Method and system for improving target positioning precision of unmanned aerial vehicle by adopting integrated time delay compensation

Abstract

The invention relates to a method and system for improving the target positioning precision of an unmanned aerial vehicle by adopting integrated time delay compensation. The method utilizes one or more unmanned aerial vehicles (UAVs) for target localization. The method comprises the following steps: converting unmanned aerial vehicle navigation data and sensor data into a local East Northeast Ad (ENU) coordinate system; constructing a geometric intersection positioning model, wherein the model introduces a time delay deviation parameter representing a synchronization error between an unmanned aerial vehicle navigation system and a photoelectric pod laser load thereof as an unknown variable; and an IGG-III robust estimation algorithm is adopted, and under the constraint of physical elevation, a generated nonlinear equation set is solved, so that high-precision and high-robustness target position estimation is obtained. The method may also include a pre-filtering step to reject data with a large miss distance error. According to the technical scheme, complex hardware synchronization equipment is not needed, and the target positioning precision can be provided under the condition that the data abnormal value exists.

Description

Technical Field

[0001] This invention generally relates to the field of remote sensing and navigation technology. More specifically, this invention relates to methods and systems for determining the geographic coordinates of stationary or quasi-stationary targets on the ground using data acquired from one or more unmanned aerial vehicles (UAVs). The technical fields involved in this invention include geodetic positioning, sensor fusion, robust statistical estimation, and signal processing, and are particularly applicable to UAV-based target detection, positioning, and geographic information acquisition. Background Technology

[0002] In long-range target localization, unmanned aerial vehicles (UAVs) have become a key platform for acquiring target location information. Accurately determining the geographical location of ground targets is one of their core tasks. However, existing technologies still face many challenges in achieving high-precision and high-reliability target localization.

[0003] Traditional UAV target localization methods typically rely on direct georeferencing, which uses the UAV's own position information (usually provided by Global Navigation Satellite System / Inertial Navigation System, GNSS / INS) and the pointing vectors of onboard sensors (such as electro-optical / infrared pods) to calculate the target's position. The accuracy of these methods is directly limited by the positioning accuracy of the UAV's GNSS / INS and the pointing accuracy of the sensor gimbal. The error chain is long, and even small errors in any link can be amplified, leading to poor final positioning results.

[0004] To improve accuracy, the industry typically employs two methods. One is to use higher-specification inertial navigation systems (INS), such as fiber optic IMUs, to enhance attitude measurement accuracy, thereby improving long-range positioning accuracy. The other is a method based on repeated positioning and averaging using multi-point observations. This method involves a single UAV flying around the target or multiple UAVs cooperating to observe the target from different geometric positions. Theoretically, redundant observations can mitigate the impact of random errors and improve the reliability of the positioning solution. However, higher-specification INS systems significantly increase system costs and cannot solve the problem of positioning errors amplifying significantly with distance due to attitude errors. Furthermore, repeated positioning by multiple UAVs still relies heavily on the quality of the input observation data, resulting in limited performance improvement.

[0005] Furthermore, in dynamic sensor fusion systems, time synchronization is one of the core bottlenecks affecting final accuracy. It is generally accepted in the art that the time system of an unmanned aerial vehicle (UAV) platform is typically based on the time provided by its onboard satellite navigation system (GNSS). Nevertheless, a small but not negligible time delay is still introduced between the UAV's main flight controller / GNSS unit and the sensor payload (such as an electro-optical pod) due to data processing, internal bus communication, and software triggering. For highly dynamic platforms like UAVs, the accuracy of sensor fusion is often not limited by the inherent noise of the sensors themselves, but rather by the quality of time synchronization between different data streams. Studies have shown that millisecond-level time synchronization errors can lead to significant positioning deviations. Existing technologies typically rely on complex hardware synchronization schemes to address this issue, such as implementing dedicated timestamp circuits on field-programmable gate arrays (FPGAs), using pulse-of-seconds (1PPS) signals, or specialized hardware triggering mechanisms. These schemes undoubtedly increase the system's size, weight, power consumption, and cost (SWaP), which is not ideal for UAV platforms with extremely stringent SWaP constraints. Therefore, effectively compensating for time synchronization errors without increasing hardware complexity is a key challenge for improving UAV positioning accuracy.

