rus
Issue #6/2023
A. V. Lenshin, E. V. Kravtsov, S. A. Sitnikov
Operational Capability Assessment Means of Optical-Electronic Intelligence of the Infrared Range
Operational Capability Assessment Means of Optical-Electronic Intelligence of the Infrared Range
DOI: 10.22184/1993-7296.FRos.2023.17.6.474.486
Operational Capability Assessment Means of Optical-Electronic Intelligence of the Infrared Range
A. V. Lenshin, E. V. Kravtsov, S. A. Sitnikov
Military Educational and Scientific Center of the Air Force “Air Force Academy named after Professor N. E. Zhukovsky and Yu. A. Gagarin” (VUNC VVS “VVA”), Voronezh, Russia
A methodological approach is proposed to improve the efficiency of assessing the capabilities of infrared reconnaissance tools, based on the use of graphoanalytic and automated algorithms, as well as the use of the values of the radiated area and maximum dimensions of objects averaged in the range of infrared reconnaissance of the values of the radiation coefficients of objects and typical backgrounds, object and background temperatures, atmospheric transmittance values, averaged over the range of operating wavelengths and implemented using graphs of the dependence of the probability of detection on the range at various fixed values of the coefficient of exploration conditions calculated in advance for the typed parameters of infrared reconnaissance facilities of various bases with acceptable accuracy. Recommendations have been developed to counteract the means of infrared reconnaissance in conditions of unsatisfactory intelligence availability.
Keywords: infrared reconnaissance, object of reconnaissance, probability of detection, efficiency of assessment
Article received: 30.06.2023
Article accepted: 02.08.2023
Introduction
The analysis of armed conflicts and local wars of recent years allows us to conclude that in modern warfare, in which a significant role is assigned to the transmission of information through the propagation of electromagnetic waves, technical means of reconnaissance (TSR) play the most important role in ensuring high combat effectiveness of means of armed struggle [1]. Currently, infrared (IR) imaging systems are widely used, receiving signals in the IR region of the spectrum and converting them into visible images. This is due to the fact that it is in this area that the bulk of the own electromagnetic radiation of the majority of objects of natural and artificial origin surrounding us is concentrated [2, 3].
Qualitative changes in the development of IR range optoelectronic reconnaissance means placed on various carriers, the rapidly changing operational situation, a significant increase in the capabilities of IR reconnaissance means (IR) and their impact on the outcome of hostilities require in-depth study of the experience of organizing and using IR means, operational forecasting of intelligence placement and intelligence availability. The interests of national security require the development of counteraction measures (PD) in the proposed areas of deployment of objects to increase their survivability, create promising and improve existing complexes and systems of PD ICR [4, 5].
The main tasks of the IR species control are the following:
verification and evaluation of the concealment of the state of preparation, construction, commissioning and repair of military facilities (VO);
monitoring of maintenance of the mode of vital activity and blackout on objects;
control of the effectiveness of the disguise of weapons, military equipment (IWT) units and divisions;
control of the secrecy of routine maintenance, restoration and loading and unloading operations at the military and industrial complex;
control of technical and organizational measures for disinformation in the positional area;
aerial IR view control of unoccupied field positions in order to assess their masking capacity (when selecting positions), as well as the formation of an image bank;
aerial IR view control of field positions (areas) immediately before occupation and after their abandonment [4, 5].
Problem statement
The existing methods provide high reliability of the assessment in the presence of the necessary completeness of the initial data. However, in a dynamically changing radio-electronic environment, their use for a number of objective reasons is often not possible. Therefore, increasing the efficiency of assessing the capabilities of ICR funds is an urgent scientific task.
The main tasks in the implementation of protective measures against ICR means are to exclude the possibility (decrease the probability) of detecting an object, determining the parameters and characteristics of the object, as well as object recognition. Methods of reducing information about the object of intelligence are divided into passive concealment (reducing the level of the object’s own radiation, reducing its reflective properties), active concealment (suppression, functional damage to the main elements of the means of IR) and disinformation (creating false objects) [4, 6].
In [7], a methodological approach is proposed to increase the efficiency of assessing the capabilities of television intelligence using the values of the brightness coefficients of intelligence objects, backgrounds and coatings, the minimum size of the object with acceptable reliability.
Operational assessment of the capabilities of ICR can be implemented using two algorithms – graphoanalytic and automated [8].
The graphoanalytic algorithm is based on the preliminary generalization of data on the means of exploration, the object of exploration, the conditions of exploration by typing them and presenting them in the form of tabular information and graphical dependencies, which are used to obtain numerical values for evaluating the capabilities of the ICR. This algorithm assumes the presence of the specified data, graphs and elementary calculations, for which computing tools are not required.
