Target drone flight path correction method based on multi-source fusion navigation

Abstract

The invention discloses a target aircraft flight trajectory correction method based on multi-source fusion navigation, and relates to the technical field of trajectory correction. According to the method, multi-source navigation information such as AI visual navigation, inertial measurement and barometric altitude can be fully fused, short-time disturbance and long-time deviation are effectively distinguished through conjoint analysis of short-time envelope and trend envelope, and the sensitivity and reliability of trajectory anomaly detection are remarkably improved; by setting a direction consistency screening and grading weight suppression mechanism, misjudgment caused by air pressure fluctuation or instantaneous noise in low-altitude flight can be suppressed, so that the false alarm rate is reduced; the method has the advantages that the method is simple and easy to implement, an envelope difference accumulated evidence and a threshold linkage triggering mechanism are utilized, early warning of trajectory deviation is achieved, the timeliness of abnormal response in a flight task is improved, stable and accurate trajectory correction can be dynamically generated in a changeable flight environment, and robustness and convergence of the correction process are guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of trajectory correction, in particular to a target aircraft flight trajectory correction method based on multi-source fusion navigation. Background Art

[0002] With the widespread use of unmanned aerial vehicles in scenarios such as military training, range testing, and scientific research verification, the demand for precise control and correction of flight trajectories is increasing. During the execution of flight missions, target drones are often affected by factors such as satellite navigation signal obstruction, multipath interference, accumulated errors in the inertial navigation system, and fluctuations in barometric altitude measurements. This often leads to deviations in heading, normal, and altitude, which in turn affect the precise completion of the mission. In recent years, multi-source navigation fusion technology has become an important research direction for flight trajectory correction because it can comprehensively utilize multiple information sources such as visual navigation, inertial measurement, and barometric altitude to achieve highly robust and high-precision navigation solutions. However, in dynamic and complex environments, how to effectively identify and suppress trajectory deviations caused by environmental disturbances, sensor noise, or abnormal maneuvers, and how to achieve precise corrections while ensuring real-time performance, remains a technical bottleneck in this field.

[0003] CN113095504B discloses a target trajectory prediction system and prediction method, which realizes the prediction and correction of the target trajectory by establishing a target maneuvering characteristic model set and combining forward prediction with backward feedback correction mechanism. This method has certain advantages in trajectory modeling and maneuvering intention recognition, and is particularly suitable for targets with obvious maneuvering patterns. However, trajectory correction relies on the calculation of the matching degree between the maneuvering characteristic model and the actual trajectory, as well as the posterior correction of parameters based on the global importance map model, which makes it difficult to respond in time to transient disturbances or atypical maneuvering deviations that occur during flight. In addition, the correction process of this method relies more on global prediction parameter adjustment, and lacks a real-time, localized deviation identification and correction mechanism based on multi-source navigation data, resulting in insufficient detection sensitivity and correction accuracy of trajectory deviations within a short time window.

[0004] CN111857177B discloses a method, apparatus, device, and medium for generating remotely controlled target commands. By combining pilot control commands with the target's dynamics, kinematic characteristics, and the motion characteristics of the simulated object, this method generates corrected equivalent control commands to achieve target flight control. This method emphasizes human-machine integration and matching simulation characteristics, making it suitable for remote control tasks. However, this solution's trajectory correction process relies on manual control and pre-designed control laws. It lacks the ability to dynamically analyze multi-source navigation data during flight and cannot effectively identify and suppress deviations caused by navigation signal disturbances, sensor errors, or sudden environmental changes. Furthermore, this method focuses on corrections at the control command level and lacks refined, multi-stage processing of flight trajectory deviations in different directions (heading, normal, and altitude). It also lacks an automated correction mechanism for real-time envelope analysis and trend monitoring. Summary of the Invention

[0005] In view of the problems existing in the prior art in correcting the flight trajectory of a target aircraft, the present invention is proposed.

[0006] Therefore, the problem to be solved by the present invention is how to achieve high-precision and real-time correction of the flight trajectory.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions: In the first aspect, the present invention provides a target aircraft flight trajectory correction method based on multi-source fusion navigation, which includes: S1: determining the heading direction deviation, normal direction deviation and altitude deviation based on the multi-source navigation data stream, and generating a short-time envelope and a trend envelope respectively within the sliding time window; S2: calculating the envelope difference according to the short-time envelope and the trend envelope, and generating a one-way cumulative evidence, when the one-way cumulative evidence exceeds the set cumulative threshold within a continuous time period and the short-time envelope threshold and the trend envelope threshold are triggered, generating a warning mark; and defining the continuous time period as the current time period; S3: when the warning mark is in a valid state, selecting the time anchor point closest to the current time period from the task segment anchor point set, performing segment integration on the heading parameters and envelope difference from the time anchor point to the current time period, and generating a candidate correction value sequence; S4: using the optimal correction value as a constraint condition in the multi-source navigation data fusion solution process, and outputting the corrected trajectory data and state estimation update value.

