Method, device and equipment for performance evaluation of reconnaissance monitoring equipment and medium

Abstract

This invention discloses a method, apparatus, equipment, and medium for evaluating the performance of reconnaissance and surveillance equipment. The method includes measurement correlation of the same equipment; measurement correlation of similar equipment; measurement correlation of dissimilar equipment; statistical processing of the correlation results; and calculation of equipment performance. This invention evaluates equipment performance using historical data, comprehensively considering the impact of different environments, different targets, different target states, and different individual equipment on the performance of reconnaissance and surveillance equipment, thus providing a more comprehensive understanding and grasp of the dynamic performance of various types of equipment in actual environments.

Description

Technical Field

[0001] This invention belongs to the field of equipment performance monitoring technology, and in particular relates to methods, devices, equipment and media for evaluating the performance of reconnaissance and surveillance equipment. Background Technology

[0002] With the widespread application of science and technology, tremendous changes have been brought about in the combat styles, force design, and weaponry deployment of future warfare. These changes are reflected in the complex electromagnetic environment of the battlefield, the high-speed mobility of targets, and their dispersed deployment. Consequently, the needs for reconnaissance and surveillance have also changed, placing higher demands on the capabilities of reconnaissance and surveillance equipment in target detection, identification, and continuous tracking.

[0003] The performance of reconnaissance and surveillance equipment is mainly calculated through comprehensive calculations based on the equipment's factory parameters and evaluation models designed for specific problems. However, the current comprehensive evaluation algorithms lack uniformity in evaluation dimensions and standards, resulting in problems such as low universality, low robustness, and difficulty in algorithm interactivity. They also suffer from deficiencies such as incomplete consideration of environmental factors and lack of coverage of target types, making it impossible to grasp the actual performance of reconnaissance and surveillance equipment in key monitoring areas and to support dynamic performance assessment of reconnaissance and surveillance equipment under actual deployment conditions. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies by providing a method, device, equipment and medium for evaluating the performance of reconnaissance and surveillance equipment. It establishes a model that can detect and track enemy targets through continuous detection of targets, and considers the performance of the equipment from three aspects: spatial coverage, measurement accuracy and measurement correctness, in order to meet the application requirements of the equipment.

[0005] The objective of this invention is achieved through the following technical solution:

[0006] A method for evaluating the performance of reconnaissance and surveillance equipment, the method comprising:

[0007] The correlation between measurement data of the same equipment is fused using a multi-hypothesis correlation method. The probability of each candidate hypothesis is calculated to determine the set of feasible correlation hypotheses at a specific time. The measurement data of the equipment is transformed into the tracks of different targets in the correlation fusion.

[0008] The spatial tracks of measurement data from similar equipment are matched in terms of heading and speed, and then the tracks are spatiotemporally correlated, including time synchronization, spatial alignment and position correlation.

[0009] The measurement data of heterogeneous equipment is converted into characteristic parameters. A fuzzy correlation matrix is ​​constructed on the characteristic parameters. By performing fuzzy correlation matrix operations with a set cutoff threshold level, the correlation analysis of multi-sensor feature vectors is realized to determine the correlation of each feature vector element under a given cutoff threshold level, thereby establishing the interrelationship of heterogeneous equipment measurement data.

[0010] Based on the correlation between measurement data of the same equipment, the correlation between measurement data of similar equipment, and the correlation between measurement data of dissimilar equipment, the energy indicators of all equipment are quantified, and the energy indicators of equipment with inconsistent dimensions are normalized, and the evaluation results of the comprehensive performance of the equipment are calculated by weighting.

[0011] Furthermore, when performing course and speed matching between spatial tracks measured by similar equipment, a threshold value is set to reflect the error range.

[0012] Furthermore, the time synchronization includes selecting a device as a reference center, using the time of the reference center as the standard time, and unifying the time of all devices within a preset range to the standard time.

[0013] Furthermore, the spatial alignment includes unifying the coordinate systems used by equipment operating on different platforms.

[0014] Furthermore, the location association includes selecting an association algorithm to calculate the association between two tracks measured by different equipment. If multiple association relationships exist, the two tracks are considered to be tracks of the same target.

[0015] Furthermore, the method of normalizing equipment energy indicators with inconsistent dimensions is adopted by the extreme value comparison method.

[0016] Furthermore, the weighted calculation of the overall performance evaluation results of the equipment includes determining the weight of each equipment energy index, then obtaining the quantitative value of the equipment capability by weighting the equipment capability index, then averaging the equipment capabilities, and finally obtaining the overall performance evaluation result of the reconnaissance and surveillance equipment.

