An air and space situation analysis method based on a whole life cycle of a flight path

Abstract

The application discloses a kind of air situation analysis methods based on track full life cycle, comprising the following steps: step 1, extraction typical track event;Step 2, establish track full life cycle model;Step 3, realize the quantitative analysis of air situation;Step 4, abnormal event identification and extraction.The air situation analysis method based on track full life cycle proposed in the application introduces track full life cycle model into the field of air situation estimation for the first time, and can deeply understand air situation on this basis.The application can make the process of situation analysis clear, the result of situation analysis more accurate and more intelligent, so as to provide more definite and accurate situation information for later decision makers.

Description

Technical Field

[0001] This invention relates to an air situation analysis method, and more particularly to an air situation analysis method based on the entire life cycle of a flight path. Background Technology

[0002] Air situation refers to the current state and evolving trends of various aerial targets and environmental elements; the situational elements refer to the targets, environment, events, and estimates that constitute the air situation. Air situation analysis involves a comprehensive qualitative and quantitative analysis of various airborne military forces and the adversarial environment, and based on this, predicts future adversarial situations. Its purpose is to provide a comprehensive air situation map based on the conclusions of the situation analysis, offering information to assist commanders in decision-making. The task of air situation analysis is to generate a comprehensive air situation map regarding the location and activities of adversarial forces, analyze ongoing events, and predict future events.

[0003] The most direct manifestation of air situation is flight paths and related events. A comprehensive quantitative analysis of historical flight path information and event information throughout their entire lifecycle can effectively enhance our understanding of the air situation. Therefore, it is necessary to research situation analysis methods based on the entire lifecycle of flight paths, providing insights and solutions for air situation estimation in command and control systems. Existing situation analysis methods primarily rely on human judgment, failing to fully utilize available information. Summary of the Invention

[0004] Purpose of the invention: The technical problem to be solved by the present invention is to provide an air situation analysis method based on the entire life cycle of a flight path, which addresses the shortcomings of the existing technology.

[0005] To address the aforementioned technical problems, this invention discloses an air situation analysis method based on the entire lifecycle of a flight path, comprising the following steps:

[0006] Step 1: Extract typical flight track events. This involves comprehensively analyzing various types of typical flight track information in the air situation, including: flight tracks of friendly targets in the air, flight tracks of detected enemy targets, flight tracks of various fixed targets, and flight tracks of various mobile platform targets. The entire life cycle of the target, from its emergence to its disappearance, is analyzed to extract typical flight track events.

[0007] Step 2: Establish a full lifecycle model of the trajectory. Based on the typical trajectory events described in Step 1, introduce the time dimension, spatial dimension and logical dimension to model the full lifecycle of the target's trajectory and record the entire lifecycle of the target from its creation to its disappearance.

[0008] Step 3: Achieve quantitative analysis of air situation based on the full lifecycle model of flight paths;

[0009] Step 4, Abnormal Event Identification and Extraction, involves identifying and extracting abnormal events based on the quantitative analysis results of the 3D distribution map of attribute events, thematic situation map, and air situation correlation map, thus completing the air situation analysis method based on the entire life cycle of flight tracks.

[0010] Beneficial effects:

[0011] This invention clarifies the situation analysis process, makes the results more accurate and intelligent, and provides more explicit and accurate situation information for later decision-makers. Attached Figure Description

[0012] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, and the advantages of the present invention in the above and / or other aspects will become clearer.

[0013] Figure 1 This is the overall flowchart of the air situation analysis method based on the entire life cycle of the flight path in this invention.

[0014] Figure 2 This is a diagram of the full lifecycle model of the flight path in this invention.

[0015] Figure 3 This is a three-dimensional distribution diagram of attribute events in this invention.

[0016] Figure 4 This is a flowchart of the abnormal event analysis process in this invention. Detailed Implementation

[0017] This invention proposes an air situation analysis method based on the entire lifecycle of a flight path, comprising the following steps:

[0018] Step 1: Extract typical flight track events. The method includes: comprehensively analyzing various types of typical flight track information in the air situation, including: flight tracks of our own targets in the air, flight tracks of detected enemy targets, flight tracks of various fixed targets, and flight tracks of various mobile platform targets. Analyze the entire life cycle of the target from its emergence to its disappearance, and extract typical flight track events.

