A Kalman innovation direction finding correction method assisted by heading angle

Abstract

The present invention proposes a heading angle-assisted Kalman innovation direction finding correction method. First, the heading angle of the search and rescue aircraft is obtained by an onboard inertial unit of the search and rescue aircraft, and the onboard search and rescue radio station measures the side angle of the search and rescue target relative to the search and rescue aircraft, and the heading angle information of the search and rescue aircraft is combined with the radio direction finding, so as to calculate the azimuth of the search and rescue target; the azimuth of the search and rescue target is used as a state value, a Kalman filter recursive formula is established, and Kalman innovation information is constructed to perform a first correction on the direction finding result, and then the heading angle is used to perform a second correction, so as to effectively correct the wild value of the radio direction finding, thereby greatly improving the accuracy and reliability of radio direction finding for aviation search and rescue.

Description

Technical Field

[0001] The invention relates to the technical field of aviation search and rescue radio direction finding, and in particular to a heading angle-assisted Kalman innovation direction finding correction method. Background Art

[0002] The aviation search and rescue system consists of two parts: an airborne search and rescue radio station and a distress and life-saving terminal. It uses radio to measure the distance, direction, and communication position of people and equipment in distress. It has the characteristics of rapid response, wide search and rescue range, and short search and rescue time. It is widely used in searching for people in plane crashes on the battlefield and in search and rescue activities in natural disasters such as shipwrecks and earthquakes.

[0003] Limited by the installation environment of search and rescue aircraft, existing airborne search and rescue radio stations use a dual-antenna interferometer method to find the direction of distress and rescue terminals. The angle between the two is determined by measuring the phase difference between the electrical signals generated by the distress and rescue terminal direction-finding signal on the two receiving antennas of the airborne search and rescue radio station. However, in long-distance direction-finding of nearly 100 kilometers, the incident elevation angle of the radio search and rescue signal is low, and the antenna phase center is easily disturbed by interference signals such as multipath, resulting in distortion of the search and rescue signal and low signal-to-noise ratio of the received signal. In addition, the installation environment of the airborne antenna is complex, and multipath and obstruction are serious, which greatly affects the antenna pattern. The accuracy, availability and reliability of radio direction-finding deteriorate sharply, and a large number of wild values ​​are generated in the direction-finding results, resulting in increased direction-finding errors or even complete failure, which greatly reduces the effectiveness of aviation search and rescue.

[0004] Existing methods usually improve the accuracy of radio direction finding by increasing the number of antenna elements and the spacing between antennas, but they still cannot effectively solve the problem of direction finding in complex airborne obstruction environments. In addition, this method will not only increase the cost of the aviation search and rescue system, but also put forward more stringent requirements on the aviation search and rescue airborne platform. Inertial navigation does not rely on any external information, nor is it affected by external electromagnetic interference. It is often used in combination with satellite navigation, vision, laser and other navigation sources to provide highly reliable position, speed and attitude information for the carrier. A few scholars have combined inertial navigation with ultra-wideband, Bluetooth and other technologies to improve the accuracy of indoor ranging and direction finding, but existing methods are only suitable for short-range scenarios and require multiple anchor nodes for assistance, and cannot be applied to wide-area aviation search and rescue scenarios. Summary of the invention

[0005] In order to overcome the shortcomings of the prior art and improve the direction finding accuracy and reliability in wide-area aerial search and rescue scenarios, the present invention provides a heading angle-assisted Kalman innovation direction finding correction method, which combines the heading angle information of the search and rescue aircraft with the radio direction finding, performs a first correction on the direction finding result by constructing Kalman innovation information, and then uses the heading angle for a second correction, thereby effectively correcting the wild value of the radio direction finding, greatly improving the accuracy and reliability of aerial search and rescue radio direction finding.