[0017] On the other hand, the present invention also provides a performance evaluation device for reconnaissance and surveillance equipment, the device comprising:

[0018] The same equipment measurement association module uses a multi-hypothesis association method to perform association fusion between measurement data of the same equipment, calculates the probability of each candidate hypothesis, and determines the set of feasible association hypotheses at a specific time. The measurement data of the equipment is transformed into the tracks of different targets in the association fusion.

[0019] The similar equipment measurement association module performs course matching and speed matching between spatial tracks of similar equipment measurement data, and then performs spatiotemporal association of the tracks, which includes time synchronization, spatial alignment and position association.

[0020] The heterogeneous equipment measurement correlation module converts heterogeneous equipment measurement data into characteristic parameters, constructs a fuzzy correlation matrix for the characteristic parameters, and performs correlation analysis of multi-sensor feature vectors by performing fuzzy correlation matrix operations with a set cutoff threshold level, so as to determine the correlation of each feature vector element under a given cutoff threshold level, thereby establishing the mutual correlation relationship of heterogeneous equipment measurement data.

[0021] The performance evaluation module quantifies all equipment energy indicators based on the correlation between measurement data of the same equipment, the correlation between measurement data of similar equipment, and the correlation between measurement data of dissimilar equipment, normalizes equipment energy indicators with inconsistent dimensions, and calculates the evaluation result of the overall equipment performance using a weighted average.

[0022] On the other hand, the present invention also provides a computer device, which includes a processor and a memory, wherein the memory stores a computer program, and the computer program is loaded and executed by the processor to implement any of the above-described reconnaissance and surveillance equipment performance evaluation methods.

[0023] On the other hand, the present invention also provides a computer-readable storage medium storing a computer program, which is loaded and executed by a processor to implement any of the above-described methods for evaluating the performance of reconnaissance and surveillance equipment.

[0024] The beneficial effects of this invention are as follows:

[0025] This invention evaluates equipment performance by correlating measurement data from the same equipment, similar equipment, and dissimilar equipment. It comprehensively considers the impact of different environments, different targets, different target states, and different individual equipment on the performance of reconnaissance and surveillance equipment, thereby gaining a more comprehensive understanding and mastery of the dynamic performance of various types of equipment in actual environments. Attached Figure Description

[0026] Figure 1 This is an architecture diagram of the reconnaissance and surveillance equipment performance evaluation method provided in this embodiment;

[0027] Figure 2 This is a schematic diagram of the correlation analysis based on fuzzy feature-based heterogeneous information in this embodiment;

[0028] Figure 3 This is a structural block diagram of the reconnaissance and surveillance equipment performance evaluation device provided in this embodiment. Detailed Implementation

[0029] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0030] 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.

[0031] The performance of reconnaissance and surveillance equipment is mainly calculated through comprehensive calculations based on the equipment's factory parameters and evaluation models designed for specific problems. However, the current comprehensive evaluation algorithms lack uniformity in evaluation dimensions and standards, resulting in problems such as low universality, low robustness, and difficulty in algorithm interactivity. They also suffer from deficiencies such as incomplete consideration of environmental factors and lack of coverage of target types, making it impossible to grasp the actual performance of reconnaissance and surveillance equipment in key monitoring areas and to support dynamic performance assessment of reconnaissance and surveillance equipment under actual deployment conditions.

[0032] To address the aforementioned technical problems, the following embodiments of the reconnaissance and surveillance equipment performance evaluation method, apparatus, equipment, and medium of the present invention are proposed.

[0033] Example 1

[0034] Reference Figure 1 ,like Figure 1 The diagram shown is an architecture diagram of the reconnaissance and surveillance equipment performance evaluation method provided in this embodiment. This embodiment considers measuring equipment performance from three aspects: spatial coverage, measurement accuracy, and measurement correctness, specifically involving six indicators: maximum detection range, maximum elevation angle, angle measurement accuracy, range measurement accuracy, false alarm probability, and missed alarm probability.

[0035] For the correlation between measurements from the same sensor, a multi-hypothesis correlation method is adopted. The basic idea of ​​this method is that each measurement data may be one of three possibilities: target echo, false alarm, or new target echo. By correlating the correlation hypothesis set at time k-1 with the current data set, multiple candidate hypotheses at time k are obtained. The probability of each candidate hypothesis is calculated to determine the feasible correlation hypothesis set at time k.

