Adaptive feedback orientation estimation method, device and system

Abstract

The invention discloses a self-adaptive feedback orientation estimation method, device and system, and belongs to the field of navigation.The method comprises the steps that in the orientation estimation process of an aircraft navigation system or a ground simulation navigation system, a self-adaptive error decision feedback mechanism is introduced and used for adjusting the angle allowable range according to an error result; the influence of large fluctuation on a measurement calculation result is reduced, and error correction is carried out on a test result; the stepping size is adjusted according to an error correction result to support rapid convergence to an error range under the condition of large errors; and finally, after the error is reduced, the stepping is slowed down, so that the error is further accurately corrected, and a final accurate result is obtained. According to the invention, the problem of inaccurate azimuth angle calculation caused by noise or other interferences is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of navigation, more particularly, to a self-adaptive feedback azimuth estimation method, device and system. BACKGROUND

[0002] TACAN (tactical air navigation) as a short-range radio navigation system is one of the main equipment of aircraft and navigation. It is divided into ground station and airborne equipment two parts, provides azimuth and slant range information for aviation target, realizes polar coordinate positioning. At present, the information transmission of TACAN system mainly adopts analog body structure, so there are problems of poor anti-noise ability, low positioning accuracy and the like. The existing scheme has the following technical problems: when large jitter occurs, the fitting result of the existing method will be severely impacted, and the azimuth angle calculation accuracy and efficiency need to be further improved. SUMMARY

[0003] The present application aims at overcoming the shortcomings of the prior art, and provides a self-adaptive feedback azimuth estimation method, device and system, which solves the problem of inaccurate azimuth angle calculation caused by noise or other interference.

[0004] The purpose of the present application is achieved by the following scheme: A self-adaptive feedback azimuth estimation method, comprising the following steps: In the azimuth estimation process of the aircraft navigation system or the ground simulation navigation system, an adaptive error decision feedback mechanism is introduced, which is used to adjust the angle allowable range according to the error result, so as to reduce the influence of large fluctuation on the measurement calculation result, and at the same time, the error correction of the test result is carried out; the step size is adjusted according to the error correction result, which is used to support fast convergence to the error range under the condition of large error; finally, after the error is reduced, the step is slowed down to further accurately correct the error, and the final accurate result is obtained.

[0005] Further, the adaptive error decision feedback mechanism is introduced, which is used to adjust the angle allowable range according to the error result, so as to reduce the influence of large fluctuation on the measurement calculation result, and at the same time, the error correction of the test result is carried out; the step size is adjusted according to the error correction result, which is used to support fast convergence to the error range under the condition of large error; finally, after the error is reduced, the step is slowed down to further accurately correct the error, and the final accurate result is obtained. S1, the nth sampling signal of the receiving end is Sn, and the n previous samplings jointly constitute an envelope signal [S1, S2... Sn]; S2, the envelope signal enters the angle calculation module, the signal is divided into subsets according to the sampling time, each subset contains the same number of sampling signals, then a corresponding time-based angle calculation function matrix is constructed, and the angle θ(i) is calculated according to the function matrix; S3, the obtained θ(i) data is sent to a calculation unit, and the following calculation is performed: , wherein, is the output angle value of the calculation module, m is the number of taps, and L represents the number of taps, which is defined by the user; represents the coefficient of each tap, the initial value is defined by the user, and the updating mode is:

[0006] wherein, u(i) is a step factor, which is an array defined by the user from large to small, the initial value is defined by the user, and e(i) represents an error function, which is given by an error calculation unit; is angle information, which is given by a hierarchical screening module; S4, the βi data is sent to the hierarchical screening module, and the following calculation operation is performed:

[0007] wherein, is the output result of the hierarchical screening module, represents , k = 1 ~ i-1, and a decision is made on in a decision calculation unit:

[0008] wherein, is an array defined by the user from large to small, and the initial value can be set by the user; then δ(i) is output, and is sent to the calculation unit of step S3; at the same time, the δ(i) data and the actual sampling signal S(i) are sent to an error calculation unit to calculate the error e(i) between S(i) and the data H(i)×δ(i) fitted at the sampling point; S5, e(i) is fed back to the calculation unit of step S3 and the decision calculation unit of ; wherein, the error e(i) is input to the decision calculation unit, when the error is within a set threshold range, a control signal is sent to the hierarchical screening module, remains unchanged; when the error exceeds the threshold, a control signal is sent to the hierarchical screening module, and is reduced.

