阅读说明：本技术 一种基于α-β-γ滤波器的全自动着陆雷达引导噪声抑制方法 (Full-automatic landing radar guiding noise suppression method based on alpha-beta-gamma filter ) 是由 张秀林 甄冲 王翼丰 桂敬玲 丁岩 于 2020-12-10 设计创作，主要内容包括：本申请提供了一种基于α-β-γ滤波器的全自动着陆雷达引导噪声抑制方法,包括：1)建立机动目标跟踪模型；2)建立雷达测量噪声模型；3)确定α-β-γ滤波器的等效传递函数,以及含有α-β-γ滤波器的引导律结构；4)判定α-β-γ滤波器稳定性,从而确定α-β-γ滤波器的参数α、β、γ。本申请提供的基于α-β-γ滤波器的全自动着舰雷达引导噪声抑制方法,相比于传统的卡尔曼滤波方法,α-β-γ滤波器在较好地保证目标跟踪及预测精度的同时,能够大幅减小计算量和计算时间,具有良好的实时性,同时滤波器结构相对简单,更有利于工程实现；而与α-β滤波器相比,其对系统高频噪声有更好的抑制效果。(The application provides a full-automatic landing radar guiding noise suppression method based on an alpha-beta-gamma filter, which comprises the following steps: 1) establishing a maneuvering target tracking model; 2) establishing a radar measurement noise model; 3) determining an equivalent transfer function of the alpha-beta-gamma filter and a guide law structure containing the alpha-beta-gamma filter; 4) the stability of the alpha-beta-gamma filter is determined, and the parameters alpha, beta and gamma of the alpha-beta-gamma filter are determined. Compared with the traditional Kalman filtering method, the alpha-beta-gamma filter can greatly reduce the calculated amount and the calculated time while better ensuring the target tracking and predicting precision, has good real-time performance, and is relatively simple in structure and more beneficial to engineering realization; compared with an alpha-beta filter, the filter has better suppression effect on system high-frequency noise.)

1. A full-automatic landing radar guiding noise suppression method based on an alpha-beta-gamma filter is characterized by comprising the following steps:

1) establishing a maneuvering target tracking model;

2) establishing a radar measurement noise model;

3) determining an equivalent transfer function of the alpha-beta-gamma filter and a guide law structure containing the alpha-beta-gamma filter;

4) the stability of the alpha-beta-gamma filter is determined, and the parameters alpha, beta and gamma of the alpha-beta-gamma filter are determined.

2. The method for suppressing the noise of the fully-automatic landing radar guidance based on the alpha-beta-gamma filter as claimed in claim 1, wherein in the step 1, the maneuvering target tracking model mainly comprises a uniform acceleration model discrete expression as follows:

X(k+1)＝FX(k)+W(k)

in the formula, X is a state vector, F is a state transition matrix, W is zero-mean gaussian white noise, and k is time, where the state transition matrix F and the zero-mean gaussian white noise W satisfy:

wherein T is the sampling interval from the time k to the time k +1,is white noise;

equation of state of the uniform acceleration model discrete time system:

the covariance matrix Q is:

in the formula (I), the compound is shown in the specification,is the noise variance.

3. The fully automatic landing radar guided noise suppression method based on α - β - γ filter according to claim 1, characterized in that in step 2, the radar measurement noise model mainly comprises a flash angle noise model based on markov power spectrum type:

where f is the frequency, ω is its corresponding angular velocity, f_mFor a jump in half-power frequency, omega_mFor its corresponding jump half-power angular velocity, W₀Is the power spectral density at zero frequency, and S is formed by white noise unit_m(ω) shaping filter G required for corresponding angular noise_m(s) is:

and simultaneously comprises a Gaussian ranging noise spectrum S (f) as follows:

wherein a is 1.665, so thatWhen s (f) is 0.5.