[0036] Let Ω k Z represents the set of correlation hypotheses at time k; k ={Z(1),Z(2),…,Z(k)} is the set of all measurements up to time k; Let z be the set of all measurements at time k, where each target is associated with at most one measurement that falls within its tracking gate; i (k) has three possibilities: ① continuation of an existing target track; ② measurement of a new target; ③ false measurement. Ω k By associating the correlation hypothesis set Ω at time k-1 k-1 The measurement set Z(k) at time k is obtained.

[0037] To calculate the hypothesis probability, we introduce an associated event θ(k) to describe the association between the measurement data and the target. We assume τ is the number of measurements from the existing target, ν is the number of measurements from the new target, and ψ is the number of spurious measurements. To facilitate the calculation of the hypothesis probability, we define...

[0038]

[0039]

[0040]

[0041] According to the above formula, the number of confirmed tracks in θ(k) is:

[0042]

[0043] The number of new tracks in θ(k) is

[0044]

[0045] The number of spurious measurements in θ(k) is

[0046] ψ=m k -τ-ν

[0047] set up For the l-th candidate hypothesis, it is given by Ω k-1 One of the assumptions in And obtained from the associated event θ(k), The posterior probability is

[0048]

[0049] If measuring z i If (k) is a continuation of an existing target track, then it follows the mean. (One-step prediction of the measurement), with variance of The Gaussian distribution of (new information covariance matrix) is denoted as N. t [z i (k)];If z is measured i If (k) is a measurement of the new target or a spurious measurement, then it follows a uniform distribution within the tracking gate. Based on the above assumptions, we can derive the following hypothesis. probability iteration formula

[0050]

[0051] In the formula, c is the normalization constant, and μ f (ψ), μ n (ν) are the probability mass functions for false measurements and new target numbers, respectively. Let be the detection probability of target t.

[0052] From this, the probability of each candidate hypothesis can be calculated, and the associated hypothesis set Ω can be determined. k Hypothesis deletion can be used to obtain hypotheses. There are two methods for hypothesis deletion: one is to set a threshold and retain hypotheses whose probability exceeds the threshold; the other is to sort all candidate hypotheses by probability and retain those with higher probabilities. The latter method does not require setting a threshold and avoids the situation where hypotheses are still being deleted when the number of hypotheses is already small, but this method requires sorting the hypotheses, which consumes a lot of computing resources. Over time, two hypotheses may become increasingly similar, at which point they need to be merged, keeping one and deleting the other.

[0053] For the correlation and fusion of data between similar equipment, since the sensor's own measurement data has been transformed into the tracks of different targets in the correlation between the same sensor measurements, the correlation between the measurements of similar sensors mainly involves track correlation.

[0054] If the input information includes heading and speed, the spatial tracks can first be matched for heading and speed to narrow down the correlation range. A threshold value is set during matching to reflect the error range. Next, the tracks are spatiotemporally correlated, mainly divided into time synchronization and position correlation, as detailed below:

[0055] ①Time synchronization

[0056] Time synchronization primarily deals with data from multiple sensors occurring simultaneously. Typically, one sensor is selected as a reference center, and its time is used as the standard time to align the times of all local sensors to that reference center. An interpolation / extrapolation method is proposed.

[0057] Both interpolation and extrapolation methods utilize the fundamental laws of kinematics to establish a motion model of the target. The most typical algorithm is the Lagrange three-point interpolation method.

[0058] The principle is as follows: Assume t i-1 t i t i+1 The time measurement data is y i-1 y i yi+1 To calculate the interpolation point t mi The measured value y at time mi Then, the Lagrange three-point interpolation method is used to calculate y. mi for:

[0059]

[0060] In fact, this utilizes three measured values ​​y i-1 y i y i+1 A quadratic polynomial was constructed as an approximation function, and then the interpolation point time on this function was taken as the function value of the independent variable.

[0061] ② Spatial alignment

[0062] In a multi-sensor data fusion system, since different sensors operate on their own platforms and may use different coordinate systems, it is necessary to unify the coordinate systems before making relevant judgments and synthesizing data from different sensors. That is, the data needs to be converted to the common coordinate system of the fusion center.

