CN114021600A - Adaptive smoothing filtering method for target azimuth - Google Patents

Abstract

The invention discloses a self-adaptive smooth filtering method of a target azimuth, which comprises the following steps: let the current target position be X_kIntercepting a target by an electronic system, wherein the measured target direction is X_mThen smooth the filtered target azimuth X_k+1Is obtained by the following formula: x_k+1′＝X_k+ΔT·V_k；E_k＝X_m－X_k+1′；X_k+1＝X_k+1′+k₁·E_k(ii) a Wherein, X_k+1' As the predicted target azimuth, Δ T represents the data sampling period, V_kRepresenting the change rate of the current target azimuth, and the initial value is 0; e_kA difference value representing a current measured target orientation and a predicted target orientation; k is a radical of₁Indicating the target orientation change coefficient. The method of the invention adopts the fuzzy self-tuning smoothing filter to carry out the target azimuth smoothing filtering, and the actual operation shows that the method can effectively resist the interference, improve the azimuth tracking rapidity and reduce the azimuth smoothing filtering overshoot.

Description

Adaptive smoothing filtering method for target azimuth

Technical Field

The invention belongs to the field of target signal processing, and particularly relates to a method for processing a functional target azimuth of an electronic system

Background

Accurate indication of the target location is one of the core functions of many electronic systems, being a necessary function to ensure that the electronic system performs a specific task. The current target position is influenced by a platform, an antenna, a radio frequency channel, a complex signal and the like, and vibration is inevitably generated; with the maneuvering of the platform and the target, the azimuth is often not tracked. In order to realize accurate, rapid and stable indication of the target position, an effective smoothing filtering method needs to be used.

The target azimuth indication of the existing electronic system does not adopt an azimuth filtering method; some simple average value-based methods cannot accurately reflect the real orientation of the target and cannot form a high-reliability target situation; some batch processing methods based on maximum likelihood estimation have the problems of linear error, ill-conditioned condition and small signal-to-noise ratio, include delay processing of all measurements, have large calculated amount and poor real-time performance; some recurrence type algorithms based on Kalman filtering show instability and filtering divergence, and the calculated amount is large.

Disclosure of Invention

The invention aims to solve the problems that the current electronic system target azimuth indication cannot accurately reflect the real azimuth of a target, cannot form a high-reliability target situation and the like.

The purpose of the invention is realized by the following technical scheme:

an adaptive smoothing filtering method for target orientation, the adaptive smoothing filtering method comprising:

let the current target position be X_kIntercepting a target by an electronic system, wherein the measured target direction is X_mThen smooth the filtered target azimuth X_k+1Is obtained by the following formula:

X_k+1′＝X_k+ΔT·V_k (1)

E_k＝X_m－X_k+1′ (2)

X_k+1＝X_k+1′+k₁·E_k (3)

wherein, X_k+1' As the predicted target azimuth, Δ T represents the data sampling period, V_kRepresenting the change rate of the current target azimuth, and the initial value is 0; e_kA difference value representing a current measured target orientation and a predicted target orientation; k is a radical of₁Indicating the target orientation change coefficient.

According to a preferred embodiment, the filtered target azimuth rate of change V is smoothed_k+1Comprises the following steps:

where k2 represents the target orientation rate coefficient of change, V_k+1For X_k+2' calculation.

According to a preferred embodiment, the parameter k₁And k₂Based on the systematic deviation E_kAnd rate of change of deviation

Using fuzzy self-tuning smoothing filter to parameter k₁，k₂And carrying out setting on line.

According to a preferred embodiment, a fuzzy self-tuning smoothing filter is used for the parameter k₁，k₂The online setting process comprises the following steps:

1) input variable fuzzification: determining two input variables and discourse domain of a smoothing filter and selecting a membership function of the smoothing filter according to the performance index and the algorithm complexity of an electronic system;

2) obtaining a self-tuning rule according to experience and a field debugging result, and generating a fuzzy rule table;

3) finally, the obtained membership degree is subjected to defuzzification by adopting a gravity center method to obtain accurate k₁，k₂And outputting the value.

The aforementioned main aspects of the invention and their respective further alternatives can be freely combined to form a plurality of aspects, all of which are aspects that can be adopted and claimed by the present invention. The skilled person in the art can understand that there are many combinations, which are all the technical solutions to be protected by the present invention, according to the prior art and the common general knowledge after understanding the scheme of the present invention, and the technical solutions are not exhaustive herein.

The invention has the beneficial effects that: the method of the invention adopts the fuzzy self-tuning smoothing filter to carry out the target azimuth smoothing filtering, and the actual operation shows that the method can effectively resist the interference, improve the azimuth tracking rapidity and reduce the azimuth smoothing filtering overshoot.

Drawings

FIG. 1 is a schematic diagram of the fuzzy self-tuning smoothing filter of the present invention;

FIG. 2 is the effect of the azimuthal smoothing filter of the present invention;

fig. 3 is a membership function of the coefficients k1, k2 of the fuzzy self-tuning smoothing filter of the present invention.

Detailed Description

The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.

It should be noted that, in order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described below, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments.

Referring to fig. 1, the present invention discloses a method for adaptive smoothing filtering of a target azimuth, wherein the method for adaptive smoothing filtering comprises:

let the current target position be X_kIntercepting a target by an electronic system, wherein the measured target direction is X_mThen smooth the filtered target azimuth X_k+1Is obtained by the following formula:

X_k+1′＝X_k+ΔT·V_k (1)

E_k＝X_m－X_k+1′ (2)

X_k+1＝X_k+1′+k₁·E_k (3)

wherein, X_k+1' As the predicted target azimuth, Δ T represents the data sampling period, V_kRepresenting the change rate of the current target azimuth, and the initial value is 0; e_kA difference value representing a current measured target orientation and a predicted target orientation; k is a radical of₁Indicating the target orientation change coefficient.