[0009] Further, in step S2, the diversity number is defined by the user.

[0010] Further, in step S5, the threshold is defined by the user.

[0011] An adaptive feedback orientation estimation device includes a processor and a memory, the memory storing a computer program that, when loaded by the processor, executes the method described in any of the preceding methods.

[0012] An adaptive feedback orientation estimation system includes the adaptive feedback orientation estimation device as described above.

[0013] The beneficial effects of this invention include: This invention utilizes error threshold control and decision feedback to perform secondary processing on the angle calculation results, eliminating angle calculation results severely affected by noise. Furthermore, by employing feedback control of the error judgment results, it enables rapid adjustment when the error is large, obtaining results within the error range. Once the error decreases, fine adjustment is possible to obtain an envelope curve that more closely matches the reference signal. To demonstrate its effectiveness, the proposed scheme was compared with the least squares method in a TACAN system with the same signal-to-noise ratio. Test results show that the proposed scheme can obtain more accurate azimuth information, thereby further improving system performance.

[0014] This invention solves the problem of inaccurate azimuth calculation caused by noise or other interference. Specifically, it introduces an adaptive error decision feedback method, which automatically adjusts the allowable angle range based on the error result, reducing the impact of large fluctuations on the measurement calculation result, while also performing more refined error correction on the test result. The adaptive error decision feedback method also automatically adjusts the step size based on the error result, supporting rapid convergence to the error range under large error conditions, and slowing down the step size after the error decreases to precisely correct the error and obtain accurate results. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A schematic diagram of the Tacon envelope signal; Figure 2 This is a flowchart of the method according to an embodiment of the present invention; Figure 3 A comparison of the envelope curves of the received signal, the reference signal, and the new algorithm. Detailed Implementation

[0017] All features disclosed in all embodiments of this specification, or steps in all methods or processes implied in the disclosure, may be combined and / or extended or replaced in any way, except for mutually exclusive features and / or steps.

[0018] Tacon envelope signal such as Figure 1 As shown, in order to reduce the impact of noise, jitter, etc., and to quickly obtain accurate azimuth angles, in a preferred embodiment, the present invention particularly relates to improvements in aircraft navigation systems and ground-based simulated navigation systems. Specifically, an improved adaptive feedback azimuth estimation method is proposed, such as... Figure 2 As shown, it includes the following steps: In orientation estimation, an adaptive error decision feedback method is introduced. This method adjusts the allowable angle range based on the error results to reduce the impact of large fluctuations on the measurement calculation results, while simultaneously providing more refined error correction. The step size is adjusted based on the error results to support rapid convergence to the error range under large error conditions, and to slow down the step size as the error decreases, precisely correcting the error and obtaining accurate results. More specifically, this includes the following steps: S1, the nth sampled signal at the receiving end is Sn, and the previous n samples together form the envelope signal [S1,S2...Sn]; S2, the envelope signal enters the angle calculation module. First, the signal is divided into diversity Mi according to the sampling time (where i represents the number of diversity, i=[1,2...k], the number of diversity is defined by the user, and each diversity contains the same number of sampled signals). Then, the corresponding time-based angle calculation function matrix Hi is constructed to calculate θ(i). S3, then the obtained θi data is sent to the calculation module for the following calculations:

[0019] Where L represents the number of taps, which is defined by the user; This represents the coefficient for each tap. The initial value is defined by the user, and the update method is as follows:

[0020] Where u(i) is the step size factor, which is a user-defined array in descending order, with an initial value defined by the user, and e(i) represents the error function, which is given by the error calculation unit. The angle information is provided by the hierarchical filtering module; S4, βi data is sent to the hierarchical filtering module, where the following calculation operations are performed:

[0021] in, express The average value of (k=1~i-1), and:

[0022] in A user-defined array in descending order of size, with initial values ​​that the user can set themselves.