[0008] As a preferred solution of the target aircraft flight trajectory correction method based on multi-source fusion navigation described in the present invention, wherein: the S1 includes: a multi-source navigation data stream including AI visual navigation data, inertial measurement data and air pressure altitude data; calculating the heading direction deviation, normal direction deviation and altitude deviation within a sliding time window, and preliminarily forming a heading deviation sequence, a normal deviation sequence and an altitude deviation original sequence; based on the altitude deviation original sequence, a hierarchical weight suppression method is used for dynamic correction, and a dynamic weight factor is assigned to the altitude deviation according to the altitude change rate to suppress the instantaneous peak caused by air pressure fluctuations during low-altitude flight.

[0009] As a preferred solution of the target aircraft flight trajectory correction method based on multi-source fusion navigation described in the present invention, wherein: the S1 also includes: after the weight adjustment, in the sliding time window, the heading direction deviation sequence, the normal direction deviation sequence and the height deviation sequence are respectively screened for direction consistency, and the disturbance points with opposite directions to the adjacent time slices and amplitudes lower than the adjacent median are eliminated to obtain the corresponding short-time candidate sequence; on the short-time candidate sequence, the upper and lower bounds are extracted according to the preset upper and lower quantiles, and the dual constraints of the envelope width lower limit and the upper limit of the adjacent time slice change rate are set to prevent the envelope from being too narrow or instantaneous jump, and the heading short-time envelope, the normal short-time envelope and the height short-time envelope are output; in the same sliding Within the time window, the short-term envelope midline sequence of each deviation direction is taken as the trend motif, and the median sliding smoothing is adopted to obtain the heading trend motif, normal trend motif and altitude trend motif; the short-term envelope midline is the arithmetic mean of the upper and lower bounds of the heading short-term envelope, normal short-term envelope and altitude short-term envelope, forming three midline curves that change with time; the local slope and local variance of each trend motif within the current sliding time window are calculated; the product of the absolute value of the local slope and the slope coefficient and the product of the local variance and the variance coefficient are added to the basic offset to obtain the upper offset; the lower offset adopts the same method but takes the negative direction; the heading trend envelope, normal trend envelope and altitude trend envelope are generated.

[0010] As a preferred solution of the target aircraft flight trajectory correction method based on multi-source fusion navigation described in the present invention, the generation of the one-way cumulative evidence includes: respectively calculating the centerline difference sequence between the heading short-time envelope and the heading trend envelope, the centerline difference sequence between the normal short-time envelope and the normal trend envelope, and the centerline difference sequence between the altitude short-time envelope and the altitude trend envelope, and using each difference sequence as the input for the one-way cumulative evidence calculation; for the envelope difference sequence in each direction, only the positive difference that is consistent with the historical deviation trend direction is retained, and the one-way cumulative evidence sequence is constructed using an exponential decay accumulation method within the sliding time window.

[0011] As a preferred solution of the target aircraft flight trajectory correction method based on multi-source fusion navigation described in the present invention, the setting of the short-time envelope threshold includes: extracting the difference between the maximum and minimum values ​​from the short-time envelope midline sequence of the current sliding time window, and multiplying it by the fluctuation sensitivity coefficient, and the obtained value is the short-time envelope threshold; the fluctuation sensitivity coefficient is preset according to the flight mission type; the setting of the trend envelope threshold includes: performing sign consistency detection on the trend envelope slope in the current sliding time window; in the sign consistency section, superimposing the mean of the trend envelope slope with the variance correction term, and multiplying it with the directional offset limit set by the mission as the trend envelope threshold; the calculation process of the variance correction term includes: normalizing the value of the local variance in the current sliding time window according to the maximum and minimum values ​​in the corresponding window, and multiplying it with the stability weight calculated by the trend envelope directional consistency.

[0012] As a preferred solution of the target aircraft flight trajectory correction method based on multi-source fusion navigation described in the present invention, the generation of the candidate correction amount sequence includes: taking the time anchor point as the starting boundary of the segment integration; within the time range from the time anchor point to the current time period, respectively intercepting the heading parameter sequence and the centerline difference sequence of the corresponding direction; within the time range, performing an integration operation based on the time attenuation weight on the heading parameter sequence, and at the same time performing an integration operation after trend direction consistency screening on the centerline difference sequence, and weighting the integration results of the two according to the stability weight to form a candidate correction amount sequence; the heading parameter sequence is a sequence intercepted by the heading direction deviation sequence within the time range.

[0013] As a preferred solution of the target aircraft flight trajectory correction method based on multi-source fusion navigation described in the present invention, the selection of the optimal correction amount includes: dividing the candidate correction amount sequence into several continuous sub-segments according to the sliding time window, and each sub-segment covers a set of candidate correction amounts of continuous or partially overlapping time slices; for each sub-segment, the proportion of positive differences between the heading parameter sequence and the corresponding centerline difference sequence after trend direction consistency screening is counted as the track correction consistency index of the sub-segment; the track correction consistency index of each sub-segment is multiplied by the stability weight to obtain a weighted consistency value; the weighted consistency values ​​of all sub-segments are compared, and the candidate correction amount corresponding to the sub-segment with the highest weighted consistency value is selected as the optimal correction amount.

