SiaNav: A Predictive Time Horizon and Symbolic 3D Navigation System for UAVs that can operate in environments where GPS signals are jammed.

Abstract

This invention describes an alternative UAV navigation system called SiaNav, capable of operating in environments where GPS signals are interrupted, jammed, or spoofed. The system represents position in a symbolic three-dimensional format (ib, DIR+Qen, Qay) relative to a fixed reference point, the Zero Point, instead of absolute coordinates. The SiaLex architecture, which uses a combination of modern numerals and symbols derived from the ancient Egyptian alphabet, enables secure and unconventional encoding of position data. SiaNav also predicts the future position of the UAV with a short-term predictive layer (RI) and keeps drift under control with a periodic Bounded Drift Reset method. These corrections, made with references from GPS or the Ground Control Station, enable the system to detect spoofing attempts and continue operating independently of GPS. Thus, SiaNav offers a reliable, robust, and flexible navigation solution even in challenging environments.

Description

1 DEFINITION SiaNav is a Predictive Time Horizon and Symbolic 3D Navigation System for UAVs that can operate in environments where GPS signals are jammed. TECHNICAL FIELD This invention belongs to the field of navigation, positioning, and relative navigation systems developed for unmanned aerial vehicles. The invention specifically addresses situations where GPS / GNSS signals are limited, interrupted, interfered with, or It is geared towards alternative navigation systems that can operate in operational environments where they may be misled. The system developed within the scope of this invention combines relative coordinate calculations, symbolic data representation, error management, recalibration mechanisms, and short-term position estimation processes. The system also uses symbolic navigation data, unlike traditional absolute coordinate structures. It offers an alternative navigation architecture that utilizes this structure. BACKGROUND OF THE INVENTION Current navigation systems used in unmanned aerial vehicles largely rely on GPS / GNSS-based absolute coordinate data. Despite their widespread use, GPS-based navigation structures have several operational limitations. The main problems encountered in GPS / GNSS-based systems are as follows: 1. Signal Loss In dense urban areas, narrow passageways, enclosed structures, or geographical obstacles, GPS / GNSS signals can weaken or disappear completely. 2. Jamming GPS / GNSS signals can be disrupted by external electromagnetic interference, significantly reducing navigation accuracy. 3. Deception (Spoofing) Unmanned aerial vehicles can be misdirected by spurious GPS / GNSS data, posing a risk to operational safety. 2 4. Limitations of Traditional Coordinate Structures Current navigation systems mostly represent coordinate data in directly readable absolute coordinate formats. This can increase the risks of data capture, analysis, or manipulation. 5. Sensor-Induced Cumulative Deviation In systems that rely solely on internal sensors, measurement errors can accumulate over time, leading to a decrease in relative positional accuracy. For these reasons, there is a need for alternative navigation systems that can provide the following features: • Ability to operate partially independently of GPS / GNSS systems, • Ability to provide relative navigation continuity, • Ability to use alternative data representation structures, • Ability to control sensor-induced deviations, • Ability to perform short-term location estimation, • Ability to implement recalibration mechanisms, • Ability to detect spoofing and similar security threats. The SiaNav system, developed as part of this invention, offers an alternative navigation architecture to meet these needs. SUMMARY OF THE INVENTION This invention relates to a relative navigation system called SiaNav, which uses a fixed reference point as the basis for navigation calculations. Within the system, position data is represented using relative parameters, as opposed to traditional absolute coordinate structures. The SiaNav system generates relative navigation data using the following basic parameters: • horizontal distance information, • Directional angle information, • information on the angle within the quarter, • altitude information. The system also includes an optional short-term position estimation mechanism. Thanks to this structure... Estimated relative position data can be generated within a specific time horizon. The SiaNav system uses a fault management mechanism called Bounded Drift Reset. This allows the system to recalibrate by comparing relative navigation data with external reference data at specific time intervals. The system also includes security checks to detect GPS / GNSS-based spoofing attempts. It includes 35 mechanisms. 3 Within the SiaNav system, relative navigation data can be represented in an alternative format using a symbolic data structure called SiaLex. The system architecture incorporates relative navigation calculations, symbolic data representation, error handling, recalibration, learning mechanisms, and safety control structures. Thanks to this structure, the system can be used as an auxiliary or alternative navigation solution in operational environments where classic GPS / GNSS-based navigation systems are limited. Explanation of the Figures Figure 1: Block diagram showing the SiaNav system architecture. Figure 2: Diagram showing the relative coordinate structure based on Zero Point. Figure 3: Flowchart of the drift reset and spoof detection process. EXPLANATION OF REFERENCES IN THE FIGURES The reference numbers used in the figures of the invention and their corresponding technical components are explained below. The reference numbers are used to establish a clear and systematic technical link between the figures and the description of the invention. Reference No. Description 1 UAV platform 2 SiaNav core 3 IMU sensors 4 Barometric sensor 5 Magnetometer 6 GPS / GNSS module 7 GCS communication unit 8 Memory unit 9 MCU processor unit 10 Drift reset unit 11 Spoofing detection unit 12 SiaLex encoding unit 13 Prediction unit 14 Data stream links 15 Local NEU reference structure 16 Zero Point reference point 17 Relative position vector 18 DIR direction angle 19 ib horizontal distance parameter 4 Qen angle parameter21 Qay altitude parameter22 East axis23 North axis24 Local NEU coordinate frame25 Position point P26 Horizontal projection line27 Vertical projection line28 Relative position line29 North direction indicator30 East direction indicator31 Startup block32 Sensor data reading block33 Position calculation block34 Reference data acquisition block35 Error