Formal modeling method for radar signal processing software

Abstract

The invention discloses a formalized modeling method for radar signal processing software, and relates to a formalized modeling technology. By combining the characteristics of high reliability and strong real-time performance of radar signal processing software, identifying the functions and functions of the software, constructing an environment entity and analyzing the static and dynamic characteristics of the entity, the formalized modeling of the software is finally completed, and the radar signal processing software can be verified to have high reliability and high real-time performance when facing a complex electromagnetic environment. And whether efficiency such as inconsistent software logic and mutual exclusion of time sequences deviates from expectation or not is solved.

Description

Technical Field

[0001] The present invention relates to formal modeling technology, specifically a method for formal modeling of radar signal processing software with strong real-time performance, which identifies the duties and functions of the software in a complex electromagnetic environment, constructs environmental entities, analyzes the static and dynamic characteristics of the entities, and ultimately completes the formal modeling of the software. Specifically, it relates to a formal modeling method for radar signal processing software. Background Art

[0002] The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.

[0003] Radar signal processing software is a core component of radar systems, responsible for processing and analyzing the raw signals received by the radar to extract target information. Radar signal processing software requires extremely high real-time performance (millisecond response time) and complexity (signal density in the millions per second). This requires the software to process large amounts of data in a very short timeframe to ensure target detection and tracking. The modern battlefield is increasingly complex, filled with numerous drones, radar equipment, communications equipment, navigation systems, and other devices. The rapidly increasing signal density has led to spectrum congestion, causing this software to exhibit performance issues such as logical inconsistencies and timing conflicts when processing large amounts of signal data, which deviates from expectations.

[0004] Formal modeling, a mathematical and logical approach used to precisely describe and analyze system behavior and properties, offers significant advantages in ensuring system correctness, reliability, and verifiability. However, due to its rigid specifications and constraints, formal modeling struggles to adapt to dynamic, real-time data environments, increasing the difficulty of flexible adjustments. In real-time data processing, the timed automata in formal models, based on discrete state transitions and linear time constraints, struggle to effectively handle such nonlinear dynamic behavior and often fail to respond quickly to data changes. Consequently, formal modeling is increasingly struggling to meet the demands of increasingly real-time radar signal processing software. Summary of the Invention

[0005] The purpose of the present invention is to address the current situation in which radar signal processing software exhibits software logic inconsistencies and timing mutual exclusion when facing complex electromagnetic environments, resulting in problems such as inaccurate target capture, disordered reporting, and performance deviation from expectations. The present invention provides a formal modeling method for radar signal processing software. By combining the high reliability and strong real-time characteristics of radar signal processing software, the functions and capabilities of the software are identified, environmental entities are constructed, and the static and dynamic characteristics of the entities are analyzed. Finally, the formal modeling of the software is completed. This method can verify whether the radar signal processing software exhibits problems such as software logic inconsistencies and timing mutual exclusion when facing complex electromagnetic environments, which deviate from expectations.

[0006] The technical solutions of the present invention are as follows: A formal modeling method for radar signal processing software, including: Step S1: Constructing the environment entity in the radar signal processing software according to the rules and constraints; Step S2: Construct the static characteristics of the radar signal processing software; analyze the attributes and value ranges of the environmental entities, define the initial state, communication interface and protocol of the module, and map the static parameters to the formal model; Step S3: construct a formal description of the entity; Step S4: constructing dynamic characteristics of radar signal processing software; Step S5: Based on the static characteristics obtained in step S2 and the dynamic characteristics obtained in step S4, all software entities are constructed to form a formal model; Step S6: Verify software logic consistency.

[0007] Furthermore, the environmental entity includes: a compliance entity, a causal entity and a lexical entity.

[0008] Furthermore, the construction of the compliant entity includes: Based on the main users of the software, the acquirers of software information, and the changers of software data, the rules and constraints followed by the obedient entities are determined, the obedient entities are identified, and the attributes, behaviors and relationships of the obedient entities with other entities are clarified.

[0009] Furthermore, the construction of the causal entity includes: Based on whether it can be controlled and whether it can control other devices, the rules and constraints followed by the causal entity are determined, the causal entity is identified, and the attributes, behavior and relationship of the causal entity with other entities are clarified.

[0010] Furthermore, the lexical entity includes: Determine the rules and constraints of lexical entities based on the physical representation of the data, identify lexical entities, and clarify the attributes, behaviors, and relationships of lexical entities with other entities.

