Cognitive confrontation simulation method based on quantum cognitive theory

Abstract

The invention discloses a cognitive countermeasure simulation method based on a quantum cognitive theory, and the method comprises the steps: extracting a working state of a target radar through an electronic reconnaissance system; the method comprises the following steps: representing a quantum superposition state of characteristic parameters of N radar pulses intercepted by a detection receiver in a k-order basis vector of a Hilbert space as # imgabs0 #; defining a characteristic extraction processing process of an electronic reconnaissance system on a signal intercepted by the detection receiver as an event A; after a decision maker of the electronic reconnaissance system observes the event A, the judgment of the radar state is defined as an event B, and the probability of the event is obtained in combination with the # imgabs1 #. Regarding the event A and the event B as being in a quantum superposition state Ggt; after the event A occurs, the quantum superposition state G of the event A and the event B is updated, and then the quantum probability of the event B is calculated. According to the method, the quantum cognitive theory is introduced into cognitive deviation modeling, cognitive deviation generated by an electronic reconnaissance system signal processing method and cognitive deviation of a decision maker are regarded as a quantum superposition state, and cognitive confrontation simulation is achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of electronic countermeasures, and in particular relates to a cognitive countermeasure simulation method based on quantum cognitive theory. Background Art

[0002] The electronic reconnaissance system is one of the core systems of cognitive electronic warfare. Cognitive confrontation is a new concept of information confrontation that emerged with electromagnetic spectrum warfare. That is, in the process of cognitive electronic confrontation, the two sides of the confrontation perceive the other side's state through their respective electronic reconnaissance systems and adjust their respective confrontation strategies in real time to maximize the efficiency of confrontation. However, any electronic reconnaissance system has measurement errors. Different parameter judgment criteria will cause the electronic reconnaissance system to have cognitive biases on the measurement parameters. When the system operator observes the measurement value with system cognitive bias, the human cognitive bias will be superimposed on the system cognitive bias, increasing the misjudgment of the current state.

[0003] Currently, there is no method that can effectively simulate real cognitive confrontation. Summary of the invention

[0004] In order to overcome the above-mentioned shortcomings of the prior art, the purpose of the present invention is to provide a cognitive confrontation simulation method based on quantum cognitive theory.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is:

[0006] A cognitive confrontation simulation method based on quantum cognitive theory, the steps are as follows:

[0007] Step 1: Extract the working state of the target radar through the electronic reconnaissance system. According to the principle of quantum state superposition, the characteristic parameters of the N radar pulses intercepted by the detection receiver are expressed in the quantum superposition state of the k-order basis vector of the Hilbert space as Among them, α i is the quantum state |x i >The probability amplitude, is α i The phase, x i represents the i-th characteristic parameter.

[0008] When measuring a characteristic parameter of a radar pulse, there are multiple measurement results due to the presence of multiple threshold judgments in the signal processing method used. The present invention regards this phenomenon as a quantum superposition state.

[0009] Step 2: Define the feature extraction and processing process of the electronic reconnaissance system on the intercepted signal of the detection receiver as event A, and define the judgment of the radar state made by the decision maker of the electronic reconnaissance system after observing event A as event B; the probability of the event is expressed as:

[0010]

[0011] in, Indicates that for x i The quantum probability of event A and event B at the time includes the impact of the cognitive bias of the two events.

[0012] This step uses quantum superposition The quantum probabilities of the corresponding characteristic parameters were calculated.

[0013] Step 3: Consider event A and event B as being in a quantum superposition state |G>, that is, in the same Hilbert space V, event A is represented by v A ∈V expansion, event B is caused by v B ∈V support, when the events are incompatible, P A P B ≠P B P A ;

[0014] The probability of event A is p(A) = ||P A |G>|| 2 , after observing event A, the quantum superposition state is corrected to Conditional probability p(B|A)=||P B |G|| 2 , then the probability of event B occurring after event A occurs is p(A)×p(B|A)=||P B P A |G>|| 2 ;

[0015] where v A 、v B are the subspaces of event A and event B on the Hilbert space V, that is, the value range of event A and event B, P A , P B are the quantum probabilities of event A and event B occurring respectively.

[0016] Event A and event B can be regarded as two superimposed states in quantum mechanics, represented by |G>. After event A occurs, this step updates the quantum superposition state G of events A and B, and then calculates the quantum probability of event B.

