Position information fusion method of multi-source position sensor for guided landing of aircraft

Abstract

The invention belongs to the technical field of aircraft guided landing, and particularly relates to an aircraft guided landing multi-source position sensor position information fusion method, which is used for efficiently and accurately fusing position information measured by various types of position sensors based on a federated Kalman filtering method. The measurement precision of the position information in the guided landing process of the aircraft can be effectively improved, and the smoothness of the measured position information is ensured.

Description

Technical Field

[0001] The present application belongs to the technical field of aircraft guided landing, and specifically relates to a method for fusing position information of multi-source position sensors for aircraft guided landing. Background Art

[0002] Aircraft guided landing technology can realize autonomous landing of the aircraft, which can greatly reduce the operating burden of the pilot.

[0003] When using aircraft guided landing technology to guide an aircraft to perform autonomous landing, it is necessary to use a position sensor to continuously measure position-related information for positioning. However, there is currently a lack of a sensor that can perform accurate position measurement over the entire landing distance of the aircraft.

[0004] Some types of position sensors have strong anti-interference ability, but low accuracy, and are greatly affected by distance. The error is large when the distance is far. Some types of position sensors have high accuracy but are easily interfered with. Various types of position sensors have their own accurate measurement distance range. In this regard, currently, the aircraft guided landing technology is used to guide the aircraft to land autonomously. Most of them are designed to switch between different position sensors within different distance ranges to measure position information. This technical solution has problems such as inappropriate switching timing of position sensors, low accuracy of position information measurement, and uneven data switching.

[0005] This application is proposed in view of the above-mentioned technical defects. Summary of the invention

[0006] The purpose of the present application is to provide a method for fusing position information of multi-source position sensors for guiding aircraft landing, so as to overcome or alleviate at least one aspect of the known technical defects.

[0007] The technical solution of this application is: A method for fusing position information of multi-source position sensors for guiding landing of an aircraft, comprising: Step 1: dividing the position sensor into a reference position sensor and a plurality of sub-position sensors; Step 2: constructing multiple local filters using the position measurement information of the reference position sensor and the multiple sub-position sensors, and constructing a main filter using the position measurement information of the reference position sensor and the position fusion information of each local filter; Step 3: Distribute the global optimal estimation information among the local filters to obtain the position fusion information, state estimation information and process noise covariance matrix of each local filter; Step 4: Update the position fusion information and state estimation information of each local filter according to time; Step 5: Use the main filter to update the position fusion information and state estimation information of each local filter according to time; Step 6: Measure and update the position fusion information and state estimation information of each local filter; Step 7: Use the main filter to perform global optimal estimation on the position fusion information and state estimation information of each local filter to obtain the global optimal estimation information.

[0008] Optionally, in the above-mentioned aircraft guidance landing multi-source position sensor position information fusion method, the allocation principle followed by each local filter in step 3 is: ; in, For the The local filter is State estimation information of round iteration; For the The local filter is The information distribution coefficient matrix of round iteration; The main filter is The state estimation information of the global optimal estimation of the round iteration; For the The local filter is The noise covariance matrix of the iterative process; The main filter is The noise covariance matrix of the iterative process; For the The local filter is Position fusion information of round iterations; The main filter is The position fusion information of the global optimal estimate of round iterations; is the number of local filters, which is equal to the number of sub-position sensors.

[0009] Optionally, in the above-mentioned aircraft guided landing multi-source position sensor position information fusion method, the filtering equation used by each local filter in step 4 is: ; in, For the The local filter The position fusion information is updated according to time in round iterations; For the The local filter The state transfer matrix of round iteration; For the The local filter The state estimation information is updated according to time in each round of iteration.

[0010] Optionally, in the above-mentioned aircraft guidance landing multi-source position sensor position information fusion method, the filtering equation used by the main filter in step 5 is: ; in, For the main filter The local filter The position fusion information is updated according to time in round iterations For the main filter The local filter The state estimation information is updated according to time in each round of iteration.

[0011] Optionally, in the above-mentioned aircraft guided landing multi-source position sensor position information fusion method, the measurement update formula used by each local filter in step 6 is: ; in, For the The local filter The state estimation information of the measurement update is performed in rounds of iterations; For the The local filter The measurement modeling matrix for round iterations; For the The local filter Iterate the measurement to update the position fusion information; For the The local filter The round iteration receives The position measurement information of each sub-position sensor.

[0012] Optionally, in the above-mentioned aircraft guidance landing multi-source position sensor position information fusion method, the fusion equation used by the main filter in step seven is: ; The main filter is The position fusion information of the global optimal estimate of round iterations; The main filter is The state estimation information of the global optimal estimate of the round iteration.