[0063] Since fusion centers often process multi-source data from different locations, they no longer function as measurement centers for detection equipment. Therefore, a globally consistent geodetic coordinate system should be used in fusion centers. This means that for two-dimensional data, target positions are represented by latitude and longitude, and for three-dimensional data, they are represented by latitude, longitude, and altitude. Alternatively, a rectangular coordinate system can be used, i.e., (x, y) coordinates for two-dimensional data, or (x, y, z) coordinates for three-dimensional data. Because radar and most sensors provide target positions in polar coordinates (slant range r, azimuth α, and elevation θ), the data from the data source needs to be converted to geodetic or rectangular coordinates during data processing.

[0064] In the maritime and navigation fields, this type of coordinate transformation problem is called geodetic calculation or geodetic theme solution, which includes two parts: forward and inverse geodetic solutions. Forward geodetic solution involves finding the latitude and longitude coordinates of a given point and its distance and azimuth from another point. Inverse geodetic solution involves finding the distance and azimuth between two given latitude and longitude coordinates.

[0065] The SPL (Space Precision Location) method is a highly accurate three-dimensional spatial positioning method. It is a set of highly accurate and practical algorithms derived from the transformation between different coordinate systems in space.

[0066] This embodiment introduces the following definition of a spatial transformation matrix:

[0067]

[0068]

[0069]

[0070] In the formula, j, w, and h are longitude, latitude, and altitude, respectively; f = 1 / 298.25722, which is the flattening of the Earth's ellipsoid; Where r = 6,378,137 meters, representing the Earth's semi-major axis.

[0071] 1) SPL positive solution formula

[0072] Given the latitude, longitude, and altitude (j1, w1, h1) of point A and the horizontal coordinates (x, y, z) of point B relative to point A, find the latitude, longitude, and altitude (j2, w2, h2) of point B. The formula is as follows:

[0073]

[0074] 2) SPL inverse solution formula

[0075] Given the latitude, longitude, and altitude (j1, w1, h1) of point A and the latitude, longitude, and altitude (j2, w2, h2) of point B, find the horizontal coordinates (x, y, z) of point B relative to point A. The formula for this is:

[0076]

[0077] Using the horizontal coordinates of point B relative to point A, the distance, azimuth, and elevation angle of point B relative to point A can be easily obtained using a three-dimensional rectangular coordinate system.

[0078] ③Location association

[0079] First, the time of two tracks measured by different sensors is synchronized through temporal correlation, followed by spatial alignment. Then, an correlation algorithm is selected to calculate the correlation between pairs of tracks. When multiple correlation relationships exist, the two tracks are considered to be tracks of the same target. Here, the modified K-nearest neighbor method is used as the track correlation algorithm.

[0080] Let the track numbers of sensors S1 and S2 be U1 = {1, 2, ..., n1} and U2 = {1, 2, ..., n2}, respectively. Let the local state estimate of sensor S1 for the i-th target at time l and its covariance matrix be... and The local state estimate and covariance matrix of sensor S2 for the j-th target at time l are as follows: and Let H0 and H1 be the following events (i∈U1, j∈U2):

[0081] H0: The trajectory i of sensor S1 is associated with the trajectory j of sensor S2 (i.e.: and (This refers to the state estimation of the same objective);

[0082] H1: The trajectory i of sensor S1 is not related to the trajectory j of sensor S2.

[0083] Let the state estimation difference between track i from sensor S1 and track j from sensor S2 be:

[0084]

[0085] In the formula n x It is the dimension of the state estimate, i∈U1,j∈U2.

[0086] Let the threshold vector be: Let N0 and K be two positive integers, where N0 ≥ 2, [N0 / 2] ≤ K ≤ N0, and N0 is the number of association tests. The principle of track association in the MK-NN method is: if K out of N0 association tests satisfy the following condition:

[0087]

[0088] If H0 is accepted, then H1 is accepted.

[0089] Considering that the state estimation at times l = 1, 2, ..., N0 is processed stepwise in the track association test, to make the association assignments after l > N0 more reliable, the MK-NN algorithm links the current test with its history, dividing the entire association test into: the association period, the check period, and the maintenance period. The algorithm is in the association period when the number of track points entering the fusion node is less than N0. When l > N0, the algorithm enters the check period for association assignment. Although the association assignment at time l = N0 has formed with a high probability, there is still a possibility of erroneous associations. To detect such errors, a check period is specifically established in the algorithm. Since the reliability of the system tracks that have passed the association period is already very high, a strict test condition (test statistic) is chosen to check the correctness of the association pairs.

[0090] The test statistic is defined as:

[0091]

[0092] In the formula, tracks i and j represent pairs that have been identified as related at time l.

