Signal sorting result comprehensive evaluation method based on AHP

Abstract

The quality of signal sorting directly affects the signal recognition performance of a radar radiation source and the subsequent decision judgment of a combat system, so that the comprehensive performance of EDW (Emitter Descriptions Words) of signal sorting needs to be evaluated. The invention provides a comprehensive evaluation method for an electronic warfare passive reconnaissance signal sorting result, and the method comprises the steps: carrying out the comprehensive performance evaluation and confidence judgment of an EDW through introducing an AHP (Analytic Hierarchy Process) according to the parameters, such as the frequency, the pulse width, the repetition period and the associated pulse number, after the sorting of a radiation source signal, carrying out the trace point association and target recognition based on the judgment result, and carrying out the recognition of the target. And the reliability and the accuracy of a signal sorting result are greatly improved. The technology provides a stable and reliable comprehensive evaluation method for the signal sorting result (EDW) in the complex electromagnetic environment, improves the accuracy and target recognition probability of signal sorting in the complex electromagnetic environment, and reduces the batch increasing rate of the system.

Description

Technical Field

[0001] The invention belongs to the field of signal processing, and in particular relates to a comprehensive evaluation method for signal sorting results based on AHP. Background Art

[0002] Radar emitter signal sorting refers to the process of separating individual radar pulse sequences from randomly interlaced pulse streams. Signal sorting is one of the core steps of electronic warfare passive reconnaissance. The quality of signal sorting directly affects the radar emitter signal recognition performance and the subsequent decision-making of the combat system. With the widespread application of various radar new technologies, the electromagnetic environment is becoming increasingly complex, the number of radar emitters is surging, the pulses of various emitters overlap seriously, and the radar signal modulation is becoming increasingly complex and the parameters are changing. The information battlefield has put forward higher and higher requirements on the correctness and reliability of emitter signal sorting. Therefore, it is necessary to conduct a comprehensive performance evaluation of EDW (Emitter Description Words) for signal sorting. Summary of the invention

[0003] To this end, the present invention proposes a comprehensive evaluation method for the sorting results of electronic warfare passive reconnaissance signals. By introducing the Analytical Hierarchy Process (AHP), the EDW is comprehensively evaluated and the confidence judgment is made based on the frequency, pulse width, repetition period, number of associated pulses and other parameters after the radiation source signal is sorted. Based on the results after the judgment, point trace association and target identification are performed, which greatly improves the reliability and accuracy of the signal sorting results. This technology provides a robust and reliable comprehensive evaluation method for signal sorting results (EDW) in complex electromagnetic environments, improves the accuracy of signal sorting and the probability of target identification in complex electromagnetic environments, and reduces the system batch increase rate. Specifically, it includes the following contents:

[0004] 1) Building an EDW comprehensive performance evaluation system based on AHP

[0005] The analytic hierarchy process (AHP) is a multi-attribute decision-making method that combines qualitative and quantitative methods. It can combine expert experience with rational analysis and achieve a comprehensive evaluation of target performance through a component judgment matrix. The premise of building an evaluation model is to establish a suitable evaluation system. Combined with the needs of radar radiation source signal sorting in a complex electromagnetic environment, this design proposes three indicators: reliability, accuracy, and robustness to measure the comprehensive performance of the radiation source EDW. The specific meanings of the three indicators are as follows:

[0006] Reliability: the degree to which parameters distinguish different types of signals. For signal sorting, the parameter differences between different signals vary. The higher the degree to which parameters distinguish different signals, the higher the reliability of signal sorting.

[0007] Accuracy: the degree of accuracy of parameter measurement. Since there are measurement errors in the signal detection process, the error size varies with different parameters and measurement mechanisms. The higher the accuracy of the parameters, the higher the accuracy of signal sorting. Robustness: the degree to which the signal parameters are affected by noise. The better the stability of the parameters, the less susceptible they are to noise, and the more stable and reliable the parameter recognition results are. Figure 1 shown.