[0011] Furthermore, the step S2 includes: Step S21: Construct entity attributes; describe entity attributes by analyzing the capabilities, communication interfaces, instructions, and transmission protocols of obedient entities, causal entities, and lexical entities; Step S22: Construct entity values; clarify the values, applicable scope and boundaries of the entity attributes in step S21; Step S23: constructing the static characteristics of the radar signal processing software; constructing the attributes and values ​​of the obedient entity, the causal entity, and the lexical entity, and completing the construction of the static characteristics of the radar signal processing software.

[0012] Furthermore, in step S22 , the values ​​within and on the boundary describe normal attributes of the entity, and the values ​​outside the boundary describe abnormal attributes of the entity.

[0013] Furthermore, the step S3 includes: Step S31: Constructing entity state; clarifying the state of the entity in step S21 that changes over time; Step S32: Constructing a trigger event: Constructing a trigger event of an entity by analyzing the instructions of the obedient entity, the causal entity, and the lexical entity, and describing the changes that occur to the entity after receiving the instruction; Step S33: Construct a formal description of the entity; the formal description of the entity is expressed as: Interaction (IntN; Initiator; Receiver; Content), where IntN is the identifier of the interaction, Initiator is the initiator of the interaction, Receiver is the receiver of the interaction, and Content is the content of the interaction.

[0014] Furthermore, the step S4 includes: Step S41: Describe the coverage of entity changes. Based on step S3, integrate the scheduling, combination, timing, and state changes of each module of the radar signal processing software into a timed automaton. Use timed automaton technology to describe the sequence, branches, and loops of radar signal processing. Construct state change characteristics for normal software scenarios for compliant inputs, and construct state change characteristics for abnormal software scenarios for out-of-range, out-of-bounds, and timeout inputs. Step S42: Describe the real-time nature of entity changes; use timed automata technology to describe radar signal processing characteristics, and construct real-time characteristics of software state changes based on the software's normal scenario processing time range within, on, and outside the boundaries.

[0015] Furthermore, the step S6 includes: performing software logic consistency verification based on the formal model obtained in step S5 to verify whether there is any conflict or contradiction in the logical sequence of operations or events in the expected software behavior.

[0016] Compared with the existing technology, the beneficial effects of the present invention are: This paper proposes a formal modeling method for radar signal processing software. Its effectiveness is mainly reflected in the following aspects: Reliability - This invention helps ensure the behavioral logic consistency of radar signal processing software, improving the accuracy and continuity of target acquisition by such software.

[0017] Scalability - This invention lays the model foundation for radar signal processing software by formally modeling it, providing support for subsequent verification of other features of this type of software. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic diagram of the radar signal processing software compliance entity; Figure 2 This is a schematic diagram of the cause-effect entities of radar signal processing software; Figure 3 This is a schematic diagram of lexical entities in radar signal processing software; Figure 4 It is a static characteristic diagram of the entity task processing module; Figure 5 A schematic diagram for the formal description of the entity; Figure 6 It is a schematic diagram of software entity coverage; Figure 7 It is a schematic diagram of the real-time performance of the software entity; Figure 8 This is a schematic diagram of the software formal model; Figure 9 Schematic diagram of software logic consistency verification. DETAILED DESCRIPTION

[0019] It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.

[0020] The features and performance of the present invention are further described in detail below with reference to the embodiments.

[0021] Example 1 A formal modeling method for radar signal processing software combines the high reliability and strong real-time characteristics of radar signal processing software to identify the software's duties and functions. The scheduling, combination, timing, and state changes of each software module are integrated into a timed automaton. The environment entity is constructed and the static and dynamic characteristics of the entity are analyzed. Finally, the formal modeling of the software is completed. The effectiveness of the formal modeling method is demonstrated by verifying the logical consistency of the software. The method includes the following steps: Step S1: Constructing the environment entity in the radar signal processing software according to the rules and constraints; Step S2: Construct the static characteristics of the radar signal processing software; analyze the attributes and value ranges of the environmental entities, define the initial state, communication interface and protocol of the module, and map the static parameters to the formal model; Step S3: construct a formal description of the entity; Step S4: constructing dynamic characteristics of radar signal processing software; Step S5: Based on the static characteristics obtained in step S2 and the dynamic characteristics obtained in step S4, all software entities are constructed to form a formal model; Step S6: Verify software logic consistency.

[0022] In this embodiment, specifically, the environmental entity includes: a compliance entity, a causal entity, and a lexical entity.