[0006] The technical solution of the present invention is:

[0007] The method for correcting heading angle-assisted Kalman innovation direction finding comprises the following steps:

[0008] Step 1: Obtain the heading angle of the rescue aircraft at time k through the onboard inertial unit of the rescue aircraft The onboard search and rescue radio measures the side angle of the search and rescue target relative to the search and rescue aircraft. , combined with the heading angle of the search and rescue aircraft The side angle of the rescue aircraft relative to the rescue target , calculate the azimuth of the search and rescue target at time k ;

[0009] Step 2: Use the Kalman innovation detection method to correct the measurement results of the airborne search and rescue radio:

[0010] Step 2.1: Establish the Kalman filter equation:

[0011]

[0012] In the Kalman filter equation, express The state value at the moment, The state value at the moment is The azimuth of the search and rescue target at all times, express The time measurement value, The time measurement value is Measurement results of airborne search and rescue radio at all times; express The state value at the moment, The state value at the moment is The azimuth of the search and rescue target at all times, is the state transfer matrix, is the observation matrix, is the process noise vector, is the measurement noise vector;

[0013] use Status update value at the moment and the covariance update matrix , and the process noise vector The covariance matrix of and the measurement noise vector The covariance matrix of , according to the Kalman filter recursive formula:

[0014]

[0015] Get the recursive prediction value , recursive covariance prediction matrix , Kalman filter gain at time , The covariance update matrix at time and Status update value at the moment ; Where I is the identity matrix, and the superscript "T" indicates the transpose of the matrix;

[0016] Step 2.2: According to the Kalman filter equation, we get The innovation covariance matrix at time and the square mean of the covariance matrix of the new information m moments before moment k ;use The innovation covariance matrix at time and the square mean of the covariance matrix of the new information m moments before moment k , according to the formula

[0017]

[0018] right Time measurement value Make corrections, including express Time measurement correction value; is the regulating factor, is the gradual disappearance coefficient; the adjustment factor for

[0019]

[0020] Fading coefficient for

[0021]

[0022] Where b is the vanishing constant;

[0023] Step 3: Calculate the change in the measurement correction value at adjacent moments:

[0024]

[0025] And according to the criteria:

[0026]

[0027] Determine whether further corrections are needed. is the set threshold value;

[0028] Step 4: When When the value obtained in step 2 is Time measurement correction value Based on the formula

[0029]

[0030] get Measurement correction value of inertial navigation heading angle assistance at all times ,in and is the coefficient, and ;

[0031] The final result Time measurement correction result for:

[0032]

[0033] and The final expression of the time-corrected direction finding result is:

[0034]

[0035] Will Direction finding results corrected at all times As the state update value at time k, it is brought into the Kalman filter recursive formula in step 2 to participate in Prediction of the moment.

[0036] Furthermore, in step 2.2, The innovation covariance matrix at time According to the formula

[0037]

[0038] Calculated.

[0039] Furthermore, in step 2.2, The square mean of the innovation covariance matrix m moments before the moment According to the formula

[0040]

[0041] Calculated, where express Time measurement value.

[0042] Furthermore, the state transfer matrix And the observation matrix Take the value [ 1 , 1 ] T .

[0043] Furthermore, the airborne search and rescue radio adopts a dual-antenna interferometer system.

[0044] Furthermore, the range of the vanishing constant b is [0.90, 0.99].

[0045] Furthermore, .

[0046] Beneficial effects:

[0047] The present invention introduces the heading angle of the airborne inertial navigation system into the detection and correction of aerial search and rescue direction finding, makes a first correction to the direction finding result by constructing Kalman innovation information, and then makes a second correction using the heading angle, which can effectively correct the wild value of radio direction finding, greatly improving the accuracy and reliability of aerial search and rescue radio direction finding.

[0048] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0050] Figure 1 The diagram is a schematic diagram of the relationship between the azimuth angle, the heading angle and the side angle of the present invention, wherein the azimuth angle is the horizontal angle between the north direction line of the search and rescue aircraft and the azimuth line of the search and rescue target in a clockwise direction. The heading angle is the angle between the longitudinal axis of the search and rescue aircraft and the north direction, expressed as The side angle is the angle between the heading line of the search and rescue aircraft and the azimuth line of the search and rescue target, expressed as express.

[0051] Figure 2 is a schematic diagram of the relationship between the change in side angle and the change in heading angle of the present invention, wherein the heading angle at time k is , the side angle is , from time k to time k+1, the changes of heading angle and side angle are and .

