CN107544063A - A kind of Forecasting Methodology of target RCS under radar tracking state - Google Patents
A kind of Forecasting Methodology of target RCS under radar tracking state Download PDFInfo
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Abstract
The invention belongs to Radar Technology field, the Forecasting Methodology of the target RCS under radar tracking state a kind of is disclosed, for predicting the RCS values of target subsequent time during radar tracking, including：The exponent number of sets target radar cross section RCS predictive filter is M, and target is obtained from N according to target tracking algorismPThe M+1 moment is to NPThe distance and speed of moment target, and from NPThe M+1 moment is to NPMoment radar is tried to achieve from N to the observation angle of targetPThe M+1 moment is to NPThe target range and motion model at moment, obtain target in NPThe target velocity at+1 moment and the angle predicted value of target range, the autocorrelation matrix of target RCS values and the cross-correlation column vector of target RCS values are tried to achieve by the observation angle of target, pass through the covariance matrix of Doppler's vector regulation target observation component, target RCS predictive filter coefficient is tried to achieve, is obtained in NPThe target RCS at+1 moment predicted value.
Description
Technical Field
The invention belongs to the technical field of radars, and particularly relates to a method for predicting a target RCS (radar cross section) in a radar tracking state, which can be used for predicting a radar scattering cross section RCS value at the next moment of a target in a radar tracking process.
Background
The Radar Cross Section (RCS) of a target is a parameter used to measure the scattering properties of the target, and the RCS of the target is generally defined by the intensity of the backscattered energy. The RCS of the target is mainly related to the structure and surface medium of the target, radar frequency, polarization mode, attitude angle of the target and other factors.
According to radar equations, the target RCS directly influences the magnitude of the target echo power, so that the target RCS directly influences the signal-to-noise ratio of the target echo signal. The magnitude of the target signal-to-noise ratio has an important influence on resource scheduling in the multi-station radar and power allocation in the phased array radar. When the radar tracks the target, if the target RCS and the target distance are known, the signal-to-noise ratio of the target echo signal can be estimated. Under the condition that the signal-to-noise ratio of the target is known, the multi-station radar can call the radar with high signal-to-noise ratio to observe the target, so that the resource scheduling is more reasonable; the phased array radar can distribute the required transmitting power according to the signal-to-noise ratio of the target, so that the radar power is more effectively utilized. Therefore, in the radar tracking state, the prediction of RCS of a research target has important significance.
At present, documents for researching RCS prediction of a target in a radar tracking state are less, and the RCS change is less considered in the existing signal-to-noise ratio prediction method, and most of the RCS value of the target is only assumed to be kept unchanged. In practical application, the RCS value of the target is continuously changed along with the change of the relative position of the target and the radar.
Disclosure of Invention
In view of the above problems in the prior art, an object of the present invention is to provide a method for predicting a target RCS in a radar tracking state, which realizes the prediction of the RCS value of the target at the next tracking time.
In order to achieve the purpose, the invention is realized by adopting the following technical scheme.
A method for predicting RCS of a target in a radar tracking state, the method comprising the steps of:
step 1, determining the order of a prediction filter of a target RCS as M, and acquiring a secondary N received by a radar P Time M +1 to N P Historical target echo amplitude observation vector of timeWherein xi is k Represents the target echo amplitude observed value at the kth time, and N P ＞M，N P K and M are natural numbers respectively, k belongs to (N) p -M+1，...，N p ) The initial value of k is N p -M+1；
Respectively obtaining target slave N by adopting a target tracking algorithm P -M +1 time to N P In the time, the target distance and the target speed are set for each time, and an initial N is set P -a target observation angle at time M + 1;
step 2, under the assumption that the flight of the target and the attitude angle of the target are not changed, estimating a target observation angle at the k +1 moment according to the target distance, the target speed and the target observation angle at the k moment; let the value of k take N in turn p -M+1，...，N p Until the current N is obtained P A target observation angle at time + 1;
step 3, according to N P Time M +1 to N P Obtaining an autocorrelation matrix phi of a target RCS (radar cross section) at a target observation angle at a moment, wherein the autocorrelation matrix phi of the target RCS is an M multiplied by M dimensional matrix;
step 4, according to N P Time M +1 to current N P Obtaining a cross-correlation vector eta of the target RCS at the target observation angle of +1 moment, wherein the cross-correlation vector eta of the target RCS is a column vector of M multiplied by 1 dimension;
step 5, obtaining a covariance matrix of a historical target echo amplitude observation vector xi according to an autocorrelation matrix phi of the target RCS;
according to the cross-correlation vector eta of the target RCS, the current N is expressed P Observed value of target echo amplitude at +1 momentA cross correlation matrix with the historical target echo amplitude observation vector xi;
step 6, according to the current N P Observed value of target echo amplitude at +1 momentThe historical target echo amplitude observation vector xi tracks a target function of a weight of a prediction filter; obtaining the optimal weight value of the prediction filter;
step 7, calculating to obtain the current N according to the optimal weight of the prediction filter and the observation vector xi of the echo amplitude of the historical target P A target echo amplitude prediction value at +1 time, thereby obtaining a target echo amplitude prediction value according to the current N P Solving current N by target echo amplitude predicted value at +1 moment P The target RCS prediction value at time + 1.