[0023] In this embodiment, specifically, the construction of the compliant entity includes: Based on the main users of the software, the acquirers of software information, and the changers of software data, the rules and constraints followed by the obedient entities are determined, the obedient entities are identified, and the attributes, behaviors and relationships of the obedient entities with other entities are clarified.

[0024] In this embodiment, specifically, the construction of the causal entity includes: Based on whether it can be controlled and whether it can control other devices, the rules and constraints followed by the causal entity are determined, the causal entity is identified, and the attributes, behavior and relationship of the causal entity with other entities are clarified.

[0025] In this embodiment, specifically, the lexical entity includes: Determine the rules and constraints of lexical entities based on the physical representation of the data, identify lexical entities, and clarify the attributes, behaviors, and relationships of lexical entities with other entities.

[0026] In this embodiment, specifically, step S2 includes: Step S21: Construct entity attributes; describe entity attributes by analyzing the capabilities, communication interfaces, instructions, and transmission protocols of obedient entities, causal entities, and lexical entities; Step S22: Construct entity values; clarify the values, applicable scope and boundaries of the entity attributes in step S21; Step S23: constructing the static characteristics of the radar signal processing software; constructing the attributes and values ​​of the obedient entity, the causal entity, and the lexical entity, and completing the construction of the static characteristics of the radar signal processing software.

[0027] In this embodiment, specifically, in step S22 , the values ​​within and on the boundary describe normal attributes of the entity, and the values ​​outside the boundary describe abnormal attributes of the entity.

[0028] In this embodiment, specifically, step S3 includes: Step S31: Constructing entity state; clarifying the state of the entity in step S21 that changes over time; Step S32: Constructing a trigger event: Constructing a trigger event of an entity by analyzing the instructions of the obedient entity, the causal entity, and the lexical entity, and describing the changes that occur to the entity after receiving the instruction; Step S33: Construct a formal description of the entity; the formal description of the entity is expressed as: Interaction (IntN; Initiator; Receiver; Content), where IntN is the identifier of the interaction, Initiator is the initiator of the interaction, Receiver is the receiver of the interaction, and Content is the content of the interaction.

[0029] In this embodiment, specifically, step S4 includes: Step S41: Describe the coverage of entity changes. Based on step S3, integrate the scheduling, combination, timing, and state changes of each module of the radar signal processing software into a timed automaton. Use timed automaton technology to describe the sequence, branches, and loops of radar signal processing. Construct state change characteristics for normal software scenarios for compliant inputs, and construct state change characteristics for abnormal software scenarios for out-of-range, out-of-bounds, and timeout inputs. Step S42: Describe the real-time nature of entity changes; use timed automata technology to describe radar signal processing characteristics, and construct real-time characteristics of software state changes based on the software's normal scenario processing time range within, on, and outside the boundaries.

[0030] In this embodiment, specifically, step S6 includes: performing software logic consistency verification based on the formal model obtained in step S5 to verify whether there are any conflicts or contradictions in the logical sequence of operations or events in the expected software behavior.

[0031] Example 2 Example 2 is a further explanation of Example 1, and is a formal modeling method for radar signal processing software, comprising the following steps: Step S1: constructing an environment entity; Step S11: Constructing obedient entities: Based on the three aspects of "primary users of the software", "acquirers of software information", and "changers of software data", determine the rules and constraints followed by obedient entities, identify obedient entities, and clarify the attributes, behaviors, and relationships of obedient entities with other entities. Based on the radar signal processing business, analyze the six functions of "task management", "command and monitoring", "situation monitoring", "state monitoring", "neighbor coordination", and "training management", and identify obedient entities including "commander", "analyst", and "maintenance personnel", such as Figure 1 .

[0032] Step S12: Constructing causal entities: Determine the rules and constraints followed by causal entities based on the two aspects of "being controllable" and "being able to control other devices", identify causal entities, and clarify the attributes, behaviors, and relationships of causal entities with other entities. Based on the radar signal processing business, analyze the five functions of "loading control", "self-test control", "parameter control", "reset control", and "recording control", and identify causal entities including "receiving device", "sending device", "management control device", and "data processing device", such as Figure 2 .

[0033] Step S13: Constructing lexical entities: Determine the rules and constraints of lexical entities based on the "physical representation of data", identify lexical entities, and clarify the attributes, behaviors, and relationships of lexical entities with other entities. According to the radar signal processing business, analyze the four functions of "adding data", "deleting data", "querying data", and "modifying data", and identify lexical entities including "database", such as Figure 3 .

