A path planning method based on location sensitivity and probability-driven approach

Abstract

This invention belongs to the field of path planning technology and relates to a path planning method based on location sensitivity and probability-driven approaches. The method includes: reading or constructing an electronic map and setting a starting point, ending point, and geographical range; within the geographical range, setting a corresponding sensitivity level for each area according to importance rules; setting the sensitivity of each intersection interval based on the starting point, ending point, road distribution within the geographical range, and regional sensitivity levels; initializing the passing indicators and out-of-detection-range indicators for each intersection interval and setting an upper limit for the detection range; the target starts from the starting point and moves along the road towards the ending point, deriving a probability interval based on the sensitivity level probability ratio and calculating a random floating-point number, which is then weighted inversely by the Markov chain probability and the number of times the intersection interval has been passed, repeating this step until the ending point is reached or the upper limit of the detection geographical range is reached, after which the target arrives at the ending point according to the shortest path rule. The planning method of this invention significantly improves detection efficiency and reliability and prevents target deception.

Description

Technical Field

[0001] This invention relates to the field of path planning technology, and in particular to a path planning method based on location sensitivity and probability-driven approaches. Background Technology

[0002] Satellite navigation systems (such as GPS and BeiDou) use satellite navigation signals to locate devices. Satellite navigation simulators, on the other hand, can generate simulated satellite navigation signals based on set parameters such as time and location, causing receiving devices to misjudge their own spatial and temporal location. This characteristic is used in inducible security detection.

[0003] In induced security detection, simulated location points are set, and simulated signals are sent using a simulator to detect whether the target exhibits abnormal behavior. Furthermore, the simulator can upgrade single-point simulation to path simulation, designing paths, dividing frequency bands, and combining signals to create navigation signals, thus creating the illusion of movement for the target.

[0004] Currently, path design often involves specifying start and end points and generating fixed paths using specific algorithms. While this method can meet the requirements of routine satellite navigation testing, it has limitations in security detection scenarios. On the one hand, the security sensitivity varies at different locations; on the other hand, reusing fixed paths can easily trigger anti-spoofing measures by the target, making it difficult to meet the stringent requirements of security detection and authentication applications. Summary of the Invention

[0005] The purpose of this invention is to address the problems of insufficient dynamism in existing path planning, difficulty in preventing the target from being activated and anti-spoofing, and low detection accuracy or even inability to counter inductive security detection applications. This invention proposes a path planning method based on location sensitivity and probability-driven approach to achieve security detection applications.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] This invention provides a path planning method based on location sensitivity and probability-driven approaches, the method comprising:

[0008] Read or build electronic maps and set the starting point, ending point, and geographical scope;

[0009] Within a geographical scope, a corresponding sensitivity level is set for each region based on importance rules;

[0010] Based on the starting point, ending point, road distribution within the geographical area, and the sensitivity level of each area, the sensitivity of each intersection section is set;

[0011] Initialize the passing indicators and out-of-detection-range indicators for each intersection section, and set the upper limit of the detection range;

[0012] The target starts from the starting point and moves along the road towards the destination. Path planning is carried out based on the intersection intervals passed through with different probabilities. The path planning is based on the probability ratio of the sensitivity level to obtain the probability interval and calculate the random floating-point number. The probability is then weighted inversely by the Markov chain probability and the number of times the intersection interval is passed. This step is repeated until the destination is reached or the upper limit of the detection geographical range is reached, and then the shortest path rule is followed to reach the destination.

[0013] As one possible implementation, path planning specifically involves: calculating the probability ratio of different forward directions based on the sensitivity level of the intersection interval; calculating several probability intervals according to the probability ratio; generating a floating-point random number within the total probability interval, and then multiplying it by the probability ratio and the inverse weighted sum of the intersection interval indicators for different directions to determine which direction the target should move at this intersection; the target moves to the next intersection according to the determined forward direction and increments the number of times this intersection interval has been traversed by 1, repeating this step to calculate the forward direction and move to the next intersection, until the target reaches the destination and ends this method or exceeds the detection geographical range; if it exceeds the detection geographical range, the number of times it exceeds the detection geographical range is incremented by 1 and it is determined whether the number of times the detection geographical range has been exceeded has reached the upper limit; if it has not reached the upper limit of the number of times the detection geographical range has been exceeded, the target moves back to the next intersection and repeats this step; otherwise, if it reaches the upper limit of the number of times the detection geographical range has been exceeded, the target reaches the destination according to the shortest path rule.

[0014] As one possible implementation, the geographical scope is a rectangular area that includes both the starting point and the ending point.

