CN102176000A - Sea clutter suppression method for marine radar - Google Patents
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Abstract
The invention discloses a sea clutter suppression method for a marine radar. Most of the existing marine radars can not process the nearby sea clutter, and the existing sea clutter suppression methods usually suppress the sea clutter according to a fixed template, which can result in the great attenuation in the amplitude of a nearby target signal or even result in miss detection. The sea clutter suppression method provided for overcoming the above disadvantages comprises the following steps: initializing parameters and data, estimating parameters, suppressing the sea clutter and updating an estimation sample. The method can estimate the sea clutter according to different sea clutter distributions selected according to different sea conditions, and determine whether the sample data need to be updated for re-estimation according to the suppression effect, thereby maximally reducing the influence to the target while suppressing the sea clutter as much as possible, and obviating the great attenuation in the amplitude of the nearby target signal.
Description
Technical Field
The invention belongs to the technical field of marine radars, and particularly relates to inhibition of sea clutter in the marine radars.
Background
The ship radar is one of indispensable navigation equipment of ships, but the detection and tracking performance of the ship radar is often influenced by electromagnetic waves (sea clutter) scattered by the surface of sea waves, and the sea clutter problem is more serious especially when small targets flying in low altitude and sea sweepback are detected. It is well known that clutter increases the false alarm probability or correspondingly decreases the detection probability at constant false alarm rates. This may be more severe for sea clutter, which is moving and fluctuating and thus difficult to eliminate than ground clutter. Most of the existing marine radars do not process the near-sea clutter of the radar, so that the phenomenon of large cake at the center can be caused. In the existing sea clutter suppression method, sea clutter suppression is generally performed according to a fixed template, so that the amplitude of a near target signal is greatly attenuated, and even missing detection is caused.
Disclosure of Invention
The invention aims to solve the problem that the amplitude of a near target signal can be greatly attenuated while radar near sea clutter suppression is carried out in the conventional marine radar sea clutter suppression method, and provides a marine radar sea clutter suppression method.
In order to achieve the purpose, the technical scheme of the invention is as follows: a method for suppressing sea clutter of a marine radar comprises the following steps:
s1, initializing, namely completing the initialization setting of parameters and the initialization of data, and specifically comprising the following steps:
s11, selecting the distribution of sea clutter according to the actual sea situation and the performance of the radar;
s12, setting parameters of a suppression method, N: number of scan lines processed in a sample update period, M: number of scanning lines required for sample estimation, α: re-estimated threshold, β: a threshold value of binarization;
s13, reading M scanning line data, and marking as { Ti(k) M, k 1, 2, Num, represented by the formula
Calculating the data point number of the sea clutter on one scanning line, wherein n is the data point number of the sea clutter on one scanning line, D is the farthest distance between the sea clutter near the radar and the radar, Range is the detection distance of the radar, and Num is the number of the data points on one scanning line, and obtaining the first n scanning line data points { T } of M scanning linesi(k) M, k is 1, 2.. n }, and the average value is recorded as
And storing as sample data;
s2, parameter estimation and data mapping, wherein sample data C (k) and step S11 are used for selecting the distribution of the sea clutter and estimating the parameters of the distribution to obtain sea clutter data { B (k) }, wherein k is 1
Carrying out data mapping to obtain mapped sea clutter data D (k);
s3, reading N scanning line data and carrying out sea clutter suppression treatment, namely carrying out subtraction operation on the N scanning line data and the sea clutter data D (k) estimated in the step S2, carrying out binarization treatment on the result according to a threshold value beta, reading the first N point data of the N-M +1 to N scanning data while carrying out sea clutter suppression when the suppression is carried out to N-M, and taking the average value of the data and recording the average value as the average value
And storing;
s4, judging whether the detection distance of the radar is changed or not, if so, executing a step S13, otherwise, executing a step S5;
s5, updating sample data if the sample data meets the requirement
Then step S3 is executed without re-estimating the sea clutter data; if not, using the first n scan line data points of the M stored in step S3The average value C' (k) updates the sample data C (k), and step S2 is executed to re-estimate the parameters.
The distribution of the sea clutter in step S11 is rayleigh distribution, log-normal distribution, weibull distribution, and K distribution.
It is assumed that a sample data sequence for estimating the sea clutter distribution on one scan line of the radar is Z ═ ZiI is 1, Ln, and n is the number of data points. The specific process of parameter estimation described in step S2 is as follows:
(1) rayleigh distribution: the Rayleigh distribution probability density function is as follows
Wherein σ2If the average power is the power, then the parameter sigma of the Rayleigh distribution can be estimated according to the sample data;
(2) lognormal distribution: the probability density function of the lognormal distribution is as follows
Wherein σ is a shape parameter and is a lognormal distribution ln ziThe standard deviation of (a), the tailing of the probability density distribution of which becomes longer as σ increases; μ is a scale parameter and is lognormal distribution ln ziIs measured. Taking the parameters between 0.5 and 1.2 according to different sea conditions, namely estimating parameters sigma and mu of lognormal distribution according to sample data;
(3) weibull distribution: the weibull distribution probability density function is as follows,
wherein q is a scale parameter, p is a shape parameter, the value range is more than 0 and less than or equal to 2, and the estimation of the distribution parameters (p, q) is as follows:
the method comprises the following steps:
the Weibull distribution of sea clutter is empirical and is set to (p)0，q0) Designing an iteration interval (p) of p, q0-δp，p0+δp)，(q0-δq，q0+δq) Wherein δp，δqDetermining the size of p and q iteration intervals according to values preset according to actual conditions, wherein q is used as an outer loop and p is used as an inner loop, performing curve fitting, recording the difference value between curve data and sample data of each fitting, and taking the p and q values of the iteration as parameters when the difference value is smaller than a given threshold value.