[0034] Step 2: Build software static features; Construct the static characteristics of the radar signal processing software, including the basic parameters of the radar signal, module initial state, communication interface, parameter protocol, etc., and finally map the static parameters of the software into a formal model.

[0035] Step S21: Constructing Entity Attributes: Describe the entity's attributes by analyzing the "capabilities," "communication interfaces," "instructions," and "transmission protocols" of the obedient entity in step S11, the causal entity in step S12, and the lexical entity in step S13. For example, the static attributes of the "task processing module" in the "management and control device" include "capabilities—load functions," "communication interfaces—Ethernet," "instructions—load data, load responses," and "transmission protocols—TCP / IP."

[0036] Step S22: Construct entity values: Define the values, applicable scope, and boundaries of the entity attributes in step S21. Values ​​within and on the boundaries describe normal attributes of the entity, while values ​​outside the boundaries describe abnormal attributes of the entity.

[0037] Step S23: Constructing software static characteristics: Construct the attributes and values ​​of the obedient entity in step S11, the causal entity in step S12, and the lexical entity in step S13 to complete the construction of the software static characteristics. Taking the "task processing module" in the "management control device" as an example, the static characteristics are as follows: Figure 4 .

[0038] Step S3: construct a formal description of the entity; Step S31: Constructing entity state: clarifying the state of the entity in step S21 that changes over time.

[0039] Step S32: Construct a trigger event: Construct the entity's trigger event by analyzing the "instructions" of the obedient entity in step S11, the causal entity in step S12, and the lexical entity in step S13, and describe the changes that occur to the entity after receiving the "instructions".

[0040] Step S33: Constructing a formal description of the entity: The formal description of the entity is expressed as: Interaction (IntN; Initiator; Receiver; Content), where IntN is the identifier of the interaction, Initiator is the initiator of the interaction, Receiver is the receiver of the interaction, and Content is the content of the interaction. Figure 5 .

[0041] Step S4: Constructing software dynamic characteristics; Construct the dynamic characteristics of radar signal processing software, including task switching timing, state transition logic, pre- and post-conditions of each module, and finally map the dynamic operating parameters of the software into a formal model.

[0042] Step S41: Describe the coverage of entity changes: Based on step S3, integrate the scheduling, combination, timing and state changes of each software module into the timed automaton. Use the timed automaton technology to describe the sequence, branching and loop of radar signal processing. For compliant inputs, construct the state change characteristics of the software normal scenario. For inputs that exceed the range, cross the boundary, or time out, construct the state change characteristics of the software abnormal scenario, such as Figure 6 .

[0043] Step S42: Describe the real-time nature of entity changes: Use timed automata technology to describe radar signal processing characteristics, and construct real-time characteristics of software state changes for situations within, on, and outside the time range of normal software scenarios, such as Figure 7.

[0044] Step S5: Form a formal model: construct all software entities according to the static characteristics of the software obtained in step S2 and the dynamic characteristics of the software obtained in step S4 to form a formal model, such as Figure 8 .

[0045] Step S6: Verify the consistency of software logic: Verify the consistency of software logic based on the formal model obtained in step S5 to verify whether there are conflicts or contradictions in the logical sequence of operations or events in the expected software behavior, such as Figure 9 .

[0046] Example 3 In order to verify the effectiveness of the present invention, this embodiment was applied to a certain type of radar signal processing software. By constructing the software's environmental entities and analyzing the static and dynamic characteristics of the entities, a total of 3 types of obedient entities, 4 types of causal entities, and 1 type of lexical entities were constructed, with more than 200 entity-related capabilities and attributes, completing the formal modeling of the software. In the process of verifying the logical consistency of the software, more than 10 conflicts were found in the software due to inconsistent timing. For example, the timing of the manually issued working current power-on and the working current power-off when over-temperature occurs mutually exclusive, resulting in failure of protection when over-temperature occurs, and other mutually exclusive software problems, such as Figure 9 After resolving these issues, the radar signal processing software experienced 15 fewer response conflicts during 8 hours of continuous operation, accounting for approximately 13.2%. Furthermore, the average response rate to radar signals in a 3 million pulse density scenario increased by 13ms, accounting for approximately 9.6%, as shown in Table 1. This demonstrates the effectiveness of the formal modeling approach for radar signal processing software.