[0015] As one possible approach, a corresponding sensitivity level is set for each area based on importance rules. Specifically, the facilities are divided into no fewer than three importance levels.

[0016] As one possible implementation, the sensitivity level is positively correlated with the path probability level.

[0017] As one possible implementation, the intersection interval is the interval between two intersections, which includes several areas with the same or different sensitivity levels.

[0018] As one possible implementation, setting the sensitivity of each intersection interval means adding or weighting the sensitivity levels of multiple areas within that intersection interval.

[0019] As one possible implementation, the total probability interval of several probability intervals is a set of probability intervals that are left-closed, right-closed, and decomposed.

[0020] As one possible implementation, the first interval in a plurality of probability intervals is left-closed and right-closed, while the other intervals are left-open and right-closed.

[0021] As one possible implementation, at this intersection, the direction the target moves is determined by: weighting the probability of different directions inversely proportional to the number of times the intersection interval is passed.

[0022] Beneficial effects

[0023] The path planning method based on location sensitivity and probability proposed in this invention has the following advantages compared with the prior art:

[0024] 1. The path planning method proposed in this invention assigns different sensitivity levels to different areas during induced confidentiality detection, and then sets the probability of the forward direction at each intersection based on the sensitivity level. Path planning is then performed dynamically based on the probability, which can improve detection efficiency and enhance the reliability of the detection.

[0025] 2. The path planning method proposed in this invention designs paths based on location sensitivity, which can prevent the target from entering anti-spoofing mode and achieve highly accurate security detection in induced security detection scenarios, especially in areas with high security sensitivity.

[0026] 3. The path planning method proposed in this invention transforms the overall path planning into a process of multiple route selections. Probabilistic decision-making is used in each route selection, making the simulated target action path more likely to pass through highly sensitive locations. Multiple simulations will adopt different action paths, and the planned path is more in line with the actual action patterns of the target. This makes the induced confidentiality detection more realistic and improves detection efficiency.

[0027] 4. The path planning method proposed in this invention is based on Markov chains, and each planning may generate a different path, which can prevent the target under test from activating anti-spoofing measures. Attached Figure Description

[0028] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings:

[0029] Figure 1 This is a flowchart of the path planning method based on location sensitivity and probability-driven principles of the present invention.

[0030] Figure 2 This is a diagram showing the possible outcomes of the first path planning and the probability of each subsequent move.

[0031] Figure 3 This is a diagram showing the possible outcomes of the second path planning and the probability of each forward move. Detailed Implementation

[0032] To facilitate a clear description of the technical solutions in the embodiments of the present invention, the terms "first" and "second" are used to distinguish identical or similar items with essentially the same function and effect. For example, the first threshold and the second threshold are merely used to distinguish different thresholds and do not limit their order. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and that the terms "first" and "second" are not necessarily different.

[0033] It should be noted that in this invention, the terms "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in this invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0034] In this invention, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one" or similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, "at least one of a, b, or c" can represent: a, b, c, a combination of a and b, a combination of a and c, a combination of b and c, or a, b, and c, where a, b, and c can be single or multiple.

[0035] This invention aims to provide a path planning method based on location sensitivity and probability-driven principles. The method includes: reading or constructing an electronic map and setting a starting point, ending point, and geographical range; within the geographical range, setting a corresponding sensitivity level for each area according to importance rules; setting the sensitivity of each intersection interval based on the starting point, ending point, road distribution within the geographical range, and regional sensitivity levels; initializing the passing indicators and out-of-detection-range indicators for each intersection interval and setting an upper limit for the detection range; the target starts from the starting point and moves along the road towards the ending point, deriving a probability interval based on the sensitivity level probability ratio and calculating a random floating-point number, which is then weighted inversely by the Markov chain probability and the number of times the intersection interval has been passed, repeating this step until the ending point is reached or the upper limit of the detection geographical range is reached, after which the target arrives at the ending point according to the shortest path rule. This planning method significantly improves detection efficiency and reliability while preventing target deception.

[0036] The implementation process of the path planning method of this invention is described in [reference]. Figure 1This is to achieve high-precision and secure detection applications. The simulated targets are motor vehicles, non-motor vehicles, or pedestrians. The specific implementation method is as follows:

[0037] Read or build electronic maps and set the starting point, ending point, and geographical scope;

[0038] As one possible implementation, the geographical scope is a rectangular area that includes both the starting point and the ending point.

[0039] For example, the starting point and the ending point are located on the road on the electronic map.

[0040] For example, electronic maps are built in real time using SLAM.

[0041] Within a geographical scope, a corresponding sensitivity level is set for each region based on importance rules;

[0042] As one possible approach, each area is assigned a corresponding sensitivity level, and the facilities are divided into no fewer than three importance levels based on their level of importance.