calculation block36 Threshold control block37 Drift evaluation block38 Drift reset block39 State update block40 Timer reset block41 Safe mode block42 GCS notification block43 Periodic timer block44 Operation flow direction45 Drift recording block46 Estimated output block47 Spoof evaluation block48 Threshold comparison link49 Calibration pipeline50 Safe mode pipeline51 Drift feedback pipeline52 Timer feedback pipeline53 Output data pipeline54 System loop link Detailed explanation 1. GENERAL INTRODUCTION TO THE SYSTEM This invention relates to an alternative navigation system developed for unmanned aerial vehicles (UAVs) (1) that aims to reduce complete reliance on GPS. The system aims to maintain position continuity, especially in operational conditions where GPS signal loss, intentional jamming and spoofing attacks occur. The SiaNav core (2), developed within the scope of the invention, uses a relative position architecture based on a fixed reference point, unlike classical navigation systems based only on absolute latitude and longitude coordinates. The reference point defined as "Zero Point" (16) in the system forms the basis of local coordinate calculations. In the system in question, the position of the UAV (1) is determined by the horizontal distance parameter ib (19), the direction angle DIR (18), The quadrant angle parameter Qen (20) and the height parameter Qay (21) are defined using these parameters. These parameters are calculated within the local NEU coordinate frame (24). The system uses data from sensors such as the IMU sensor (3), barometric sensor (4), magnetometer (5) and GPS / GNSS module (6). The GPS / GNSS module (6) is a continuously main sensor within the system. It is configured not as a navigation source, but as an auxiliary reference source used for recalibration purposes in specific situations. Data received from the sensors is processed via the MCU processor unit (9) and transferred to the SiaNav core (2). The system also includes a drift reset unit (10), a deception detection unit (11), a prediction unit (13) and a SiaLex encoding unit (12). The drift reset unit (10) reduces error accumulation by analyzing the difference between the relative position data calculated at specific time intervals and the reference data. The spoofing detection unit (11) evaluates possible spoofing situations by checking the consistency of the data received from the GPS / GNSS module (6) with other sensor data in the system. The estimation unit (13) is an optional unit that enables the system to predict the position in the short term future. It includes a predictive mechanism. Thanks to this structure, the system can maintain directional continuity in the event of temporary signal losses or data interruptions. The SiaLex encoding unit (12) includes an auxiliary encoding layer that enables the representation of relative coordinate information in a non-standard symbolic data structure. This structure expresses coordinate data differently from directly readable classical coordinate formats. It enables this to be done. The system architecture is modular and operates via data flow connections (14), and location calculations are performed using the local NEUreference structure (15). 35 2. ZERO POINT – REFERENCE POINT 2.1 Definition and Purpose 6 Zero Point reference point (16) is a fixed georeference point with predefined geodetic coordinates. All relative position calculations within the system are performed based on this reference point. Calculated relative position vector (17) within the local NEU coordinate frame (24), north It is created using the axis (23), east axis (22) and elevation direction. The following basic parameters are obtained as a result of relative position calculations: · horizontal distance parameter (19), · DIR direction angle (18), · Qen angle parameter (20), · Qay altitude parameter (21). The Zero Point reference point (16) forms the basic reference for the prediction calculations, drift reset operations and recalibration processes within the system. 2.2 Zero Point Selection Criteria Zero Point reference point (16) is determined according to the following criteria: • having a geodetically verifiable fixed geographical location, • It must be documented on standard coordinate systems, • It can be aligned with true north, • It must be operationally verifiable and reliable. Reference data used within the system can be stored in the memory unit (8) or GCS It can be received via communication unit (7). 2.3 Geodetic Representation and Local Framework Absolute coordinate data are obtained according to the WGS-84 reference system. Altitude data are evaluated according to the mean sea level reference whenever possible. Initially, the local NEU coordinate frame (24) is created by the system. This The frame consists of the following axes: · north axis (23), · eastern axis (22), • Upward elevation axis. Relative position vector (17) using ΔN, ΔE and ΔU components of the target location The data are generated and converted into the parameters ib (19), DIR (18), Qen (20) and Qay (21). 2.4 Deriving Location Elements from Zero Point The relative position vector (17) is analyzed using the horizontal projection line (26) and the vertical projection line (27). 7 The horizontal distance parameter ib (19) is obtained by calculating the shifts on the north axis (23) and the east axis (22). Direction information is defined using the DIR direction angle (18) and the Qen angle parameter (20). The directional structure within the system can be divided into four main quadrant regions. Altitude information is represented by the Qay altitude parameter (21). The position point P (25) is defined on the relative position line (28). 2.5 Writing and Coding Structure Relative coordinate data generated within the system can be converted into a symbolic data structure using the SiaLex encoding unit (12). The encoding structure allows for the simultaneous use of modern numerical values ​​and non-standard symbolic characters. The encoding process enables alternative representations of coordinate data. 2.6 Multiple Reference Points Depending on operational requirements, the system can have multiple Zero Point reference points (16) It allows for identification. Switching between different reference points can be done without altering the system's fundamental operating principle. 2.7 Security and Data Integrity Zero Point reference data can be stored in the memory unit (8) and verified when needed. It can be controlled through various mechanisms. Reference point changes made within the system can be recorded and verified via the GCS communication unit (7). 