[0008] 2) EDW comprehensive performance evaluation characteristic parameter selection

[0009] The performance of the radiation source signal sorting EDW is comprehensively evaluated, that is, the reliability, accuracy and robustness of the characteristic parameters of the EDW are comprehensively evaluated. Whether the characteristic parameters to be evaluated are selected appropriately directly affects the performance of the EDW. Conventional parameters that can characterize the performance of the EDW mainly include carrier frequency, pulse width, repetition period, angle of arrival, amplitude (signal-to-noise ratio). In addition, the number of PDWs that generate the EDW in the detection time slot and whether there is a phenomenon such as pulse loss should also be considered comprehensively. Therefore, this design selects six indicators of signal quality (signal-to-noise ratio SNR), angle of arrival DOA, pulse width PW, repetition period PRI, carrier frequency RF, and number of pulses (pulse correlation) for comprehensive consideration.

[0010] When evaluating actual parameters, it is necessary to comprehensively consider the combat scenarios of the reconnaissance system, such as the operating frequency band of the radiation source, the working mode of the radiation source within this frequency band, the signal waveform, etc. Therefore, the reliability of each parameter is defined. Taking the pulse width of the sorting signal EDW as an example, the reliability metric is calculated using an S-curve, and the calculation formula is as follows:

[0011]

[0012] Pulse width (PW) reliability curve is as follows Figure 2 shown.

[0013] The pulse width values ​​0.5us, 5.25us and 10us in formula (1) can be adjusted according to the actual combat frequency band. Taking a low frequency band as an example, if the PW of the EDW after sorting is less than 0.5us, the reliability of the EDW is considered to be low. When the PW is greater than 10us, the reliability of the EDW is considered to be high. The same method can be used to measure the reliability of parameters such as arrival angle, signal-to-noise ratio, repetition period, and carrier frequency.

[0014] In addition, the number of pulses (pulse relevance) in this design refers to the comprehensive consideration of how many PDWs are associated with the EDW generated in the time slot, and how many TOAs of four consecutive PDWs satisfy the PRI relationship. The specific definition is as follows:

[0015] Assume that the reconnaissance window length is T win, the number of pulses of the i-th EDW is N i , repetition period T i , then the reliability of the pulse number (pulse correlation) is defined as:

[0016]

[0017] The closer the ratio of the number of pulses correlated in EDW to the theoretical calculated value is to 1, the higher the reliability of the pulse number (pulse correlation) is.

[0018] 3) EDW comprehensive performance evaluation process based on AHP

[0019] a) Build an EDW performance evaluation model

[0020] Based on the selection of characteristic parameters in the comprehensive evaluation of EDW performance, the EDW performance evaluation model constructed in this design is as follows: Figure 3 shown.

[0021] b) Constructing a judgment matrix

[0022] According to the Star relative importance rating table, the criteria layer and indicators in the EDW performance evaluation model are scored to construct a judgment matrix. The Star relative importance rating table is shown in Table 1:

[0023] Table 1. Relative importance of Star

[0024] scale meaning 1 The i-th factor has the same influence as the j-th factor 3 The i-th factor has a slightly stronger influence than the j-th factor 5 The i-th factor has a stronger influence than the j-th factor 7 The influence of the i-th factor is significantly stronger than that of the j-th factor 9 The influence of the i-th factor is definitely stronger than that of the j-th factor

[0025] In Table 1, the unrepresented 2, 4, 6, and 8 indicate that the impact of the ith factor on the jth factor is between the two adjacent levels mentioned above.

[0026] The constructed judgment matrices A, B1, B2, and B3 are as follows:

[0027]

[0028]

[0029] In formula (3), a 12 =7 means that the influence of reliability index on EDW related parameters is significantly weaker than that of accuracy index. The meanings of other elements in matrix A and B1, B2, B3 are similar.

[0030] c) Perform consistency check on judgment matrices A, B1, B2, and B3 respectively

[0031] The consistency test process is as follows: First, define the consistency index CI, the expression is as follows:

[0032]

[0033] In the above formula, n is the sum of the diagonal elements of the judgment matrix A, which is also the sum of the characteristic roots of A.

[0034] Then calculate the consistency ratio CR, the expression is as follows:

[0035]

[0036] In the above formula, RI is the random consistency index, and its value is related to the order of the judgment matrix, as shown in Table 2.

[0037] Table 2 Consistency Index (RI)

[0038] Degree 1 2 3 4 5 6 7 8 9 RI 0 0 0.58 0.9 1.12 1.24 1.32 1.41 1.45

[0039] When the consistency ratio CR<0.1, the judgment matrix is ​​considered to meet the consistency requirements. Otherwise, the judgment matrix needs to be readjusted and the above calculations are repeated until the consistency requirements are met.