The technical scheme of the invention has the characteristics and further improvements that:
(1) In step 2, a target observation angle alpha at the moment k +1 is determined according to the target distance, the target speed and the target observation angle at the moment k k+1 Specifically, the following formula is adopted for calculation:
wherein r is k Indicating the target distance at time k, v k Indicating the target speed at time k, α k A target observation angle at time k is shown, and T represents a pulse repetition interval;
let the value of k take N in turn p -M+1，...，N p Until the current N is obtained P Target observation angle at time + 1.
(2) In step 3, according to N P -M +1 time to N P Target observation of time of dayAnd angle, obtaining an autocorrelation matrix phi of the target RCS as follows:
where ρ (·) represents a correlation coefficient function, whose expression is: ρ (u) 1 )＝jinc(4f c d sin(u 1 /2)/c)，u 1 As input to a function of the correlation coefficient, f c Denotes radar carrier frequency, d denotes mean value of target size, c =3 × 10 8 m/s denotes the speed of light, jinc (·) denotes the jinc function, which is defined as:J 1 (. Represents a Bessel function of the first kind, u 2 As input to the jinc function, α k Represents the target observation angle at time k, k ∈ (N) p -M+1，...，N p )。
(3) In step 4, according to N P Time M +1 to N P And obtaining a cross-correlation vector eta of the target RCS at the target observation angle at +1 moment as follows:
wherein the content of the first and second substances,representing the current N P Target observation angle at time +1 [ ·] T Representing the transposition operation and p (·) the correlation coefficient function.
(4) The step 5 specifically comprises the following steps:
obtaining a covariance matrix C of the target echo amplitude observation vector according to the autocorrelation matrix phi of the target RCS:
C＝E(ξξ H )＝σ s 2 diag(a d )Φdiag(a * d )；
wherein E (-) represents expectation, ξ represents N P Time M +1 to N P Historical target echo amplitude observation vector, sigma, at a time s 2 Variance of observed value representing amplitude of target echo, a d Represents N P -M +1 time to N P The doppler steering vector of the target at the time instant,a * d denotes a d Conjugation of (c) (. 1) H Represents conjugate transpose, diag (·) represents vector diagonalization;
according to the cross-correlation vector eta of the target RCS, the current N is expressed P Observed value of target echo amplitude at +1 momentAnd a cross correlation matrix B between the observation vector and the observation vector xi of the echo amplitude of the historical target:
wherein the content of the first and second substances,represents the current N P The doppler steering vector of the target at time + 1.