[0093]

[0094] These represent their state estimates and error covariance, respectively, with β being a correction coefficient set to compensate for insufficient process noise estimation. ω ij (l) is to obey nx χ of degrees of freedom 2 Distribution. Therefore, it can be based on χ². 2 The verification is used to check the correctness of the track association.

[0095] It should be noted that for systems where the track covariance cannot be directly obtained, The target's measurement variance matrix can be used instead, but the coefficients need to be adjusted appropriately.

[0096] For the correlation and fusion of data from different types of equipment, since the target parameters detected by different sensors are of different categories, how to establish a unified correlation metric for these different types of intelligence is a key issue in achieving correlation processing.

[0097] Characterization and reduction of multi-source intelligence "attributes" are fundamental to the correlation processing of multi-source heterogeneous information. Multi-source heterogeneous information correlation technology mainly involves characterization and reduction of heterogeneous information, such as vectorizing target motion parameters (position, orientation, velocity, etc.) as a set of vector elements for feature vector association. Based on this, correlation analysis is performed using a unified feature vector. This allows the measurement of positional information, feature information, and attribute information to be unified into a feature vector, enabling the use of multi-factor correlation analysis, fuzzy clustering analysis, statistical tests, and other methods for data correlation analysis and association processing. (Refer to...) Figure 2 ,like Figure 2 The diagram shown is a correlation analysis diagram based on fuzzy feature-based heterogeneous information in this embodiment.

[0098] Multivariate statistical correlation analysis using fuzzy feature vectorization: First, the detection data from multiple sensors are standardized and transformed into fuzzy membership functions. The target fuzzy membership function can be set according to the physical mechanism and distribution of the intelligence feature information. For example, due to interference and jitter, the fuzzy membership function of the radar radiation source frequency feature parameter can be set as follows:

[0099]

[0100] Where, x j , σ ij These represent the j-th index of the frequency measurement value, and the object being identified is A. i The mean and standard deviation of the j-th indicator.

[0101] Secondly, for various technical intelligence information parameters with fuzzy characteristics, a multivariate statistical analysis method is used to construct a fuzzy correlation matrix. By setting an appropriate cutoff threshold level λ for fuzzy correlation matrix operation, the correlation analysis of multi-sensor feature vectors is realized to determine the correlation of each feature vector element under a given cutoff threshold level λ, thereby establishing the mutual correlation relationship of heterogeneous intelligence information.

[0102] Among them, the fuzzy statistic F is used to select the optimal threshold λ for the fuzzy clustering level parameter. Let there be a set of n feature vector elements X = {x1, x2, ..., xn}. n}, and assuming they are divided into r-related classes, then the fuzzy statistic is:

[0103]

[0104] in, It is the mean of the k-th feature parameter of the i-th class; D1 is the mean of the k-th characteristic parameter of all elements in the universe of discourse; D2 is the Mahalanobis distance.

[0105]

[0106] The numerator describes the distance between elements within a class, while the denominator describes the distance between classes. Therefore, the threshold corresponding to the maximum F value is the optimal threshold: λ = max{F}. That is, when D² < λ, the two sets of equipment data are considered correlated and require fusion; otherwise, they are considered uncorrelated.

[0107] After completing the data association of the same, similar, and dissimilar equipment, the equipment energy indicators are quantified, specifically:

[0108] For spatial coverage indicators:

[0109] Assume that after data on similar equipment is correlated and fused, the fused distance values ​​between all targets and equipment are obtained, denoted as set D = [d1, d2, d3, ..., d n If the maximum distance max{D} in the distance fusion result is taken as the maximum measurement distance of this type of equipment, then the maximum distance in the distance fusion result will be taken as the maximum measurement distance of this type of equipment.

[0110] Assume that after data from similar equipment is correlated and fused, the fused values ​​of the azimuth angles between all targets and equipment are obtained, denoted as set Θ = [α1, α2, α3, ..., α n If the maximum angle max{Θ} in the azimuth fusion result is taken as the maximum measurement angle of this type of equipment, then the maximum angle in the azimuth fusion result will be taken as the maximum measurement angle of this type of equipment.