[0040] According to formula (7), the eigenvalues ​​and normalized eigenvectors of the judgment matrix A are calculated, and the calculation results are as follows:

[0041] λ Amax =3.1044,ω A =[a1,a2,a3]=[0.7766 0.1530 0.0704]

[0042] According to formula (8) and the consistency index in Table 2, the consistency index CI of the judgment matrix A is calculated A =0.0522 and consistency ratio CR A =0.09, due to CR A <0.1, it is considered that the judgment matrix A satisfies the consistency test.

[0043] Using the same method as above, the eigenvalues ​​and weight vectors of the comparison matrices B1, B2, and B3 in the criterion layer are calculated, and the consistency test results are as follows:

[0044]

[0045]

[0046]

[0047]

[0048] From the above calculation results, it can be seen that the consistency test results of the judgment matrices B1, B2, and B3 are all less than 0.1, which meets the consistency test requirements.

[0049] d) Calculate the total hierarchical ranking weight vector

[0050] Assume that there are m factors X1, X2, …, X m The order of the total goals is a1, a2, …, a m Assume that the index layer has n elements Y1, Y2, ..., Yn. When calculating the weight, first calculate the n elements for the factor X in the criterion layer. j (j=1,2,…,m) hierarchical single ordering b 1j ,b 2j ,…,b nj Then, the total hierarchical order of the indicator layer is as follows:

[0051] Y1:a1b 11 +a2b 12 +…+a m b 1m

[0052] Y2:a1b 21 +a2b 22 +…+a m b 2m

[0053] …

[0054] Y n :a1b n1 +a2b n2 +…+a m b nm

[0055] The weight of the i-th factor in the indicator layer to the overall goal is

[0056]

[0057] The weights of the six parameters of the indicator layer are calculated respectively, and the results are shown in Table 3.

[0058] Table 3 Total ranking weights of indicator layers

[0059] parameter Weight Pulse Width <h2 style=";text-align:left;direction:ltr"><![CDATA[ω1=a1b <h2 style=";text-align:left;direction:ltr"> 11 <h2 style=";text-align:left;direction:ltr"> +a2b<h2 style=";text-align:left;direction:ltr"> 21 <h2 style=";text-align:left;direction:ltr"> +a3b<h2 style=";text-align:left;direction:ltr"> 31 <h2 style=";text-align:left;direction:ltr"> <0.1193]]><h2 style=";text-align:left;direction:ltr"> Signal-to-Noise Ratio <h2 style=";text-align:left;direction:ltr"><![CDATA[ω2=a1b <h2 style=";text-align:left;direction:ltr"> 12 <h2 style=";text-align:left;direction:ltr"> +a2b<h2 style=";text-align:left;direction:ltr"> 22 <h2 style=";text-align:left;direction:ltr"> +a3b<h2 style=";text-align:left;direction:ltr"> 32 <h2 style=";text-align:left;direction:ltr"> <0.1285]]><h2 style=";text-align:left;direction:ltr"> Carrier frequency <h2 style=";text-align:left;direction:ltr"><![CDATA[ω3=a1b <h2 style=";text-align:left;direction:ltr"> 13 <h2 style=";text-align:left;direction:ltr"> +a2b<h2 style=";text-align:left;direction:ltr"> 23 <h2 style=";text-align:left;direction:ltr"> +a3b<h2 style=";text-align:left;direction:ltr"> 33 <h2 style=";text-align:left;direction:ltr"> <0.1886]]><h2 style=";text-align:left;direction:ltr"> Repeat cycle <h2 style=";text-align:left;direction:ltr"><![CDATA[ω4=a1b <h2 style=";text-align:left;direction:ltr"> 14 <h2 style=";text-align:left;direction:ltr"> +a2b<h2 style=";text-align:left;direction:ltr"> 24 <h2 style=";text-align:left;direction:ltr"> +a3b<h2 style=";text-align:left;direction:ltr"> 34 <h2 style=";text-align:left;direction:ltr"> <0.3696]]><h2 style=";text-align:left;direction:ltr"> Angle of arrival <h2 style=";text-align:left;direction:ltr"><![CDATA[ω5=a1b <h2 style=";text-align:left;direction:ltr"> 15 <h2 style=";text-align:left;direction:ltr"> +a2b<h2 style=";text-align:left;direction:ltr"> 25 <h2 style=";text-align:left;direction:ltr"> +a3b<h2 style=";text-align:left;direction:ltr"> 35 <h2 style=";text-align:left;direction:ltr"> <0.0230]]><h2 style=";text-align:left;direction:ltr"> Pulse number <h2 style=";text-align:left;direction:ltr"><![CDATA[ω6=a1b <h2 style=";text-align:left;direction:ltr"> 16 <h2 style=";text-align:left;direction:ltr"> +a2b<h2 style=";text-align:left;direction:ltr"> 26 <h2 style=";text-align:left;direction:ltr"> +a3b<h2 style=";text-align:left;direction:ltr"> 36 <h2 style=";text-align:left;direction:ltr"> <0.1640]]><h2 style=";text-align:left;direction:ltr">

