1-9, characterized in that, include: The detection and analysis unit is used to acquire radar data from the radar in real time. In each scanning cycle, it acquires the radar beam pointing angle and the target's azimuth angle relative to the radar in real time. It determines the beam pointing angle, the antenna horizontal beamwidth and azimuth angle in the radar data, and performs agglomeration processing on the target points generated that meet the determination to obtain the agglomerated effective points. The target determination unit is used to acquire the accuracy data of the radar, analyze the accuracy data, generate random errors that match the radar, and superimpose the random errors onto the valid point traces to obtain the target point traces containing the errors. The target calculation unit is used to analyze radar data, calculate the detection probability corresponding to different distance segments within the detection area, obtain the current distance of the target corresponding to the target point, determine the detection probability of the distance segment where the current distance is located, and generate random numbers according to the normal distribution random function. The target matching unit is used to determine the random number and its corresponding detection probability to obtain the matching point.

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