The automated algorithm is based on the use of analytical relations, which are formed in the form of a software-algorithmic implementation. This algorithm assumes the presence of computing facilities with a pre-installed evaluation program.
The purpose of the article is the further development of operational methods for assessing the intelligence availability of ICR objects, as well as the development of recommendations on ICR PD related to the presentation of requirements for masking an object in relation to specific conditions.
Operational assessment of ICR capabilities using a graphoanalytic algorithm
The database for the graphoanalytic algorithm includes the following source data:
According to the means of reconnaissance, evaluation and analysis of the result: graphs of the dependence of the probability of detection on the range WP = f(ДP) for the lower and upper boundary values of the assessment at various fixed values of the coefficient of the conditions of exploration UP, calculated in advance for the typed parameters of the means by type of exploration; the height of aerial caviar (for various options) – HP, km;
For the object of exploration: tabular data with the values of the radiated area SОБ and the maximum size of the typed objects Lmax;
According to the conditions of exploration: data on the values of the radiation coefficients of objects εо and typical backgrounds averaged in the IR range εф; the type of background; the temperature values of the object Tо and background Tф (if absent Tо = Tф); the value of the atmospheric transmittance averaged in the range of operating wavelengths τа (may be absent); the range of exploration ДР.
The graphoanalytic algorithm for the operational assessment of the capabilities of the ICR relative to a critical object (CVO) is illustrated in Figure 1 and includes the following procedure:
Step 1. Input of initial data;
Step 2. The value of the coefficient of exploration conditions UP is calculated using the measured or accepted temperatures of the object and background, reference data on the average values of the radiation coefficients of the object and background in a given wavelength range, according to the formula
UP = [Tо − Tф + 35(εо − εф)] · τа; (1)
Step 3. According to the WP = f(ДP) schedule, an assessment of the capabilities of the ICR is carried out according to the calculated value of the UP: a) at an unknown range for the probability corresponding to the normative value, the range of the intelligence availability limit is determined; b) at a known range of ДP reconnaissance, the probability of detection is determined along the curve for the UP value found in step 1;
Step 4. A decision is made to implement the PD measures. When solving on PD and having standard means with known parameters in a given wavelength range, their implementation is carried out, leading to a decrease in the UP coefficient due to a corresponding change in the temperature of the object, the radiation coefficient or the transmission coefficient of the atmosphere (for example, due to the use of aerosols). The effectiveness of the measures taken is evaluated according to steps 1 and 2 of the algorithm.
Thus, for the implementation of the graphoanalytic algorithm, it is necessary to type data on the TSR of the ICR, the CVO and the conditions of exploration.
Operational assessment
of ICR capabilities using an automated algorithm
It is advisable to consider an automated algorithm for the operational assessment of the capabilities of ICR for two cases.
Case 1: The typification of the object and the means of IR reconnaissance has been carried out
Database for an automated algorithm (case 1):
By means of intelligence: file data with the values of the coefficients of the type of intelligence CP, calculated in advance for the typed parameters of the means by types of intelligence (aerial reconnaissance, ground reconnaissance, etc.) for the upper boundary assessment; the height of conducting aerial reconnaissance (for various typed variants) – HP, km;
By the object of intelligence: file data with the values of the radiated area SОБ;
According to the conditions of exploration: file data on the values of the emission coefficients of εо objects and typical εф backgrounds averaged in the range of IR, as well as on the average values of the parameters of PD means; the values of the temperatures of the Tо object and the Tф background (in the absence of Tо = Tф); the background type; the value of the atmospheric transmittance averaged in the range of operating wavelengths τа (may be absent); reconnaissance range ДР (may be absent).
The automated algorithm for evaluating the capabilities of ICR for case 1 is illustrated in Figure 2 and assumes the following procedure:
Step 1. Input of initial data;
Step 2. The value of the coefficient of exploration conditions UP is calculated according to the formula (1);
If the range of ДР exploration is set:
Step 3. The generalized parameter x is determined by the formula
; (2)
Step 4. The probability of W0 detection is determined using the expression [9]
; (3)
Step 5. If the calculated probability value satisfies condition W0 > W0 ДОП, a conclusion is made about the need for measures of PD ICR;
Step 6. When solving on the ICP and the availability of funds with known parameters, the measures of the ICP PD are implemented. Paragraphs 1–3 of the evaluation algorithm are carried out with corresponding changes in (1) due to the measures of the ICR PD.
If the ДР reconnaissance range is not set:
Step 7. The functional dependence of the W0 = f(ДP) probability on the reconnaissance range is calculated according to the formula (3). When calculating W0, the value of the ДР range changes in the interval (100–10 000) km for space reconnaissance (KR), (0–70) km for aerial reconnaissance (VR), depending on the variant of VR means, and (0–20 km) for ground reconnaissance (HP), depending on the placement option;
Step 8. The W0 = f(ДP) dependence is visualized and the boundary value of the detection zone of the ДОГР (W0 ДОП) object is given as the result of the evaluation. The values of the ДОГР are compared with the approximate (expected) range of the LL reconnaissance ДРO and the range of the optical horizon of the ДOГ (for aerial and ground reconnaissance) and a conclusion is made about the degree of intelligence availability and the need for measures of the ICR PD.