[0006] Furthermore, during target localization, the UAV's own maneuvering (such as turning and changing speed), external airflow disturbances, and significant changes in the field of view can cause instantaneous deviations in the onboard electro-optical pod's target tracking. When the sensor's line of sight fails to precisely align with the target, the emitted laser beam may illuminate the background or clutter near the target, resulting in outliers in the laser ranging information that significantly deviate from the true value—i.e., gross errors. If these gross errors are not addressed, they will directly contaminate the localization process. Traditional estimation algorithms, such as the classic least squares method, are highly sensitive to these outliers; a single erroneous measurement can severely distort the final localization result. Research shows that in complex environments, the IGG-III scheme outperforms other robust schemes (such as the Huber function) and can significantly improve localization accuracy.

[0007] In summary, existing technologies have significant shortcomings in the field of UAV target localization: they require high time synchronization, are sensitive to outliers, and often rely on expensive and complex hardware to solve these problems. Therefore, the industry urgently needs a new method that can solve these problems at the algorithmic level, achieving high-precision target localization at a lower cost and with greater robustness. Summary of the Invention

[0008] The present invention aims to overcome the above-mentioned defects of the prior art and provide a UAV target positioning method and system. The method has high accuracy, robustness to data anomalies, and can uniquely compensate for time synchronization errors between system components without relying on dedicated synchronization hardware.

[0009] The technical problem to be solved by this invention is achieved through a multi-stage processing flow. This flow first transforms the UAV state data to a local coordinate system, followed by two stages of data filtering. The core of this invention lies in constructing a classic geometric intersection positioning model and introducing the time delay deviation between the UAV navigation system and the sensor payload as an unknown variable to be solved into the positioning equations. Finally, the IGG-III robust estimation algorithm is used, and the nonlinear equations are solved under physical elevation constraints.

[0010] Compared with the prior art, the present invention has the following significant advantages: (1) Improve positioning accuracy: By simultaneously solving for the target position and time delay deviation in the positioning model, this invention fundamentally eliminates a systematic error source that has long plagued traditional methods, thereby significantly improving the accuracy of the final positioning result.

[0011] (2) Enhance system robustness: This invention adopts a two-level data filtering strategy that combines "off-target amount" gross error elimination and IGG-III robust estimation, and adds physical-level elevation constraints, so that the positioning solution process has a high tolerance for contaminated sensor data, ensuring the reliability of the results.

[0012] (3) Reduce system complexity and cost: By compensating for latency deviations at the algorithm level, this invention avoids the need for expensive and complex hardware synchronization schemes, effectively reducing the size, weight, power consumption and total cost (SWaP-C) of the system, making it easier to deploy and expand on various UAV platforms.

[0013] (4) Improve operational flexibility: This method is applicable to various target positioning scenarios, including single UAVs flying around the target (circling) or multiple UAVs coordinating observation from different directions (multi-aircraft coordination), and has good adaptability and flexibility. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of a system architecture according to an embodiment of the present invention; Figure 2 This is a detailed flowchart of a high-precision target positioning method according to an embodiment of the present invention. Detailed Implementation

[0015] The technical solution of the present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. It should be understood that these descriptions are for illustrative purposes only and are not intended to limit the invention in any way.

[0016] Figure 1 This is a schematic diagram of a system architecture according to an embodiment of the present invention. The diagram depicts one or more unmanned aerial vehicles (UAVs), each equipped with a GNSS receiver, an inertial measurement unit (IMU), and sensor payloads (such as an electro-optical pod with a laser rangefinder), observing a ground target.

[0017] Figure 2 This is a detailed flowchart of a high-precision target localization method according to an embodiment of the present invention. The flowchart visually illustrates the complete processing steps from data acquisition to outputting the final target coordinates.

[0018] 1.1 System Architecture and Operating Environment The system described in this invention includes at least one drone. In a typical embodiment, each drone is equipped with the following core components: (1) Global Navigation Satellite System (GNSS) Receiver: Used to acquire the three-dimensional position and velocity information of the UAV in a global reference coordinate system (such as WGS84) and to provide a high-precision time reference for the entire system.