[0111] For measurement accuracy indicators:

[0112] Suppose the result of the association and fusion of heterogeneous data is T = {T1, T2, T3, ... T} n}, where T k (k = 1, 2, 3, ..., n) represents the measurement information of the k-th target, i.e., the orientation information α. k and distance information d k Meanwhile, assuming that the result of data fusion for similar equipment is T′={T′1,T′2,T′3,…T′m}, where T′ k (k = 1, 2, 3, ..., m) represents the measurement information of the k-th target, i.e., the orientation information α′. k and distance information d′ k The correlation fusion result T between heterogeneous data and the correlation fusion result T′ between similar equipment data are correlated to obtain the correlation result T between T and T′. i ={T i1 ,T i2 ,T i3 ,…T in} and T′ i ={T′ i1 ,T′ i2 ,T′ i3 ,…T′ in The accuracy of azimuth measurement and distance measurement for the associated target is denoted as Θ. r ={θ i1 ,θ i2 ,θ i3 ,…,θ in} and D r ={γ i1 ,γ i2 ,γ i3 ,…,γ in},in

[0113]

[0114]

[0115] Finally, max{Θ r As the angular measurement accuracy of this equipment, max{D r The ranging accuracy of this equipment is considered.

[0116] For measurement accuracy metrics:

[0117] Suppose the result of the association and fusion of heterogeneous data is T = {T1, T2, T3, ... T} n}, where T k (k = 1, 2, 3, ..., n) represents the measurement information of the k-th target, i.e., the orientation information α. k and distance information d k Meanwhile, assuming that the result of data fusion for similar equipment is T′={T′1,T′2,T′3,…T′ m}, where T′ k (k = 1, 2, 3, ..., m) represents the measurement information of the k-th target, i.e., the orientation information α′. k and distance information d′ kThe correlation fusion result T between heterogeneous data and the correlation fusion result T′ between similar equipment data are correlated to obtain the correlation result T between T and T′. i ={T i1 ,T i2 ,T i3 ,…T in} and T′ i ={T′ i1 ,T′ i2 ,T′ i3 ,…T′ in}, then the probability of a false alarm from the equipment is

[0118]

[0119] The probability of the equipment missing an alarm is

[0120]

[0121] After the indicators are quantified, a comprehensive performance evaluation of the equipment is conducted, which includes:

[0122] Since the units and connotations of the indicators such as maximum measurement distance, maximum measurement angle, distance measurement accuracy, angle measurement accuracy, false alarm probability, and false alarm probability are inconsistent, we consider normalizing the data with inconsistent dimensions and then using the analytic hierarchy process (AHP) to calculate the evaluation results of the equipment's comprehensive performance.

[0123] In terms of data normalization, the extreme value comparison method is used. This involves identifying the best detection range, optimal elevation angle, final ranging accuracy, optimal false alarm probability, and false alarm probability for a certain type of equipment through methods such as literature review and expert consultation. By comparing the measured values ​​with the optimal values, all values ​​can be converted into dimensionless values ​​between 0 and 1, thus achieving data normalization. Let the normalized value of the maximum measurement distance be A1, the normalized value of the maximum elevation angle be A2, the normalized value of the angle measurement accuracy be B1, the normalized value of the maximum ranging distance be B2, the normalized value of the false alarm probability be C1, and the normalized value of the false alarm probability be C2.

[0124] In terms of data weighted averaging, since the equipment performance consists of three capabilities and six capability indicators, it is necessary to determine the weight of each layer of elements, then obtain the quantitative value of the capability by weighting the capability indicators, and finally obtain the comprehensive evaluation result of the reconnaissance and surveillance equipment performance by weighted averaging of the capabilities.

[0125] ①Indicator Comparison Matrix

[0126] Weight determination is a key step in the analytic hierarchy process. First, the importance of lower-level indicators to higher-level indicators is determined. Based on the importance of lower-level indicators to higher-level indicators, the importance of lower-level indicators among themselves is determined. Combining the meaning of the importance scale in Table 1, a judgment matrix is ​​constructed. By obtaining the eigenvectors of the judgment matrix, the weights of the lower-level indicators are obtained.

[0127]

[0128] Table 1. Meaning of the 9-scale and its reciprocal scale

[0129] Taking spatial range as the higher-level indicator as an example, it includes three elements: maximum measurement distance, maximum pitch angle, and A1 and A2 respectively. The following judgment matrix can be obtained:

[0130]

[0131] ② Consistency check

[0132] The purpose of consistency testing is to check whether the comparison judgment matrix contains logical errors (or illogical errors), and to determine whether the error caused by such errors is acceptable. A comparison judgment matrix that passes the consistency test is valid; otherwise, it needs to be corrected. When the comparison judgment matrices M are completely consistent, we have λ max =n, where λ max Let λ be the largest eigenvalue of the judgment matrix M, and let M be the order of the judgment matrix M; when M is not completely consistent, λ max >n; the greater the inconsistency, the smaller λ max The greater the difference between λ and n, the better. Therefore, λ can be used. max -n is used as an indicator to measure the consistency of the matrix. Therefore, the consistency index C is defined as:

[0133]

[0134] The larger C is, the worse the consistency, and vice versa. In addition, considering that the consistency deviation may also be caused by random factors, when checking whether the consistency of the judgment matrix is ​​satisfactory, C should also be compared with the average random consistency index I (which is only related to the order n of the comparison judgment matrix M) to obtain the test number R.