When deciding on the use of PD tools, paragraphs 1–3 of the evaluation algorithm are carried out, taking into account changes due to the measures of the PD of the ICR. In this case, the value of the reconnaissance range obtained at step 6 of the algorithm is substituted into expression (3). The presented automated algorithm for assessing the capabilities of ICR (Figure 2) for the case when the typing of the object and the means of exploration is carried out meets the requirements of increasing efficiency and can be used to predict the intelligence availability of the CVO.
Case 2: The typification of the object and the means of exploration has not been carried out
Database for an automated algorithm (case 2):
By means of reconnaissance: reference data with the values of the parameters of the reconnaissance means: the elementary field of view of the thermal imager (angular resolution) γОЭС, threshold sensitivity by temperature Δt; the height of conducting aerial caviar – HР, km;.
By the object of reconnaissance: data with the values of the geometric parameters of the object: SОБ – the radiated area of the object; Lmax – the maximum linear size of the object;
According to the conditions of exploration: reference data on the values of the radiation coefficients of objects εо and typical backgrounds εф averaged in the IR range, as well as on the average values of the parameters of the PD means; the values of the temperatures of the object Tо and background Tф (in the absence of Tо = Tф); the value of the atmospheric transmittance averaged in the range of operating wavelengths τа (may be missing); ДР reconnaissance ranges.
An automated algorithm for evaluating the capabilities of ICR for case 2 is shown in Figure 3.
The automated algorithm for evaluating the capabilities of ICR includes the following sequence of actions:
Step 1. Input of initial data;
Step 2. Using the expression (1), the coefficient of exploration conditions UP is calculated.
If the reconnaissance range ДР is set:
Step 3. The linear resolution of the thermal imager LPC is determined
LPC = γОЭС ДР ; (4)
Step 4. The conditional minimum Lmin size is calculated using the formula
Lmin = SОБ / Lmax ; (5)
Step 5. The fulfillment of the conditions is checked:
а) < 1, < 1; b) < 1, ≥ 1;
c) ≥ 1, < 1; d) ≥ 1, ≥ 1.
The generalized parameter x is calculated:
for the condition а) x ≈ − 3,2;
for the condition b) x ≈ − 3,2;
for the condition c) x ≈ − 3,2;
for the condition d) x ≈ − 3,2.
Step 6. The probability of object detection is determined by expression (3);
Step 7. If the calculated probability value satisfies the condition W0 > W0 ДОП, a conclusion is made about the need for measures of PD ICR. With the decision on the P and the availability of funds with known parameters, the measures of the PD of the ICR are implemented.
Otherwise, paragraphs 1, 4 and 5 of the evaluation algorithm are carried out with corresponding changes in the value of the coefficient of conditions (paragraph 1) due to the measures of the ICR PD.
If the reconnaissance range ДР is not set:
Step 8. Using the expression (1), the coefficient of exploration conditions UP is calculated;
Step 9. The functional dependence of the W0 = f(ДP) probability on the reconnaissance range is calculated. When calculating W0, the ДP range value changes in the interval (100–10 000) km for KR, (0–70) km for BP, depending on the variant of BP means, and (0–20 km) for HP, depending on the placement option. The calculation is performed for condition b) with the corresponding parameter x;
Step 10. The dependence W0 = f(ДP) is visualized and the boundary values of the detection zone of the object ДPГР = W0 ДОП are given as the result of the evaluation. The values of ДPГР are compared with the approximate (expected) range of exploration ДPО and the range of the optical horizon ОГ, and a conclusion is made about the degree of intelligence availability and the need for measures of PD IR.
When deciding on the use of PD tools, paragraphs 1, 4 and 5 of the evaluation algorithm are carried out, taking into account changes due to PD measures. In this case, the value of the reconnaissance range is substituted into the expression for calculating the parameter x. The automated algorithm for assessing the capabilities of the exploration ICR, shown in Figure 3, is characterized by flexibility and scalability, which allows it to be used, including in the absence of typed data on the means of exploration, the status quo and the conditions for conducting ICR. The results of modeling the W0 = f(ДP) dependence for different resolution conditions are shown in Figure 4. The graphs are obtained for space-based ICR facilities with the parameters of the reconnaissance facility close to optimal.