[0017] Compared with the prior art, the present invention has the following beneficial effects:

[0018] 1. According to the various complex factors faced by the opposing sides in cognitive electromagnetic spectrum warfare, the present invention introduces quantum cognitive theory into the modeling of cognitive bias, and regards the cognitive bias caused by the signal processing method of the electronic reconnaissance system and the cognitive bias of decision makers such as commanders and operators as quantum superposition states, providing theoretical support for the accurate description and simulation of cognitive bias in cognitive electronic warfare.

[0019] 2. Based on the analysis of the causes of human cognitive bias, the present invention concludes that human cognitive bias cannot be described by a certain mathematical model. In order to quantitatively describe human cognitive bias, the cognitive bias generated by event B (decision maker's judgment) is set as k p ∈[0,2) random number, when 0 <k p <1 is low cognitive bias, and 1 <k p When <2, it indicates high cognitive bias.

[0020] 3. According to the signal processing method and quantum Bayesian network reasoning mechanism of the electronic reconnaissance system, the quantum probability model of the cognitive bias of the electronic reconnaissance system and the cognitive bias of the decision maker is defined, and a basic analysis framework for the impact of cognitive bias on the electromagnetic situation decision-making results is constructed.

[0021] 4. Define the quantum entanglement coefficient λ of the cognitive bias of the electronic reconnaissance system and the cognitive bias of commanders and intelligence analysts i , and realized the quantitative analysis of the impact of cognitive bias on electromagnetic situation decision-making results. Taking the detection receiver measuring the time domain parameters of intercepted radar pulses as an example, the effectiveness of the proposed method was verified. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 The modeling and simulation flow chart of the present invention.

[0023] Figure 2 Schematic diagram of a quantum Bayesian network.

[0024] Figure 3 This is the time domain waveform of the intercepted pulse.

[0025] Figure 4 A histogram-based pulse amplitude measurement diagram.

[0026] Figure 5 This is the pulse time domain parameter measurement diagram.

[0027] Figure 6 Schematic diagram of the impact of cognitive bias on electromagnetic situation decision-making results. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present invention.

[0029] The impact of cognitive bias on cognitive confrontation cannot be described by a definite mathematical model, but can only be represented by random numbers distributed in a finite interval. This is very similar to the physical phenomenon described by quantum cognitive theory. Therefore, this theory can be used to describe cognitive bias in cognitive electronic confrontation. This paper takes the cognitive bias generated in cognitive radar confrontation as the object, regards the deviation generated by the measurement error of the electronic detection system and the cognitive bias of the decision maker as a quantum entangled state, and uses quantum cognitive theory to establish a mathematical model of cognitive bias. Taking the measurement of radar pulse time domain parameters as an example, the influence of cognitive bias on the situation judgment results is analyzed, and the experimental results verify the effectiveness of the proposed method.

[0030] The following are some basic theoretical bases and related concept definitions for understanding the present invention.

[0031] 1. Basic principles of quantum cognition

[0032] (1) Quantum state and superposition principle:

[0033] Quantum cognitive theory draws on the superposition principle of quantum mechanics and believes that a person's cognitive state can be in a superposition of multiple possibilities like a quantum state. For example, in the decision-making process, an individual may consider multiple options at the same time, and these options coexist to some extent, rather than being either this or that in traditional logic.

[0034] (2) Probability and uncertainty:

[0035] Quantum cognitive theory believes that the state of cognition and decision-making can be described by wave functions, and the superposition and collapse of these wave functions reflects the volatility and uncertainty of cognitive states. Similar to the probability distribution and uncertainty principle in quantum mechanics, quantum cognition also emphasizes the uncertainty and probability of the cognitive process. This means that we cannot accurately predict the outcome of a specific cognitive event, but can only give a probability range for its occurrence.

[0036] (3) Entanglement and interference:

[0037] In some cases, there may be a quantum entanglement-like relationship between different cognitive elements, that is, their states are interdependent. In addition, interference may occur between different cognitive paths or options, affecting the final cognitive results and thus leading to cognitive bias.

[0038] (4) Contextual relevance:

[0039] Quantum mechanics is fundamentally context-dependent, and this property is also applied to quantum cognition. Cognitive processes are often affected by specific contexts, resulting in different cognitive patterns and results in different situations. Quantum cognition and cognitive bias are two concepts with different emphases in the field of cognitive science.