[0019] The trajectory is defined as: target information generated after first-level signal-level information fusion, second-level position-level information fusion, and third-level attribute information fusion in the JDL model (reference: Information Fusion Concepts, Methods and Applications [M]. Beijing: National Defense Industry Press, 2012.11, 323-359). The purpose of this invention is to utilize trajectory information and analyze and understand typical trajectory events throughout the trajectory's entire lifecycle to provide support for fourth-level situation estimation and fifth-level threat estimation. The trajectory is represented by a quintuple:

[0020] Track=<Track_No,Track_Type,Track_XY,Track_Attribute,Track_Interaction>

[0021] Among them, Track_No is the track identifier, which is globally unique; Track_Type is the track type, which describes the category of the track; Track_XY are the track motion parameters, including the target position information after the second level of information fusion; Track_Attribute is the track attribute, which includes the attribute information after the third level of attribute information fusion; Track_Interaction is the track interaction, which describes the interaction relationship between tracks.

[0022] The aforementioned track events are defined as facts and circumstances that have significance or impact throughout the entire lifecycle of the track. The basic attributes of track events are represented by a quintuple as follows:

[0023] Event =<Event_ID,Event_Time,Event_Space,Event_Subject,Event_Description>

[0024] Wherein: Event_ID is the event identifier, indicating the name of the event, and is globally unique; Event_Time is the start and end time of the event; Event_Space is the spatial location description of the event; Event_Subject is the subject to which the event belongs, which can be multiple subjects; and Event_Description is a detailed description of the event. Qualitative and quantitative analysis is performed across multiple dimensions, including track type, track motion parameters, track attributes, and track interactions, throughout the entire lifecycle of a track from its creation to its disappearance, to extract typical track events.

[0025] Step 2, establish a full lifecycle model of the trajectory, including the following methods:

[0026] Based on the typical trajectory events described in step 1, time, space, and logical dimensions are introduced to model the entire lifecycle of the target's trajectory, recording the entire lifecycle of the target from its creation to its disappearance, as detailed below:

[0027] Step 2-1: Distinguish track events from three dimensions: time dimension, which is the time when the track event occurs, corresponding to the start and end time of the event Event_Time; space dimension, which is the location or geographical area where the track event occurs, corresponding to the spatial location description of the event Event_Space; and definition logic dimension, including: the importance level, abnormal trends, and correlation of the track event, corresponding to the topic Event_Subject and the detailed description of the event Event_Description.

[0028] The logical dimension is divided into an attribute layer, a topic layer, and a correlation layer. The attribute layer distinguishes the basic attributes of various track events, including the identification of both parties, platform type, and motion parameters. The topic layer distinguishes the impact of various track events on the air situation. The correlation layer indicates that the current target event is related to other targets or other resources.

[0029] Step 2-2: Define quantitative standards for the three dimensions, and locate each track event in the three dimensions;

[0030] Steps 2-3: All track events for the same target constitute the full lifecycle model of the track.

[0031] Step 3, quantify the air situation, specifically including: quantifying the air situation based on the flight path lifecycle model.

[0032] Step 3-1, perform time dimension quantitative analysis: classify and statistically analyze all track events in different time periods, and perform statistics once a day, every two days, every week or every half month to analyze the time dimension pattern characteristics;

[0033] Step 3-2, conduct spatial dimension quantitative analysis: classify and statistically analyze all flight track events in different airspaces and areas of responsibility, and analyze the spatial dimension regularity characteristics for typical areas such as civil aviation flight corridors, military aviation training areas and enemy target reconnaissance areas;

[0034] Step 3-3, perform logical dimension quantitative analysis: Quantify all track events sequentially according to the attribute layer, theme layer, and association layer. Specific methods include:

[0035] Step 3-3-1, Attribute-level quantitative analysis: Quantitative analysis is performed on the two-party identification attributes, platform type attributes, and motion parameter attributes of all track events. Combining the temporal and spatial regularity characteristics, a three-dimensional distribution map of attribute events is generated.