[0060] The consistency ratio of the EDW comprehensive evaluation model is calculated as follows:

[0061]

[0062] The calculation results show that the consistency test results of the EDW comprehensive performance evaluation model are met, that is, the evaluation model is effectively constructed.

[0063] Therefore, after obtaining the sorting result EDW, the weight coefficients of the six parameters of pulse number, signal-to-noise ratio, pulse width, repetition period, carrier frequency, and arrival angle associated with EDW are comprehensively compared. When the evaluation result is greater than the threshold (threshold reference value 0.75), it is considered that the EDW has a high reliability, and the EDW can be processed for subsequent point trace association and data storage.

[0064] The beneficial effects of the present invention are

[0065] 1) Improve the accuracy of radiation source signal sorting in complex electromagnetic environments and reduce the batch increase rate

[0066] AHP is used to comprehensively evaluate the parameters of signal sorting and EDW generation (pulse signal-to-noise ratio SNR, arrival angle DOA, pulse width PW, repetition period PRI, carrier frequency RF, and pulse number) to obtain the confidence judgment of the sorted EDW. According to the confidence judgment result, it is decided whether to perform subsequent point trace association and data storage, thereby improving the accuracy of signal sorting in complex electromagnetic environments and reducing the system batch increase rate.

[0067] 2) Use self-evaluation processing technology to improve the robustness of signal sorting technology in complex electromagnetic environments

[0068] When conducting comprehensive evaluation of EDW, the detection signal-to-noise ratio of the radiation source signal, the number of correlated pulses of signal sorting and other indicators are comprehensively utilized to conduct comprehensive evaluation and reliability judgment of EDW, thereby improving the robustness of signal sorting technology in complex electromagnetic environments.

[0069] 3) Improve the accuracy of data storage in complex electromagnetic environments

[0070] It realizes the comprehensive evaluation and reliability judgment of the sorted EDW, greatly improves the accuracy of data storage, and thus improves the probability of identifying the target radiation source. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 This is a diagram of the EDW comprehensive performance evaluation system based on AHP.

[0072] Figure 2 Schematic diagram of the pulse width (PW) reliability curve.

[0073] Figure 3 Schematic diagram of EDW comprehensive performance evaluation model.

[0074] Figure 4 This is the signal sorting result of scene 1 (before improvement).

[0075] Figure 5 This is the signal sorting result of scene 1 (after improvement).

[0076] Figure 6This is the signal sorting result of scene 2 (before improvement).

[0077] Figure 7 This is the signal sorting result for scenario 2 (after improvement). DETAILED DESCRIPTION

[0078] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0079] Taking a passive detection system in a certain frequency band as an example, the instantaneous bandwidth is set to 800MHz, the sampling rate is set to 960MHz, and the parameters of three radiation sources are simulated to verify the effectiveness of this design method.

[0080] Scenario 1: Radiation source 1 (frequency agility + fixed repetition rate), radiation source 2 (staggered repetition rate + fixed carrier frequency), radiation source 3 (jittered repetition rate + fixed carrier frequency), the specific parameters are shown in the following table:

[0081] Table 4 Parameters of three radiation sources (Scenario 1)

[0082]

[0083]

[0084] Figure 4 and Figure 5 The results of signal sorting batch increase and sorting accuracy before comprehensive evaluation of EDW parameters and after comprehensive evaluation of EDW designed by this method are given.