[0052] Figure 3 The present invention is a flow chart of Kalman innovation field value correction based on heading angle assistance. DETAILED DESCRIPTION

[0053] Embodiments of the present invention are described in detail below. The embodiments are exemplary and intended to be used to explain the present invention, but should not be construed as limiting the present invention.

[0054] The heading angle-assisted Kalman innovation direction finding correction method in this embodiment includes the following steps:

[0055] Step 1: If Figure 1 As shown in the figure, a global coordinate system is established based on the true north direction, and the heading angle of the search and rescue aircraft at time k is obtained by the airborne inertial unit. The onboard search and rescue radio measures the side angle of the search and rescue target relative to the search and rescue aircraft. , combined with the heading angle of the search and rescue aircraft The side angle of the rescue aircraft relative to the rescue target , calculate the azimuth of the search and rescue target at time k , subscript k represents a timestamp; in this embodiment, the airborne search and rescue radio station adopts a dual-antenna interferometer system.

[0056] Step 2: Use the Kalman innovation detection method to correct the measurement results of the airborne search and rescue radio station, which specifically includes the following steps:

[0057] Step 2.1: Establish the Kalman filter equation:

[0058]

[0059] In the Kalman filter equation, we use express The state value at the moment, i.e. The azimuth of the search and rescue target at all times, express The measured value at that moment, Measurement results of airborne search and rescue radio at all times; express The state value at the moment, i.e. The azimuth of the search and rescue target at all times, is the state transfer matrix, is the observation matrix. In this embodiment, , Take the value [ 1 , 1 ] T ; is the process noise vector, is the measurement noise vector;

[0060] And use Status update value at the moment and the covariance update matrix , and the process noise vector The covariance matrix of and the measurement noise vector The covariance matrix of , according to the Kalman filter recursive formula:

[0061]

[0062] Get the recursive prediction value , recursive covariance prediction matrix , Kalman filter gain at time , The covariance update matrix at time and Status update value at the moment ; Where I is the identity matrix, and the superscript "T" indicates the transpose of the matrix;

[0063] Step 2.2: Exploitation The innovation covariance matrix at time and the square mean of the covariance matrix of the new information m moments before moment k , according to the formula

[0064]

[0065] right Time measurement value Make corrections, including express The measurement correction value at the moment When The measurement is valid at all times and no correction is required. The original measurement value remains unchanged at the moment; When the uncertainty of the measured value is considered to be large, the measured value needs to be corrected. The correction principle satisfies the adjustment factor and the extinction coefficient constraints.

[0066] The innovation covariance matrix at time According to the formula

[0067]

[0068] Calculated.

[0069] The square mean of the innovation covariance matrix m moments before the moment According to the formula

[0070]

[0071] Calculated, where express Time measurement value.

[0072] The regulatory factor for Matrix and The ratio of the trace of the matrix is ​​used to measure the gap between the two. The larger the gap, The smaller the accuracy of the actual measurement value at a moment, the smaller the weight, and the corresponding weight of the predicted measurement value is larger:

[0073] .

[0074] In addition, considering the influence of the data at the near moment, different weights are assigned to the m data. The consideration principle of the weight is The closer the time is, the greater the weight is assigned, and the method of gradually fading exponent is used to achieve this:

[0075]

[0076] Wherein, b is a vanishing constant, and its value range in this embodiment is [0.90, 0.99], is the corresponding extinction coefficient. According to the recursive relationship, the extinction coefficient can be obtained for:

[0077] .

[0078] Step 3: Use the heading angle information to further constrain the corrected direction finding value and set the threshold As the detection threshold, it is used to further determine whether the direction finding value is an outlier.

[0079] Since most search and rescue aircraft are helicopters, their flight speed is limited and they are usually far away from the target, such as Figure 2 As shown, therefore, from Time has come At time , the change in the side angle of the search and rescue target relative to the search and rescue aircraft The change in heading angle with the search and rescue aircraft Almost equal, this relationship can still hold true within a few seconds, that is:

[0080]

[0081] Therefore, calculate the change in the measurement correction value at adjacent moments:

[0082]

[0083] And according to the criteria:

[0084]

[0085] Determine whether further correction is needed. The threshold value Select according to the requirements of the direction finding index. If the value is too large, it is easy to misjudge the normal measurement value as an outlier, and if the value is too small, it is easy to miss the outlier.