3. MAIN COMPONENTS OF THE SYSTEM 3.1 Functional Overview The SiaNav system consists of two main structures: a hardware layer and a software / firmware layer. The hardware layer includes the components that enable the collection, processing, and transmission of physical sensor data. The software / firmware layer, on the other hand, handles relative navigation calculations, error control processes, recalibration operations, and symbolic data encoding mechanisms. It ensures its implementation. The basic navigation calculations used within the system are performed by the SiaNav kernel (2). This structure consists of the relative position vector (17), ib horizontal distance parameter. 8 (19), DIR direction angle (18), Qen angle parameter (20) and Qay altitude parameter (21) data are generated. The software layer also works with the drift reset unit (10), deception detection unit (11), prediction unit (13) and SiaLex encoding unit (12). 3.2 Hardware Components 3.2.1 Operation and Control Unit The system uses an MCU processor unit (9). This processor unit can consist of ESP32 class or functionally equivalent microcontrollers. The MCU processor unit (9) processes sensor data in real time and executes navigation algorithms. It provides. Memory unit (8) enables the storage of the following data: · Zero Point reference point (16) data, • system threshold values • time intervals, • operating modes, • verification data, • Digital signature records. External data storage units can be used for long-term recording operations on demand. 3.2.2 Basic Sensors The system uses an IMU sensor (3), a barometric sensor (4), a magnetometer (5) and a GPS / GNSS module (6). The IMU sensor (3) enables the measurement of acceleration and angular motion information. The sensor structure can be in 6-DOF or 9-DOF configurations. The barometric sensor (4) is used for relative altitude calculations. This sensor enables the determination of altitude changes relative to the mean sea level reference. The magnetometer (5) helps to determine the true north reference in direction calculations. The GPS / GNSS module (6) is not the main navigation source within the system, but only It is configured as an auxiliary reference unit used for recalibration purposes in specific situations. 3.2.3 Communication Interfaces The system includes a GCS communication unit (7). This unit enables data exchange with the ground control station. 9 The following operations can be performed via the GCS communication unit (7): • Reference coordinate transfer, • Obtaining recalibration data, • status update transmission, • Transmission of safety alerts. Hardware-level data communication interfaces include I²C, SPI, UART, and USB. 3.2.4 Power and System Management Low-noise power regulation architectures are used within the system. This architecture is controlled by the MCU processor. It ensures the stable operation of the sensor components with unit (9). The system may also include temperature and voltage monitoring mechanisms. These mechanisms help to maintain safe working conditions. 3.2.5 Security and Hardware Protection Optional security elements can be used within the system. This structure is Zero Point. It ensures the protection of reference point (16) data and verification keys. Physical tamper protection mechanisms can be implemented for the memory unit (8) and sensor connections. Additionally, a read-only mode can be implemented within the system to prevent unauthorized data modifications during flight. 3.3 Software / Firmware Components 3.3.1 Startup and Initial Operation When the system is started, the signed Zero Point reference point (16) in memory unit (8) is loaded. The local NEU coordinate frame (24) is created and the default threshold values ​​are applied. During the startup process, drift records are reset and system operating parameters are activated. 3.3.2 Sensor Data Collection The IMU sensor (3) produces data at a high sampling frequency. The barometric sensor (4) and the magnetometer (5) can operate at lower sampling frequencies. Digital filtering operations can be applied to sensor data. 3.3.3 SiaNav Core The SiaNav kernel (2) performs relative position calculations. The relative position vector (17) is calculated using the shifts on the north axis (23) and the east axis (22). The horizontal distance parameter ib (19) is derived from the components ΔN and ΔE. The DIR direction angle (18) and the Qen angle parameter (20) form the directional position information. Qay altitude parameter (21) is calculated based on the Zero Point reference point (16). The SiaLex encoding unit (12) enables the conversion of relative coordinate data into a symbolic data structure. 3.3.4 Estimation Unit The estimation unit (13) is able to calculate the short-term future position of the UAV (1). This system uses data on changes in speed, direction, and altitude to predict future relative position values. 3.3.5 Error Checking and Recalibration The drift reset unit (10) compares SiaNav data with external reference data at specific time intervals. Error calculation block (35), threshold control block (36) and drift evaluation block (37) are used during comparison operations. In case of deviations below the determined threshold values, partial correction is applied by the drift reset block (38). The snoozing detection unit (11) assesses the reliability of the data provided by the GPS / GNSS module (6). It is analyzing. If the defined security thresholds are exceeded, the system can switch to the safe mode block (41) and send a warning via the GCS notification block (42). The following recalibration modes can be applied within the system: • Direct GPS / GNSS reference, • GCS-based reference validation. 3.3.6 Registration and Diagnosis The system can store records of navigation operations in the memory unit (8). The record structure may include the following data groups: • Data prior to SiaNav, • external reference data, • Data corrected after SiaNav. 11 The system can also generate alerts in case of sensor errors, data deviations, and security breaches. 3.4 Data Structures and Data Formats The following data structures can be used within the system: • Zero Point referral record, • measuring frame, • Relative position data, • calibration records, • Symbolic SiaNav data sequences. These data structures can be stored in the memory unit (8) or in the GCS communication unit (7) It can be transferred via [link / method]. 3.5 Interfaces and Integration The system can operate with both external and internal data interfaces. External interfaces include ground control station connections and reference data transmission. Internal interfaces facilitate data communication between the sensor abstraction layer and hardware components. It provides. 