[0040] 2. Cognitive Bias

[0041] 2.1 Definition

[0042] Cognitive bias refers to the phenomenon that people’s cognitive results are distorted or deviate from objective facts due to the influence of factors such as themselves or the situation in the cognitive process of perception, understanding, judgment, memory or reasoning. These biases may lead to misinterpretation of the real world and wrong decision-making.

[0043] Cognitive biases have the following characteristics:

[0044] Universality: Almost everyone is susceptible to cognitive biases in their cognitive processes.

[0045] Hiddenness: Cognitive biases are often difficult for individuals to detect because they occur unconsciously.

[0046] Diversity: There are many types of cognitive biases, such as anchoring effect, confirmation bias, availability heuristic, etc.

[0047] Common types:

[0048] Anchoring effect: Over-reliance on the first piece of information obtained (the "anchor") when making decisions and using it as a benchmark for making adjustments.

[0049] Confirmation bias: The tendency to seek out and interpret information that supports one's existing views and to ignore evidence that contradicts them.

[0050] Availability heuristic: judging the likelihood or frequency of an event based on information that is easily retrieved from memory.

[0051] Emotional heuristics: making quick decisions and solving problems based on current emotions rather than objective facts and rational analysis.

[0052] 2.2 Anchoring Effect

[0053] The anchoring effect in cognitive bias is a common psychological phenomenon that significantly affects people's decision-making process. The anchoring effect refers to the fact that when people make decisions, they will over-rely on or "anchor" the information they initially received (i.e., the "anchor point") and make subsequent judgments and decisions based on this information.

[0054] 3. Cognitive Electronic Warfare

[0055] Quantum cognition and cognitive electronic warfare are two different but interrelated fields, each with unique characteristics and development directions in technology and application. Cognitive electronic warfare is usually defined as an electronic warfare form based on electronic warfare equipment with cognitive performance, focusing on autonomous and interactive electromagnetic environment learning capabilities and dynamic and intelligent confrontation task processing capabilities.

[0056] 4. Cognitive bias based on quantum probability theory

[0057] Quantum probability theory is the mathematical basis of quantum decision theory. Quantum probability theory is established in a separable Hilbert space, and all basic events are represented by subspaces of the Hilbert space.

[0058] 4.1 Hilbert space

[0059] Hilbert Space is a linear space with inner product, completeness and measurability. It is a basic concept in the field of functional analysis in mathematics. The following is a detailed definition of Hilbert Space:

[0060] 1) Linear space: First of all, Hilbert space is a linear space, that is, it satisfies the closed property of addition and multiplication. This means that any two vectors in the space can be added, and the result remains in the space; at the same time, a vector can be multiplied by a scalar, and the result also remains in the space. The basic properties of linear space include the existence, uniqueness, distributive law, and associative law of zero elements.

[0061] 2) Inner product space: In the inner product space, in addition to the basic properties of linear space, the concept of inner product is also introduced. The inner product is a binary operation that maps two vectors to a scalar and satisfies properties such as positive definiteness, conjugate symmetry, and linearity. Through the inner product, we can define concepts such as the length (or norm), angle, and orthogonality of a vector. In Hilbert space, if the inner product of two vectors is zero, then the two vectors are said to be orthogonal.

[0062] 3) Completeness: Hilbert space is complete, which means that any Cauchy sequence in the space converges to an element in the space. This property ensures that most concepts in calculus can be extended to Hilbert space without obstacles.

[0063] 4) Measurability: Due to the existence of inner product, vectors in Hilbert space have length, so the distance between vectors can be defined. This makes Hilbert space a measurable space, which is convenient for numerical analysis and calculation.

[0064] 4.2 Basic Definition of Quantum Probability

[0065] Definition 1: A quantum state is a set of quantum representations used to represent the superposition state of basic events. Assume that there are k-order basis vectors |x1>,|x2>,…|x in the Hilbert space. k >, the quantum superposition state is expressed as Among them, α i is the quantum state |x i >The probability amplitude, is α i The probability of an event is equal to the square of the projection length,

[0066] Here, '|·>' represents the quantum state of the observed quantity. In the electronic confrontation scenario, both parties observe the state of the other party through the detection receiver of their respective electronic reconnaissance systems. Therefore, the present invention first needs to extract the working state of the target radar through the electronic reconnaissance system, and then, based on the principle of quantum state superposition, the characteristic parameters of the N radar pulses intercepted by the detection receiver are expressed in the quantum superposition state of the k-order basis vector of the Hilbert space as Among them, x i In some specific embodiments, the probability amplitude is estimated using a histogram method.