[0043] As one possible implementation, the sensitivity level is positively correlated with the path probability level.

[0044] For example, critical infrastructure is classified as level 5; secondary critical infrastructure as level 4; third critical infrastructure as level 3; general infrastructure as level 2; non-critical infrastructure as level 1; and no infrastructure as level 0. The levels set here from 0 to 5 represent the level of no sensitivity, increasing sequentially, with 5 being the highest level of sensitivity, corresponding to the most important area. This number also corresponds to the probability level when calculating the path later.

[0045] Based on the starting point, ending point, road distribution within the geographical area, and the sensitivity level of each area, the sensitivity of each intersection section is set;

[0046] As one possible implementation, the intersection interval is the interval between two intersections, which includes several areas with the same or different sensitivity levels.

[0047] As one possible implementation, setting the sensitivity of each intersection interval means adding or weighting the sensitivity levels of multiple areas within that intersection interval.

[0048] For example, at an intersection, turning left will pass through two areas with sensitivity levels of 1 and 3, totaling 4; going straight will pass through two areas with sensitivity levels of 2 and 4, totaling 6; turning right will pass through three areas with sensitivity levels of 0, 1 and 1, totaling 2.

[0049] Initialize the passing indicators and out-of-detection-range indicators for each intersection section, and set the upper limit of the detection range;

[0050] The target starts from the starting point and moves along the road towards the destination. Path planning is carried out based on the intersection intervals passed through with different probabilities. The path planning is based on the probability ratio of the sensitivity level to obtain the probability interval and calculate the random floating-point number. The probability is then weighted inversely by the Markov chain probability and the number of times the intersection interval is passed. This step is repeated until the destination is reached or the upper limit of the detection geographical range is reached, and then the shortest path rule is followed to reach the destination.

[0051] One possible implementation method is path planning, which involves calculating the probability ratio of different forward directions based on the sensitivity level of the intersection interval.

[0052] For example, at a certain intersection, the current probability ratio of turning left, going straight, and turning right is 4:6:2. Turning left at this intersection will pass through two areas with sensitivity levels of 1 and 3, totaling 4; going straight will pass through two areas with sensitivity levels of 2 and 4, totaling 6; turning right will pass through three areas with sensitivity levels of 0, 1, and 1, totaling 2.

[0053] For example, when a target moves to an intersection, different probabilities of directions are assigned based on the sensitivity level of different areas passed through, which serves as the basis for determining which direction the target should move in.

[0054] If the destination is reached using the above method, the path planning ends. If the target exceeds the geographical range, it reverses direction at the point outside the range, retraces its steps, and reaches the next intersection to choose a direction to proceed. If there are two or more selectable directions, the out-of-range indications of the already traversed route are incremented by 1 and used as the denominator to weight the probabilities of different directions, generating corresponding probability intervals. A floating-point random number is then generated to determine which direction the target should proceed at this intersection.

[0055] Based on the probability ratio, several probability intervals are calculated according to the probability proportion; a floating-point random number within the total probability interval is generated, and then multiplied by the probability ratio and the inverse proportional weighted sum of the intersection intervals indicating different directions to determine which direction the target should move at this intersection; the target moves to the next intersection according to the determined direction and increments the number of times it has passed through this intersection interval by 1. This step is repeated to calculate the direction of travel and move to the next intersection until the target reaches the destination or exceeds the detection geographical range; if it exceeds the detection geographical range, the number of times it exceeds the detection geographical range is incremented by 1 and it is determined whether the number of times it exceeds the detection geographical range has reached the upper limit. If it has not reached the upper limit of the number of times it exceeds the detection geographical range, the target moves back to the next intersection and repeats this step; otherwise, if it reaches the upper limit of the number of times it exceeds the detection geographical range, the target reaches the destination according to the shortest path rule.

[0056] As one possible implementation, the total probability interval of several probability intervals is a set of probability intervals that are left-closed, right-closed, and decomposed.

[0057] As one possible implementation, the first interval in a plurality of probability intervals is left-closed and right-closed, while the other intervals are left-open and right-closed.

[0058] For example, within the range [0,10], a floating-point random number is generated. Combining the probability ratio of moving in different directions, within the range [0,10], according to the probability ratio obtained in the previous step, the probability interval of the floating-point type (left open and right closed) is calculated proportionally (except for the first interval, which is left closed and right closed), accurate to three decimal places. For example, with a probability ratio of 4:6:2, the resulting probability interval is [0,3.33], (3.33,8.33], (8.33,10).