3.6 Reliability and Environmental Working Conditions The system is configured for use on small and medium-sized UAV platforms. The system architecture is designed to withstand operational conditions such as vibration, temperature variation, and transient sensor failures. In case of sensor or reference loss, the system can only continue navigation with the SiaNav core (2). 3.7 System Variants The system can be implemented in the following configurations: • low-cost configuration, • High-precision configuration, • portable platform configuration, • Fixed platform configuration. While the hardware components used are interchangeable, the basic operating principle of the system remains the same. 3.8 Component Summary The system consists of: MCU processor unit (9), IMU sensor (3), barometric sensor (4), magnetometer (5), GPS / GNSS module (6), GCS communication unit (7), memory unit (8), drift reset unit (10), 12 It consists of basic components such as the deception detection unit (11), the prediction unit (13) and the SiaLex encoding unit (12). 3.9 Non-Restrictive Statement Sampling rates, thresholds, time intervals, and hardware used within the system. These are parameters whose configurations can be changed. The use of functionally equivalent components does not alter the fundamental operating principle of the invention. 4. SIALEX CODING SYSTEM 4.1 Definition and Purpose The SiaLex encoding system refers to the symbolic data encoding structure used within the SiaNav kernel (2). The system enables the representation of relative navigation data in a way different from standard coordinate formats. SiaLex encoding processes are carried out by the SiaLex encoding unit (12). The horizontal distance parameter ib (19), the direction angle DIR (18), and the angle Qen created within the system The parameter (20), Qay altitude parameter (21) and optional forecast data can be converted into a symbolic data structure. The main objectives of the SiaLex system are as follows: • Non-standard representation of navigation data, • Consistent storage of relative coordinate records, • data transfer can be carried out in a compact manner, • Creating a data format that differs from classical coordinate structures that are directly readable. 4.2 Components of the Coding Structure The SiaLex coding system consists of the following basic components: 1. Modern numerical values, 2. Symbolic character structures, 3. Hybrid spelling rules. The system allows for the use of both modern numerals and non-standard symbolic characters. The coding structure allows for the alternating writing of digital segments. 4.3 Symbol Structure The symbolic data structures used within the SiaLex system may include the following example transliterations: 13 Numeric Value SymbolicCharacter0 nefer1 wa2 senwy3 khemet4 ifed5 diw6 sis7 sefekh8 khemen9 pesedj10 medj100 shet1000 kha These symbols are used as example structures within the system and can be replaced with functionally equivalent symbolic character structures. 4.4 General Writing Rules The following syntax rules can be applied within the SiaLex data structure: · Within the horizontal distance parameter (19) and the altitude parameter (21) of ib, kilometer and meter sections can be separated by a distinguishing character. · The DIR direction angle (18) can be represented together with the directional region information. · The angle parameter Qen (20) can be written in integer and fractional parts. · The forecast data generated by the forecasting unit (13) can be encoded together with the time parameter. The directional definitions used within the system can be expressed with the following example directional structures: Meh Lab, · Meh Ime, Re Lab, Re Ime. These directional definitions are used to create quadrant-based directional classifications. 4.5 Coding Examples Relative navigation data generated within the system can be represented in the following formats: Example 1: ib shet 5 - diw 2 14 Example 2: Qay nefer - khemen 5 Example 3: Re Lab Qen ifed 7 - khemet 5 Example 4: ib 9 - shet 5 4 0 | Re Lab Qen ifed 7 - khemet 5 | Qay nefer - khemen 5 These examples illustrate the use of the SiaLex data structure, but the system is not limited to these examples. 4.6 System Specifications The SiaLex encoding system can provide the following features: • Non-standard data representation, • compact data transfer, • Symbolic data representation structure • Extensible coding architecture, • Creation of an alternative navigation data format. The system architecture allows for the addition of new symbolic data groups in future versions. 4.7 Result The SiaLex coding system is used within the SiaNav kernel (2) for relative navigation data. It creates a data encoding structure that enables its representation in symbolic form. Thanks to the system, navigation data can be generated, stored, and transmitted in a way that differs from standard coordinate representations. 5.: ERROR MANAGEMENT AND LEARNING MECHANISM 5.1 General Description The SiaNav system uses a relative navigation architecture that can operate without being constantly dependent on the GPS / GNSS module (6). In such systems, positional deviations can occur because sensor data can generate cumulative errors over time. In order to control the accumulation of errors that may occur within the system, the drift reset unit (10), error calculation block (35), threshold control block (36), drift evaluation block (37) and deception detection unit (11) work together. Thanks to this structure, the system maintains relative navigation accuracy within certain limits. It is able to protect. 5.2 Bounded Drift Reset Principle The system employs a fault management method called Bounded Drift Reset. Within the scope of this method, relative position data generated by the SiaNav kernel (2) at specific time intervals are compared with external reference data. Comparisons can be made using the following reference sources: · GPS / GNSS module (6), · Reference data received via GCS communication unit (7). Error calculation block (35), threshold control block (36) and drift evaluation block (37) are used during comparison operations. Partial correction is applied by the drift reset block (38) for deviations that remain below the specified error thresholds. If the specified security thresholds are exceeded, the spoofing detection unit (11) can mark the relevant reference source as an unreliable data source. The threshold values ​​used within the system are configurable parameters and vary depending on the application. It can be changed. The SiaNav system does not use the GPS / GNSS module (6) as the main navigation source. The GPS / GNSS module (6) is configured as an auxiliary reference source used only for limited error correction and recalibration purposes. 