[0036] Step 3-3-2, Thematic Layer Quantitative Analysis: Cluster analysis is performed on all track events according to the thematic situation, and the thematic situation map is generated by combining the temporal and spatial regularity characteristics.

[0037] Step 3-3-3, Quantitative analysis of the correlation layer: Perform correlation analysis on all related flight track events to generate an air situation correlation diagram.

[0038] The specific method for quantitative analysis described in step 3 is as follows:

[0039] Track events targeting the target<tn,t,(x,y),position,amplify> Where tn represents the target number, t represents the time of occurrence, (x, y) represents the spatial location, position represents the topic information, and ampulify represents the detailed information; the quantized representation of the track event is d. i for:

[0040] s i =sqrt((x i -x i-1 ) 2 +(y i -y i-1 ) 2 ) / (t i -t i-1 (1)

[0041] d i =s i-1 +s i-2 -s i (2)

[0042] Among them, s i The measure of event i, x i The x-coordinate of event i is represented by x. i-1 The x-coordinate of event i-1 is represented by y. i The y-coordinate represents the vertical coordinate of event i. i-1 The y-coordinate of event i-1, t i t represents the time of event i. i-1 s represents the time of event i-1. i-1 s represents the metric for event i-1. i-2 The measure representing event i-2;

[0043] An improved Hausdorff distance-based approach is used for event quantization, and the calculation formula is as follows:

[0044]

[0045]

[0046] d=min(d(A,B),d(B,A)) (5)

[0047] Where d represents the quantitative analysis result of track events, d(A,B) represents the quantification of event A and event B, d(B,A) represents the quantification of event A and event B, and a k Let b represent the k-th element of event A. k Let a represent the k-th element of event B. l Let b represent the l-th element of A. lLet represent the l-th element of event B, A represent the set of events A, B represent the set of events B, and d represent the final quantization result.

[0048] Step 4, anomaly event identification and extraction, specifically includes: identifying and extracting anomalies based on the quantitative analysis results of the 3D distribution map of attribute events, thematic situation map, and air situation correlation map.

[0049] Step 4-1: Based on the 3D distribution map of attribute events, set the threshold for abnormal event occurrence according to the event attribute type to filter out abnormal events;

[0050] Step 4-2: Based on the topic situation map, set the threshold for abnormal events according to the situation topic, and filter out abnormal events;

[0051] Step 4-3: Based on the air situation correlation diagram, set the threshold for abnormal events according to the correlation and filter out abnormal events.

[0052] The threshold for the occurrence of abnormal events is obtained based on the mean of a random variable:

[0053]

[0054] in, Indicates the threshold for the occurrence of abnormal events, α n Let d be a random variable representing event n. n This represents the quantitative analysis result of event n, where n represents the event sequence and N represents the total number of events.

[0055] Example:

[0056] To better understand the air situation and assist commanders in making command decisions, this invention proposes an air situation analysis method based on the entire lifecycle of flight paths, providing research and solutions for air situation analysis problems in command and control systems. Figure 1 As shown, the main steps of this method include:

[0057] Step 1: Extract typical track events;

[0058] Step 2: Establish a full lifecycle model of the flight path;

[0059] Step 3: Perform quantitative analysis of the air situation.

[0060] Step 4: Abnormal event identification and extraction.

[0061] The processing flow of this invention is as follows: Figure 1 As shown, the main steps include:

[0062] Step 1: Extract typical track events;

[0063] The basic properties of a track event can be represented by a 5-tuple as follows: Event =<Event_ID,Event_Time,Event_Space,Event_Subject,Event_Description>

[0064] Where: Event_ID is the event identifier, indicating the name of the event, which is globally unique; Event_Time is the start and end time of the event; Event_Space is the spatial location description of the event; Event_Subject is the subject to which the event belongs; Event_Description is a detailed description of the event.

[0065] Typical track event identifiers can be defined as target generation events, target identification events, target maneuvering events, anti-radiation events, target allocation events, target engagement events, and target disappearance events, etc. Their specific meanings are as follows:

[0066] The target generation event refers to the track event immediately after the target track is captured;

[0067] Target recognition events refer to track events that identify target attributes.