4. The fully automatic landing radar guided noise suppression method based on α - β - γ filter according to claim 1, wherein in step 3, the equivalent transfer function of α - β - γ filter mainly includes the state estimation equation of the target as:

wherein Z is a measured value;

the prediction equation for the target is:

the equivalent transfer function of the alpha-beta-gamma filter position, velocity, acceleration estimates is:

wherein z is a discrete field symbol, a₁、a₂、a₃、b₀、b₁、b₂Are coefficients, each coefficient satisfying table 1:

[ Table 1]

The corresponding continuous domain expression is as follows:

wherein s is a continuous field symbol.

5. The fully automatic landing radar guided noise suppression method based on α - β - γ filter according to claim 1, characterized in that in step 4, α - β - γ filter stability decision comprises a characteristic equation of the equivalent transfer function in discrete domain by the system:

f(z)＝z³-(3-α-β-γ)z²+(3-2α-β+γ)z-(1-α)

the conditions for obtaining system stability are as follows:

6. the fully automatic landing radar guided noise suppression method based on α - β - γ filter according to claim 1, wherein in step 4, determining the parameters α, β, γ comprises obtaining α, β, γ from a critical damping state selection method in relation to the triple real root R of the system characteristic equation as:

Technical Field

The application belongs to the technical field of flight control, and particularly relates to a full-automatic landing radar guiding noise suppression method based on an alpha-beta-gamma filter.

Background

The full-automatic carrier landing system measures the position of the aircraft by using a carrier-based tracking radar and a digital computer, calculates the required pitching and rolling control instructions, and automatically controls the aircraft to fly according to an expected track so as to help a pilot to safely land under the conditions of low visibility, night, severe sea conditions, air disturbance and the like.

When the aircraft enters the radar interception window, the carrier-based precision tracking radar continuously tracks the flight track of the aircraft to obtain the flight distance, the azimuth and the pitch angle of the aircraft relative to a radar measurement coordinate system until the aircraft lands on a ship or flies back. The filtering is to filter various types of noise contained in the echo signal as much as possible, reduce or eliminate the influence caused by the noise, and finally obtain the optimal state estimation. In the actual radar target motion process, the target can make various maneuvers, and the whole filtering system is nonlinear. The noise pollution to be eliminated in the process is generally divided into two types, one type is an error generated by each extension module of the radar, and the other type is clutter interference of various electromagnetism, meteorology and the like in the atmospheric space. The ship-borne guidance radar is necessarily in a complex electromagnetic environment, such as signals of other ships in a fleet, radar electronic systems of aircrafts and other radar avionics on the ships, which all cause clutter influences of different degrees on guidance information. Since the guiding radar may also face electromagnetic interference, an interference signal with too large amplitude may even cause the radar system to crash and fail to operate. The radar noise signal is dominated by medium-high frequency and high frequency.

In the full-automatic carrier landing system, as the main frequency distribution area of the radar signal is consistent with the working frequency area of the carrier landing system, if the noise content in the pitching instruction is higher, the airplane bumps when entering the field. The excessive control actions can also cause the pilot to feel uncomfortable, influence the pilot to control the safe carrier landing of the airplane, and if the sensitivity of radar noise in a control command can be reduced, the carrier landing can be safer. However, if the gain is decreased in order to reduce the noise in the pitch command, the stability of the closed loop system is adversely affected. The conventional method uses a tracking filter, but amplifies noise when the height and the differential value thereof are obtained, and if an additional filter is added, the response speed of the system is reduced.

The current commonly used filtering methods include Kalman filtering, alpha-beta filtering and the like. The alpha-beta filter is a steady-state solution form of a Kalman filter with a uniform motion equation. Although kalman filtering is recursive and is a linear filtering method, it does not need to store previous state information, and only needs to calculate the estimator at the previous time and the current observed value to obtain the current state estimate at each recursive operation. However, compared with the α - β filter, the coefficient is not fixed, the calculation format is relatively complicated, and the matrix operation is involved, the data storage amount is large, and the operation amount increases with respect to the number of stages of another filter. Practice shows that the Kalman filter is complex in structure and sensitive to a target model, the selection of model noise has direct influence on filtering precision, in addition, the calculated amount of the Kalman filter is sharply increased by the third power of state dimension, the problem of poor real-time performance exists, and meanwhile, the fault rate is increased along with the increase of a navigation subsystem. On the other hand, the alpha-beta filter can only be used for processing uniform motion, and the use condition is strict.