[0086] Step 4: When , it indicates that the direction finding value needs to be further corrected. Time measurement correction value Based on the formula

[0087]

[0088] Adding the heading angle change correction value can further correct the direction finding value, giving this correction value a higher credibility, and obtain Measurement correction value of inertial navigation heading angle assistance at all times , and is the coefficient, where In this embodiment, .

[0089] The final result Time measurement correction result for:

[0090]

[0091] and The final expression of the time-corrected direction finding result is:

[0092]

[0093] Will Direction finding results corrected at all times As the state update value at time k, it is brought into the Kalman filter recursive formula to participate in Prediction of the moment.

[0094] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention without departing from the principles and intent of the present invention.

Claims

1. A heading angle-assisted Kalman innovation direction finding correction method, characterized in that: The following steps are involved: Step 1: Obtain the heading angle of the rescue aircraft at time k through the onboard inertial unit of the rescue aircraft The onboard search and rescue radio measures the side angle of the search and rescue target relative to the search and rescue aircraft. , combined with the heading angle of the search and rescue aircraft The side angle of the rescue aircraft relative to the rescue target , calculate the azimuth of the search and rescue target at time k ; Step 2: Use the Kalman innovation detection method to correct the measurement results of the airborne search and rescue radio: Step 2.1: Establish the Kalman filter equation: In the Kalman filter equation, express The state value at the moment, The state value at the moment is The azimuth of the search and rescue target at all times, express The time measurement value, The time measurement value is Measurement results of airborne search and rescue radio at all times; express The state value at the moment, The state value at the moment is The azimuth of the search and rescue target at all times, is the state transfer matrix, is the observation matrix, is the process noise vector, is the measurement noise vector; use Status update value at the moment and the covariance update matrix , and the process noise vector The covariance matrix of and the measurement noise vector The covariance matrix of , according to the Kalman filter recursive formula: Get the recursive prediction value , recursive covariance prediction matrix , Kalman filter gain at time , The covariance update matrix at time and Status update value at the moment ; Where I is the identity matrix, and the superscript "T" indicates the transpose of the matrix; Step 2.2: According to the Kalman filter equation, we get The innovation covariance matrix at time and the square mean of the covariance matrix of the new information m moments before moment k ; use The innovation covariance matrix at time and the square mean of the covariance matrix of the new information m moments before moment k , according to the formula right Time measurement value Make corrections, including express Time measurement correction value; is the regulating factor, is the gradual disappearance coefficient; the adjustment factor for Fading coefficient for Where b is the vanishing constant; Step 3: Calculate the change in the measurement correction value at adjacent moments: And according to the criteria: Determine whether further corrections are needed. is the set threshold value; Step 4: When When the value obtained in step 2 is Time measurement correction value Based on the formula get Measurement correction value of inertial navigation heading angle assistance at all times ,in and is the coefficient, and ; The final result Time measurement correction result for: and The final expression of the time-corrected direction finding result is: Will Direction finding results corrected at all times As the state update value at time k, it is brought into the Kalman filter recursive formula in step 2 to participate in Prediction of the moment.

2. The heading angle-assisted Kalman innovation direction finding correction method according to claim 1, characterized in that: In step 2.2, The innovation covariance matrix at time According to the formula Calculated.

3. The heading angle-assisted Kalman innovation direction finding correction method according to claim 1, characterized in that: In step 2.2, The square mean of the innovation covariance matrix m moments before the moment According to the formula Calculated, where express Time measurement value.

4. The heading angle-assisted Kalman innovation direction finding correction method according to claim 1, characterized in that: State transition matrix And the observation matrix Take the value .

5. The heading angle-assisted Kalman innovation direction finding correction method according to claim 1, characterized in that: The airborne search and rescue radio uses a dual-antenna interferometer system.

6. The heading angle-assisted Kalman innovation direction finding correction method according to claim 1, characterized in that: The range of the vanishing constant b is [0.90, 0.99].

7. The heading angle-assisted Kalman innovation direction finding correction method according to claim 1, characterized in that: 。

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