An anti-aircraft guided missile (SAM) with averaged dimensions was chosen as the object of reconnaissance. The conditions for conducting IR reconnaissance in terms of temperature differences between the object and the background and differences in radiation coefficients are assumed to be close to minimal. Condition (a) can be attributed to the condition of the worst resolution, condition (d) – the best resolution. The other two conditions (b) and (c) occupy an intermediate position, closer, however, to condition (d). In Fig. 4, the number 1 indicates the curve for condition (a) < 1, < 1; number 2 – for condition (d) ≥ 1, ≥ 1; number 3 – for condition (b) < 1, ≥ 1.
The analysis of the dependencies in Figure 4 allows us to conclude that for condition (a), the probability of detecting W0 varies from one to 0.02 in almost the entire range of IR ranges, for the other two conditions (b), (d), the range of guaranteed detection ranges is determined from condition W0 ≥ 0,9.
The conditions (a) of the worst and (d) of the best resolution can be taken as the boundary conditions for the methodology of operational assessment of the capabilities of ICR. In order to increase efficiency, it is planned to carry out the typing of intelligence objects by the radiated area SОБ, intelligence means by the options for placing equipment on carriers and the values of the coefficient of the type of intelligence СР. As an example, Figure 5 shows the dependences of the probability of detection on the range at different values of the coefficient of exploration conditions UР, which are obtained for space ICR.
The dependencies in Figure 5 are obtained for the lower boundary estimate of x ≈ − 3,2. The selection of the appropriate curve (Figure 5) is made after calculating the coefficient of exploration conditions UР in accordance with expression (1) based on measuring the temperatures of the object and the background, as well as determining the radiation coefficients from reference data.
The average value in the operating wavelength range can be taken as the atmospheric transmission coefficient τа. In the absence of reference data, τа is assumed to be equal to one. The implementation of counteraction measures is associated with a change in the coefficient of exploration conditions UР and their effectiveness is evaluated similarly.
Conclusion
The article proposes a methodological approach to improve the efficiency of assessing the capabilities of IR, based on the use of graphoanalytic and automated algorithms, the use of a minimum of initial data (the value of the radiated area of the object, the maximum size of the typed objects, averaged in the range of IR values of the radiation coefficients of objects and typical backgrounds, the type of background, the temperature values of the object and background, the value of the atmospheric transmittance, averaged over the range of operating wavelengths) and implemented using pre-calculated graphs of the dependence of the probability of detection on the range for the lower and upper boundary values of the estimate at various fixed values of the coefficient of exploration conditions calculated in advance for the typed parameters of means by types of ground-based, air-based and space-based reconnaissance with acceptable accuracy. The use of an automated algorithm makes it possible to significantly increase the efficiency of the methodology for assessing the capabilities of ICR funds. The totality of the identified factors makes it possible to assess the intelligence availability of protection objects, choose the most effective measures for IR PD and apply them in advance, which significantly reduces the informativeness of enemy reconnaissance in the IR range.
REFERENCES
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Tarasov V. V., Yakushenkov Yu. G. Infrared systems of the «looking» type. – M.: Logos, 2004. 444 p. ISBN 5‑94010‑372‑8. (in Russ.).
Wolf U. Handbook of infrared technology / Ed. U. Wolf, G. Cisis. In 4 tt. t. 3. Instrument base of IR systems. – M.: Mir, 1999. 472 p. ISBN 5‑03‑002926‑5. (in Russ.).
Menshakov Yu. K. Fundamentals of protection from technical intelligence. – M.: CPI “Mask”, 2017. 572 p. ISBN 978‑5‑9069‑5518‑0. (in Russ.).
Menshakov Yu. K. Theoretical foundations of technical intelligence / Edited by Yu. N. Lavrukhin. – M.: Publishing House of Bauman Moscow State Technical University, 2008. 536 p. ISBN 978‑5‑7038‑3019‑2. (in Russ.).
Menshakov Yu. K. Fundamentals of protection from technical intelligence / Under the general editorship of M. P. Sychev. – M.: Publishing House of Bauman Moscow State Technical University, 2011. 478 p. ISBN 978‑5‑7038‑3382‑7. (in Russ.).
Lenshin A. V., Kravtsov E. V., Ryumshin R. I., Sidorenko I. A. Increasing the Assessment Efficiency of Television Reconnaissance Facilities. Photonics Russia. 2022; 16 (8): 624–633. DOI: 10.22184/1993‑7296.FRos.2022.16.8.624.633.
Kravtsov E. V., Kupin I. V., Tatarintsev S. V., Ryumshin R. I. Certificate of state registration of a computer program. The program of operational assessment of optoelectronic intelligence capabilities / No. 2019614147 (RF); dated 01.04.2019. (in Russ.).
Lenshin A. V., Kravtsov E. V. Functional method of generalized parameters for operational assessment of the capabilities of technical means of intelligence. Radio engineering and telecommunication systems. 2021; 43(3): 23–32. (in Russ.).
