CN111308433A - Sea clutter simulation method containing texture information - Google Patents
Sea clutter simulation method containing texture information Download PDFInfo
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- CN111308433A CN111308433A CN202010222144.7A CN202010222144A CN111308433A CN 111308433 A CN111308433 A CN 111308433A CN 202010222144 A CN202010222144 A CN 202010222144A CN 111308433 A CN111308433 A CN 111308433A
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Abstract
The invention provides a sea clutter simulation method containing texture information, which can be used for high-resolution sea clutter simulation and belongs to the technical field of radar signal processing, and the specific implementation process comprises the following steps: (1) setting simulation parameters; (2) simulating sea clutter by adopting a SIRP model; (3) calculating sea surface surge parameters; (4) calculating a sea clutter space-time texture information matrix; (5) texture information is added to the simulation data. According to the method, the sea clutter simulation data can be obtained through calculation according to the set sea clutter parameters, the simulated sea clutter follows the composite K distribution in amplitude, the realistic texture information is contained, and the simulation data can be provided for carrying out algorithm researches such as sea surface target detection.
Description
The technical field is as follows:
the invention belongs to the technical field of radar signal processing, and particularly relates to a sea clutter simulation method containing texture information.
Background art:
the sea clutter is used as a main component in an echo signal when the radar observes the sea, the scattering mechanism is complex, the influence factors are numerous, especially for a high-resolution radar, the amplitude of the radar has strong non-Gaussian property, the frequency spectrum has obvious offset, particularly under a high sea condition, sea surface fluctuation has obvious expression in the radar echo, at the moment, sea clutter data obtained by a traditional random distribution simulation method is greatly different from sea clutter obtained by actual measurement, and the application requirement is difficult to meet. Therefore, under the condition of high resolution, a proper simulation model is established for the sea clutter, the texture information under different sea conditions is reflected, and the method has important significance for improving the simulation effect of the sea clutter and the like.
The traditional sea clutter simulation method mainly aims at amplitude statistical characteristics, usually adopts lognormal distribution, Weibull distribution, K distribution, composite Gaussian distribution and the like to describe sea clutter, and for a high-resolution radar, a ball invariant random process method and a zero memory nonlinear transformation method are often adopted to add specific inter-pulse correlation characteristics, so that simulated sea clutter data show more complex characteristics. Aiming at Doppler spectral characteristics, common representative models mainly comprise a Lee model and a Walker model, the Lee model expresses a sea clutter Doppler power spectrum as three spectral components, namely a Gaussian spectral component related to Bragg scattering, a Lorenztian spectral component related to Burst scattering and a Voigian spectral component related to Whitecap scattering, the Walker model is used for approximating and simplifying the Lee model, and the combined Gaussian model of the three components in the Lee model is used for describing the sea clutter Doppler spectrum. The models can well reflect the amplitude statistical characteristic and the Doppler characteristic of the sea clutter, but the periodically appearing texture information in the sea clutter is not considered, the generated sea clutter does not show a surge structure, the sea clutter is obviously different from the actual sea clutter of the high sea condition, and the data characteristic of the high-resolution radar for sea observation under the high sea condition is not met.
The invention content is as follows:
1. technical problem to be solved
The invention aims to overcome the defects of the existing sea clutter simulation technology, so that the simulated sea clutter data not only meet the statistical characteristics, but also embody the texture information under high sea conditions, and provide more reliable data for developing algorithm researches such as sea surface target detection and the like.
2. Technical scheme
The invention discloses a sea clutter simulation method containing texture information, which comprises the following specific steps:
firstly, setting simulation parameters according to simulation requirements, wherein the parameters comprise radar parameters, environment parameters and the like;
simulating sea clutter by adopting a SIRP (sphere inverse Random process) model, and generating simulated sea clutter data with statistical characteristics complying with composite K distribution;
thirdly, calculating surge parameters, calculating parameters such as average surge period, average speed, average distance and the like and positions of all wave crests according to the set sea clutter simulation parameters;
step four, calculating a space-time texture information matrix, and calculating space textures and time textures according to the calculated surge parameters so as to obtain the space-time texture information matrix;
and step five, multiplying the simulated sea clutter data obtained in the step two by the texture information matrix to obtain the simulated sea clutter data containing the texture information.
In the third step, the distance between wave peaks of the sea clutter is simulated by utilizing uniform distribution, in the fourth step, a sine function is utilized to describe the texture fluctuation of the sea clutter, the sine function is related to factors such as the motion speed and the direction of sea waves, and in the fifth step, the texture information matrix is multiplied by the sea clutter data obtained in the second step to obtain sea clutter simulation data containing texture information. Factors such as radar parameters, environmental parameters and the like are considered in the simulation process.