Disclosure of Invention

The present application is directed to providing a fully automatic landing radar guided noise suppression method based on an α - β - γ filter to solve or mitigate at least one of the problems of the background art.

The technical scheme of the application is as follows: a full-automatic landing radar guiding noise suppression method based on an alpha-beta-gamma filter comprises the following steps:

1) establishing a maneuvering target tracking model;

2) establishing a radar measurement noise model;

3) determining an equivalent transfer function of the alpha-beta-gamma filter and a guide law structure containing the alpha-beta-gamma filter;

4) the stability of the alpha-beta-gamma filter is determined, and the parameters alpha, beta and gamma of the alpha-beta-gamma filter are determined.

In step 1, the maneuvering target tracking model mainly comprises a uniform acceleration model discrete expression as follows:

X(k+1)＝FX(k)+W(k)

in the formula, X is a state vector, F is a state transition matrix, W is zero-mean gaussian white noise, and k is time, where the state transition matrix F and the zero-mean gaussian white noise W satisfy:

wherein T is the sampling interval from the time k to the time k +1,is white noise;

equation of state of the uniform acceleration model discrete time system:

the covariance matrix Q is:

in the formula (I), the compound is shown in the specification,is the noise variance.

In step 2, the radar measurement noise model mainly includes a jump angle noise model based on a markov power spectrum type:

where f is the frequency, ω is its corresponding angular velocity, f_mFor a jump in half-power frequency, omega_mFor its corresponding jump half-power angular velocity, W₀Is the power spectral density at zero frequency, and S is formed by white noise unit_m(ω) shaping filter G required for corresponding angular noise_m(s) is:

and simultaneously comprises a Gaussian ranging noise spectrum S (f) as follows:

wherein a is 1.665, so thatWhen s (f) is 0.5.

In step 3, the equivalent transfer function of the α - β - γ filter mainly includes the state estimation equation of the target:

wherein Z is a measured value;

the prediction equation for the target is:

the equivalent transfer function of the alpha-beta-gamma filter position, velocity, acceleration estimates is:

wherein z is a discrete field symbol, a₁、a₂、a₃、b₀、b₁、b₂Are coefficients, each coefficient satisfying table 1:

[ Table 1]

The corresponding continuous domain expression is as follows:

wherein s is a continuous field symbol.

Wherein, in step 4, the alpha-beta-gamma filter stability decision comprises a characteristic equation of an equivalent transfer function of the system in a discrete domain:

f(z)＝z³-(3-α-β-γ)z²+(3-2α-β+γ)z-(1-α)

the conditions for obtaining system stability are as follows:

in step 4, determining the parameters α, β, γ includes obtaining a relation between α, β, γ and a triple true root R of the system characteristic equation by a critical damping state selection method, where the relation is:

compared with the traditional Kalman filtering method, the alpha-beta-gamma filter can greatly reduce the calculated amount and the calculated time while better ensuring the target tracking and predicting precision, has good real-time performance, and is relatively simple in structure and more beneficial to engineering realization; compared with an alpha-beta filter, the filter has better suppression effect on system high-frequency noise.

Drawings

In order to more clearly illustrate the technical solutions provided by the present application, the following briefly introduces the accompanying drawings. It is to be expressly understood that the drawings described below are only illustrative of some embodiments of the invention.

FIG. 1 is a schematic diagram of the formation of Markov noise according to the present invention.

FIG. 2 is a schematic diagram of the Markov noise generation of the present invention.

Fig. 3 is a schematic diagram of a pilot law structure including an α - β - γ filter according to the present invention.

Fig. 4 is a block diagram of a tracking filter designed according to the present invention.