5.3 Triple Recording Mechanism Every recalibration operation performed within the system can be logged. Calibration records may include the following data groups: SiaNav data before the first correction, 2. External reference data, SiaNav data after the 3rd revision. These records can be stored in the memory unit (8) and used for diagnostic purposes when needed. Thanks to its three-part record structure, long-term failure behavior can be analyzed and system performance can be evaluated. 16 5.4 Learning and Adaptation Mechanism An optional adaptation mechanism can be implemented within the system. This system allows for the evaluation of sensor behavior using historical calibration records. The learning mechanism may include the following processes: • Storing timestamped error logs, • analysis of sensor deviations, • Updating correction coefficients, • long-term drift behavior assessment. This structure can be implemented using rule-based or linear tuning methods. Learning processes are carried out locally within the system and do not change the symbolic data structure used by the SiaLex encoding unit (12). This design helps reduce the number of errors between drift reset operations. 5.5 GPS Spoofing and Security Check The deception detection unit (11) is used within the system. The snoozing detection unit (11) compares the data provided by the GPS / GNSS module (6) with the relative navigation data generated by the IMU sensor (3), barometric sensor (4), magnetometer (5) and SiaNav core (2). If the defined security thresholds are exceeded, the system can perform the following actions: • Marking the relevant GPS data as unreliable data, · Switching to safe mode block (41), · Sending alerts via GCS notification block (42), · Continue navigation using only the SiaNav kernel (2). Thanks to this design, the system can maintain navigation continuity even in the event of attacks such as GPS spoofing and jamming. 5.6 Conclusion The error handling structure used within the system includes the following key elements: • Bounded Drift Reset-based error correction mechanism, • adaptation and learning mechanism, • GPS spoofing detection structure, • Safe mode and error logging mechanism. 17 By enabling these structures to work together, the system is able to improve relative navigation accuracy and maintain operational robustness. 6. PRACTICAL APPLICATIONS AND OPERATIONAL WORK 6.1 Purpose and Scope The SiaNav system was developed to provide relative navigation continuity in operational environments where the GPS / GNSS module (6) can be used in limited, intermittent or unreliable conditions. The system can operate in different mission scenarios using a symbolic navigation architecture running on the UAV platform (1). The application examples and operational scenarios described in this section are provided to illustrate how the invention works and do not limit its scope. 6.2 Operating Modes Multiple operational modes can be implemented within the system. 6.2.1 Symbolic Mode Only In this operating mode, the system produces relative navigation outputs using the local NEUcoordinate frame (24) created around the Zero Point reference point (16). The system outputs include the following parameters: · horizontal distance parameter (19), · DIR direction angle (18), · Qen angle parameter (20), · Qay altitude parameter (21). In this operating mode, navigation can be maintained without using external reference data. 6.2.2 Prediction Mode A relative position forecast for the future can be generated using the forecast unit (13). The system is capable of calculating estimated relative position values ​​after a specific time horizon. The prediction outputs can optionally be converted into a symbolic data structure via the SiaLex encoding unit (12). 6.2.3 GPS Calibrated Mode In this operating mode, the drift reset unit (10) processes the data generated by the SiaNav core (2). It compares with the reference data provided by the GPS / GNSS module (6). Limited corrections can be applied to deviations that fall below the specified error thresholds. 18 The spoofing detection unit (11) can detect unreliable GPS data and consider the relevant reference data invalid. 6.2.4 GCS Calibrated Mod The system can operate with external reference data received via the GCS communication unit (7). This operating mode can be used as an alternative reference source in cases where the data provided by the GPS / GNSS module (6) is unreliable. 6.2.5 RF Silent Mode In this operating mode, the system can only function using internal relative navigation data, limiting wireless communication operations. The system is in RF silent mode: • relative navigation calculations, • forecasting processes, • internal data records It is able to continue. 6.2.6 Multiple Zero Point Modes Multiple Zero Point reference points (16) can be defined within the system. Depending on the mission requirements, it is possible to switch between different reference points. Transitions between reference points can be performed without altering the system's fundamental navigation structure. 6.3 Operational Workflow The system's operational process may consist of the following stages: 6.3.1 Pre-departure Procedures When the system starts: · Signed Zero Point reference point (16) is being loaded, · IMU sensor (3), barometric sensor (4) and magnetometer (5) are checked, • Threshold values ​​are being activated. 6.3.2 Takeoff and Climb After launch, the SiaNav kernel (2) starts generating relative navigation outputs. The system can create the following data structure: · horizontal distance parameter (19), 19 · DIR direction angle (18), · Qen angle parameter (20), · Qay altitude parameter (21). If necessary, the forecasting unit (13) can be activated. 6.3.3 Navigation and Mission Procedure During the mission, the system continues to generate relative navigation data. The drift reset unit (10) can perform recalibration operations at specific time intervals. The system can also exchange external data via the GCS communication unit (7). 6.3.4 Turning and Landing During the descent phase, the system can store relative navigation records in the memory unit (8). Calibration records and operational data history can then be used for analysis. 6.4 Operational Use Scenarios The system can be used in the following operational scenarios: urban areas, • Coastal and maritime operations, mountainous regions • Industrial and RF intensive areas, • Logistics and mapping tasks. In urban areas, the system can overcome GPS signal interference by using a relative navigation structure. In coastal operations, the Qay altitude parameter (21) can be calculated using the mean sea level reference. Altitude accuracy can be improved in mountainous regions by using barometric sensor (4) data. In areas with high RF intensity, the system can operate in RF silent mode. 6.5 Interoperability The system allows for conversions between symbolic data structures and classical coordinate systems. The DIR direction angle (18) and the Qen angle parameter (20) can be converted to directional coordinates. The horizontal distance parameter ib (19) can be related to the relative ΔN and ΔE components. The Qay altitude parameter (21) can be evaluated according to the mean sea level reference. System outputs can be generated as ASCII-based data sequences. 