[0014] As a preferred solution of the target aircraft flight trajectory correction method based on multi-source fusion navigation described in the present invention, the output of the corrected trajectory data and state estimation update value includes: taking the optimal correction amount as the constraint input, combining the multi-source navigation data stream of the current time period, performing constraint update in the Kalman-type fusion solution process, generating a corrected trajectory data sequence, and extracting the estimated update values ​​of position, velocity and attitude, and outputting them together with the corrected trajectory data sequence as the output result.

[0015] In a second aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of the target aircraft flight trajectory correction method based on multi-source fusion navigation as described in the first aspect of the present invention are implemented.

[0016] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of the target aircraft flight trajectory correction method based on multi-source fusion navigation as described in the first aspect of the present invention are implemented.

[0017] The beneficial effects of the present invention are as follows: the present invention can fully integrate multi-source navigation information such as AI visual navigation, inertial measurement and pressure altitude, and effectively distinguish short-term disturbances from long-term deviations through the joint analysis of short-term envelopes and trend envelopes, significantly improving the sensitivity and reliability of trajectory anomaly detection; by setting up direction consistency screening and hierarchical weight suppression mechanisms, it can suppress misjudgments caused by pressure fluctuations or transient noise in low-altitude flight, thereby reducing the false alarm rate; and by utilizing envelope difference accumulation evidence and threshold linkage triggering mechanisms, it can achieve early warning of trajectory deviations, improve the timeliness of abnormal responses in flight missions, and dynamically generate stable and accurate trajectory corrections in a changing flight environment, ensuring the robustness and convergence of the correction process. The present invention significantly enhances the navigation reliability and mission execution safety of the target aircraft in complex mission environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 This is a flow chart of the target drone flight trajectory correction method based on multi-source fusion navigation.

[0020] Figure 2 A flow chart for generating one-way cumulative evidence. DETAILED DESCRIPTION

[0021] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0022] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0023] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0024] Figure 1 FIG. 1 is a flow chart of a target drone flight trajectory correction method based on multi-source fusion navigation according to an embodiment of the present invention. Figure 1 As shown, the target aircraft flight trajectory correction method based on multi-source fusion navigation includes: S1: Determine the heading direction deviation, normal direction deviation, and altitude deviation based on multi-source navigation data streams, and generate short-term envelopes and trend envelopes within a sliding time window, respectively.

[0025] Among them, the multi-source navigation data stream includes AI visual navigation data, inertial measurement data and barometric altitude data.

[0026] S1.1: Calculate the heading direction deviation, normal direction deviation, and altitude deviation within the sliding time window to preliminarily form the original sequence of heading deviation, normal deviation, and altitude deviation.

[0027] Specifically, within the sliding time window, for each time slice, the preset heading obtained by the heading solution model based on the AI ​​visual navigation data is differentiated from the actual heading output by the inertial measurement unit hour by hour to obtain the heading direction deviation value; the actual position of the flight trajectory in the normal direction is differentiated from the preset trajectory position hour by hour to obtain the normal direction deviation value; the pressure altitude data is converted into an altitude value, and is differentiated from the preset flight altitude hour by hour to obtain the altitude deviation value. The three-directional deviation values ​​of each time slice are stored in chronological order to preliminarily form the original sequence of heading deviation, normal deviation and altitude deviation, which serve as input for subsequent differential processing and weight correction. The length and time step of the sliding window can be preset according to the type of flight mission to ensure that the data in the window covers small track fluctuations while smoothing occasional disturbances.

[0028] S1.2: Based on the original sequence of altitude deviations, a hierarchical weight suppression method is used for dynamic correction. Dynamic weight factors are assigned to altitude deviations according to the altitude change rate to suppress instantaneous peaks caused by pressure fluctuations during low-altitude flight.

[0029] In specific operations, within the sliding time window, for the altitude deviation value of each time slice, three factors are first calculated: the altitude change rate, the ratio of the current altitude deviation amplitude to the entire window altitude deviation range, and the consistency of the deviation change trend of adjacent time slices. When the altitude change rate is small and the deviation amplitude is in the upper range of the window range and the trend direction is continuous and consistent, the maximum weight is assigned; when the altitude change rate is large or the deviation amplitude is in the lower range of the window range and the trend direction is opposite to that of the adjacent segments, the minimum weight is assigned; for other intermediate cases, the weight is calculated by linear interpolation based on the altitude change rate and amplitude ratio. This weight is multiplied by the corresponding time slice altitude deviation value to obtain the weighted altitude deviation. This corrected sequence replaces the original altitude deviation sequence and is used as the final input for generating the short-term envelope and trend envelope together with the heading deviation sequence and normal deviation sequence.