(5) The step 6 specifically comprises the following steps:
(6a) According to the current N P Observed value of target echo amplitude at +1 momentAnd constructing an objective function about the weight of the prediction filter by using the historical target echo amplitude observation vector xi:
where w represents the prediction filter weights, z represents the interference signal,means for obtaining a value of w when (-) is minimized;
(6b) Solving the objective function related to the weight of the prediction filter to obtain the optimal weight of the prediction filter
Wherein R is z A covariance matrix representing an interference signal, which indicates a dot product;
(6c) Covariance matrix R of interference signal z For the diagonal matrix, the optimal weight of the prediction filter is simplified as follows:wherein the content of the first and second substances,
(6) The step 7 specifically comprises the following steps:
according to the optimal weight w of the prediction filter opt ＝[w 1 ，…w i ，…，w M ] T The historical target echo amplitude observation vectorCalculating to obtain the current N P Target echo amplitude predicted value at +1 momentAccording to the current N P Target echo amplitude predicted value at +1 momentFind the current N P Target RCS prediction value at +1 timeWhere Σ represents a summation operation, w i For optimal weight w of prediction filter opt Is the ith element of (1, 2.. Ang., M)。
According to the technical scheme, the RCS predicted value of the target at the next tracking time is obtained by predicting the RCS value of the target at the next time through the observation angle change of the target at the current time and the observation angle change of the target at the next time and the prediction filter.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flowchart of a method for predicting a target RCS in a radar tracking state according to an embodiment of the present invention;
FIG. 2 is a schematic view of a model of scattering points of an airplane in a simulation experiment according to the present invention;
FIG. 3 is a schematic diagram of a motion model of a target in a simulation experiment according to the present invention;
FIG. 4 is a schematic diagram showing comparison between the true value, the observed value and the predicted value of the echo amplitude of a target under different pulse observation frequencies in a simulation experiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
An embodiment of the present invention provides a method for predicting a target RCS in a radar tracking state, as shown in fig. 1, the method includes:
step 1, setting the order of a prediction filter of the radar scattering cross section RCS of a target to be M. Radar receiving target from N P -M +1 to N P Historical echo amplitude observation vector of timeξ k Represents observation data at the k-th time (·) T Representing a fetch-transpose operation, N P And k is a natural number. Setting a target observed value at the current moment asThis example assumes that all observations obey a mean of zero and a variance of σ s 2 Complex gaussian distribution.
And the significance of the predictor order M is that the RCS value at the current moment is predicted by using observation data of M moments before the current moment. The value of M does not exceed the maximum allowable calculation amount.
Step 2, under the assumption that the flight of the target and the attitude angle of the target are not changed, the distance information r of the target at any k moment can be obtained by using a target tracking algorithm k And velocity information v k According to the target distance r at time k k Target velocity v at time k k And a target observation angle alpha at time k k The observation angle of the target at the k +1 moment can be calculated according to the motion model of the target
Wherein arccos (-) represents an inverse cosine function, cos (-) represents a cosine function, sin (-) represents a sine function, T r Indicating the pulse repetition interval, in this example setting the starting time angleAt 0.43rad.
The target tracking algorithm comprises target tracking based on Kalman filtering, target tracking based on particle filtering and target tracking based on wiener filtering. The target tracking algorithm based on Kalman filtering is selected but not limited in the embodiment.
Step 3, (3.1) according to from N P -M +1 to N P The target observation angle at that time can be used to determine the autocorrelation matrix Φ of the target RCS, which is an M × M matrix in the form of
Where ρ represents a correlation coefficient function, the expression of which is:
ρ(u 1 )＝jinc(4f c d sin(u 1 /2)/c)
wherein u is 1 Input variable representing a function of the correlation coefficient, f c Denotes radar carrier frequency, d denotes mean value of target size, c =3 × 10 8 m/s represents the speed of light, jinc (·) represents the jinc function, which is defined as:
J 1 (. Represents a Bessel function of the first kind, u 2 An input variable that is a jinc function;
(3.2) according to N P Target speed of timeTarget distanceAnd target observation angleCalculated by step 2Observation angle at presentThe cross-correlation vector η of the target RCS values can be expressed as an mx 1 column vector, which is in the specific form:
wherein the content of the first and second substances,target observation angle representing current time [ ·] T Denotes transposition and ρ (·) denotes a correlation coefficient function.
The RCS value of the target is only related to the observation angle, and when the two observation angles are separated by a small distance, the RCS values of the target corresponding to the two observation angles have strong correlation. According to the relevance of the target RCS value, a predictor can be used for completing the prediction of the target RCS;
the precondition of using the correlation prediction is that the historical echo amplitude observation vector and the observation value at the current moment are not statistically independent, namely, the historical echo amplitude observation vector and the observation value at the current moment are correlated, and the condition for judging the correlation is as follows:
namely thatWhere φ represents the difference in observation angle at any two times i, j, i.e., φ = α j -α i ＝Δα j ，φ max Representing the maximum observation angle that can be predicted using correlation.