[0059] As one possible implementation, at this intersection, the direction the target moves is determined by: weighting the probability of different directions inversely proportional to the number of times the intersection interval is passed.

[0060] For example, if the destination is reached using the method described above, the path planning ends. If the destination is outside the geographical range, the target reverses direction at the location outside the range, goes back, reaches the next intersection, and chooses a direction to proceed. If there are two or more possible directions to choose from, the route already taken is discarded, and the probability of different directions is calculated using the method described above. A corresponding probability interval is generated, and a floating-point random number is generated to determine which direction the target should proceed at this intersection.

[0061] For example, the inverse weighting of the number of times the intersection interval is passed is 3, and the coefficient is 1 / (number of times the intersection interval is passed + A)*B; the values ​​of A[0.2-10] and B range from [0.1-5].

[0062] As a specific implementation, there are several intersections within the detected geographical area, and each route planning step involves passing through several intersections, meaning several planning steps are required. The probability of choosing the nth route as the forward direction in the m-th step is denoted as... Since the probability of choosing a route each time is related to the previous choices, it can be denoted as conditional probability. For example, the probability of choosing route n1 on the first attempt is denoted as... The second route was chosen The probability is denoted as The third route was chosen. The probability is denoted as By continuing in this manner, the y-th route selection... The probability is denoted as This leads to a Markov chain based on a probabilistic programming path.

[0063] Suppose that in a path planning operation, it takes 6 planning steps to get from the starting point to the destination, and each step has... , ... There are several routes to choose from. Since the route is chosen based on different probabilities each time, under the same conditions, the possible outcomes of two path planning operations and the probability of moving forward each time are as follows: Figure 2 , Figure 3 As shown. Further explanation is needed: because the next route segment is chosen probabilistically at each intersection, even with the same starting point, ending point, and security sensitivity levels for each area, the route selection may differ each time. Since the outcome of each route selection depends not only on the current probability but also on previous choices, the results of two path planning iterations can differ significantly. Furthermore, during each route selection, the probability of choosing a high-sensitivity area is greater than that of a low-sensitivity area, so the path is more likely to pass through high-sensitivity areas.

[0064] Different locations often contain objects with different levels of sensitivity. For example, ordinary residential communities have very low security sensitivity, while aerospace units have very high security sensitivity. Conversely, if a residential community is located next to an aerospace unit, its security sensitivity level will be correspondingly higher.

[0065] In induced confidentiality testing and certification, if there are areas with different levels of confidentiality sensitivity within the simulated geographical area, the following operations will be performed to achieve excellent testing results:

[0066] S11. Set boundary conditions; boundary conditions refer to the geographical area to be detected.

[0067] S12. Generate path constraints based on constraint principles; constraint principles refer to the fact that the generated path cannot exceed the above boundary conditions, as well as the start and end points;

[0068] S13. Calculate the probability of the current node choosing different directions of travel based on path constraints;

[0069] S14. Select a jump point according to the first preset principle to form a new path; the first preset principle refers to assigning a jump probability from high to low according to the sensitivity level.

[0070] S15. Determine whether the jump is reasonable according to the second preset principle. The second preset principle refers to determining whether the route has been passed. If so, prioritize the route that has not been passed and give it a higher weight. That is, assign different weights to routes that have been passed and routes that have not been passed, and avoid routes that have been passed with a high probability. If the boundary is exceeded, return directly to the boundary. If the boundary is exceeded three times or more, go directly to the destination without considering the jump probability.

[0071] S16. Record the generated paths and generate a model based on the results of multiple path planning.

[0072] Before planning the running path of the target, the method of the present invention needs to set up areas with different sensitivity levels and assign different weights to areas with different sensitivity levels, so that the probability of passing through high-sensitivity level locations is higher than that of low-sensitivity level locations.

[0073] Conventional path planning is global planning, meaning the planned path is fixed each time. The method proposed in this invention dynamically generates paths based on Markov chains, determining the direction of travel with probability at each intersection; when the detection area is large, the planned paths may be different multiple times.

[0074] Because security detection often requires repeated detection of the same target, and with the advancement of technology, the anti-spoofing capabilities of the detected target are constantly improving, if the same path is used to induce each detection, the detected target may recognize that the current environment is not real, thereby activating anti-spoofing measures and rendering security detection ineffective; the probability of transition at each step at the intersection.

[0075] Security surveillance often requires multiple satellite navigation detections of the same target to simulate its movement path and induce detection. The goal of satellite navigation induced detection is to make the target believe it is on the simulated path. The usual practice is to use the same path for each detection. However, with technological advancements, the anti-spoofing capabilities of the targets are constantly improving. If the same path is used for each induced detection, the target may recognize that the current environment is not real, thereby activating anti-spoofing measures and rendering the security surveillance ineffective.