[0030] S1.3: After weight adjustment, the heading direction deviation sequence, normal direction deviation sequence, and altitude deviation sequence are screened for direction consistency within the sliding time window. The disturbance points with opposite directions to the adjacent time slices and amplitudes lower than the adjacent median are eliminated to obtain the corresponding short-term candidate sequences.

[0031] The screening results form a short-term candidate sequence for heading, normal, and altitude. This process removes instantaneous disturbance points, retains continuous trend changes, and ensures that only effective deviation fluctuations are considered when generating the short-term envelope.

[0032] S1.4: On the short-time candidate sequence, extract the upper and lower bounds according to the preset upper and lower quantiles, and set dual constraints of the envelope width lower limit and the upper limit of the rate of change of adjacent time slices to prevent the envelope from being too narrow or instantaneous jumps. Output the heading short-time envelope, normal short-time envelope, and altitude short-time envelope.

[0033] Specifically, the dual constraints include: Constraint 1: Envelope width lower limit constraint, the logic is that the upper limit minus the lower limit must not be less than the deviation range within the current sliding window multiplied by the width adjustment coefficient. If this is not met, the boundary is extended to the lower limit; Constraint 2: Inter-time slice change rate constraint, the logic is that the increase or decrease of the envelope boundary of the current time slice must not exceed the increase or decrease of the envelope boundary of the previous time slice multiplied by the change rate coefficient. If it exceeds, it is adjusted proportionally. The two constraints are combined to generate the final short-term envelope boundary, and the heading, normal, and altitude directions are processed independently.

[0034] It can be seen that by constraining the lower limit of the width to ensure that the short-term envelope covers the actual trajectory fluctuations, the correction error caused by being too narrow can be prevented; by constraining the rate of change of adjacent time slices to avoid instantaneous jumps, a continuous and smooth envelope can be formed.

[0035] S1.5: Within the same sliding time window, take the short-term envelope midline sequence of each deviation direction as the trend motif and apply median sliding smoothing to obtain the heading trend motif, normal trend motif, and altitude trend motif. The trend motif reflects the medium- and long-term variation trend of the deviation and provides a basis for the calculation of local offsets.

[0036] S1.6: The short-term envelope centerline is the arithmetic average of the upper and lower limits of the heading short-term envelope, the normal short-term envelope, and the altitude short-term envelope, forming three time-varying centerline curves; Calculate the local slope and local variance of each trend motif in each time slice within the current sliding time window; add the product of the absolute value of the local slope and the slope coefficient, and the product of the local variance and the variance coefficient, to the basic offset to obtain the upper offset; the lower offset uses the same method but takes the negative direction; the offset obtained in this way will be dynamically adjusted with the trend change amplitude (variance) and trend change speed (slope). The envelope is close when the change is slow, and the envelope is automatically relaxed when the change is fast to avoid excessive cutting of the real trajectory change; generate the heading trend envelope, normal trend envelope and altitude trend envelope.

[0037] For example, the local slope is calculated as follows: Trend Motif Each time slice in the sliding time window , use front and back fixed windows Linear regression fitting is performed on time slices, where n is a constant and the slope is defined as the fitting straight line coefficient:

[0038] in, Indicates the The deviation direction in the time slice The local slope of For trend motif in time slice Deviation value (heading, normal or altitude); is the window time mean; is the mean of the trend motif within the window.

[0039] The local variance is calculated as follows: Trend motifs within the same sliding time window , the local variance represents the deviation fluctuation amplitude within the window, and the calculation formula is:

[0040] in, For the The deviation direction in the time slice The local variance of ; this variance is used to reflect the fluctuation amplitude of the trend within the sliding window and is used to dynamically adjust the envelope offset.

[0041] Based on the local slope and local variance, the upper offset and Defined as:

[0042]

[0043] in, For the The basic offset in each deviation direction is preset by the task type; is the slope coefficient, which is used to adjust the impact of trend change speed on the envelope; is the variance coefficient, which is used to adjust the impact of trend fluctuation amplitude on the envelope; To express the dynamic expansion amount contributed by the speed of trend change, Indicates the dynamic expansion amount contributed by the trend fluctuation amplitude.

[0044] S2: Calculate the envelope difference based on the short-term envelope and the trend envelope, and generate one-way cumulative evidence. When the one-way cumulative evidence exceeds the set cumulative threshold within a continuous time period and the short-term envelope threshold and the trend envelope threshold are triggered, generate a warning mark; and define the continuous time period as the current time period.

[0045] The generation of one-way cumulative evidence includes the following steps: First, the centerline difference sequence between the heading short-term envelope and the heading trend envelope, the centerline difference sequence between the normal short-term envelope and the normal trend envelope, and the centerline difference sequence between the altitude short-term envelope and the altitude trend envelope are calculated respectively, and each difference sequence is used as the input for the one-way cumulative evidence calculation.

[0046] Among them, the calculation of each midline difference sequence is carried out according to the corresponding relationship of time slices, that is, the short-term envelope midline and the trend envelope midline are subtracted on the same time slice to ensure the logical alignment of the data, and the output result is used as the input for the subsequent one-way cumulative evidence construction.