6.6 Operational Safety Controls Security restrictions can be applied within the system. This structure may include the following operations: • geographical limitation • altitude limitation, • safety threshold control, • GPS reliability analysis. The spoofing detection unit (11) can consider the relevant GPS data invalid if the defined error thresholds are exceeded for a long time. 6.7 Adjustable Parameters The following parameters can be configured and implemented within the system: • estimated time horizon, • calibration intervals, • drift reset threshold values, • sensor sampling rates, • Multiple Zero Point transition policies. Changing these parameters does not alter the fundamental operating principle of the system. 6.8 Example Data Structures Relative navigation data generated within the system can be represented in the following example formats: Example 1: ib 9 - shet 5 4 0 | Re Lab Qen ifed 7 - khemet 5 | Qay nefer - khemen 5 Example 2: RI ib … - … | Re Lab Qen … | Qay … ; t=60 These examples are for illustrative purposes only, and the system is not limited to these data formats. 6.9 Operational Limitations New Zero Point reference points (16) can be defined at long operating distances. Since the Qay altitude parameter (21) can be affected by atmospheric conditions, local pressure calibration can be applied. 21 The accuracy of the DIR direction angle (18) and the Qen angle parameter (20) may vary depending on the actual north alignment. 6.10 Data Recording and Privacy Structure Operational records created within the system can be stored with timestamps. The ternary record structure can contain the following data groups: • Data prior to correction, • Reference data, • Data after correction. Zero Point reference point (16) data and verification keys secure data storage methods It can be protected by using [method / technology]. 6.11 Conclusion SiaNav system offers a relative navigation architecture capable of operating in different mission environments with multiple operation modes. The system incorporates relative navigation, bounded drift reset, prediction mechanism, symbolic data structure, and By using security control mechanisms together, operational continuity can be ensured. Thanks to this structure, the system can be used as an alternative or supplementary navigation system to classic GPS / GNSS-based navigation solutions. 7. GPS AND SIANAV COMPARISON 7.1 General Description Traditional GPS-based navigation systems represent location data using absolute coordinates. This structure is typically based on latitude, longitude, and altitude data. The SiaNav system uses a relative navigation architecture. Within the system, position data is converted into a symbolic data structure based on the Zero Point reference point (16). The basic relative navigation parameters used within the SiaNav system are as follows: · horizontal distance parameter (19), · DIR direction angle (18), · Qen angle parameter (20), · Qay altitude parameter (21). Thanks to this structure, location data is presented as relative symbolic data instead of direct absolute coordinates. It can be represented in this way. 7.2 Comparative Structure GPS systems generate location information directly using the WGS-84 coordinate system. 22 The SiaNav system is: · Zero Point reference point (16), · local NEU coordinate framework (24), · relative position vector (17), · SiaLex encoding unit (12) It creates an alternative data structure using this method. Within the system, absolute coordinates can be converted into the following relative parameters: · horizontal distance parameter (19), · DIR direction angle (18), · Qen angle parameter (20), · Qay altitude parameter (21). These conversion processes are carried out by the SiaNav kernel (2). 7.3 Operational Comparison GPS systems operate by relying on global satellite signals. The SiaNav system can perform relative navigation operations using the IMU sensor (3), barometric sensor (4), magnetometer (5) and local reference structures. The GPS / GNSS module (6) can only be used for recalibration purposes in certain situations, not as the main navigation source within the system. The SiaNav system can provide the following operational advantages: • relative navigation continuity, • alternative symbolic data structure, • GPS reliability check, • spoofing detection, • RF silent operation support, • Multiple Zero Point support. 7.4 Security and Data Structure The symbolic data structure used within the SiaNav system creates a data representation that differs from standard coordinate formats. The SiaLex encoding unit (12) produces data sequences different from directly readable coordinate structures by converting relative navigation data into symbolic form. This structure allows navigation data to be represented in an alternative data format. 7.5 Result 23 Unlike classic GPS systems based on absolute coordinates, the SiaNav system uses a relative and symbolic navigation architecture. The system uses an alternative navigation structure with Zero Point reference point (16), relative position vector (17), local NEU coordinate frame (24) and SiaLex coding unit (12). It constitutes. Thanks to this structure, the system can be implemented as an auxiliary or alternative navigation solution to GPS-based navigation systems. 8. LEARNING AND ADAPTATION MECHANISM 8.1 General Description The SiaNav system can include an optional learning and adaptation mechanism to reduce cumulative sensor deviations that may occur during relative navigation operations. The structure in question works together with the drift reset unit (10), error calculation block (35), threshold control block (36), drift evaluation block (37) and memory unit (8). The learning mechanism analyzes error behaviors without altering the system's fundamental symbolic data structure. It enables analysis. 8.2 Basic Working Structure The learning mechanism is based on the analysis of past calibration records. The following data groups can be evaluated together within the system: SiaNav data before the first correction, 2. Reference data, SiaNav data after the 3rd revision. These records can be stored in the memory unit (8) and used to evaluate sensor behavior over time. 8.3 Data Record Structure Learning records created within the system may include the following data fields: • timestamp • Relative navigation data prior to correction, • Reference navigation data, • Navigation data after correction. These data structures enable the analysis of long-term error behavior. 