[0023] Subsequently, δ(i) is output and simultaneously fed into the calculation unit; at the same time, the δ(i) data and the actual sampled signal S(i) (where i represents the total number of sampling points corresponding to θ(i)) are jointly fed into the error calculation unit to calculate the error e(i) = H(i) × δ(i) - S(i) between S(i) and the data H(i) × δ(i) fitted to the sampling point, and then feed e(i) back to the calculation unit and the decision calculation unit; S5, input the error e(i) into the decision calculation unit. When the error is within the set threshold range (the threshold is user-defined), send a control signal to the hierarchical screening module. The error remains unchanged; when the error exceeds the threshold, a control signal is sent to the graded screening module, and then... Turn it down.

[0024] It should be noted that the decision calculation unit simultaneously counts the number of times the error exceeds the threshold. If the number exceeds the user's requirement, a control signal is sent to the calculation unit to increase u(i); if the error consistently meets the requirement, a control signal is sent to the calculation unit to decrease u(i), and a control signal is also sent to the hierarchical screening module to... Adjust the setting to achieve more precise error control.

[0025] Angle calculation can be performed in various ways, including but not limited to the least squares method. Any calculation module can use this method, so it will not be elaborated here.

[0026] The following embodiments are provided to verify the technical effects of the solutions in the embodiments of the present invention: To verify the correctness of the azimuth estimation method proposed in the embodiments of the present invention, simulation verification was performed using a simulation platform (such as MATLAB platform), and the parameter settings are shown in Table 1.

[0027] Table 1 TACAN signal parameter setting table

[0028] Based on the parameters in Table 1, calculate matrices θ1 to θk using the received envelope signal; The obtained θ(i) data is then fed into the calculation module for the following calculations:

[0029] L=4, w(m) represents the coefficient of each tap, with an initial value of [1,0,0,0].

[0030] The calculated result β(i) data is sent to the hierarchical filtering module, where the following calculation operations are performed:

[0031] in, express The average value of (k=1~i-1), when i=1, = D(1)=0; Then, a decision is made on D(i):

[0032] in, A user-defined array in descending order of size, here [0.05, 0.1, 0.3, 0.5, 0.8], is initialized to 0.3. When D(i) < 0.3, , and subsequently Output, and simultaneously feed into the computation unit; when D(i) > 0.3, The result is then output and simultaneously fed into the computing unit.

[0033] Subsequently, the δ(i) data and the actual sampled signal S(i) (where i represents the total number of sampling points corresponding to θ(i)) are sent together to the error calculation unit to calculate the error e(i) = H(i) × δ(i) - S(i) between S(i) and the data H(i) × δ(i) fitted to the sampling point. Then, e(i) is fed back to the calculation unit and the decision calculation unit.

[0034] The error e(i) is input into the decision calculation unit. At this time, the gate range is [-0.3~0.3]. When the error is within the set threshold range, a control signal is sent to the hierarchical screening module. The error remains unchanged; when the error exceeds the threshold, a control signal is sent to the graded screening module, and then... Reduce it to 0.1.

[0035] After receiving the errors e(i) and δ(i), the computing unit updates the four taps. , following the expression .

[0036] Where u(i) is the step size factor, which is a user-defined array from largest to smallest [0.5, 0.2, 0.05], and the initial value is selected as u(1) = 0.2; The new data θ(i+1) is then fed into the calculation module for the following calculations:

[0037] The obtained results are then used for cyclical calculations. During this process, the calculation unit counts the number of times the error exceeds a threshold. If it exceeds 10 times (user-defined), a control signal is sent to the calculation unit to adjust u(k) to 0.2. If the error consistently meets the requirements (e.g., more than 50 times), a control signal is sent to the calculation unit to modulate u(k) to 0.05. Simultaneously, a control signal is sent to the grading and filtering module to... Reduce it to 0.1 for more precise error control.

[0038] The azimuth curve calculated based on this is as follows: Figure 3 As shown, for ease of comparison, Figure 3 also presents the azimuth curves obtained using the least squares method, i.e., a comparison of the envelope curves of the received envelope signal, the reference signal, and the new method. Figure 3 As shown. Figure 3 Central reference azimuth ( Figure 3 The blue line represents the 35° setting in Table 1. The azimuth curve calculated using the least squares method ( Figure 3 The black line shows a significant fluctuation compared to the reference azimuth angle, primarily due to increased error in angle calculation caused by noise. To reduce this error, this invention utilizes a novel estimation method to obtain the azimuth curve as shown below. Figure 3 As shown by the red line, its fluctuation amplitude is smaller compared to the least squares curve.