ABOUT AUTHORS
A. V. Len’shin, Doctor of Technical Sciences, Professor of the Department, Military Educational and Scientific Center of the Air Force “Air Force Academy named after Professor N. E. Zhukovsky and Yu. A. Gagarin” (VUNC VVS “VVA”), e-mail: andrey-lenshin@yandex.ru; Voronezh, Russia.
ORCID:0000-0001-7540-9351
E. V. Kravtsov, Doctor of Technical Sciences, Associate Professor, Head of Department, Military Educational and Scientific Center of the Air Force “Air Force Academy named after Professor N. E. Zhukovsky and Yu. A. Gagarin” (VUNC VVS “VVA”), Voronezh, Russia.
ORCID:0009-0009-5254-760X
S. A. Sitnikov, student, Military Educational and Scientific Center of the Air Force “Air Force Academy named after Professor N. E. Zhukovsky and Yu. A. Gagarin” (VUNC VVS “VVA”), Voronezh, Russia.
ORCID:0009-0009-5990-8419
CONFLICT OF INTEREST
The authors declare no conflict of interest. The manuscript was prepared in the course of joint work and agreed upon by all authors.
A. V. Lenshin, E. V. Kravtsov, S. A. Sitnikov
Military Educational and Scientific Center of the Air Force “Air Force Academy named after Professor N. E. Zhukovsky and Yu. A. Gagarin” (VUNC VVS “VVA”), Voronezh, Russia
A methodological approach is proposed to improve the efficiency of assessing the capabilities of infrared reconnaissance tools, based on the use of graphoanalytic and automated algorithms, as well as the use of the values of the radiated area and maximum dimensions of objects averaged in the range of infrared reconnaissance of the values of the radiation coefficients of objects and typical backgrounds, object and background temperatures, atmospheric transmittance values, averaged over the range of operating wavelengths and implemented using graphs of the dependence of the probability of detection on the range at various fixed values of the coefficient of exploration conditions calculated in advance for the typed parameters of infrared reconnaissance facilities of various bases with acceptable accuracy. Recommendations have been developed to counteract the means of infrared reconnaissance in conditions of unsatisfactory intelligence availability.
Keywords: infrared reconnaissance, object of reconnaissance, probability of detection, efficiency of assessment
Article received: 30.06.2023
Article accepted: 02.08.2023
Introduction
The analysis of armed conflicts and local wars of recent years allows us to conclude that in modern warfare, in which a significant role is assigned to the transmission of information through the propagation of electromagnetic waves, technical means of reconnaissance (TSR) play the most important role in ensuring high combat effectiveness of means of armed struggle [1]. Currently, infrared (IR) imaging systems are widely used, receiving signals in the IR region of the spectrum and converting them into visible images. This is due to the fact that it is in this area that the bulk of the own electromagnetic radiation of the majority of objects of natural and artificial origin surrounding us is concentrated [2, 3].
Qualitative changes in the development of IR range optoelectronic reconnaissance means placed on various carriers, the rapidly changing operational situation, a significant increase in the capabilities of IR reconnaissance means (IR) and their impact on the outcome of hostilities require in-depth study of the experience of organizing and using IR means, operational forecasting of intelligence placement and intelligence availability. The interests of national security require the development of counteraction measures (PD) in the proposed areas of deployment of objects to increase their survivability, create promising and improve existing complexes and systems of PD ICR [4, 5].
The main tasks of the IR species control are the following:
verification and evaluation of the concealment of the state of preparation, construction, commissioning and repair of military facilities (VO);
monitoring of maintenance of the mode of vital activity and blackout on objects;
control of the effectiveness of the disguise of weapons, military equipment (IWT) units and divisions;
control of the secrecy of routine maintenance, restoration and loading and unloading operations at the military and industrial complex;
control of technical and organizational measures for disinformation in the positional area;
aerial IR view control of unoccupied field positions in order to assess their masking capacity (when selecting positions), as well as the formation of an image bank;
aerial IR view control of field positions (areas) immediately before occupation and after their abandonment [4, 5].
Problem statement
The existing methods provide high reliability of the assessment in the presence of the necessary completeness of the initial data. However, in a dynamically changing radio-electronic environment, their use for a number of objective reasons is often not possible. Therefore, increasing the efficiency of assessing the capabilities of ICR funds is an urgent scientific task.
The main tasks in the implementation of protective measures against ICR means are to exclude the possibility (decrease the probability) of detecting an object, determining the parameters and characteristics of the object, as well as object recognition. Methods of reducing information about the object of intelligence are divided into passive concealment (reducing the level of the object’s own radiation, reducing its reflective properties), active concealment (suppression, functional damage to the main elements of the means of IR) and disinformation (creating false objects) [4, 6].