8.4 Adaptation and Correction Mechanism 24 Correction factors can be generated by analyzing error behavior within the system. The learning mechanism may include the following processes: • Evaluation of past error data, • Creating a sliding record window, • Identifying error trends, • Updating correction coefficients, • Reducing sensor deviations. Calibration records that fall below the specified error thresholds can be used for learning processes. Learning operations can be temporarily suspended when an unreliable data source is detected by the spoofing detection unit (11). The learning processes used within the system can be implemented using rule-based or linear correction methods. 8.5 Safety and Stability Measures Security restrictions may be implemented within the system to maintain the stability of the learning mechanism. This structure may include the following operations: • Temporary suspension of learning processes, • Limiting the correction coefficients, • Adaptation reset during reference changes, • Learning control during RF silent mode. These mechanisms make it possible to keep the error correction behavior within the system under control. 8.6 Operational Advantages The adaptation mechanism can provide the following advantages: • Improving long-term navigation accuracy, • Reducing sensor-induced deviations, • Low processing power requirements, • Applicability in embedded systems, • Optional activation structure. The system can improve relative navigation accuracy without requiring complex filtering algorithms. 8.7 Result The learning and adaptation mechanism used within the SiaNav system enables the analysis of sensor-related error behaviors and the improvement of relative navigation accuracy over time. This structure allows the system to maintain operational resilience while preserving its symbolic navigation architecture. It can increase. 9. SECURITY AND DATA INTEGRITY 9.1 General Description SiaNav is not just a system that performs relative navigation calculations; it also incorporates data integrity, reference validation, error traceability, and security checks. It offers an integrated navigation architecture that includes various mechanisms. The safety structures used within the system work together with the Zero Point reference point (16), memory unit (8), GCS communication unit (7), deception detection unit (11), drift reset unit (10) and safe mode block (41). Thanks to this structure, the system is protected against unauthorized data modifications, unreliable reference sources, and It can provide protection against GPS spoofing attempts. 9.2 Zero Point Protection Structure Zero Point reference point (16) data can be stored in memory unit (8) together with the validation data. The following security measures can be applied to reference data within the system: • digital signature verification, • Verification key check, • hash-based integrity check, • Unauthorized data denial process. Zero Point reference point (16) verification when received via GCS communication unit (7) It can be processed. Reference data that fails the validation process may be rejected by the system. 9.3 Triple Recording Mechanism Every recalibration operation performed within the system can be logged. Calibration records may include the following data groups: SiaNav data before the first correction, 2. Reference data, SiaNav data after the 3rd revision. These records can be stored in memory unit (8) in a timestamped form. 26 Thanks to its three-tiered record structure, operational decisions within the system can be traced. 9.4 GPS Spoofing Detection The deception detection unit (11) continuously monitors the data provided by the GPS / GNSS module (6). It is able to analyze. If the error thresholds defined within the system are exceeded for an extended period, the relevant GPS data may be marked as an unreliable data source. In this case, the system can perform the following operations: • temporarily suspending the learning mechanism, · Switching to safe mode block (41), · continue navigation with only SiaNav kernel (2), · Sending alerts via GCS notification block (42). This structure enables the system to maintain operational continuity against attacks such as spoofing and jamming. 9.5 Operational Security Policies Operational security policies can be implemented within the system. This structure may include the following operations: • Geofencing-based area delimitation, • altitude limitation, • Reference access control, • Security threshold management. When the Zero Point reference point (16) is changed, the system can implement an access control procedure to reduce sudden position jumps. The system can also prevent exceeding the specified maximum operational altitude values. 9.6 Integration with External Security Protocols The system is compatible with external authentication and encryption protocols. The following security methods can be applied during data transfer via the GCS communication unit (7): • Key-based authentication, • encrypted data transfer, • Secure communication protocols. While these security operations are being implemented, the symbolic data structure created by the SiaLex encoding unit (12) can be preserved. 27 9.7 Result In addition to its relative navigation architecture, the SiaNav system includes integrated security and data integrity structures. Within the system: · Zero Point reference point (16) verification, • Triple recording mechanism, • GPS reliability check, • spoofing detection, • safe operating modes, • Operational security policies They can be implemented together. Thanks to this structure, the system can provide relative navigation continuity in operational environments requiring high reliability. CONCLUSION SiaNav system offers a relative and symbolic navigation architecture developed for unmanned aerial vehicles. The system consists of: Zero Point reference point (16), relative position vector (17), local NEU coordinate frame (24), drift reset unit (10), deception detection unit (11) and SiaLex encoding unit (12) It creates an alternative navigation structure using 35. Within the system: • relative navigation calculations, 28 • Bounded drift reset mechanism, • Symbolic data encoding structure • prediction mechanism, • learning and adaptation processes, • security and verification structures They can be implemented together. The SiaNav system can provide navigation continuity in operational environments where the GPS / GNSS module (6) is unreliable or has limited usability. Thanks to this structure, the system can assist in civil, industrial, research and operational applications. It can be implemented as an alternative navigation solution.