[0039] In other aspects of the invention, an adaptive feedback orientation estimation apparatus is also provided, comprising a processor and a memory, wherein the memory stores a computer program that, when loaded by the processor, executes the method as described in any of the preceding claims.

[0040] In other aspects of the invention, an adaptive feedback orientation estimation system is also provided, including the adaptive feedback orientation estimation device as described above.

[0041] The units described in the embodiments of the present invention can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0042] According to one aspect of the present invention, a computer program product or computer program is provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and executes the computer instructions, causing the computer device to perform the methods provided in the various optional implementations described above.

[0043] In another aspect, embodiments of the present invention also provide a computer-readable medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the methods described in the above embodiments.

Claims

1. An adaptive feedback orientation estimation method, characterized in that, Includes the following steps: In the orientation estimation process of aircraft navigation systems or ground simulation navigation systems, an adaptive error decision feedback mechanism is introduced to adjust the allowable angle range according to the error results, so as to reduce the impact of large fluctuations on the measurement and calculation results, and at the same time correct the test results for errors. The step size is adjusted based on the error correction results to support rapid convergence to the error range under large error conditions; finally, after the error is reduced, the step size is slowed down to further refine the error and obtain the final accurate result.

2. The adaptive feedback orientation estimation method according to claim 1, characterized in that, The aforementioned adaptive error decision feedback mechanism is used to adjust the allowable angle range based on the error results, so as to reduce the impact of large fluctuations on the measurement and calculation results, and at the same time correct the test results for errors. The step size is adjusted based on the error correction results to support rapid convergence to the error range under large error conditions. Finally, after the error decreases, the step size is slowed down to further refine the error correction, specifically including the following sub-steps: S1, the nth sampled signal at the receiving end is Sn, and the previous n samples together form the envelope signal [S1,S2...Sn]; S2, the envelope signal enters the angle calculation module, the signal is divided into diversity according to the sampling time, each diversity contains the same number of sampled signals, and then the corresponding time-based angle calculation function matrix is ​​constructed, and the angle θ(i) is calculated according to the function matrix; S3, the obtained θ(i) data is sent to the calculation unit for the following calculation: ， in, The calculation module outputs angle values, where m is the number of taps and L represents the number of taps, which is defined by the user. This represents the coefficient for each tap. The initial value is defined by the user, and the update method is as follows: Where u(i) is the step size factor, which is a user-defined array from largest to smallest, with an initial value defined by the user, and e(i) represents the error function, which is given by the error calculation unit; The angle information is provided by the hierarchical filtering module; S4, send the βi data into the hierarchical filtering module and perform the following calculation operations: in, Output results for the hierarchical screening module. express The average value, k=1~i-1, and in the decision calculation unit for Execution of the judgment: in, The user-defined array is arranged from largest to smallest, and the initial value can be set by the user. Then, δ(i) is output and sent to the calculation unit in step S3. At the same time, the δ(i) data and the actual sampled signal S(i) are sent to the error calculation unit to calculate the error e(i) = H(i) × δ(i) - S(i) between S(i) and the data H(i) × δ(i) fitted to the corresponding sampling point. S5, feed e(i) back to the calculation unit in step S3 and the... The decision calculation unit; wherein, the error e(i) is input to the decision calculation unit, and when the error is within the set threshold range, a control signal is sent to the hierarchical screening module. The error remains unchanged; when the error exceeds the threshold, a control signal is sent to the graded screening module, and then... Turn it down.

3. The adaptive feedback orientation estimation method according to claim 2, characterized in that, In step S2, the number of diversity elements is defined by the user.

4. The adaptive feedback orientation estimation method according to claim 2, characterized in that, In step S5, the threshold is user-defined.

5. An adaptive feedback orientation estimation device, characterized in that, It includes a processor and a memory, wherein the memory stores a computer program that, when loaded by the processor, executes the method as described in any one of claims 1 to 4.

6. An adaptive feedback orientation estimation system, characterized in that, It includes the adaptive feedback orientation estimation device as described in claim 5.

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