[0047] To ensure the stability of the difference, for time slices where the trend envelope direction changes abruptly, the sliding median of the adjacent time slices is used to replace the outliers, preventing the difference sequence from being amplified by transient noise. The units of the difference sequence remain consistent with the original deviation sequence, eliminating the need for unit conversion in subsequent cumulative calculations, allowing direct application to one-way cumulative evidence construction.

[0048] Secondly, for the envelope difference sequence in each direction, only the positive differences that are consistent with the direction of the historical deviation trend are retained, and the accumulated evidence is concentrated on abnormal changes in the trend deviating from the positive direction, eliminating the misjudgment of normal fluctuations; and an exponential decay accumulation method is used within the sliding time window to construct a one-way cumulative evidence sequence.

[0049] Specifically, within the sliding time window, the filtered differences are accumulated using an exponential decay method to generate a one-way cumulative evidence sequence. The exponential decay coefficient makes the differences in the recent time slices contribute more to the cumulative evidence, while the influence of the differences in the more distant time slices gradually weakens. The specific calculation formula is as follows:

[0050] in, Indicates time slice Previous The cumulative evidence value in each direction, Indicates the corresponding screening difference, An exponential decay coefficient, ranging from 0 to 1, is set based on the mission type. For example, it can be 0.9 for precision guidance missions (emphasizing recent data), 0.7 for cruise surveillance missions (balancing historical data), and 0.5 for maneuver training missions (rapidly responding to changes). This method ensures that accumulated evidence reflects both current deviations and recent trend changes, achieving dynamic weight fusion.

[0051] In an embodiment of the present invention, the setting of the short-time envelope threshold includes: extracting the difference between the maximum value and the minimum value from the short-time envelope midline sequence of the current sliding time window, and multiplying it by the fluctuation sensitivity coefficient. The obtained value is the short-time envelope threshold; the fluctuation sensitivity coefficient is preset according to the flight mission type.

[0052] In an embodiment of the present invention, the setting of the trend envelope threshold includes: performing sign consistency detection on the trend envelope slope within the current sliding time window; within the sign consistency segment, superimposing the mean of the trend envelope slope with the variance correction term, and multiplying it with the direction offset limit set by the task as the trend envelope threshold.

[0053] In this embodiment of the present invention, the variance correction term is calculated by normalizing the local variance within the current sliding time window to the maximum and minimum values ​​within the corresponding window, and then multiplying the result by the stability weight calculated based on the directional consistency of the trend envelope. This reflects the dynamic adjustment of the threshold to the trend fluctuation amplitude. This ensures that the threshold changes adaptively with trend stability, avoiding false alarms when stability is low, while also enhancing sensitivity when the trend deviates significantly.

[0054] Furthermore, the continuous time period is defined as the current time period, which is used to judge the unidirectional cumulative evidence. For example, assuming that multi-source navigation data is continuously collected during the flight mission, the short-term envelope and trend envelope are continuously calculated according to the sliding time window, and the difference and cumulative evidence analysis are performed. The timeline includes Wait, and when and The envelope difference begins to exceed the threshold at the middle time. Among them, the continuous time period refers to the time interval from the moment the envelope difference first exceeds the threshold to the end of this detection cycle or the triggering of the preset condition (for example → The current time period is an identified time period that needs to be directly referenced by subsequent steps in the flight data processing flow. If only the continuous time period is mentioned, it is just a statistical result and has not been explicitly assigned as the "current" target interval to be processed; and this interval will be used to perform task segment backtracking and correction value generation in the future, so it must have a unique and callable identity identifier, that is, assigning the continuous time period the identity of "current time period", which is equivalent to a variable binding. If it is not defined, there may be multiple continuous time periods in the flight process, and the subsequent correction steps may reference the wrong time period, resulting in miscalculation of the correction value, for example Over threshold → defined as the current time period, Exceeding the threshold again → new current time period (overwriting the old one), while S3 only processes the most recent current time period to ensure that the correction amount is consistent with the real-time status.

[0055] S3: When the warning flag is in a valid state, the time anchor point closest to the current time period is selected from the mission segment anchor point set, and the heading parameters and envelope differences from the time anchor point to the current time period are segmentally integrated to generate a candidate correction value sequence.

[0056] S3.1: Use the time anchor point as the starting boundary of the segment integration, and intercept the heading parameter sequence and the centerline difference sequence of the corresponding direction within the time range from the time anchor point to the current time range. The heading parameter sequence is the intercepted sequence of the heading direction deviation sequence within the time range.

[0057] The mission segment anchor point set consists of key time points pre-defined during the mission planning phase. Each time anchor point corresponds to a location in the flight path with typical constraints, such as a turn point, the start of an ascent, or the end of a descent. Time anchor points serve as a baseline reference throughout the navigation correction process and are selected to ensure complete coverage and data consistency within the correction calculation interval.