[0019] (2) Inertial Measurement Unit (IMU): It is used to provide attitude information (roll, pitch, yaw) of the UAV as well as high-frequency motion acceleration and angular velocity data. After being fused with GNSS data, it can provide smooth navigation status with a high update rate.

[0020] (3) Sensor payload: Typically a rotatable photoelectric / infrared gimbal pod, which integrates: a) Laser rangefinder: Used to directly measure the straight-line distance from the UAV to the target it is pointing at.

[0021] b) Imaging sensor (such as a visible light or infrared camera): used to capture images of the target and measure the target's "off-target amount", that is, the pixel offset of the target in the center of the sensor's field of view.

[0022] This method is applicable to scenarios where the UAV can observe the target from multiple different geometric perspectives. This can be achieved in two ways: (1) Flyaround positioning: A single UAV flies around the target in a circular or elliptical trajectory, thereby acquiring observation data from different azimuth angles at different times.

[0023] (2) Multi-drone cooperative positioning: Multiple drones are deployed in different locations around the target to cooperate in observing the target.

[0024] To accurately capture the platform's high dynamic characteristics, all relevant sensor data, including the UAV's position, speed, laser range, and miss distance, are collected and recorded at a high sampling rate of no less than 10 Hz.

[0025] 1.2 Data Coordinate Transformation and Reference System Definition Step 1: Establish a local reference benchmark. First, collect all raw location data reported by the UAV throughout the observation period. This data is typically in WGS84 geodetic coordinates (latitude, longitude, and elevation). Calculate the arithmetic mean of all these location points and define this average location point as the local reference benchmark. .

[0026] Step 2: Coordinate system transformation to ENU system. Transform the position and velocity vectors of all UAVs from the WGS84 coordinate system to a coordinate system based on a reference point. The coordinate system is a local tangent plane coordinate system with the origin at the East-North-Up (ENU) coordinate system. In this coordinate system, the X-axis points due east, the Y-axis points due north, and the Z-axis is perpendicular to the Earth's surface and points upward.

[0027] 1.3 Data Synchronization and Pre-filtering Step 3: Multi-Platform Time Alignment. In scenarios involving multiple drones, it is essential to align the data streams from different platforms in terms of time. This invention employs a "second-level alignment" strategy, which divides the data from all platforms into a common time interval measured in seconds based on the data's timestamp. This coarse alignment method ensures that the observation data participating in the same calculation were collected at similar time points.

[0028] Step 4: Data aggregation and statistical analysis. Within each second-level time interval... Inside, from the same drone Statistical calculations are performed on the data within the specified interval to smooth high-frequency noise and extract representative features of that interval. Total observation time (in seconds). This refers to the number of drones.

[0029] (1) Calculate the UAV position coordinates and velocity components The mean.

[0030] (2) Calculate the mean value of the laser range measurement. .

[0031] (3) Calculate the miss distance in the sensor image coordinate system mean in direction and root mean square error ,in Representing the coordinates of the photograph in the image coordinate system direction and direction.

[0032] Step 5: Gross Error Removal Based on Miss Rate. Before proceeding with the localization calculation, a data pre-filter is performed to remove obviously erroneous observations. This step sets a miss rate threshold. Data blocks that are at the second level and meet any of the following conditions will be discarded: (1) The average miss distance is greater than the threshold, i.e. This condition aims to eliminate data where the average sensor reading is significantly deviated from the target.

[0033] (2) The root mean square error of the miss distance is greater than the threshold, i.e. This condition aims to eliminate data from sensors that exhibit highly unstable or drastic pointing.

[0034] The miss distance directly reflects the sensor tracking system's ability to keep the target centered in the field of view; its concept is similar to the "lateral miss distance" in UAV landing guidance. A larger average miss distance indicates the presence of systematic pointing errors, while a larger root mean square error of the miss distance indicates random jitter or unstable tracking. Both of these situations indicate that the laser rangefinder value associated with the miss distance... The reliability of this measurement is questionable because it may be measuring clutter near the target or the measurement itself may be too noisy due to poor tracking lock. Therefore, through this filtering step, the algorithm can remove low-quality ranging data before geometric intersection calculation, preventing it from contaminating subsequent solution processes.