[0067] The present invention defines the feature extraction and processing process of the electronic reconnaissance system on the intercepted signal of the detection receiver as event A, and defines the radar state judgment made by the decision maker of the electronic reconnaissance system after observing event A as event B. Then, the above formula Indicates that for x i The quantum probability of event A and event B at the time includes the impact of the cognitive bias of the two events.

[0068] Before the intercepted signal is processed, the opponent's state is a superposition state of multiple modes. When a specific signal processing algorithm is used to extract the opponent's state characteristics, that is, after event A occurs, the quantum superposition state wave function representing the radar state is Collapse into a fixed state, due to the different decision rules adopted by the signal processing algorithm, will lead to different collapse states, thus causing cognitive bias of event A.

[0069] Definition 2: In the same Hilbert space V, the quantum superposition state is |G>. Let event A be caused by v A ∈V expansion, event B is caused by v B∈V, when the events are incompatible, the projection algorithm is not commutative, that is, P A P B ≠P B P A .

[0070] Then the probability of event A is p(A) = ||P A |G>|| 2 , after observing event A, the quantum superposition state is corrected to Conditional probability p(B|A)=||P B |G|| 2 , then the probability of the event "after event A occurs, event B occurs" is p(A)×p(B|A)=||P B P A |G>|| 2 Among them, v A 、v B are the subspaces of event A and event B on the Hilbert space V, that is, the value range of event A and event B, P A , P B are the quantum probabilities of event A and event B respectively. A 、v B The range of the subspace is the distribution range of the characteristic values ​​of the radar pulse after feature extraction, which is determined by the number N of radar pulses.

[0071] This definition states that when there is a sequence between two interrelated events A and B, their quantum probabilities should be calculated in the order in which the events occur. For example, when the detection receiver does not intercept the other party's signal (event A), the intelligence processing personnel cannot make a judgment on the other party's working status (event B). The processing result of event A is the premise for the occurrence of event B, and the cognitive bias of event A will be passed on to event B.

[0072] As mentioned above, human cognitive bias is much more complicated than the bias of the detection receiver. The main factor causing decision makers’ cognitive bias is the anchoring effect, which mainly causes cognitive biases:

[0073] 1) Authority obedience bias: Authority obedience bias in detective intelligence analysis refers to the phenomenon that intelligence analysts use the will or opinions of the commander in charge, the intelligence analysis team to which they belong, and experts with rich practical experience and seniority as the starting point and reference point of intelligence analysis during the intelligence analysis process, or even directly use the suggestions of the above groups as the final conclusion, and then collect relevant confirming information to support the conclusion through the intelligence analysis process.

[0074] 2) Egocentric bias: The anchoring effect will lead to egocentric bias of decision makers. Existing research results show that the selection of opinions is the core of social interaction, but it is often difficult for people to fully understand the opinions and views of others. The process of accepting other people's opinions is actually a process in which egocentric bias continues to manifest: although people accept other people's opinions, this process is to use their original opinions as the initial anchor value, and make insufficient adjustments on this basis, so as to constantly reconcile the differences between their own opinions and those of others.

[0075] Due to the huge amount of information and high randomness of electronic reconnaissance information, commanders and intelligence processing personnel often have different cognitive responses to the same information stimulus, resulting in exchanges and collisions between different viewpoints. Then this kind of brainstorming process often leads to both authority obedience bias and egocentric bias. Both authority obedience bias and egocentric bias may lead to deviations in intelligence analysis conclusions.