[0135] Define the random consistency ratio test number R as:

[0136]

[0137] When C is less than the set threshold, the comparison judgment matrix M is considered to have satisfactory consistency; when C is greater than the set threshold, the comparison judgment matrix M is considered to be inconsistent and the judgment matrix must be corrected.

[0138] ③ Weight determination

[0139] Based on the corrected judgment matrix, the weights of the lower-level indicators are obtained by calculating the eigenvectors of the judgment matrix. Let the weight of maximum measurement distance be α1, the weight of maximum pitch angle be α2, the weight of angle measurement accuracy be β1, the weight of maximum distance measurement be β2, the weight of false alarm probability be ε1, the weight of missed alarm probability be ε2, the weight of spatial coverage be α, the weight of measurement accuracy be β, and the weight of measurement correctness be ε.

[0140] ④ Comprehensive quantitative assessment

[0141] By using the above weighting method, the weights of each element are obtained. Finally, the weighted average method is used to obtain the evaluation result of the equipment performance.

[0142] E=α(α1A1+α2A2)+β(β1B1+β2B2)+ε(ε1C1+ε2C2)

[0143] This embodiment evaluates equipment performance by correlating measurement data from the same equipment, similar equipment, and dissimilar equipment. It comprehensively considers the impact of different environments, different targets, different target states, and different individual equipment on the performance of reconnaissance and surveillance equipment, thereby gaining a more comprehensive understanding and mastery of the dynamic performance of various types of equipment in actual environments.

[0144] Example 2

[0145] Reference Figure 3 ,like Figure 3 The diagram shown is a structural block diagram of the reconnaissance and surveillance equipment performance evaluation device provided in this embodiment. The device specifically includes the following structures:

[0146] The same equipment measurement association module uses a multi-hypothesis association method to perform association fusion between measurement data of the same equipment, calculates the probability of each candidate hypothesis, determines the set of feasible association hypotheses at a specific time, and transforms the equipment measurement data into tracks of different targets in the association fusion process.

[0147] The similar equipment measurement association module performs course and speed matching between spatial tracks of similar equipment measurement data, and then performs spatiotemporal association of the tracks, which includes time synchronization, spatial alignment and position association.

[0148] The heterogeneous equipment measurement correlation module converts heterogeneous equipment measurement data into characteristic parameters, constructs a fuzzy correlation matrix for the characteristic parameters, and performs fuzzy correlation matrix operations with a set cutoff threshold level to realize the correlation analysis of multi-sensor feature vectors, so as to determine the correlation of each feature vector element under a given cutoff threshold level, thereby establishing the mutual correlation relationship of heterogeneous equipment measurement data.

[0149] The performance evaluation module quantifies the energy indicators of all equipment based on the correlation between measurement data of the same equipment, the correlation between measurement data of similar equipment, and the correlation between measurement data of dissimilar equipment. It also normalizes the energy indicators of equipment with inconsistent dimensions and calculates the evaluation result of the overall performance of the equipment by weighting.

[0150] Example 3

[0151] This preferred embodiment provides a computer device that can implement the steps in any embodiment of the reconnaissance and surveillance equipment performance evaluation method provided in this application. Therefore, it can achieve the beneficial effects of the reconnaissance and surveillance equipment performance evaluation method provided in this application. For details, please refer to the previous embodiments, which will not be repeated here.

[0152] Example 4

[0153] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor. Therefore, embodiments of the present invention provide a storage medium storing multiple instructions that can be loaded by a processor to execute the steps of any embodiment of the reconnaissance and surveillance equipment performance evaluation method provided by the present invention.

[0154] The storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0155] Since the instructions stored in the storage medium can execute the steps in any of the reconnaissance and surveillance equipment performance evaluation method embodiments provided by the present invention, the beneficial effects that any of the reconnaissance and surveillance equipment performance evaluation methods provided by the present invention can achieve can be realized. For details, please refer to the previous embodiments, which will not be repeated here.