[0035] 1.4 Geometric positioning model with time delay compensation Step 6: Construct a geometric positioning model with time delay compensation. A geometric positioning model is used, employing a system of nonlinear equations to accurately simulate the geometric relationship between the UAV's state, laser measurements, and the unknown target's position. The formulas are as follows:

[0036] in, This represents the three-dimensional coordinates of the target in the ENU coordinate system. Indicates drone The position constant time delay deviation represents the systematic time offset between the timestamp of the UAV navigation data (position / velocity) and the timestamp of the sensor payload measurement (laser ranging).

[0037] The formula introduces a time bias on the left side. The correction item simulates the actual position of the UAV at the moment the laser rangefinder measures the target. Using the UAV's position and speed, through first-order motion correction, the reported position is "backtracked" or "pushed forward" to the actual moment the laser rangefinder occurred.

[0038] It should be noted that this first-order model has sufficient accuracy when the UAV platform is performing low-acceleration or uniform motion. For high-maneuverability scenarios (e.g., turning flight with angular velocity greater than a predetermined threshold), to further improve target positioning accuracy, a model including an acceleration term can be used. Second-order motion compensation model:

[0039] Among them, acceleration data It can be obtained from the drone's inertial measurement unit (IMU).

[0040] 1.5 Robust Solution of Constrained Nonlinearity Each valid data point after pre-filtering Each will provide an equation as described above. Through multiple observations from a single UAV at different locations, or through coordinated observations from multiple UAVs, a nonlinear system of equations containing multiple equations can be formed. The unknowns in this system of equations include the target's three-dimensional coordinates. And the unknown latency deviation for each drone (or multiple drones). .

[0041] This problem is essentially a nonlinear least squares problem with inequality constraints. Its goal is to solve for the parameter vector. :

[0042] This minimizes the sum of squared residuals for all observation equations while satisfying physical constraints.

[0043] Because it employs a pseudorange positioning principle similar to satellite navigation, its positioning accuracy in the elevation direction is significantly weaker than in the horizontal direction, and it may produce "false" target positioning results in the U direction in the ENU coordinate system. Therefore, to improve the stability and physical accuracy of the solution, additional constraints need to be considered.

[0044] The physical meaning of this formula is that the target altitude is lower than the average flight altitude of the drone.

[0045] Step 7: Calculation of IGG-III robust estimation with added elevation constraints. For this positioning model, IGG-III robust estimation is used for iterative solution. The solution process is as follows: (1) Parameter initialization. Set the iteration counter. and convergence threshold Given initialization parameters To ensure stable convergence of the algorithm, the parameter vector needs to be adjusted. Perform proper initialization. The recommended initialization strategy is to use the average value of the target localization results (WGS84 coordinate system) from a traditional single-unit unmanned pod, and then convert it to a coordinate system. ENU coordinate system with origin As an initial estimate, and assuming Together they form the initial parameter vector Finally, ensure that the initial elevation meets the constraints. .

[0046] (2) Iterative solution.

[0047] a) Calculate the residuals: For each observation pair That is, each drone exist Calculate the residuals from the observed data at time:

[0048] b) Calculate the Jacobian matrix. Based on the current... And velocity, construct the Jacobian matrix:

[0049] c) Adjusting the weights. First, normalize the residuals using the absolute deviation of the median, i.e.

[0050] Then adjust the ranging weight matrix according to the IGG-III weight function. :

[0051] In the matrix In Chinese, the formula for calculating diagonal elements is as follows:

[0052] For threshold parameters, usually selected , .

[0053] d) Solve the normal equations. Solve the system of linear equations using the weighting function:

[0054] e) Update parameters.

[0055]

[0056] f) Elevation constraint handling. If Keep the original value; if Then forced ( This is to ensure that the constraints are met.

[0057] g) Check for convergence. If or If the iteration stops, the result is output. .

[0058] Step 8: Target Location Output. After obtaining the robust estimation results from IGG-III, the target location is obtained. The high-precision estimate in the ENU coordinate system is then converted back to WGS84 geodetic coordinates via the coordinate axes.