3. Advantageous effects
Compared with the prior art, the sea clutter simulation method comprising the texture information has the advantages that:
(1) the method can calculate corresponding surge structure information according to set scene parameters, and further calculate a sea clutter time-space texture information matrix, so that the texture information of the sea clutter is combined with an actual scene;
(2) according to the method, the sea clutter texture is modeled by using the sine function, a texture structure similar to actual measurement data can be added to simulated sea clutter data based on a distribution model, the sea clutter data containing texture information is obtained, and effective simulation data are provided for sea clutter texture information extraction research and sea surface target detection algorithm research.
Description of the drawings:
FIG. 1 is a flow chart of a process for carrying out the present invention;
FIG. 2 is simulated sea clutter data without added texture information;
FIG. 3 is simulated sea clutter data with texture information.
The specific implementation mode is as follows:
referring to the attached figure 1 of the specification, the processing flow of the invention specifically comprises the processes of parameter setting, surge related parameter calculation, space-time texture information matrix calculation, texture addition for simulated sea clutter data and the like. The specific implementation is described in detail below.
1. Setting sea clutter simulation parameters
And setting parameters related to sea clutter simulation, such as radar parameters, environment parameters and the like according to simulation requirements. The radar parameters comprise carrier frequency, sampling frequency, bandwidth, range resolution R, pulse repetition frequency PRF, polarization mode and detection direction thetarAnd the like. The environmental parameters include sea state and wind speed VwNoise to noise ratio and direction of wave motion thetas。
2. Sea clutter data with simulation statistical characteristics obeying composite K distribution
The composite K distribution is one of typical models for describing the distribution characteristics of the sea clutter at present, a plurality of simulation methods are provided, and the SIRP (sphere inverse Random process) model can be adopted to simulate the sea clutter in the specific implementation. The process of generating data by the SIRP model is described in detail in many publications, which relate to the generation of gaussian distribution sequences and some series of filtering processes, and will not be described herein.
3. Calculating relevant parameters of surge
And calculating parameters such as average surge period, average speed, average distance and the like according to the set sea clutter simulation parameters.
From wind speed VwAnd the average surge period T is calculated according to the wave height H:
wherein g is the gravitational acceleration, and further the average velocity V is obtained:
the average distance D between the peaks of the surge wave is V.T according to the period and the speed.
The texture of the sea clutter is influenced by a plurality of factors, the distance between wave peaks is not a fixed constant, and the distance between the wave peaks is statistically processed in the specific sea clutter texture simulation. Suppose diDistance from ith peak to radar, | di+1-diL is the distance between the (i + 1) th peak and the ith peak, and the distance is expressed by random numbers satisfying uniform distribution, that is
|di+1-di|～U(D-δD,D+δD) (3)
Wherein, U (D-delta)D,D+δD) Is distributed over a range of [ D-delta ]D,D+δD]And the mean is a uniform distribution of D. DeltaDUsually 0.1-0.2 times of D is selected, and the adjustment can be carried out according to the actual sea condition.
And (4) taking the distance between the radar and the first peak as a reference point, and obtaining the distance value from each peak to the radar in the simulation according to the uniform distribution of the formula (3).
4. Computing spatio-temporal texture information matrices
And calculating space texture and time texture according to the surge motion parameters to further obtain a space-time texture information matrix.
The sea clutter texture information is related to the surge, and a large amount of measured data show that the fluctuation structure of the sea clutter texture information is similar to a sine function, so that the surge structure is simulated by the sine function. According to the surge related parameters (average period T, average distance D, average speed V and positions D of all peak points) calculated in the step 3i) The spatial texture may be generated using a sine function, wherein the spatial texture between the ith peak and the (i + 1) th peak may be described as:
wherein α is an adjustment coefficient, and the value range is [0,1], where the spatial texture w (0, d) is the result of w (t, d) when time t is 0, and a more general expression is as follows:
where Δ d is the distance that the peak advances in the radar detection direction within t, and is represented by V · t · cos (θ)s-θr)。
The above formula is an expression based on continuous time and continuous distance, the simulated radar system usually uses discontinuous transmission signals in a pulse mode, meanwhile, echo signals are also the result after discretization, and on the basis of the formula (5), the radar working parameter range resolution R and the pulse repetition frequency PRF are substituted into the above formula to obtain a texture information matrix w (n, m) corresponding to a range unit and a pulse unit:
wherein N is the serial number of the transmitted pulse, N is more than 0 and less than or equal to N, M is the serial number of the distance unit, M is more than or equal to 1 and less than or equal to M, N and M are the total pulse number and the farthest distance unit number set in the simulation, and
5. adding texture information to simulated sea clutter data
And (3) calculating to obtain the sea clutter data containing the space-time texture information by using a formula (7) based on the space-time texture matrix w (n, m) obtained in the step 4 and the sea clutter simulation data c (n, m) obtained by the simulation in the step 2.
c'(n,m)＝w(n,m)·c(n,m) (7)
To verify the validity of the simulation method, a carrier frequency F is setc9GHz, sampling frequency Fs10MHz, distance resolution R15 m, pulse repetition frequency PRF 5kHz, polarization mode Pol HH, detection direction thetarPi/2. The environmental parameters comprise sea State State 6, noise-to-noise ratio CNR 10dB and wave direction thetasThe simulation experiment was performed with a simulation time length of 4s of 3 pi/4.