[0156] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for performance evaluation of a reconnaissance surveillance equipment, characterized by, The method comprises: The association between the same equipment measurement data is associated by using a multi-hypothesis association method for association fusion, the probability of each candidate hypothesis is calculated, the feasible association hypothesis set at a specific time is determined, and the measurement data of the equipment is converted into the tracks of different targets in the association fusion; The spatial tracks between the same type of equipment measurement data are matched in heading and speed, and then the tracks are associated in space and time, the space alignment includes selecting an equipment as a reference center, taking the time of the reference center as a standard time, and unifying the times of the equipment within a preset range to the standard time; and the position association includes selecting an association algorithm to calculate the association of the tracks obtained by two different equipment measurements, and if there are multiple association relationships, the two tracks are considered as the tracks of the same target; The measurement data of different types of equipment is converted into characteristic parameters, a fuzzy association matrix is constructed for the characteristic parameters, and the correlation analysis of the feature vectors of multiple sensors is realized by setting the fuzzy association matrix operation of the cutoff threshold level to determine the correlation of each feature vector element under the given cutoff threshold level, thereby establishing the mutual association relationship of the measurement data of different types of equipment; According to the association between the same equipment measurement data, the association between the same type of equipment measurement data, and the association between the measurement data of different types of equipment, all equipment energy indicators are quantified, the equipment energy indicators with inconsistent dimensions are normalized, and the evaluation results of the comprehensive performance of the equipment are calculated by weighting, wherein the calculation of the evaluation results of the comprehensive performance of the equipment by weighting includes determining the weight of each equipment energy indicator, obtaining the quantized value of the equipment capability by weighting the equipment capability indicators, then obtaining the comprehensive evaluation results of the performance of the reconnaissance surveillance equipment by weighted average of the equipment capability, and finally obtaining the comprehensive evaluation results of the performance of the reconnaissance surveillance equipment.

2. The method of performance evaluation of reconnaissance surveillance equipment according to claim 1, characterized in that, The space alignment includes unifying the coordinate systems used by the equipment working on different platforms.

3. The method of performance evaluation of reconnaissance surveillance equipment according to claim 1, characterized in that, The normalization of the equipment energy indicators with inconsistent dimensions adopts the maximum value comparison method.

4. A reconnaissance surveillance equipment performance evaluation device, characterized by The device is applied to the reconnaissance surveillance equipment performance evaluation method of claim 1, and the device comprises: A same equipment measurement association module, which associates the association between the same equipment measurement data by using a multi-hypothesis association method for association fusion, calculates the probability of each candidate hypothesis, and determines the feasible association hypothesis set at a specific time, and the measurement data of the equipment is converted into the tracks of different targets in the association fusion; A same type of equipment measurement association module, which matches the spatial tracks between the same type of equipment measurement data in heading and speed, and then associates the tracks in space and time, and the space alignment includes time synchronization, space alignment, and position association; A different type of equipment measurement association module, which converts the measurement data of different types of equipment into characteristic parameters, constructs a fuzzy association matrix for the characteristic parameters, and realizes the correlation analysis of the feature vectors of multiple sensors by setting the fuzzy association matrix operation of the cutoff threshold level to determine the correlation of each feature vector element under the given cutoff threshold level, thereby establishing the mutual association relationship of the measurement data of different types of equipment. The heterogeneous equipment measurement correlation module converts the heterogeneous equipment measurement data into characteristic parameters, constructs a fuzzy correlation matrix for the characteristic parameters, and realizes correlation analysis of the multi-sensor feature vectors through fuzzy correlation matrix operation with a set threshold level, to determine the correlation of each feature vector element under the given threshold level, thereby establishing the correlation relationship of the heterogeneous equipment measurement data; The performance evaluation module quantifies all equipment energy indicators according to the correlation between the same equipment measurement data, the correlation between the same type of equipment measurement data, and the correlation between the heterogeneous equipment measurement data, normalizes the equipment energy indicators with inconsistent dimensions, and weightedly calculates the evaluation result of the comprehensive performance of the equipment.

5. A computer device, comprising: The computer device comprises a processor and a memory, and the memory stores a computer program which is loaded and executed by the processor to implement the reconnaissance surveillance equipment performance evaluation method according to any one of claims 1-3.

6. A computer-readable storage medium, characterized in that, The storage medium stores a computer program which is loaded and executed by the processor to implement the reconnaissance surveillance equipment performance evaluation method according to any one of claims 1-3.

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