Claims

1. A method for improving the target positioning accuracy of unmanned aerial vehicles (UAVs) using integrated time delay compensation, characterized by: Includes the following steps: 1) Receive multiple sets of measurement datasets from at least one UAV, each set of measurement datasets including UAV position data, UAV speed data, and distance data to the target measured by sensor payload within a predetermined time interval; 2) Based on the UAV position data in the multiple sets of measurement datasets, calculate an average UAV position, and establish a local northeast ENU coordinate system with this average UAV position as the origin; 3) Transform the UAV position data and UAV velocity data from the global coordinate system to the local ENU coordinate system; 4) Construct a nonlinear equation system based on a geometric model, where the position of the target to be determined in the local ENU coordinate system, and a time delay deviation parameter representing the time offset between the UAV's position and velocity data and the distance data are all treated as unknown variables. 5) A robust estimation algorithm is used to solve the nonlinear equations to determine the location of the target.

2. The method for improving the target positioning accuracy of an unmanned aerial vehicle (UAV) using integrated time delay compensation as described in claim 1, characterized in that: The robust estimation algorithm is the IGGIII scheme.

3. The method for improving the target positioning accuracy of an unmanned aerial vehicle (UAV) using integrated time delay compensation as described in claim 1, characterized in that: Before constructing the nonlinear equation system, a pre-filtering step is also included, which includes: 1) Receive the miss distance data associated with the distance data; 2) Set the miss threshold; 3) Remove measurement datasets whose off-target data exceeds the off-target threshold.

4. A method for improving the target positioning accuracy of an unmanned aerial vehicle (UAV) using integrated time delay compensation as described in claim 3, characterized in that: The miss distance data includes the mean miss distance and the root mean square error of the miss distance. When the sum of the squares of the mean miss distance is greater than the square of the threshold, or when the root mean square error of the miss distance is greater than the threshold, the corresponding measurement dataset is discarded.

5. A method for improving the target positioning accuracy of an unmanned aerial vehicle (UAV) using integrated time delay compensation as described in claim 1, characterized in that: When solving the nonlinear equations, an elevation constraint is added, namely, the elevation value of the target in the local ENU coordinate system is less than zero.

6. A method for improving the target positioning accuracy of an unmanned aerial vehicle (UAV) using integrated time delay compensation as described in claim 1, characterized in that: The at least one drone includes multiple drones, and a unique time delay deviation parameter is solved for each drone.

7. A method for improving the target positioning accuracy of an unmanned aerial vehicle (UAV) using integrated time delay compensation as described in claim 1, characterized in that: The measurement dataset is aggregated within a second-level time interval, and the UAV position data, speed data, and distance data are the average values ​​calculated within the interval. Furthermore, the time interval and aggregation method are improved by adding or reducing the time interval and using the median method for aggregation.

8. A method for improving the target positioning accuracy of an unmanned aerial vehicle (UAV) using integrated time delay compensation according to claim 1, characterized in that: The position of the UAV in the geometric model needs to be compensated. Position compensation adopts a first-order motion model that includes velocity or a second-order motion model that includes velocity and acceleration terms. Specifically, the first-order motion model is as follows: Specifically, the second-order motion model is as follows: in, For the target location to be determined, For the first The first drone Average drone location over a time interval For the first The first drone The average speed of the drone over a time interval For the first The first drone The average acceleration of the drone over a time interval For the first The first drone Average distance measurements over a time interval For the first The time delay deviation parameters of the drone, among which , Total observation time (in seconds). , This refers to the number of drones.

9. A system for improving the target positioning accuracy of unmanned aerial vehicles (UAVs) by employing integrated time delay compensation, characterized in that: The system includes: 1) At least one drone, i.e. The UAV is equipped with a navigation unit for acquiring position and speed data, and a sensor payload for measuring the distance to the target; 2) A processing unit configured to execute instructions to implement the method as described in any one of claims 1 to 8.

10. A system for improving the target positioning accuracy of an unmanned aerial vehicle (UAV) using integrated time delay compensation as described in claim 9, characterized in that: The processing unit is configured to use the IGG-III scheme as a robust estimation algorithm to solve the nonlinear equations, taking into account elevation constraints.

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