Fig. 5 is a graph comparing the filtering effect of the position channel when R is 0.85.

Fig. 6 is a graph comparing the filtering effect of the filter in the speed channel when R is 0.85.

Fig. 7 is a graph comparing the filtering effect of the acceleration channel when R is 0.85.

Detailed Description

In order to make the implementation objects, technical solutions and advantages of the present application clearer, the technical solutions in the embodiments of the present application will be described in more detail below with reference to the drawings in the embodiments of the present application.

In order to achieve the technical problem, the method for suppressing the noise guided by the fully automatic landing radar based on the alpha-beta-gamma filter provided by the application comprises the following steps:

s1, establishing a maneuvering target tracking model;

s2, establishing a radar measurement noise model;

s3, determining an equivalent transfer function of the alpha-beta-gamma filter and a guide law structure containing the alpha-beta-gamma filter;

s4, judging the stability of the alpha-beta-gamma filter and determining the parameters alpha, beta and gamma of the alpha-beta-gamma filter.

The specific process of establishing the maneuvering target tracking model in step S1 is as follows:

adopting a uniform acceleration motion model, wherein the target displacement is x (t) and the speed isAcceleration ofThe random disturbance of the disturbance acceleration is also regarded as white Gaussian noise with the average value of 0The state expression of the discrete-time system of the uniform acceleration model is:

its covariance matrix Q is then:

wherein the content of the first and second substances,is the noise variance.

The specific process of establishing the radar measurement noise model in step S2 is as follows:

a noise model based on a markov power spectral pattern is first built. Power spectrum S of the jump angle noise_m(ω) exhibits Markov type:

in the formula (f)_mFor flashing half power frequency, W₀Is the power spectral density at zero frequency.

Taking empirical data f during simulation_mSince ω is 2 pi f at 5Hz, formula (3) can also be written as:

and S_m(ω) the corresponding angular noise can be passed through the shaping filter G by unity white noise_m(s) formation. G_m(s) can be directly derived from formula (4):

in the formula 1/omega_mIs the correlation time tau_mIf f is selected_mAt 5Hz, then τ_m＝0.0318s。

Then, noise based on the gaussian power spectrum type is established. The spectrum of gaussian noise s (f) varies exponentially:

wherein a is constant at 1.665, so thatWhen s (f) is 0.5; when f is f_3dBWhen s (f) is 0.067, wherein f_3dB6 MHz. Within the system bandwidth, its power spectral density can be considered constant, and this noise can be treated as white noise.

The specific process of step 3 is as follows:

the prediction process of the alpha-beta-gamma filter obtains the best prediction value by correcting the current measurement value x (n)And to ensure that its mean square error with the future value of S (n +1) is minimal. The continuous domain expression corresponding to the equivalent transfer function is as follows:

the specific process of step 4 is as follows:

according to the characteristic equation of the equivalent transfer function of the system in the discrete domain:

f(z)＝z³-(3-α-β-γ)z²+(3-2α-β+γ)z-(1-α) (10)

the system can be stable when alpha is more than 0, beta is more than 0, gamma is more than 0, 2 alpha + beta is less than or equal to 4, 2 alpha is more than beta, alpha (beta + gamma) is more than 2 gamma. Meanwhile, the relation of alpha, beta and gamma and the triple true root R of the system characteristic equation is obtained by a critical damping state selection method, and the relation is as follows:

thus determining the values of α, β and γ.

Compared with the traditional Kalman filtering method, the alpha-beta-gamma filter can greatly reduce the calculated amount and the calculated time while better ensuring the target tracking and predicting precision, and has good real-time performance. Meanwhile, the structure is relatively simple, and engineering realization is facilitated. Compared with the alpha-beta filter, the filter has wider application and better inhibition effect on the high-frequency noise of the system. Therefore, the alpha-beta-gamma filter can filter noise in the full-automatic carrier landing system, plays a role in smoothing and estimating position and speed information, realizes good tracking of maneuvering targets, and assists the system in finishing the guiding work of the airplane.

The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.