And 4, assuming that the radar system is coherent, the Doppler phase information of the target is known, and expressing the Doppler component of the target as a Doppler guide vectorKnowing the velocity of the object motion, the doppler steering vector is also known.
Assuming that the motion state of the target is uniform motion, the Doppler guide vector at the current moment is expressed asThe covariance matrix form of the available target echo components is:
C＝E(ξξ H )＝σ s 2 diag(a d )Φdiag(a * d )
wherein C is an M × M dimensional matrix, diag (·) denotes vector diagonalization, (·) H For conjugate transposition, (.) * Representing conjugate, current time target observation dataThe cross-correlation matrix with the historical observation ξ may be expressed as:
step 5, (5.1) assumes that the spatial response variation of the target is a smooth random process, varying only with the starting angle of the signal. According to the existing echo observation data xi, the output of a predictor is represented in a linear combination mode, and the form is as follows:the performance criterion of the selected metric predictor is a minimum mean square error criterion, and the objective function can be expressed as:
w represents the weight vector of the predictor, z represents the vector formed by interference components at different moments, and the formula can be written as
R z = M × M, wherein R z A covariance matrix representing the interfering signal, wherein the interference is independent of the signal.
(5.2) the optimal weight form corresponding to the minimum mean square error can be obtained by deriving the above formula w:
wherein |, indicates a dot product;
(5.3) in practical engineering applications, the interference is statistically independent for different pulse repetition periods, so R is z For a diagonal matrix, the optimal weight can be represented as:
wherein Indicating doppler phase compensation;
(5.4) if the interference has the same variance as the observed ξ, i.e.The optimal weight is further simplified as: the target RCS prediction filter coefficient w obtained by the step can be obtained, and the expression of the predictor coefficient w is w = [ w ] 1 ，…w i ，…，w M ] T 。
Wherein, w i For the i-th element of the prediction filter coefficient w, i =1, 2.
If the correlation of the target RCS is strong, the autocorrelation matrix R may be nonsingular, which may cause errors in matrix inversion, in which case, the generalized matrix inversion operation may be used instead of the matrix inversion operation.
Step 6, according to predictor coefficient w and from N P -M +1 time to N P The observation value of the echo amplitude of the target at the moment is obtained to obtain the predicted value of the complex amplitude of the target at the current momentTo find the Nth P The predicted value of the target RCS at the time +1 isWhere Σ denotes a summation operation, i =1,2 i Is the ith element of the prediction filter coefficient w;
the effect of the present invention is further explained by combining the simulation experiment as follows:
setting simulation parameters: using a 2D radar at the origin of coordinates, setting the radar carrier frequency f c =1GHz, the radar measurement parameters are the range and azimuth of the target. The target is an airplane target with the length of 10m, the RCS scattering characteristics are subject to scattering point models, as shown in FIG. 2, the scattering coefficient of each scattering point is subject to the mean value of 0 and the variance of 1/N S The complex Gaussian distribution of the scattering points is obtained, and the scattering coefficients of different scattering points are mutually independent; wherein N is S The number of scattering points is indicated. The maximum value phi of the tracking angle difference can be obtained by calculation max ＝0.86(rad)。
Establishing a target motion model: as shown in fig. 3 (the horizontal and vertical coordinate units are meters), the target makes uniform linear motion at a speed of 200 m/s; at the first moment of radar tracking, the target is at a distance to the radar ofThe observation angle of the target isThe ratio of signal power to noise power is 15dB.