[0076] From the above analysis, it can be seen that in cognitive electronic confrontation, cognitive bias consists of two parts: electronic reconnaissance system bias and decision maker bias. The former is directly related to factors such as the complexity of the electromagnetic environment and the receiver signal processing algorithm, and can be described and modeled by mathematical methods; the latter is difficult to describe by mathematical methods, although there is currently no unified mathematical model that can cover all types of cognitive biases. The present invention constructs a basic analysis framework from the perspectives of probability theory, statistics, and decision theory. This framework reflects the impact of cognitive bias on the results of electromagnetic situation decision-making, and usually includes the following parts:

[0077] Input variables: including environmental information, personal experience, emotional state, and other factors that can influence and / or preliminarily judge the factors that may affect the decision. In the cognitive confrontation simulation of the present invention, the input variables specifically include:

[0078] 1. The electronic reconnaissance system processes the intercepted radar pulses.

[0079] 2. The random number k of cognitive bias after comprehensively considering environmental information, personal experience, emotional state, and other factors that may affect and / or initially judge the decision p .

[0080] Processing mechanism: describes how individuals process and interpret this information, and how they are affected by cognitive biases. In this invention, it mainly describes how the electronic reconnaissance system processes the characteristic parameters extracted from the intercepted radar pulses, and how to explain the signal processing method of the electronic reconnaissance system. That is, first, the electronic reconnaissance system needs to extract the radar signal characteristics. Since the processing method can cause cognitive bias, the aliasing state needs to be updated. Event B must occur based on event A, and then the impact of human cognitive bias on the output results can be estimated.

[0081] Output: The final judgment, choice or decision result.

[0082] The above framework can be Figure 2 The quantum Bayesian network implementation shown.

[0083] Attached Figure 2 In the example, event A contains the state information of one party in cognitive electronic warfare confrontation. Taking cognitive radar confrontation as an example, the working state of the target radar is extracted, that is, the characteristic parameters A of the N radar pulses are first extracted. m ,f c ,B w ,M d ,prt],x i ∈A, i=1,2,…5. Among them, T m is the pulse time domain parameter (amplitude A m , pulse width τ, etc.), f c is the center frequency, B w is the instantaneous bandwidth, M d It is the intra-pulse modulation method, and prt is the pulse repetition period.

[0084] Then, the mean d of the characteristic parameter measurements of N radar pulses is used i and variance As a measurement of radar working status and cognitive bias.

[0085] Set the cognitive bias of event B to a random number k p , that is, the cognitive bias generated by the decision maker after observing the above parameter measurement value is k p The effect of the cognitive bias of event B on event A is considered as quantum entanglement, defined as:

[0086]

[0087] Among them, k p ∈[0,2), to ensure that λ i ∈R, and k p = 0 when λ meets Criteria, where the cognitive bias of event B is 0 <k p <1 indicates low cognitive bias, <k p <2 indicates high cognitive bias. p When =0, it means that the cognitive bias is 0; R is the symbol of the one-dimensional real number domain.

[0088] Finally, the cognitive bias of events A and B causes the following bias in judging the radar working status:

[0089]

[0090] In the formula,

[0091] When the jammer's detection receiver intercepts the radar state A, it will measure and estimate the state parameters of A respectively. Due to factors such as signal sorting error and channel random fading, these measured parameter values ​​are expressed as random sequences with a length of N that obey the Gaussian distribution. The mean and variance (μ, σ 2 ) is the basis for judging its true value and random error. The usual practice is to use the intensity of the noise σ 2 Determine a threshold. When the measured value is below the threshold, it is considered that the measured value has not changed significantly. When it exceeds the threshold, it is considered that the measured value has changed significantly. It can be seen that different threshold selection principles will cause different cognitive biases. The present invention defines the cognitive bias generated by the observation event of this electronic reconnaissance system as event C. When the decision maker observes event C, the cognitive bias generated is event D. He makes a judgment on the working status of the radar: event E is a judgment that the cognitive bias does not cause a subversive error, and event F is a judgment that is significantly different from event E. According to quantum probability definition 1, when two cognitive biases are in a superposition state, the probability of event E occurring is:

[0092]

[0093] Similarly, the probability of event F occurring is:

[0094]

[0095] In the formula, let is the quantum entanglement phase difference angle of the two cognitive biases, when When , the cognitive bias phase angle of the electronic reconnaissance system is greater than the cognitive bias phase angle of the commander and intelligence analysts, resulting in an increase in the probability of event E and event F; when The two cognitive biases are orthogonal, so that p(E) and p(F) are not affected by cognitive biases; when When the phase angle of the cognitive bias of the electronic reconnaissance system is smaller than that of the commander and intelligence analysts, the probability of occurrence of event E and event F, p(E) and p(F), decreases. Then p(E) and p(F) reach their minimum values. C ,θ D are the quantum entanglement phase angles of event C and event D respectively.