[0047] Table 1 Comparison before and after modeling

[0048] The above-described embodiments merely represent specific implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of protection of the present application. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the technical concept of the present application, and all such variations and improvements fall within the scope of protection of the present application.

[0049] This background section is provided to generally present the context of the invention, and the work of the presently named inventors, the work to the extent described in this background section, and aspects of the description in this section that did not constitute prior art at the time of filing are neither explicitly nor implicitly admitted to be prior art to the present invention.

Claims

1. A formal modeling method for radar signal processing software, characterized in that: include: Step S1: Constructing the environment entity in the radar signal processing software according to the rules and constraints; Step S2: Construct the static characteristics of the radar signal processing software; analyze the attributes and value ranges of the environmental entities, define the initial state, communication interface and protocol of the module, and map the static parameters to the formal model; Step S3: construct a formal description of the entity; Step S4: constructing dynamic characteristics of radar signal processing software; Step S5: Based on the static characteristics obtained in step S2 and the dynamic characteristics obtained in step S4, all software entities are constructed to form a formal model; Step S6: Verify software logic consistency.

2. A formal modeling method for radar signal processing software according to claim 1, characterized in that: The environmental entities include: obedient entities, causal entities and lexical entities.

3. A formal modeling method for radar signal processing software according to claim 2, characterized in that: The construction of the compliant entity includes: Based on the main users of the software, the acquirers of software information, and the changers of software data, the rules and constraints followed by the obedient entities are determined, the obedient entities are identified, and the attributes, behaviors and relationships of the obedient entities with other entities are clarified.

4. A formal modeling method for radar signal processing software according to claim 2, characterized in that: The construction of the causal entity includes: Based on whether it can be controlled and whether it can control other devices, the rules and constraints followed by the causal entity are determined, the causal entity is identified, and the attributes, behavior and relationship of the causal entity with other entities are clarified.

5. A formal modeling method for radar signal processing software according to claim 2, characterized in that: The lexical entities include: Determine the rules and constraints of lexical entities based on the physical representation of the data, identify lexical entities, and clarify the attributes, behaviors, and relationships of lexical entities with other entities.

6. A formal modeling method for radar signal processing software according to claim 2, characterized in that: The step S2 includes: Step S21: Construct entity attributes; describe entity attributes by analyzing the capabilities, communication interfaces, instructions, and transmission protocols of obedient entities, causal entities, and lexical entities; Step S22: Construct entity values; clarify the values, applicable scope and boundaries of the entity attributes in step S21; Step S23: constructing the static characteristics of the radar signal processing software; constructing the attributes and values ​​of the obedient entity, the causal entity, and the lexical entity, and completing the construction of the static characteristics of the radar signal processing software.

7. A formal modeling method for radar signal processing software according to claim 6, characterized in that: In step S22 , the values ​​inside and on the boundary describe normal attributes of the entity, and the values ​​outside the boundary describe abnormal attributes of the entity.

8. A formal modeling method for radar signal processing software according to claim 6, characterized in that: The step S3 comprises: Step S31: Constructing entity state; clarifying the state of the entity in step S21 that changes over time; Step S32: Constructing a trigger event: Constructing a trigger event of an entity by analyzing the instructions of the obedient entity, the causal entity, and the lexical entity, and describing the changes that occur to the entity after receiving the instruction; Step S33: Construct a formal description of the entity; the formal description of the entity is expressed as: Interaction (IntN; Initiator; Receiver; Content), where IntN is the identifier of the interaction, Initiator is the initiator of the interaction, Receiver is the receiver of the interaction, and Content is the content of the interaction.

9. A formal modeling method for radar signal processing software according to claim 1, characterized in that: The step S4 comprises: Step S41: Describe the coverage of entity changes. Based on step S3, integrate the scheduling, combination, timing, and state changes of each module of the radar signal processing software into a timed automaton. Use timed automaton technology to describe the sequence, branches, and loops of radar signal processing. Construct state change characteristics for normal software scenarios for compliant inputs, and construct state change characteristics for abnormal software scenarios for out-of-range, out-of-bounds, and timeout inputs. Step S42: Describe the real-time nature of entity changes; use timed automata technology to describe radar signal processing characteristics, and construct real-time characteristics of software state changes based on the software's normal scenario processing time range within, on, and outside the boundaries.

10. A formal modeling method for radar signal processing software according to claim 1, characterized in that: The step S6 includes: performing software logic consistency verification based on the formal model obtained in step S5 to verify whether there are conflicts or contradictions in the logical sequence of operations or events in the expected software behavior.

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