[0013] This application has at least the following beneficial technical effects: A method for fusing position information of multi-source position sensors during aircraft guided landing is provided. Based on the federal Kalman filter method, the position information measured by various types of position sensors is efficiently and accurately fused, which can effectively improve the measurement accuracy of position information during aircraft guided landing and ensure the smoothness of the measured position information. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a schematic diagram of a method for fusing position information of multi-source position sensors for guiding landing of an aircraft provided in an embodiment of the present application; Figure 2 It is a schematic diagram of constructing a federal Kalman sub-filter and a main filter provided in an embodiment of the present application.

[0015] In order to better illustrate the present embodiment, some contents of the drawings may be omitted, enlarged or reduced, which is only used for illustrative purposes and should not be construed as limiting the present application. DETAILED DESCRIPTION

[0016] In order to make the technical solution and advantages of the present application clearer, the technical solution of the present application will be described in further detail in detail and in detail with reference to the accompanying drawings. It can be understood that the specific embodiments described here are only partial embodiments of the present application, which are only used to explain the present application, not to limit the present application. It should be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, and other related parts can refer to the general design.

[0017] In addition, unless otherwise defined, the technical terms or scientific terms used in the description of this application should be the common meanings understood by those skilled in the art in the field to which this application belongs. The term "include" used in the description of this application means that the concepts appearing before the term include the concepts listed after the term and their equivalents, without excluding other related concepts.

[0018] In addition, the words indicating orientation used in the description of this application are only used to indicate relative directions or positional relationships. When the absolute position of the object being described changes, its relative positional relationship may also change accordingly. It should also be noted that, unless otherwise clearly specified and limited, the words "installation", "connection" and other similar words used in the description of this application should be understood in a broad sense. For example, the connection can be a fixed connection or a detachable connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium. Technical personnel in the field can understand its specific meaning in this application according to the specific circumstances.

[0019] During the movement of the aircraft, the position information measured by different types of position sensors will have deviations. Fusing the position information measured by various types of position sensors and adaptively adjusting the weights of the position information measured by various types of position sensors is an effective means to improve the measurement accuracy of the position information during the aircraft's guided landing process and ensure the smoothness of the measured position information.

[0020] The federated Kalman filter method is a typical decentralized filtering method with flexible design, good fault tolerance and high fusion accuracy. In the federated Kalman filter method, the global fusion result of the main filter is used to feedback and reset the state and estimated error matrix of each sub-filter, so that the fusion data result is accurate and the process is fast.

[0021] Based on the above, the embodiment of the present application provides a method for fusing position information of multi-source position sensors for guiding landing of an aircraft, such as Figure 1 As shown, the federal Kalman filter method is applied to efficiently and accurately fuse the position information measured by various types of position sensors, improve the measurement accuracy of the position information during the aircraft guidance landing process, and ensure the smoothness of the measured position information.

[0022] Step 1: Divide the position sensor into a reference position sensor and a plurality of sub-position sensors.

[0023] The reference position sensor is a sensor that can perform relatively accurate position measurement over the entire distance range during the aircraft's guided landing process, that is, a sensor that can perform relatively accurate position measurement for both long and short distances during the aircraft's guided landing process is selected, and its position measurement information is used as a reference.

[0024] Step 2: construct multiple local filters using the position measurement information of the reference position sensor and multiple sub-position sensors, and construct a main filter using the position measurement information of the reference position sensor and the position fusion information of each local filter, such as Figure 2 shown.

[0025] Step 3: Distribute the global optimal estimation information among the local filters to obtain the position fusion information, state estimation information and process noise covariance matrix of each local filter.

[0026] The allocation principle followed by each local filter is:

[0027] ; in, For the The local filter is State estimation information of round iteration; For the The local filter is The information distribution coefficient matrix of round iteration; The main filter is The state estimation information of the global optimal estimation of the round iteration; For the The local filter is The noise covariance matrix of the iterative process; The main filter is The noise covariance matrix of the iterative process; For the The local filter is Position fusion information of round iterations; The main filter is The position fusion information of the global optimal estimate of round iterations; is the number of local filters, which is equal to the number of sub-position sensors.

[0028] Step 4: Update the position fusion information and state estimation information of each local filter according to time.

[0029] The filtering equation used by each local filter is:

[0030] ; in, For the The local filter The position fusion information is updated according to time in round iterations; For the The local filter The state transfer matrix of round iteration; For the The local filter The state estimation information is updated according to time in each round of iteration.

[0031] Step 5: Use the main filter to update the position fusion information and state estimation information of each local filter according to time.