[0096] Since event C is the cognitive bias of the electronic reconnaissance system, and the magnitude of the cognitive bias is directly related to the signal processing algorithm adopted, the cognitive bias can be described by the parameter measurement error in event A. In practice, the parameter measurement of event A can be regarded as an independent process. Here, the time domain parameter T of the intercepted radar pulse is detected by the receiver. mTaking the measurement of event C as an example, the method of calculating the cognitive bias of event C is explained.

[0097] As attached Figure 3 As shown, the detection receiver intercepts the pulse waveform, and the receiver sampling frequency f s =100KHz, the pulse is subject to mean μ=0, variance σ 2 = 0.1 white Gaussian noise pollution. Figure 4 To estimate the noise level, set the upper and lower thresholds and use the histogram method to estimate the pulse amplitude A. m . With the different noise intensity, A m Changes within a certain range and has a quantum superposition state Where k is the range of noise variance. Obviously, when the measurement algorithm determines the pulse amplitude value, the quantum superposition state collapses to a certain value, and the difference between it and the true value of the pulse amplitude is the cognitive bias.

[0098] Attached Figure 5 Figure 1 is a schematic diagram of pulse time domain parameter measurement. The measurement algorithm first determines the upper, middle, and lower thresholds based on the noise intensity, then determines the time point when the pulse passes through the three thresholds and calculates the pulse rise time t r , fall time t f , pulse width τ, pulse repetition period prt and pulse overshoot amplitude. Since the receiver does not capture the leading edge of the first pulse, only the rise time of the four pulses can be measured here. The measurement results are shown in Table 1.

[0099] Table 1 Pulse time domain parameters (time unit: ms)

[0100]

[0101] As can be seen from Table 1, due to the influence of noise, the time domain parameters of each pulse are different. When the mean d is taken as the measurement value, the corresponding quantum superposition state collapses and the variance of each measurement value is For cognitive bias.

[0102] Event D is the cognitive bias of the decision maker. As mentioned above, it cannot be described by a definite mathematical model and can only be described by k p ∈[0,2) is a random number. When the cognitive bias of event C After confirmation, k p right Figure 1 The influence of the output of the quantum Bayesian network can be estimated by formula (3):

[0103]

[0104] In the formula, is the cognitive bias of event C, σ cb is the total cognitive bias of events C and D.

[0105]

[0106] In formula (4), λ reflects the relationship between the cognitive bias of event D and the cognitive bias of event C. Figure 6 As shown, d = 1, When cb Follow k p Change curve.

[0107] From the attached Figure 6 It can be seen that when 0 <k p When k < 1, the impact of personnel cognitive bias is lower than that of electronic reconnaissance system cognitive bias, and the effectiveness of cognitive confrontation effect evaluation depends on the bias caused by signal processing methods; when k p When it approaches 2, it is believed that cognitive bias increases exponentially, leading to serious misjudgment of the actual confrontation situation.

[0108] In summary, although both quantum cognition and cognitive bias involve the scope of cognitive science, their focus and research methods are different. Quantum cognition focuses on using the principles and methods of quantum mechanics to understand and simulate the human cognitive process; while cognitive bias focuses more on revealing and analyzing the various biases that occur in people's cognitive process and their causes and effects. Together, they enrich the understanding and knowledge of human cognitive mechanisms. Based on quantum probability theory, the present invention defines cognitive biases in cognitive electronic confrontation, and provides a method for calculating cognitive biases, thereby introducing quantum cognition into the confrontation effectiveness evaluation of cognitive electronic warfare.