Preferably, the filtered target azimuth rate of change V is smoothed_k+1Comprises the following steps:

where k2 represents the target orientation rate coefficient of change, V_k+1For X_k+2' calculation.

k₁And k₂Respectively representing the target orientation change coefficient and the target orientation change rate coefficient, and directly influencing the smooth filtering effect. In engineering application, the method is given according to experience values, in order to accurately generate the method according to experience, the technical scheme of the method utilizes the idea of fuzzy control to realize the online setting of k1 and k2, the dynamic performance of the system can be improved, and the adaptability and the robustness are enhanced.

Preferably, the parameter k₁And k₂Based on the systematic deviation E_kAnd rate of change of deviation

Using fuzzy self-tuning smoothing filter to parameter k₁，k₂And carrying out setting on line. Meet the requirements of Ek and E at different moments_kThe system block diagram of the requirements of/Δ T on the smoothing filter parameters is shown in FIG. 1.

Preferably, a fuzzy self-tuning smoothing filter is adopted to the parameter k₁，k₂The online setting process comprises the following steps:

1) input variable fuzzification: determining two input variables and discourse domain of a smoothing filter and selecting a membership function of the smoothing filter according to the performance index and the algorithm complexity of an electronic system;

2) obtaining a self-tuning rule according to experience and a field debugging result, and generating a fuzzy rule table;

3) finally, the obtained membership degree is subjected to defuzzification by adopting a gravity center method to obtain accurate k₁，k₂And outputting the value.

Examples

The method described in the invention has been implemented in some electronic system development, and the specific method is as follows:

1) the smoothing filter algorithm is implemented according to equations 1, 2, 3 and 4.

2) Smoothing filter coefficient k by fuzzy control₁And k₂And (3) setting on line, wherein the implementation method comprises the following steps:

a) input variable fuzzification: according to the system scene, performance index and algorithm complexity, determining two input variables of fuzzy control, discourse domain and selecting k₁Membership function, as shown in fig. 3.

b) Generating a reasonable fuzzy rule table

And performing fuzzy reasoning according to the input fuzzification result E and EC to generate a fuzzy rule table shown in table 1.

Wherein E and EC are fuzzy intermediate variables, E is error, and EC is error change. If E and EC belong to ZE and MP, respectively, the smoothing filter coefficient k1 is set to EP.

TABLE 1 fuzzy relation of smoothing filter coefficient k1

Note: wherein ZE (zeros, zero), SP (small positive), MP (middle positive), LP (large positive), VP (very large positive), EP (extra large positive)

c) And finally, performing defuzzification on the inferred membership degree by adopting a gravity center method to obtain accurate k 1.

Similarly, the smoothing filter coefficient k2 can be obtained according to the method described above.

3) Intercepting each target of the electronic system, calling a fuzzy self-tuning smoothing filter according to the currently measured target azimuth and the last azimuth of the target in the active library, and outputting the azimuth after smoothing filtering.

The method of the invention adopts the fuzzy self-tuning smoothing filter to carry out the target azimuth smoothing filtering, and the actual operation shows that the method can effectively resist the interference, improve the azimuth tracking rapidity and reduce the azimuth smoothing filtering overshoot. The requirements of the system are met, and the overall effect is shown in figure 2.

The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.

Claims (4)

1. An adaptive smoothing filtering method for target azimuth, the adaptive smoothing filtering method comprising:

let the current target position be X_kIntercepting a target by an electronic system, wherein the measured target direction is X_mThen smooth the filtered target azimuth X_k+1Is obtained by the following formula:

X_k+1′＝X_k+ΔT·V_k (1)

E_k＝X_m－X_k+1′ (2)

X_k+1＝X_k+1′+k₁·E_k (3)

wherein, X_k+1' As the predicted target azimuth, Δ T represents the data sampling period, V_kRepresenting the change rate of the current target azimuth, and the initial value is 0; e_kA difference value representing a current measured target orientation and a predicted target orientation; k is a radical of₁Indicating the target orientation change coefficient.

2. A method for adaptive smoothing filtering of target bearing according to claim 1, characterized in that the smoothed filtered target bearing rate of change V_k+1Comprises the following steps:

wherein k is₂Representing the target azimuth change rate coefficient, said V_k+1For X_k+2' calculation.

3. The method for adaptive smoothing of target bearing of claim 1, wherein the parameter k is₁And k₂Based on the systematic deviation E_kAnd rate of change of deviation

Using fuzzy self-tuning smoothing filter to parameter k₁，k₂And carrying out setting on line.

4. The method of adaptive smoothing of a target azimuth as claimed in claim 1, wherein the parameter k is smoothed using a fuzzy self-tuning smoothing filter₁，k₂The online setting process comprises the following steps:

1) input variable fuzzification: determining two input variables and discourse domain of a smoothing filter and selecting a membership function of the smoothing filter according to the performance index and the algorithm complexity of an electronic system;

2) obtaining a self-tuning rule according to experience and a field debugging result, and generating a fuzzy rule table;

3) finally, the obtained membership degree is subjected to defuzzification by adopting a gravity center method to obtain accurate k₁，k₂And outputting the value.

Images