[0085] Scenario 2: Radiation source 1 (linear frequency modulation + repetition rate jitter), radiation source 2 (frequency agility + fixed repetition rate), radiation source 3 (frequency agility + repetition rate jitter), the specific parameters are shown in the following table:

[0086] Table 5 Parameters of three radiation sources (Scenario 2)

[0087]

[0088] Figure 6 and Figure 7 The results of signal sorting batch increase and sorting accuracy before comprehensive evaluation of EDW parameters and after comprehensive evaluation of EDW designed by this method are given.

[0089] Depend on Figures 4 to 7 It can be seen that after adopting the EDW comprehensive performance evaluation method designed in this paper, the number of signal sorting batches in complex scenarios is reduced and the sorting accuracy is significantly improved.

[0090] The present invention is not limited to the above specific implementations, and the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made to the above implementations based on the technical essence of the present invention shall be included in the protection scope of the present invention.

Claims

1. A comprehensive evaluation method for preference sorting results based on AHP, characterized by: The specific steps include: 1) Building an EDW comprehensive performance evaluation system based on AHP The three indicators of reliability, accuracy and robustness are set by using the hierarchical analysis method to measure the comprehensive performance of the radiation source EDW; the specific meanings of the three indicators are as follows: Reliability: the degree to which the parameter distinguishes different types of signals; Accuracy: the degree of precision with which a parameter is measured; Robustness: the degree to which signal parameters are affected by noise; 2) EDW comprehensive performance evaluation characteristic parameter selection The reliability, accuracy and robustness of EDW characteristic parameters are comprehensively evaluated through six indicators: signal quality, arrival angle DOA, pulse width PW, repetition period PRI, carrier frequency RF, and pulse number; The number of pulses refers to how many PDWs are associated with the EDW generated in the time slot, and how many consecutive 4 PDWs' TOA satisfy the PRI relationship. The specific definitions are as follows: Assume that the reconnaissance window length is T win , the number of pulses of the i-th EDW is N i , repetition period T i , then the reliability of the pulse number is defined as: When the ratio of the number of pulses associated in EDW to the theoretical calculated value is closer to 1, the reliability of the pulse number is higher; 3) EDW comprehensive performance evaluation process based on AHP a) Build an EDW performance evaluation model A performance evaluation model for reliability, accuracy and robustness based on six indicators, namely, signal quality, arrival angle DOA, pulse width PW, repetition period PRI, carrier frequency RF and pulse number, is constructed, including a target layer, a criterion layer and an indicator layer; the target layer is a performance evaluation target, the criterion layer includes reliability, accuracy and robustness judgment criteria, and the indicator layer includes six indicators, namely, signal quality, arrival angle DOA, pulse width PW, repetition period PRI, carrier frequency RF and pulse number; b) Constructing a judgment matrix According to the Star relative importance rating table, the criteria layers and indicators in the EDW performance evaluation model are scored and a judgment matrix is ​​constructed; c) Check the consistency of the judgment matrix First, define the consistency index CI, the expression is as follows: In the above formula, n is the sum of the diagonal elements of the judgment matrix; Then calculate the consistency ratio CR, the expression is as follows: In the above formula, RI is the random consistency index; When the consistency ratio CR<0.1, the judgment matrix is ​​considered to meet the consistency requirements, otherwise the judgment matrix needs to be readjusted and the above calculations are repeated until the consistency requirements are met; d) Calculate the total hierarchical ranking weight vector Assume that there are m factors X1, X2, …, X m The order of the total goals is a1, a2, …, a m ; Assume that the index layer has n elements Y1, Y2, ..., Y n , when calculating the weights, first calculate the n elements against the factor X in the criterion layer j , j = 1, 2, ..., m hierarchical single ordering b 1j ,b 2j ,…,b nj , then the total hierarchical order of the indicator layer is as follows: Y1:a1b 11 +a2b 12 +…+a m b 1m Y2:a1b 21 +a2b 22 +…+a m b 2m … Y n :a1b n1 +a2b n2 +…+a m b nm The weight of the i-th factor in the indicator layer to the overall goal is: The weights of the six parameters of the indicator layer are calculated respectively to obtain the final evaluation results.

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