[0068] Target maneuvering events refer to track events that occur when target track position information is updated.

[0069] Anti-radiation alarm events refer to alarm events that detect anti-radiation behavior of a target.

[0070] Target assignment event refers to the event in which a flight path is assigned a strike weapon.

[0071] A target strike incident refers to an event in which a target is struck and dealt with.

[0072] A target disappearance event refers to a track event in which the track is deemed to have disappeared.

[0073] Step 2: Establish a full lifecycle model of the trajectory. This involves connecting all events from the trajectory's creation to its disappearance to obtain the full lifecycle model. Without human intervention, the full lifecycle model of the trajectory is as follows: Figure 2 .

[0074] Figure 2 The description of events in the China Aviation Track includes common information such as time, location, level, anomaly trends, and correlation analysis. At the same time, descriptions of different events may include unique descriptive elements. Generally, they can be divided into time-based categories.

[0075] • Target generation event (specific descriptions include target speed and number of aircraft)

[0076] • Target recognition event (specific description includes target identifier)

[0077] • Target maneuvering events (specific description includes target maneuver level)

[0078] ●Anti-radiation alarm events (specific descriptions include target and radar location)

[0079] • Target assignment event (specific description includes the target assignment object)

[0080] • Target strike event (specific description includes the target of the strike)

[0081] • Target disappearance event (specific description includes whether the target landed at an airport)

[0082] Step 3: Based on the full lifecycle model of flight paths, perform quantitative analysis of the air situation.

[0083] Step 3-1, Perform time-dimensional quantitative analysis: Classify and statistically analyze all track events in different time periods. Statistics can be performed daily, every two days, weekly, or every half month to analyze the time-dimensional patterns and characteristics.

[0084] Step 3-2, conduct spatial dimension quantitative analysis: classify and statistically analyze all flight track events in different airspaces and areas of responsibility, and analyze the spatial dimension patterns and characteristics for typical areas such as civil aviation flight corridors, military aviation training areas, and enemy target reconnaissance areas.

[0085] Step 3-3: Perform logical dimension quantitative analysis: Quantify all track events sequentially according to the attribute layer, theme layer, and association layer.

[0086] Step 3-3, which involves performing logical dimension quantization analysis, includes:

[0087] Step 3-3-1: Attribute-level Quantitative Analysis: Quantitative analysis is performed on the two-way identification attributes, platform type attributes, and motion parameter attributes of all track events. Combining the temporal and spatial regularity characteristics, a three-dimensional distribution map of attribute events is generated.

[0088] like Figure 3 As shown, the three-dimensional distribution map of attribute events consists of three dimensions: time, space, and attributes. Events can be divided into unilateral events and bilateral events, and can also be divided into three dimensions: motion parameters, platform type, and bilateral identification.

[0089] Step 3-3-2: Thematic Quantitative Analysis: Cluster analysis is performed on all track events according to the thematic situation, and the thematic situation map is generated by combining the temporal and spatial regularity characteristics.

[0090] Step 3-3-3: Correlation layer quantitative analysis: Perform correlation analysis on all related flight track events to generate an air situation correlation diagram.

[0091] Track events targeting the target<tn,t,(x,y),position,amplify> To determine whether abnormal events such as track deviation exist, the quantized representation d i for

[0092] s i =sqrt((x i -x i-1 ) 2 +(y i -y i-1 ) 2 ) / (t i -t i-1 (1)

[0093] d i =s i-1 +s i-2 -s i (2)

[0094] An improved Hausdorff distance-based approach is used for event quantization. The calculation formula is as follows:

[0095]

[0096]

[0097] d=min(d(A,B),d(B,A)) (5)

[0098] Where d represents the result of the event quantitative analysis.

[0099] Step 4: Based on the trajectory event cycle model formed in steps 1, 2, and 3, the analysis flowchart for abnormal air situation events is as follows: Figure 4 As shown. Based on the quantitative analysis results of attribute events, such as the 3D distribution map, thematic situation map, and air situation correlation diagram, abnormal events are analyzed. a Identification and extraction are performed.