According to the simulation parameter setting and the target motion model, calculating to obtain the range of the radar and target observation angle as [ -0.43,0.43] (rad), taking out the real value of the target complex amplitude within the range of [ -0.43,0.43] (rad) for simulation, drawing the real value of the target RCS within the angle of [ -0.43,0.43] (rad) into a two-dimensional curve, and comparing the predicted value obtained by the method with the real value after noise addition at different pulse observation frequencies, as shown in FIG. 4 (the abscissa is the hanging angle of the radar to the target, the unit is degree, the ordinate is the target echo amplitude, and the unit is decibel), in graph (4 a), the pulse observation frequency PVF =0.2Hz, in graph (4 b), the pulse observation frequency PVF =10Hz, and in graph (4 c), the pulse observation frequency PVF =1Hz; the simulation results show that the overall variation trend of the target RCS predicted value is the same as the real value of the target RCS, the size of the target RCS predicted value is randomly jumped near the real value, which is mainly caused by noise, and the three simulation results in the figure 4 show that the method can realize the prediction of the target RCS and can better predict the variation trend of the target RCS.
Those of ordinary skill in the art will understand that: all or part of the steps of implementing the method embodiments may be implemented by hardware related to program instructions, and the program may be stored in a computer-readable storage medium, and when executed, executes the steps including the method embodiments; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily think of the changes or substitutions within the technical scope of the present invention, and shall cover the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (7)
1. A method for predicting RCS (radar cross section) of a target in a radar tracking state is characterized by comprising the following steps:
step 1, determining the order of a prediction filter of a target RCS as M, and acquiring a secondary N received by a radar P -M +1 time to N P Historical target echo amplitude observation vector of timeWherein xi is k Represents a target echo amplitude observation at time k, and N P >M，N P K and M are natural numbers respectively, k belongs to (N) p -M+1,...,N p ) The initial value of k is N p -M+1；
Respectively obtaining target slave N by adopting a target tracking algorithm P Time M +1 to N P In the time, the target distance and the target speed are set for each time, and an initial N is set P -a target observation angle at time M + 1;
step 2, determining a target observation angle at the k +1 moment according to the target distance, the target speed and the target observation angle at the k moment; let the value of k take N in turn p -M+1,...,N p Until the current N is obtained P A target observation angle at time + 1;
step 3, according to N P -M +1 time to N P Obtaining an autocorrelation matrix phi of a target RCS (radar cross section) at a target observation angle at a moment, wherein the autocorrelation matrix phi of the target RCS is an M multiplied by M dimensional matrix;
step 4, according to N P M +1 moment to current N P Obtaining a cross-correlation vector eta of the target RCS at the target observation angle of +1 moment, wherein the cross-correlation vector eta of the target RCS is a column vector of M multiplied by 1 dimension;
step 5, obtaining a covariance matrix of a historical target echo amplitude observation vector xi according to an autocorrelation matrix phi of the target RCS;
according to the cross-correlation vector eta of the target RCS, the current N is expressed P Observed value of target echo amplitude at +1 momentA cross correlation matrix between the observation vector and a historical target echo amplitude xi;
step 6, according to the current N P Observed value of target echo amplitude at +1 momentConstructing a target function about the weight of a prediction filter by the historical target echo amplitude observation vector xi;
solving the objective function related to the weight of the prediction filter to obtain the optimal weight of the prediction filter;
step 7, calculating to obtain the current N according to the optimal weight of the prediction filter and the observation vector xi of the echo amplitude of the historical target P A target echo amplitude prediction value at +1 time, thereby obtaining a target echo amplitude prediction value according to the current N P Solving current N by target echo amplitude predicted value at +1 moment P The target RCS prediction value at time + 1.
2. The method for predicting RCS (Radar Cross section) of the target in the Radar tracking state according to claim 1, wherein in step 2, the target observation angle α at the time k +1 is determined from the target distance, the target speed and the target observation angle at the time k k+1 Specifically, the following formula is adopted for calculation:
wherein r is k Indicating the target distance at time k, v k Indicating the target speed at time k, α k Target observation angle, T, representing time k r Representing a pulse repetition interval;
let the value of k take N in turn p -M+1,...,N p Until the current N is obtained P Target observation angle at time + 1.