[0076] Based on the method proposed in this invention, the operation path can be designed according to the confidentiality sensitivity level of different locations. Multiple detections will plan different paths based on Markov chains, and will tend to pass through high-sensitivity areas, so as to make the detected target believe that it is in a real environment, thereby inducing the detected target to activate anti-spoofing measures while inducing the detected target.

[0077] Although the invention has been described herein in conjunction with various embodiments, those skilled in the art will understand and implement other variations of the disclosed embodiments by reviewing the accompanying drawings, the disclosure, and the description of the drawings, in carrying out the claimed invention. In this specification, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple components. A single processor or other unit can implement several of the functions listed in the specification. While certain measures are described in different embodiments, this does not mean that these measures cannot be combined to produce good results.

[0078] Although the invention has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made therein without departing from the spirit and scope of the invention. Accordingly, this specification and drawings are merely illustrative of the invention and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the invention. Clearly, those skilled in the art can make various alterations and modifications to the invention without departing from its spirit and scope. Thus, if such modifications and modifications fall within the scope of the invention and its equivalents, the invention is also intended to include such modifications and modifications.

Claims

1. A path planning method based on location sensitivity and probability-driven approaches, characterized in that, include: Read or build electronic maps and set the starting point, ending point, and geographical scope; Within a geographical scope, a corresponding sensitivity level is set for each region based on importance rules; Based on the starting point, ending point, road distribution within the geographical area, and the sensitivity level of each area, the sensitivity of each intersection section is set; Initialize the passing indicators and out-of-detection-range indicators for each intersection section, and set the upper limit of the detection range; The target starts from the starting point and moves along the road towards the destination. Path planning is carried out based on the intersection intervals passed through with different probabilities. The path planning is based on the probability ratio of the sensitivity level to obtain the probability interval and calculate the random floating-point number. The probability is then weighted inversely by the Markov chain probability and the number of times the intersection interval is passed. This step is repeated until the destination is reached or the upper limit of the detection geographical range is reached, and then the shortest path rule is followed to reach the destination.

2. The path planning method based on location sensitivity and probability-driven approach according to claim 1, characterized in that, The path planning is specifically as follows: Calculate the probability ratio of different directions of travel based on the sensitivity level of the intersection interval; calculate several probability intervals based on the probability ratio; Generate a floating-point random number within the total probability range, then multiply it by the probability ratio and the inverse weighted sum of the intersection intervals in different directions to determine the target's forward direction at this intersection; The target moves forward according to the determined direction to the next intersection and increments the number of times it has passed through this intersection section by 1. This process is repeated to calculate the direction of travel and then move forward to the next intersection until the target reaches the destination or exceeds the detection geographical range. If the target exceeds the detection range, the number of times it exceeds the detection range is incremented by 1, and it is determined whether the number of times the detection range has been reached. If the number of times the detection range has not been reached, the target moves back until the next intersection and repeats this step; otherwise, if the number of times the detection range has been reached, the target moves to the destination according to the shortest path rule.

3. The path planning method based on location sensitivity and probability-driven approach according to claim 1, characterized in that, The geographical range is a rectangular area that includes the starting point and the ending point.

4. The path planning method based on location sensitivity and probability-driven approach according to claim 1, characterized in that, The aforementioned importance rules are used to set corresponding sensitivity levels for each area, specifically, the facilities are divided into no fewer than three importance levels based on their importance.

5. The path planning method based on location sensitivity and probability-driven approach according to claim 1, characterized in that, The sensitivity level is positively correlated with the path probability level.

6. The path planning method based on location sensitivity and probability-driven approach according to claim 1, characterized in that, The intersection interval is the interval between two intersections, and the interval includes several areas with the same or different sensitivity levels.

7. The path planning method based on location sensitivity and probability-driven approach according to claim 6, characterized in that, Setting the sensitivity of each intersection interval means adding or weighting the sensitivity levels of multiple areas within that intersection interval.

8. The path planning method based on location sensitivity and probability-driven approach according to claim 2, characterized in that, The total probability interval of the plurality of probability intervals is a plurality of probability intervals that are left-closed, right-closed, and decomposed.

9. A path planning method based on location sensitivity and probability-driven approach according to claim 8, characterized in that, The first interval among the probability intervals is left-closed and right-closed, while the other intervals are left-open and right-closed.

10. A path planning method based on location sensitivity and probability-driven approach according to claim 2, characterized in that, The direction the target travels at this intersection is determined by a weighted average of the probability of each direction and the number of times the target passes through the intersection.

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