[0100] d of the abnormal event a When it occurs, it will be much larger than the normal event's d. n . d a From d n After filtering, it is necessary to obtain the threshold for the occurrence of abnormal events.

[0101] d is

[0102] Threshold The mean of a random variable can be obtained as follows:

[0103]

[0104] After the above steps, abnormal track yaw events will be obtained from the track maneuvering events. For different events, only different quantization representations 'd' need to be set, and the abnormal events can be obtained using the analysis method provided in this paper.

[0105] In its specific implementation, this application provides a computer storage medium and a corresponding data processing unit. The computer storage medium is capable of storing a computer program, which, when executed by the data processing unit, can run the invention's content regarding an air situation analysis method based on the entire lifecycle of a flight path, as well as some or all of the steps in various embodiments. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0106] Those skilled in the art will clearly understand that the technical solutions in the embodiments of the present invention can be implemented using computer programs and their corresponding general-purpose hardware platforms. Based on this understanding, the technical solutions in the embodiments of the present invention, or the parts that contribute to the prior art, can be embodied in the form of computer programs, i.e., software products. These computer program software products can be stored in a storage medium and include several instructions to cause a device containing a data processing unit (which may be a personal computer, server, microcontroller, MUU, or network device, etc.) to execute the methods described in various embodiments or certain parts of the embodiments of the present invention.

[0107] This invention provides a concept and method for air situation analysis based on the entire lifecycle of flight paths. Many methods and approaches exist for implementing this technical solution; the above description is merely a preferred embodiment of the invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this invention, and these improvements and modifications should also be considered within the scope of protection of this invention. All components not explicitly stated in this embodiment can be implemented using existing technologies.

Claims

1. A method for air situation analysis based on the entire lifecycle of flight paths, characterized in that, Includes the following steps: Step 1: Extract typical track events; Step 2: Establish a full lifecycle model of the trajectory. Based on the typical trajectory events described in Step 1, introduce the time dimension, spatial dimension and logical dimension to model the full lifecycle of the target's trajectory and record the entire lifecycle of the target from its creation to its disappearance. Step 3: Achieve quantitative analysis of air situation based on the full lifecycle model of flight paths; Step 4, Abnormal Event Identification and Extraction, that is, based on the quantitative analysis results of the three-dimensional distribution map of attribute events, thematic situation map and air situation correlation map, abnormal events are identified and extracted to complete the air situation analysis method based on the entire life cycle of the flight path; The specific method for establishing the full lifecycle model of the trajectory in step 2 is as follows: Step 2-1: Distinguish track events from three dimensions: time dimension, which is the time when the track event occurs, corresponding to the start and end time of the event Event_Time; space dimension, which is the location or geographical area where the track event occurs, corresponding to the spatial location description of the event Event_Space; and definition logic dimension, including: the importance level, abnormal trends, and correlation of the track event, corresponding to the topic Event_Subject and the detailed description of the event Event_Description. The logical dimension is divided into an attribute layer, a topic layer, and a correlation layer. The attribute layer distinguishes the basic attributes of various track events, including the identification of both parties, platform type, and motion parameters. The topic layer distinguishes the impact of various track events on the air situation. The correlation layer indicates that the current target event is related to other targets or other resources. Step 2-2: Define quantitative standards for the three dimensions, and locate each track event in the three dimensions; Steps 2-3: All track events for the same target constitute the full lifecycle model of the track; Step 3, which describes the quantitative analysis of the air situation, specifically includes: Step 3-1, perform time dimension quantitative analysis: classify and statistically analyze all track events in different time periods, and perform statistics once a day, every two days, every week or every half month to analyze the time dimension pattern characteristics; Step 3-2, conduct spatial dimension quantitative analysis: classify and statistically analyze all flight track events in different airspaces and areas of responsibility, and analyze the spatial dimension regularity characteristics for typical areas such as civil aviation flight corridors, military aviation training areas and enemy target reconnaissance areas; Step 3-3: Perform logical dimension quantitative analysis: Quantify all track events sequentially according to the attribute layer, theme layer, and association layer; Step 3-3, which involves performing logical dimension quantization analysis, includes: Step 3-3-1, Attribute-level quantitative analysis: Quantitative analysis is performed on the two-party identification attributes, platform type attributes, and motion parameter attributes of all track events. Combining the temporal and spatial regularity characteristics, a three-dimensional distribution map of attribute events is generated. Step 3-3-2, Thematic Layer Quantitative Analysis: Cluster analysis is performed on all track events according to the thematic situation, and the thematic situation map is generated by combining the temporal and spatial regularity characteristics. Step 3-3-3, Quantitative analysis of the correlation layer: Perform correlation analysis on all related flight track events to generate an air situation correlation diagram.