Claims

1. A cognitive confrontation simulation method based on quantum cognitive theory, characterized in that: Here are the steps: Step 1: Extract the working state of the target radar through the electronic reconnaissance system. According to the principle of quantum state superposition, the characteristic parameters of the N radar pulses intercepted by the detection receiver are expressed in the quantum superposition state of the k-order basis vector of the Hilbert space as Among them, α i is the quantum state |x i >The probability amplitude, is α i The phase, x i represents the i-th characteristic parameter; Step 2: Define the feature extraction and processing process of the electronic reconnaissance system on the intercepted signal of the detection receiver as event A, and define the judgment of the radar state made by the decision maker of the electronic reconnaissance system after observing event A as event B; the probability of the event is expressed as: Among them, P xi Indicates that for x i The quantum probabilities of event A and event B at the time of , including the impact of cognitive biases of the two events; Step 3: Consider event A and event B as being in a quantum superposition state |G>, that is, in the same Hilbert space V, event A is represented by v A ∈V expansion, event B is caused by v B ∈V expansion, when the events are incompatible, P A P B ≠P B P A ; The probability of event A is p(A) = ||P A |G>|| 2 , after observing event A, the quantum superposition state is corrected to Conditional probability p(B|A)=||P B |G A || 2 , then the probability of event B occurring after event A occurs is p(A)×p(B|A)=||P B P A |G>|| 2 ; where v A 、v B are the subspaces of event A and event B on the Hilbert space V, that is, the value range of event A and event B, P A , P B are the quantum probabilities of event A and event B occurring respectively.

2. The cognitive confrontation simulation method based on quantum cognitive theory according to claim 1 is characterized in that: In step 1, the target radar working state is extracted as follows: Extract the characteristic parameter A of the N radar pulses = [T m ,f c ,B w ,M d ,prt],x i ∈A, i=1,2,…5; where T m is the pulse time domain parameter, f c is the center frequency, B w is the instantaneous bandwidth, M d It is the intra-pulse modulation method, and prt is the pulse repetition period.

3. The cognitive confrontation simulation method based on quantum cognitive theory according to claim 2 is characterized in that: Determine the impact of cognitive biases on decision making by following these steps: Step 41, using the mean d of the characteristic parameter measurement values ​​of N radar pulses i and variance σ 2 si , as a measurement of radar working status and cognitive bias; Step 42, set the cognitive bias of event B to a random number k p ; Step 43, consider the influence of the cognitive bias of event B on event A as quantum entanglement, defined as: Step 44, define the deviation caused by the cognitive deviation of events A and B in judging the working state of the radar as: In the formula, 4. The cognitive confrontation simulation method based on quantum cognitive theory according to claim 3 is characterized in that: Set the cognitive bias of event B to a random number k p ∈[0,2), to ensure that λ i ∈R, and k p = 0 when λ meets Criteria, where the cognitive bias of event B is 0 <k p <1 indicates low cognitive bias, <k p <2 indicates high cognitive bias. p When =0, it means that the cognitive bias is 0; R is the symbol of the one-dimensional real number domain.

5. The cognitive confrontation simulation method based on quantum cognitive theory according to claim 1 is characterized in that: Construct an analytical framework for the impact of cognitive bias on electromagnetic situation decision-making results, which includes: Input variables: the processing result of the intercepted radar pulse by the electronic reconnaissance system and the cognitive bias random number k p ; Processing mechanism: describes how individuals process and interpret the signal processing methods of the electronic reconnaissance system and how they are affected by cognitive biases, that is, the process of step 3; the individuals refer to the characteristic parameters extracted by the electronic reconnaissance system from the intercepted radar pulses; Output: The final judgment, choice or decision result.

6. The cognitive confrontation simulation method based on quantum cognitive theory according to claim 1 is characterized in that: Assume that event A contains the state information of one party in cognitive electronic warfare confrontation, the cognitive bias generated by the electronic reconnaissance system observing the event is defined as event C, the cognitive bias generated by the decision maker after observing event C is event D, event E is the judgment that the cognitive bias does not cause subversive errors, and event F is the judgment that is significantly different from event E. According to step 3, when the two cognitive biases are in a superposition state, the probability of event E occurring is: The probability of event F occurring is: make is the quantum entanglement phase difference angle of the two cognitive biases, when When , the cognitive bias phase angle of the electronic reconnaissance system is greater than the cognitive bias phase angle of the decision maker, resulting in an increase in the probability of event E and event F; when The two cognitive biases are orthogonal, so that p(E) and p(F) are not affected by cognitive biases; when When the phase angle of cognitive bias of the electronic reconnaissance system is smaller than that of the decision maker, the probability of occurrence of event E and event F, p(E) and p(F), decreases. Then p(E) and p(F) reach their minimum values, where θ C ,θ D are the quantum entanglement phase angles of event C and event D respectively.

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