Claims

REQUESTS 1. The invention is a relative and symbolic navigation system for unmanned aerial vehicles; - a Zero Point reference point that is defined according to known geodetic coordinates reference unit, - a computing unit that creates a local coordinate frame based on the Zero Point reference point in question, - A navigation unit that calculates the relative position data of the target as horizontal distance, directional angle information and altitude information, - an encoding unit that converts calculated relative navigation data into a symbolic data structure, - a forecasting unit that generates estimated relative position data within a specific time horizon It includes.

2. According to Claim 1, it is a relative and symbolic navigation system, and its characteristic feature is that the navigation in question... The unit generates its relative position data as follows: ib (horizontal distance parameter), DIR (directional angle information), Qen (in-quadrant angle parameter), and Qay (altitude parameter).

3. It is a relative and symbolic navigation system according to claim 1 or 2, characterized by the fact that the coding unit in question presents numerical navigation data using both modern numerals and symbolic characters. It is about converting it into the hybrid data structure in which it is used.

4. According to Claim 3, it is a relative and symbolic navigation system, characterized by containing symbolic data expressions derived from ancient Egyptian transliterations of the symbolic characters in question.

5. It is a relative and symbolic navigation system according to any of the previous requirements, and its feature is that the Zero Point reference point is verified with a digital signature, verification key, or hash-based integrity check.

6. It is a relative and symbolic navigation system compared to any of the previous ones; its characteristic feature is: The local coordinate frame is constructed using a north-east-up coordinate structure aligned to the true north reference. 29 7. Compared to any of the previous methods, it is a relative and symbolic navigation system characterized by the classification of directional angle information using four different directional quadrants.

8. According to Claim 7, it is a relative and symbolic navigation system characterized by the inclusion of the Meh Lab, Re Lab, Re Ime, and Meh Ime directional definitions for the relevant directional quadrants.

9. Compared to any of the previous methods, it is a relative and symbolic navigation system whose characteristic is that the directional angle information is calculated as an intra-quartile angle parameter between 0° and 90°.

10. It is a relative and symbolic navigation system, according to any of the previous methods, and its characteristic feature is that it uses the velocity, direction, and relative position data of the predictor unit to generate estimated relative navigation data within a specific time horizon.

11. Unlike any of the previous methods, this is a relative and symbolic navigation system characterized by the recalibration of relative navigation data at specific time intervals by comparing it with external reference data.

12. According to Claim 11, it is a relative and symbolic navigation system, characterized by its recalibration capability. This involves using GPS / GNSS data or ground control station reference data in operations.

13. According to Claim 11, it is a relative and symbolic navigation system whose characteristic is that if the specified error thresholds are exceeded, the relevant reference data is marked as an unreliable data source.

14. It is a relative and symbolic navigation system based on any of the previous methods; its characteristic feature is the creation of a record structure consisting of pre-correction data, reference data, and post-correction data for each recalibration process.

15. Unlike any of the previous options, it is a relative and symbolic navigation system, and its feature is that it includes a learning unit that analyzes sensor-related error behavior using past calibration records.

16. According to claim 15, it is a relative and symbolic navigation system whose characteristic is that it updates the correction coefficients according to the error tendencies of the learning unit.

17. Compared to any of the previous methods, it is a relative and symbolic navigation system whose feature is to detect interference by analyzing deviations between GPS / GNSS data and relative navigation data.

18. According to Claim 17, it is a relative and symbolic navigation system; its characteristic feature is that after deception detection This involves temporarily suspending the learning processes and allowing the system to continue operating solely with relative navigation data.

19. Compared to any of the previous options, it is a relative and symbolic navigation system characterized by its inclusion of geofencing, altitude limitation, and controlled reference passage policies.

20. It is a navigation method based on any of the previous requests, and its characteristic is; - defining at least one Zero Point reference point, - Calculation of the relative position data of the target within the local coordinate frame, - the relative position data in question includes horizontal distance, directional angle information and altitude parameters. transformation, - encoding the generated navigation data using a symbolic data structure, - optional generation of estimated relative navigation data, - storage or transmission of generated data It includes the steps. 31

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