2. The air situation analysis method based on the entire life cycle of a flight path as described in claim 1, characterized in that, The method for extracting typical flight track events described in step 1 includes: comprehensively analyzing various types of typical flight track information in the air situation, including: flight tracks of friendly targets in the air, flight tracks of detected enemy targets, flight tracks of various fixed targets, and flight tracks of various mobile platform targets; analyzing the entire life cycle of the target from its emergence to its disappearance; and extracting typical flight track events.

3. The air situation analysis method based on the entire life cycle of a flight path according to claim 2, characterized in that, The trajectory mentioned in step 1 is defined as: target information generated after information fusion at the first level (signal level), the second level (position level), and the third level (attribute level) in the JDL model; the trajectory is represented by a quintuple: Track=<Track_No,Track_Type,Track_XY,Track_Attribute,Track_Interaction> Among them, Track_No is the track identifier, which is globally unique; Track_Type is the track type, which describes the category of the track; Track_XY are the track motion parameters, including the target position information after the second level of information fusion; Track_Attribute is the track attribute, which includes the attribute information after the third level of attribute information fusion; Track_Interaction is the track interaction, which describes the interaction relationship between tracks.

4. The air situation analysis method based on the entire life cycle of a flight path as described in claim 3, characterized in that, The basic attributes of the track event described in step 1 are represented by a quintuple as follows: Event=<Event_ID,Event_Time,Event_Space,Event_Subject,Event_Description> Where: Event_ID is the event identifier, indicating the name of the event, which is globally unique; Event_Time is the start and end time of the event; Event_Space is the spatial location description of the event; Event_Subject is the subject to which the event belongs; Event_Description is a detailed description of the event.

5. The air situation analysis method based on the entire life cycle of a flight path according to claim 4, characterized in that, The specific method for quantitative analysis described in step 3 is as follows: Track events targeting the target<tn,t,(x,y),position,amplify> Where tn represents the target number, t represents the occurrence time, (x, y) represents the spatial location, position represents the topic information, and ampulify represents detailed information; the track events are divided into unilateral events and bilateral events; the quantized representation of the track events... for: ； in, Indicates an event Measurement Indicates an event x-coordinate Indicates an event x-coordinate Indicates an event The ordinate, Indicates an event The ordinate, Indicates an event Time, Indicates an event Time, Indicates an event Measurement Indicates an event Measure; An improved Hausdorff distance-based approach is used for event quantization, and the calculation formula is as follows: ； in, The results of the quantitative analysis of track events, This represents the quantification of events A and B. This represents the quantification of events A and B. Represents the first event of event A. element, Indicates the first event of event B. element, Represents the first of A element, Indicates the first event of event B. element, Describe the set of events A. Describe the set of events B. This represents the final quantification result.

6. The air situation analysis method based on the entire life cycle of a flight path according to claim 5, characterized in that, The abnormal event identification and extraction described in step 4 specifically includes: Step 4-1: Based on the 3D distribution map of attribute events, set the threshold for abnormal event occurrence according to the event attribute type to filter out abnormal events; Step 4-2: Based on the topic situation map, set the threshold for abnormal events according to the situation topic, and filter out abnormal events; Step 4-3: Based on the air situation correlation diagram, set the threshold for abnormal events according to the correlation and filter out abnormal events.

7. The air situation analysis method based on the entire life cycle of a flight path according to claim 6, characterized in that, The abnormal event occurrence threshold mentioned in step 4 is obtained based on the mean of a random variable: ； in, Indicates the threshold for the occurrence of abnormal events. Indicates an event random variables, Indicates an event The quantitative analysis results Represents a sequence of events. Indicates the total number of events.

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