[0058] In the present invention, the selection rule of the time anchor point is defined as follows: find a time point closest to the current warning time period in the mission segment anchor point set, and use this time point as the starting boundary of the integral operation. In this way, it can be ensured that the correction calculation interval covers the entire process from the previous key point to the current abnormal point, thereby avoiding the omission of historical deviation information that affects the correction judgment. If the interval between the time anchor point and the current time period is too short, it means that the flight segment is changing too quickly. In this case, the present invention will automatically extend the integral interval to the previous anchor point to ensure that the integral interval has sufficient length to avoid the correction amount being unrepresentative due to the segment being too small.

[0059] It should be noted that the interception operation must ensure complete alignment with the heading parameter sequence, that is, the difference is obtained at the same time index, so as to achieve strict consistency of the two types of sequences in the time dimension.

[0060] S3.2: Within the time range, perform an integration operation on the heading parameter sequence based on the time decay weight. At the same time, perform an integration operation on the centerline difference sequence after trend direction consistency screening. The integration results of the two are weighted and synthesized according to the stability weight to form a candidate correction value sequence.

[0061] Trend direction consistency refers to whether the direction of change in the current midline difference is consistent with the overall deviation trend. If the midline difference for a particular time slice is in the opposite direction of the trend, that data point is reset to zero to avoid misleading effects during the integration process. This filtered midline difference sequence is then integrated using a time-decay weighted integration operation. This approach ensures that the integration result consists only of accumulated deviations that are consistent with the trend direction, thereby enhancing the reliability and directional indicative nature of the integration result in generating corrections.

[0062] S4: The optimal correction amount is used as a constraint in the multi-source navigation data fusion solution process, and the corrected trajectory data and state estimation update value are output.

[0063] The selection of the optimal correction amount includes the following steps: S4.1: Divide the candidate correction value sequence into a number of continuous sub-segments according to the sliding time window, each sub-segment covering candidate correction values ​​of a set of continuous or partially overlapping time slices.

[0064] The basic principle of the partitioning operation is that each subsegment covers candidate corrections for consecutive time slices, and adjacent subsegments partially overlap to ensure that the changing trends of candidate corrections are smoothly reflected in adjacent segments. This segmentation process decomposes large interval problems into multiple subproblems, effectively avoiding misjudgments caused by excessively long overall intervals. This allows each subsegment to serve as an independent evaluation unit for subsequent track correction consistency calculations, thereby improving the precision and stability of optimal correction selection.

[0065] When the optimal correction amount is successfully applied, the current time period identifier, one-way cumulative evidence counter, and warning identifier status are reset.

[0066] S4.2: For each sub-segment, the proportion of positive differences between the heading parameter sequence and the corresponding centerline difference sequence after trend direction consistency screening is calculated as the track correction consistency indicator of the sub-segment.

[0067] The specific operation is as follows: within the time range of the sub-segment, the heading parameter sequence and the corresponding centerline difference sequence are first intercepted, and the trend direction consistency screening method is used to retain only the positive differences whose direction is consistent with the overall trend. Subsequently, the proportion of the number of positive differences in the sub-segment to the total number of differences in the segment is calculated as the track correction consistency index. The numerical range of this index is between 0 and 1. The larger the value, the more concentrated the correction direction in the segment and the higher the correction credibility. This avoids the problem of conventional methods that generally only judge the size of the correction and ignore the consistency of direction, which is prone to erroneous correction judgments in the case of local fluctuations.

[0068] S4.3: Multiply the track correction consistency index and the stability weight of each sub-segment to obtain a weighted consistency value.

[0069] The weighted consistency value reflects two characteristics simultaneously: the concentration of corrections and the stability of the flight trajectory. Using only the consistency metric can lead to falsely high consistency in low-stability situations; using only the stability weight fails to reflect the reliability of the corrections. By combining these two, the present invention ensures that the evaluation value fully describes the effectiveness of the corrections.

[0070] S4.4: Compare the weighted consistency values ​​of all sub-segments and select the candidate correction value corresponding to the sub-segment with the highest weighted consistency value as the optimal correction value.

[0071] After calculating the weighted consistency values ​​for all subsegments, a horizontal comparison is performed. The segment with the highest weighted consistency value is selected as the segment corresponding to the optimal correction value, and the final optimal correction value is selected from the candidate correction values ​​for that segment. The specific operation involves first sorting the weighted consistency values ​​of all subsegments and selecting the segment with the highest value as the target segment. Then, within the candidate correction value sequence for that segment, a weighted average is calculated using a time slice attenuation weighting method, and this average is used as the final optimal correction value. This selected correction value not only reflects the directional consistency characteristics within the local segment, but also avoids interference from a single abnormal time slice through weighted averaging.

[0072] The present invention ensures that the selection process of the optimal correction amount is stable and anti-interference through a comprehensive mechanism of partitioning, weighting and screening, and is suitable for complex and changeable flight environments.