The sea clutter data obtained by the SIRP model simulation is shown in fig. 2, and the simulation data is random numbers obeying the composite K distribution. In the figure, the horizontal axis represents the time dimension and the vertical axis represents the distance dimension. The simulation data after adding texture information using the patented method is shown in fig. 3. The simulation data reflects the space-time texture of the sea clutter and the variation trend of the space-time texture, the texture direction of the sea clutter is consistent with the setting of simulation parameters, the peak distance of the sea clutter is consistent with the simulation calculation, and the comparison between the graph 2 and the graph 3 visually shows the effectiveness of the patent method on the simulation of the sea clutter texture.
In summary, the sea clutter simulation method including the texture information provided by the invention can accurately simulate the sea clutter texture structure caused by sea surface surge, the time-space texture information matrix is constructed by calculating the surge motion parameters, and then the idea of obtaining the sea clutter including the texture by adding the texture information into the simulation data is feasible and effective, so that available data is provided for the follow-up development of algorithm researches such as sea surface target detection and the like.
Claims (3)
1. A sea clutter simulation method containing texture information is characterized by comprising the following steps:
step one, setting sea clutter simulation parameters according to task requirements;
simulating sea clutter by adopting a SIRP (sphere inverse Random process) model, and generating simulated sea clutter data with statistical characteristics complying with composite K distribution;
calculating sea surface surge motion parameters including average surge period, average speed, average distance, peak positions and the like according to the set simulation parameters;
step four, calculating a sea clutter space-time texture information matrix, calculating space textures and time textures according to surge parameters, and further obtaining a space-time texture information matrix;
and fifthly, adding texture information to the sea clutter data obtained in the step two to obtain a simulation result, and multiplying the simulated sea clutter data by the texture information matrix to obtain sea clutter simulation data containing the texture information.
2. The method according to claim 1, wherein the peak positions are calculated by introducing a uniform distribution to describe the peak positions based on the average peak distance.
Suppose diDistance from ith peak to radar, | di+1-diL is the distance between the (i + 1) th peak and the ith peak, and the distance is expressed by random numbers satisfying uniform distribution, that is
|di+1-di|～U(D-δD,D+δD) (1)
Wherein, U (D-delta)D,D+δD) Is a mean value D and a distribution range [ D-delta ]D,D+δD]Is uniformly distributed. DeltaDUsually 0.1-0.2 times of D, and can be adjusted according to the set sea condition in the simulation.
And (3) taking the distance between the radar and the first peak as a reference point, and obtaining the distance value from each peak to the radar in the simulation according to the uniform distribution of the formula (1). The distance of the (i + 1) th wave peak from the radar is di+1＝di+|di+1-di|。
3. The method according to claim 1, wherein the spatiotemporal texture information matrix calculation method in step four comprises:
adopting a sine function to describe the fluctuation characteristics of the sea clutter texture, and calculating the surge related parameters (average speed V and distance d between every two wave crests) according to the third stepi) Generating spatial texture information by using a sine function, and obtaining the spatial texture information between the ith peak and the (i + 1) th peak as follows:
α is an adjustment coefficient, and the value range [0,1], where the spatial texture w (0, d) in the above equation is a result of taking t as 0 for the spatial texture w (t, d), and the texture recursion relationship between the time t and the time 0 is as follows:
w(t,d)＝w(0,d-Δd) (3)
wherein, Δ d is the distance that the wave crest advances along the radar detection direction in the time t, and has the relationship:
Δd＝V·(t-0)·cos(θs-θr) (4)
in the formula [ theta ]s,θrRespectively the wave motion direction and the radar detection direction.
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| CN112255601A (en) * | 2020-10-17 | 2021-01-22 | 中国电波传播研究所（中国电子科技集团公司第二十二研究所） | Shore-based multi-channel radar simulated airborne data diagnosis method |
| CN114488107A (en) * | 2022-04-13 | 2022-05-13 | 南方海洋科学与工程广东省实验室(广州) | Method and device for sea clutter space-time distribution and influence grading product manufacturing |
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Cited By (3)
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