In [7], a methodological approach is proposed to increase the efficiency of assessing the capabilities of television intelligence using the values of the brightness coefficients of intelligence objects, backgrounds and coatings, the minimum size of the object with acceptable reliability.
Operational assessment of the capabilities of ICR can be implemented using two algorithms – graphoanalytic and automated [8].
The graphoanalytic algorithm is based on the preliminary generalization of data on the means of exploration, the object of exploration, the conditions of exploration by typing them and presenting them in the form of tabular information and graphical dependencies, which are used to obtain numerical values for evaluating the capabilities of the ICR. This algorithm assumes the presence of the specified data, graphs and elementary calculations, for which computing tools are not required.
The automated algorithm is based on the use of analytical relations, which are formed in the form of a software-algorithmic implementation. This algorithm assumes the presence of computing facilities with a pre-installed evaluation program.
The purpose of the article is the further development of operational methods for assessing the intelligence availability of ICR objects, as well as the development of recommendations on ICR PD related to the presentation of requirements for masking an object in relation to specific conditions.
Operational assessment of ICR capabilities using a graphoanalytic algorithm
The database for the graphoanalytic algorithm includes the following source data:
According to the means of reconnaissance, evaluation and analysis of the result: graphs of the dependence of the probability of detection on the range WP = f(ДP) for the lower and upper boundary values of the assessment at various fixed values of the coefficient of the conditions of exploration UP, calculated in advance for the typed parameters of the means by type of exploration; the height of aerial caviar (for various options) – HP, km;
For the object of exploration: tabular data with the values of the radiated area SОБ and the maximum size of the typed objects Lmax;
According to the conditions of exploration: data on the values of the radiation coefficients of objects εо and typical backgrounds averaged in the IR range εф; the type of background; the temperature values of the object Tо and background Tф (if absent Tо = Tф); the value of the atmospheric transmittance averaged in the range of operating wavelengths τа (may be absent); the range of exploration ДР.
The graphoanalytic algorithm for the operational assessment of the capabilities of the ICR relative to a critical object (CVO) is illustrated in Figure 1 and includes the following procedure:
Step 1. Input of initial data;
Step 2. The value of the coefficient of exploration conditions UP is calculated using the measured or accepted temperatures of the object and background, reference data on the average values of the radiation coefficients of the object and background in a given wavelength range, according to the formula
UP = [Tо − Tф + 35(εо − εф)] · τа; (1)
Step 3. According to the WP = f(ДP) schedule, an assessment of the capabilities of the ICR is carried out according to the calculated value of the UP: a) at an unknown range for the probability corresponding to the normative value, the range of the intelligence availability limit is determined; b) at a known range of ДP reconnaissance, the probability of detection is determined along the curve for the UP value found in step 1;
Step 4. A decision is made to implement the PD measures. When solving on PD and having standard means with known parameters in a given wavelength range, their implementation is carried out, leading to a decrease in the UP coefficient due to a corresponding change in the temperature of the object, the radiation coefficient or the transmission coefficient of the atmosphere (for example, due to the use of aerosols). The effectiveness of the measures taken is evaluated according to steps 1 and 2 of the algorithm.
Thus, for the implementation of the graphoanalytic algorithm, it is necessary to type data on the TSR of the ICR, the CVO and the conditions of exploration.
Operational assessment
of ICR capabilities using an automated algorithm
It is advisable to consider an automated algorithm for the operational assessment of the capabilities of ICR for two cases.
Case 1: The typification of the object and the means of IR reconnaissance has been carried out
Database for an automated algorithm (case 1):
By means of intelligence: file data with the values of the coefficients of the type of intelligence CP, calculated in advance for the typed parameters of the means by types of intelligence (aerial reconnaissance, ground reconnaissance, etc.) for the upper boundary assessment; the height of conducting aerial reconnaissance (for various typed variants) – HP, km;
By the object of intelligence: file data with the values of the radiated area SОБ;
According to the conditions of exploration: file data on the values of the emission coefficients of εо objects and typical εф backgrounds averaged in the range of IR, as well as on the average values of the parameters of PD means; the values of the temperatures of the Tо object and the Tф background (in the absence of Tо = Tф); the background type; the value of the atmospheric transmittance averaged in the range of operating wavelengths τа (may be absent); reconnaissance range ДР (may be absent).
The automated algorithm for evaluating the capabilities of ICR for case 1 is illustrated in Figure 2 and assumes the following procedure:
Step 1. Input of initial data;
Step 2. The value of the coefficient of exploration conditions UP is calculated according to the formula (1);
If the range of ДР exploration is set:
Step 3. The generalized parameter x is determined by the formula
; (2)
Step 4. The probability of W0 detection is determined using the expression [9]
; (3)
Step 5. If the calculated probability value satisfies condition W0 > W0 ДОП, a conclusion is made about the need for measures of PD ICR;
Step 6. When solving on the ICP and the availability of funds with known parameters, the measures of the ICP PD are implemented. Paragraphs 1–3 of the evaluation algorithm are carried out with corresponding changes in (1) due to the measures of the ICR PD.