[0073] Furthermore, outputting the corrected trajectory data and the updated state estimate value includes the following steps: S4.5: Use the optimal correction value as the constraint input, combine it with the multi-source navigation data stream of the current time period, perform constraint update in the Kalman-type fusion solution process, generate a corrected trajectory data sequence, and extract the estimated update values ​​of position, velocity, and attitude as the output result together with the corrected trajectory data sequence.

[0074] A more optimal approach is to inject the optimal correction as a constraint using pseudo-observation equations. This, combined with the multi-source navigation data streams collected during the current time period, allows for constraint updates within a Kalman-style fusion solution framework. This approach dynamically modifies the state variables of the navigation solution, producing a corrected trajectory data sequence in the solution and extracting updated estimates of core states such as position, velocity, and attitude.

[0075] This approach can, to a certain extent, reduce the cumulative error introduced by a single data source and improve the overall accuracy of trajectory calculations. After the constraint fusion solution is complete, a corrected trajectory data sequence and updated state estimates are output. The trajectory data includes a corrected three-dimensional position point sequence representing the actual trajectory of the aircraft; the updated state estimates include the optimal estimates of position, velocity, and attitude within the current time period. These outputs not only provide real-time correction information to the navigation system but also provide accurate state support for the upper-level flight control module.

[0076] While outputting the correction results, key auxiliary data must also be recorded for subsequent self-calibration of thresholds and window lengths within the time window. This record includes the selection of time anchor points, the calculation of cumulative evidence values, and the values ​​of the short-term envelope threshold and trend envelope threshold. By storing this historical information, the threshold size and window length can be dynamically adjusted during subsequent sliding time window updates, adapting to changing flight conditions.

[0077] This embodiment also provides a computer device, which is suitable for a target drone flight trajectory correction method based on multi-source fusion navigation, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the target drone flight trajectory correction method based on multi-source fusion navigation proposed in the above embodiment.

[0078] The computer device may be a terminal, comprising a processor, memory, a communication interface, a display, and an input device connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores an operating system and computer programs. The internal memory provides an environment for the operating system and computer programs stored in the non-volatile storage media. The communication interface of the computer device is used to communicate with external terminals via wired or wireless communication. Wireless communication may be achieved via Wi-Fi, a carrier network, NFC (near-field communication), or other technologies. The display of the computer device may be a liquid crystal display or an electronic ink display. The input device may be a touchscreen overlay on the display, buttons, a trackball, or a touchpad on the computer device housing, or an external keyboard, touchpad, or mouse.

[0079] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for correcting the target aircraft flight trajectory based on multi-source fusion navigation proposed in the above embodiment is implemented.

[0080] In summary, the present invention can fully integrate multi-source navigation information such as AI visual navigation, inertial measurement and pressure altitude, and effectively distinguish short-term disturbances from long-term deviations through the joint analysis of short-term envelopes and trend envelopes, significantly improving the sensitivity and reliability of trajectory anomaly detection; by setting up direction consistency screening and hierarchical weight suppression mechanisms, it can suppress misjudgments caused by pressure fluctuations or transient noise in low-altitude flight, thereby reducing the false alarm rate; and by utilizing envelope difference accumulation evidence and threshold linkage triggering mechanisms, it can achieve early warning of trajectory deviations, improve the timeliness of abnormal responses in flight missions, and dynamically generate stable and accurate trajectory corrections in a changing flight environment, ensuring the robustness and convergence of the correction process. The present invention significantly enhances the navigation reliability and mission execution safety of the target aircraft in complex mission environments.

[0081] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A target drone flight trajectory correction method based on multi-source fusion navigation, characterized by: include: S1: Determine the heading direction deviation, normal direction deviation, and altitude deviation based on multi-source navigation data streams, and generate short-term envelopes and trend envelopes within a sliding time window respectively; S2: Calculate the envelope difference based on the short-term envelope and the trend envelope, and generate unidirectional cumulative evidence. When the unidirectional cumulative evidence exceeds the set accumulation threshold within a continuous time period and the short-term envelope threshold and the trend envelope threshold are triggered, generate a warning indicator; and define the continuous time period as the current time period; S3: When the warning flag is in a valid state, the time anchor point closest to the current time period is selected from the mission segment anchor point set, and the heading parameter and envelope difference from the time anchor point to the current time period are segmentally integrated to generate a candidate correction value sequence; S4: The optimal correction amount is used as a constraint in the multi-source navigation data fusion solution process, and the corrected trajectory data and state estimation update value are output.

2. The target drone flight trajectory correction method based on multi-source fusion navigation according to claim 1 is characterized in that: Said S1 comprises: Multi-source navigation data streams include AI visual navigation data, inertial measurement data, and barometric altitude data; Calculate the heading direction deviation, normal direction deviation and height deviation within the sliding time window to preliminarily form the original sequence of heading deviation, normal deviation and height deviation; Based on the original sequence of altitude deviation, a hierarchical weight suppression method is used for dynamic correction. A dynamic weight factor is assigned to the altitude deviation according to the altitude change rate to suppress the instantaneous peak caused by pressure fluctuations during low-altitude flight.