3. The method of claim 2, wherein in step 3, the target RCS is predicted according to N P -M +1 time to N P Obtaining the autocorrelation of the RCS of the target by the target observation angle of timeThe matrix Φ is:
where ρ (·) represents a correlation coefficient function, whose expression is: ρ (u) 1 )＝jinc(4f c d sin(u 1 /2)/c)，u 1 As input to the correlation coefficient function, f c Denotes radar carrier frequency, d denotes mean value of target size, c =3 × 10 8 m/s denotes the speed of light, jinc (·) denotes the jinc function, which is defined as:J 1 (. Cndot.) denotes Bessel function of the first kind, u 2 As input to the jinc function, α k Represents the target observation angle at time k, k ∈ (N) p -M+1,...,N p )。
4. The method of claim 3, wherein in step 4, the target RCS is predicted according to N P Time M +1 to N P And obtaining a cross-correlation vector eta of the target RCS at the target observation angle at +1 moment as follows:
wherein the content of the first and second substances,represents the current N P Target observation angle at time +1 [ ·] T Representing a transposition operation and ρ (·) representing a correlation coefficient function.
5. The method for predicting the RCS of the target under the radar tracking state as recited in claim 4, wherein the step 5 specifically comprises:
obtaining a covariance matrix C of the target echo amplitude observation vector according to the autocorrelation matrix phi of the target RCS:
C＝E(ξξ H )＝σ s 2 diag(a d )Φdiag(a * d )；
wherein E (-) represents expectation, ξ represents N P Time M +1 to N P Historical target echo amplitude observation vector, σ, at time s 2 Variance of observed value representing amplitude of target echo, a d Represents N P Time M +1 to N P The doppler steering vector of the target at the time instant,a * d denotes a d Conjugation of (1) H Represents conjugate transpose, diag (·) represents vector diagonalization;
according to the cross-correlation vector eta of the target RCS, the current N is expressed P Observed value of target echo amplitude at +1 momentAnd a cross correlation matrix B between the observation vector and the observation vector xi of the echo amplitude of the historical target:
wherein the content of the first and second substances,represents the current N P The doppler steering vector of the target at time + 1.
6. The method for predicting the RCS of the target under the radar tracking state as recited in claim 5, wherein the step 6 specifically comprises:
(6a) According to the current N P Observed value of target echo amplitude at +1 momentThe calendarAnd (3) a history target echo amplitude observation vector xi is used for constructing an objective function related to the weight of the prediction filter:
where w represents the prediction filter weights, z represents the interference signal,means for obtaining a value of w when (-) is minimized;
(6b) Solving the objective function related to the weight of the prediction filter to obtain the optimal weight of the prediction filter
Wherein R is z A covariance matrix representing an interference signal, which indicates a dot product;
(6c) Covariance matrix R of interference signal z For the diagonal matrix, the optimal weight of the prediction filter is simplified as follows:wherein the content of the first and second substances,
7. the method for predicting the RCS of the target under the radar tracking state according to claim 1, wherein the step 7 specifically includes:
according to the optimal weight w of the prediction filter opt ＝[w 1 ,…w i ,…,w M ] T The historical target echo amplitude observation vectorCalculating to obtain the current N P Target echo amplitude predicted value at +1 momentThereby according to said current N P Target echo amplitude predicted value at +1 momentFind the current N P Target RCS prediction value at +1 timeWhere Σ denotes a summation operation, w i For optimal weight w of prediction filter opt I ∈ (1, 2.., M).
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| CN111123231A (en) * | 2018-11-01 | 2020-05-08 | 三峡大学 | Wind turbine RCS data compression reconstruction method |
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| CN112689772A (en) * | 2019-11-19 | 2021-04-20 | 深圳市大疆创新科技有限公司 | Method and device for determining scattering sectional area of object radar and storage medium |
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| CN109802656A (en) * | 2019-01-25 | 2019-05-24 | 西安电子科技大学 | Kalman filter method based on amplitude information |
| CN109802656B (en) * | 2019-01-25 | 2022-03-04 | 西安电子科技大学 | Kalman filtering method based on amplitude information |
| CN112689772A (en) * | 2019-11-19 | 2021-04-20 | 深圳市大疆创新科技有限公司 | Method and device for determining scattering sectional area of object radar and storage medium |
| CN111208482A (en) * | 2020-02-28 | 2020-05-29 | 成都汇蓉国科微系统技术有限公司 | Radar precision analysis method based on distance alignment |
| CN115356703A (en) * | 2022-10-17 | 2022-11-18 | 中国人民解放军国防科技大学 | Surface element distribution-based rough target RCS scaling measurement method and device |
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