If the ДР reconnaissance range is not set:
Step 7. The functional dependence of the W0 = f(ДP) probability on the reconnaissance range is calculated according to the formula (3). When calculating W0, the value of the ДР range changes in the interval (100–10 000) km for space reconnaissance (KR), (0–70) km for aerial reconnaissance (VR), depending on the variant of VR means, and (0–20 km) for ground reconnaissance (HP), depending on the placement option;
Step 8. The W0 = f(ДP) dependence is visualized and the boundary value of the detection zone of the ДОГР (W0 ДОП) object is given as the result of the evaluation. The values of the ДОГР are compared with the approximate (expected) range of the LL reconnaissance ДРO and the range of the optical horizon of the ДOГ (for aerial and ground reconnaissance) and a conclusion is made about the degree of intelligence availability and the need for measures of the ICR PD.
When deciding on the use of PD tools, paragraphs 1–3 of the evaluation algorithm are carried out, taking into account changes due to the measures of the PD of the ICR. In this case, the value of the reconnaissance range obtained at step 6 of the algorithm is substituted into expression (3). The presented automated algorithm for assessing the capabilities of ICR (Figure 2) for the case when the typing of the object and the means of exploration is carried out meets the requirements of increasing efficiency and can be used to predict the intelligence availability of the CVO.
Case 2: The typification of the object and the means of exploration has not been carried out
Database for an automated algorithm (case 2):
By means of reconnaissance: reference data with the values of the parameters of the reconnaissance means: the elementary field of view of the thermal imager (angular resolution) γОЭС, threshold sensitivity by temperature Δt; the height of conducting aerial caviar – HР, km;.
By the object of reconnaissance: data with the values of the geometric parameters of the object: SОБ – the radiated area of the object; Lmax – the maximum linear size of the object;
According to the conditions of exploration: reference data on the values of the radiation coefficients of objects εо and typical backgrounds εф averaged in the IR range, as well as on the average values of the parameters of the PD means; the values of the temperatures of the object Tо and background Tф (in the absence of Tо = Tф); the value of the atmospheric transmittance averaged in the range of operating wavelengths τа (may be missing); ДР reconnaissance ranges.
An automated algorithm for evaluating the capabilities of ICR for case 2 is shown in Figure 3.
The automated algorithm for evaluating the capabilities of ICR includes the following sequence of actions:
Step 1. Input of initial data;
Step 2. Using the expression (1), the coefficient of exploration conditions UP is calculated.
If the reconnaissance range ДР is set:
Step 3. The linear resolution of the thermal imager LPC is determined
LPC = γОЭС ДР ; (4)
Step 4. The conditional minimum Lmin size is calculated using the formula
Lmin = SОБ / Lmax ; (5)
Step 5. The fulfillment of the conditions is checked:
а) < 1, < 1; b) < 1, ≥ 1;
c) ≥ 1, < 1; d) ≥ 1, ≥ 1.
The generalized parameter x is calculated:
for the condition а) x ≈ − 3,2;
for the condition b) x ≈ − 3,2;
for the condition c) x ≈ − 3,2;
for the condition d) x ≈ − 3,2.
Step 6. The probability of object detection is determined by expression (3);
Step 7. If the calculated probability value satisfies the condition W0 > W0 ДОП, a conclusion is made about the need for measures of PD ICR. With the decision on the P and the availability of funds with known parameters, the measures of the PD of the ICR are implemented.
Otherwise, paragraphs 1, 4 and 5 of the evaluation algorithm are carried out with corresponding changes in the value of the coefficient of conditions (paragraph 1) due to the measures of the ICR PD.
If the reconnaissance range ДР is not set:
Step 8. Using the expression (1), the coefficient of exploration conditions UP is calculated;
Step 9. The functional dependence of the W0 = f(ДP) probability on the reconnaissance range is calculated. When calculating W0, the ДP range value changes in the interval (100–10 000) km for KR, (0–70) km for BP, depending on the variant of BP means, and (0–20 km) for HP, depending on the placement option. The calculation is performed for condition b) with the corresponding parameter x;
Step 10. The dependence W0 = f(ДP) is visualized and the boundary values of the detection zone of the object ДPГР = W0 ДОП are given as the result of the evaluation. The values of ДPГР are compared with the approximate (expected) range of exploration ДPО and the range of the optical horizon ОГ, and a conclusion is made about the degree of intelligence availability and the need for measures of PD IR.