[0032] The filtering equation used by the main filter is:

[0033] ; in, For the main filter The local filter The position fusion information is updated according to time in round iterations For the main filter The local filter The state estimation information is updated according to time in each round of iteration.

[0034] Step 6: Measure and update the position fusion information and state estimation information of each local filter.

[0035] The measurement update formula used by each local filter is:

[0036] ; in, For the The local filter The state estimation information of the measurement update is performed in rounds of iterations; For the The local filter The measurement modeling matrix for round iterations; For the The local filter Iterate the measurement to update the position fusion information; For the The local filter The round iteration receives The position measurement information of each sub-position sensor.

[0037] Step 7: Use the main filter to perform global optimal estimation on the position fusion information and state estimation information of each local filter to obtain the global optimal estimation information.

[0038] The fusion equation used by the main filter is:

[0039] ; The main filter is The position fusion information of the global optimal estimate of round iterations; The main filter is The state estimation information of the global optimal estimate of the round iteration.

[0040] The method for fusing position information of multi-source position sensors for guiding landing of an aircraft disclosed in the above-mentioned embodiment utilizes the characteristics of good fault tolerance and high fusion accuracy of the federated Kalman filter method, uses the main filter to fuse its own state estimation with the input information of the sub-filter, and finally obtains the optimal solution for the state estimation, dynamically allocates the weights of the position measurement information of various position sensors, and can accurately, stably and reliably fuse and output the position measurement information of multiple position sensors to obtain accurate and smooth position information, thereby effectively reducing the adverse effects of fluctuations in inaccurate position information.

[0041] So far, the technical solution of the present application has been described in conjunction with the preferred embodiments shown in the accompanying drawings. Those skilled in the art should understand that the scope of protection of the present application is obviously not limited to these specific embodiments. Without departing from the principles of the present application, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the scope of protection of the present application.

Claims

1. A method for fusing position information of multi-source position sensors for guiding landing of an aircraft, characterized in that: include: Step 1: dividing the position sensor into a reference position sensor and a plurality of sub-position sensors; Step 2: constructing multiple local filters using the position measurement information of the reference position sensor and the multiple sub-position sensors, and constructing a main filter using the position measurement information of the reference position sensor and the position fusion information of each local filter; Step 3: Distribute the global optimal estimation information among the local filters to obtain the position fusion information, state estimation information and process noise covariance matrix of each local filter; Step 4: Update the position fusion information and state estimation information of each local filter according to time; Step 5: Use the main filter to update the position fusion information and state estimation information of each local filter according to time; Step 6: Measure and update the position fusion information and state estimation information of each local filter; Step 7: Use the main filter to perform global optimal estimation on the position fusion information and state estimation information of each local filter to obtain the global optimal estimation information.

2. The method for fusing position information of multi-source position sensors for guiding landing of an aircraft according to claim 1, characterized in that: The allocation principle followed by each local filter in step 3 is: ； in, For the The local filter is State estimation information of round iteration; For the The local filter is The information distribution coefficient matrix of round iteration; The main filter is The state estimation information of the global optimal estimation of the round iteration; For the The local filter is The noise covariance matrix of the iterative process; The main filter is The noise covariance matrix of the iterative process; For the The local filter is Position fusion information of round iterations; The main filter is The position fusion information of the global optimal estimate of round iterations; is the number of local filters, which is equal to the number of sub-position sensors.

3. The method for fusing position information of multi-source position sensors for guiding landing of an aircraft according to claim 2, characterized in that: The filtering equations used by each local filter in step 4 are: ； in, For the The local filter The position fusion information is updated according to time in round iterations; For the The local filter The state transfer matrix of round iteration; For the The local filter The state estimation information is updated according to time in each round of iteration.

4. The method for fusing position information of multi-source position sensors for guiding landing of an aircraft according to claim 3, characterized in that: The filtering equation used by the main filter in step 5 is: ； in, For the main filter The local filter The position fusion information is updated according to time in round iterations For the main filter The local filter The state estimation information is updated according to time in each round of iteration.

5. The method for fusing position information of multi-source position sensors for guiding landing of an aircraft according to claim 4, characterized in that: The measurement update formula used by each local filter in step 6 is: ； in, For the The local filter The state estimation information of the measurement update is performed in rounds of iterations; For the The local filter The measurement modeling matrix for round iterations; For the The local filter Iterate the measurement to update the position fusion information; For the The local filter The round iteration receives The position measurement information of each sub-position sensor.

6. The method for fusing position information of multi-source position sensors for guiding landing of an aircraft according to claim 5, characterized in that: The fusion equation used by the main filter in step 7 is: ； The main filter is The position fusion information of the global optimal estimate of round iterations; The main filter is The state estimation information of the global optimal estimate of the round iteration.

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