3. The target drone flight trajectory correction method based on multi-source fusion navigation as claimed in claim 2, characterized in that: Said S1 further comprises: After weight adjustment, the heading direction deviation sequence, normal direction deviation sequence, and altitude deviation sequence are screened for direction consistency within the sliding time window. The disturbance points with opposite directions to the adjacent time slices and amplitudes lower than the adjacent median are eliminated to obtain the corresponding short-term candidate sequences. On the short-time candidate sequence, the upper and lower bounds are extracted according to the preset upper and lower quantiles, and dual constraints of the envelope width lower limit and the upper limit of the change rate of adjacent time slices are set to prevent the envelope from being too narrow or instantaneous jump, and the heading short-time envelope, normal short-time envelope and altitude short-time envelope are output; In the same sliding time window, the short-term envelope midline sequence of each deviation direction is taken as the trend motif, and the median sliding smoothing is used to obtain the heading trend motif, normal trend motif and height trend motif. The short-term envelope centerline is the arithmetic average of the upper and lower limits of the heading short-term envelope, the normal short-term envelope, and the altitude short-term envelope, forming three centerline curves that change with time; Calculate the local slope and local variance of each trend motif within the current sliding time window; add the product of the absolute value of the local slope and the slope coefficient, and the product of the local variance and the variance coefficient, to the base offset to obtain the upper offset; the lower offset is calculated using the same method but with the direction negative; Generate heading trend envelope, normal trend envelope and altitude trend envelope.

4. The target drone flight trajectory correction method based on multi-source fusion navigation according to claim 1, characterized in that: The generation of the one-way cumulative evidence includes: Calculate the midline difference sequence between the heading short-term envelope and the heading trend envelope, the midline difference sequence between the normal short-term envelope and the normal trend envelope, and the midline difference sequence between the altitude short-term envelope and the altitude trend envelope respectively, and use each difference sequence as the input for the one-way cumulative evidence calculation; For the envelope difference sequence in each direction, only the positive differences that are consistent with the historical deviation trend direction are retained, and a one-way cumulative evidence sequence is constructed using an exponential decay accumulation method within the sliding time window.

5. The target drone flight trajectory correction method based on multi-source fusion navigation as claimed in claim 4 is characterized in that: The short-term envelope threshold is set by extracting the difference between the maximum and minimum values ​​from the short-term envelope midline sequence of the current sliding time window and multiplying it by the fluctuation sensitivity coefficient. The resulting value is the short-term envelope threshold; the fluctuation sensitivity coefficient is preset according to the flight mission type; The setting of the trend envelope threshold includes: performing a sign consistency test on the trend envelope slope within the current sliding time window; within the sign consistency section, adding a variance correction term to the mean of the trend envelope slope and multiplying it by the direction offset limit set by the task to obtain the trend envelope threshold; The calculation process of the variance correction term includes: normalizing the value of the local variance in the current sliding time window according to the maximum and minimum values ​​in the corresponding window, and multiplying it by the stability weight calculated by the trend envelope direction consistency.

6. The target drone flight trajectory correction method based on multi-source fusion navigation according to claim 1, characterized in that: The generation of the candidate correction value sequence includes: Using the time anchor point as the starting boundary of the segment integration; In the time range from the time anchor point to the current time period, respectively intercept the heading parameter sequence and the centerline difference sequence of the corresponding direction; Within the time range, the heading parameter sequence is integrated based on the time decay weight, and the centerline difference sequence is integrated after trend direction consistency screening, and the integration results of the two are weighted and synthesized according to the stability weight to form a candidate correction value sequence; The heading parameter sequence is an intercepted sequence of the heading direction deviation sequence within the time range.

7. The target drone flight trajectory correction method based on multi-source fusion navigation according to claim 1, characterized in that: The selection of the optimal correction amount includes: Divide the candidate correction value sequence into several continuous sub-segments according to the sliding time window, and each sub-segment covers the candidate correction values ​​of a set of continuous or partially overlapping time slices; For each sub-segment, the proportion of positive differences between the heading parameter sequence and the corresponding centerline difference sequence after trend direction consistency screening is counted as the track correction consistency index of the sub-segment; Multiply the track correction consistency index and stability weight of each sub-segment to obtain the weighted consistency value; The weighted consistency values ​​of all sub-segments are compared, and the candidate correction value corresponding to the sub-segment with the highest weighted consistency value is selected as the optimal correction value.

8. The target drone flight trajectory correction method based on multi-source fusion navigation according to claim 7 is characterized in that: The output corrected trajectory data and state estimation update value include: The optimal correction amount is used as the constraint input, combined with the multi-source navigation data stream of the current time period, and the constraint update is performed in the Kalman-type fusion solution process to generate a corrected trajectory data sequence, and the estimated update values ​​of position, velocity and attitude are extracted and output together with the corrected trajectory data sequence.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the target aircraft flight trajectory correction method based on multi-source fusion navigation according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the target aircraft flight trajectory correction method based on multi-source fusion navigation according to any one of claims 1 to 8 are implemented.

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