When deciding on the use of PD tools, paragraphs 1, 4 and 5 of the evaluation algorithm are carried out, taking into account changes due to PD measures. In this case, the value of the reconnaissance range is substituted into the expression for calculating the parameter x. The automated algorithm for assessing the capabilities of the exploration ICR, shown in Figure 3, is characterized by flexibility and scalability, which allows it to be used, including in the absence of typed data on the means of exploration, the status quo and the conditions for conducting ICR. The results of modeling the W0 = f(ДP) dependence for different resolution conditions are shown in Figure 4. The graphs are obtained for space-based ICR facilities with the parameters of the reconnaissance facility close to optimal.
An anti-aircraft guided missile (SAM) with averaged dimensions was chosen as the object of reconnaissance. The conditions for conducting IR reconnaissance in terms of temperature differences between the object and the background and differences in radiation coefficients are assumed to be close to minimal. Condition (a) can be attributed to the condition of the worst resolution, condition (d) – the best resolution. The other two conditions (b) and (c) occupy an intermediate position, closer, however, to condition (d). In Fig. 4, the number 1 indicates the curve for condition (a) < 1, < 1; number 2 – for condition (d) ≥ 1, ≥ 1; number 3 – for condition (b) < 1, ≥ 1.
The analysis of the dependencies in Figure 4 allows us to conclude that for condition (a), the probability of detecting W0 varies from one to 0.02 in almost the entire range of IR ranges, for the other two conditions (b), (d), the range of guaranteed detection ranges is determined from condition W0 ≥ 0,9.
The conditions (a) of the worst and (d) of the best resolution can be taken as the boundary conditions for the methodology of operational assessment of the capabilities of ICR. In order to increase efficiency, it is planned to carry out the typing of intelligence objects by the radiated area SОБ, intelligence means by the options for placing equipment on carriers and the values of the coefficient of the type of intelligence СР. As an example, Figure 5 shows the dependences of the probability of detection on the range at different values of the coefficient of exploration conditions UР, which are obtained for space ICR.
The dependencies in Figure 5 are obtained for the lower boundary estimate of x ≈ − 3,2. The selection of the appropriate curve (Figure 5) is made after calculating the coefficient of exploration conditions UР in accordance with expression (1) based on measuring the temperatures of the object and the background, as well as determining the radiation coefficients from reference data.
The average value in the operating wavelength range can be taken as the atmospheric transmission coefficient τа. In the absence of reference data, τа is assumed to be equal to one. The implementation of counteraction measures is associated with a change in the coefficient of exploration conditions UР and their effectiveness is evaluated similarly.
Conclusion
The article proposes a methodological approach to improve the efficiency of assessing the capabilities of IR, based on the use of graphoanalytic and automated algorithms, the use of a minimum of initial data (the value of the radiated area of the object, the maximum size of the typed objects, averaged in the range of IR values of the radiation coefficients of objects and typical backgrounds, the type of background, the temperature values of the object and background, the value of the atmospheric transmittance, averaged over the range of operating wavelengths) and implemented using pre-calculated graphs of the dependence of the probability of detection on the range for the lower and upper boundary values of the estimate at various fixed values of the coefficient of exploration conditions calculated in advance for the typed parameters of means by types of ground-based, air-based and space-based reconnaissance with acceptable accuracy. The use of an automated algorithm makes it possible to significantly increase the efficiency of the methodology for assessing the capabilities of ICR funds. The totality of the identified factors makes it possible to assess the intelligence availability of protection objects, choose the most effective measures for IR PD and apply them in advance, which significantly reduces the informativeness of enemy reconnaissance in the IR range.
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ABOUT AUTHORS
A. V. Len’shin, Doctor of Technical Sciences, Professor of the Department, Military Educational and Scientific Center of the Air Force “Air Force Academy named after Professor N. E. Zhukovsky and Yu. A. Gagarin” (VUNC VVS “VVA”), e-mail: andrey-lenshin@yandex.ru; Voronezh, Russia.
ORCID:0000-0001-7540-9351
E. V. Kravtsov, Doctor of Technical Sciences, Associate Professor, Head of Department, Military Educational and Scientific Center of the Air Force “Air Force Academy named after Professor N. E. Zhukovsky and Yu. A. Gagarin” (VUNC VVS “VVA”), Voronezh, Russia.
ORCID:0009-0009-5254-760X
S. A. Sitnikov, student, Military Educational and Scientific Center of the Air Force “Air Force Academy named after Professor N. E. Zhukovsky and Yu. A. Gagarin” (VUNC VVS “VVA”), Voronezh, Russia.
ORCID:0009-0009-5990-8419
CONFLICT OF INTEREST
The authors declare no conflict of interest. The manuscript was prepared in the course of joint work and agreed upon by all authors.
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