The second method comprises the following steps:
where < > denotes moment estimation, which is common knowledge in the art and will not be described in detail here.
(4) K distribution: the K distribution probability density function is as follows
Wherein, Kv(.) is a second type of modified Bessel function, c is a scale parameter which affects the average power of the clutter, a smaller c indicates a smaller clutter intensity, the shape parameter v reflects the degree of bias of the K distribution, and a smaller v indicates a more pronounced asymmetry of the distribution, with a greater deviation from the Rayleigh distribution. v generally varies from 0.1 to 10, and is a rayleigh distribution when v ∞, where Γ (v) refers to the Γ function.
And (3) estimating parameters of the K distribution by adopting a moment estimation method, and for known samples, estimating the overall corresponding origin moment of each order by adopting the origin moment of the samples:
then the empirical estimate of the moment estimate of v, c is as follows:
wherein,
the second order and the fourth order origin moments of the sample data are respectively.
The invention has the beneficial effects that: according to the sea clutter suppression method provided by the invention, a user can select different sea clutter distributions to carry out sea clutter estimation according to different sea conditions, and whether sample data needs to be updated for re-estimation is judged according to the suppression effect, so that the influence on a target is reduced to the minimum while the sea clutter is suppressed as much as possible, and the problem of great attenuation on the signal amplitude of a near target is solved.
Drawings
FIG. 1 is a schematic flow chart of the sea clutter suppression method of the marine radar of the present invention.
FIG. 2 is a schematic representation of the marine radar scan data of the present invention.
Fig. 3 is a schematic diagram of a result of performing sea clutter suppression on a piece of scan data by using weibull distribution according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of measured data of the marine radar according to the embodiment of the present invention.
Fig. 5 is a schematic diagram of a result of performing sea clutter suppression on measured data by using weibull distribution according to an embodiment of the present invention.
Detailed Description
Specific embodiments of the present invention are given below with reference to the accompanying drawings. It should be noted that: the parameters in the examples do not affect the generality of the invention.
The sea clutter characteristics include two aspects: firstly, determining the amplitude distribution and the power spectrum type of the sea clutter; secondly, parameters of the amplitude distribution and power spectrum model are determined according to a specific radar system and a radar working environment. The sea clutter amplitude distribution function is important for designing signal and information processing algorithms such as target detection, estimation, tracking and identification. The flow diagram of the embodiment of the invention is shown in fig. 1, and specifically as follows:
s1, initializing, namely completing the initialization setting of parameters and the initialization of data, and specifically comprising the following steps:
s11, selecting Weibull distribution according to the actual sea situation and the performance of the radar,
s12, setting parameters: the number of scanning lines N processed in each sample update cycle is 100, the number of scanning lines M required for each sample estimation is 15, the threshold value α of the re-estimation parameter is 0.2, the threshold value β of the binarization is 60,
s13, initializing sample data: read 15 scan lines, denoted as { T }i(k) 1, 2.. 15, k ═ 1,... Num }, represented by the formula
Calculating the number of data points of the sea clutter on one scanning line, where n is the number of data points of the sea clutter on one scanning line, as shown in fig. 2, D is the farthest distance of the sea clutter near the radar from the radar, where D is 3 nautical miles, the detection distance Range of the radar is 60 nautical miles, and the number Num of data points on one scanning line is 1000, so as to obtain the first 50 point scanning line data { T ═ T ] of 15 scanning linesi(k) 1, 2.. 15, k ═ 1,.. 50}, and the average value is taken and recorded as
And saved as sample data.
S2, parameter estimation, namely selecting the distribution of the sea clutter by sample data C (k) and the step S1, and estimating the distributed parameters to obtain sea clutter data;
estimating required parameters p and q by sample data C (k) and Weibull distribution obeyed by sea clutter, wherein the specific process is as follows:
the weibull distribution probability density function is as follows,
in the formula, q is a scale parameter and is generally smaller than the peak position of curve distribution; p is a shape parameter, the value range is more than 0 and less than or equal to 2, and when the shape parameter p is 1, the Weibull distribution is degenerated into exponential distribution; when the shape parameter p is 2, the weibull distribution degenerates to a rayleigh distribution. As the shape parameter p decreases, the tailing of the probability density distribution becomes longer; it is assumed here that the data sequence for estimating the sea clutter distribution on one scan line of the radar is Z ═ ZiI is 1, L n, and n is the number of data points.
The Weibull distribution of the general sea clutter is empirical, is set as (1, 15) here, and is taken as iteration intervals (0, 2) and (0, 20) of design p and q, iterative operation is carried out, curve fitting is carried out, the difference value of the curve fitted each time and the actual radar data is recorded, and when the difference value is smaller than a given threshold value
And then, the p and q values of the iteration are used as estimated parameters, and the sea clutter data { B (k) }, wherein k is 1.
Since the estimated sea clutter data is a sample of the probability distribution, and the data interval is (0, 1), mapping of the data, i.e. mapping the obtained sea clutter data to the range corresponding to the scan line data, i.e. according to the equationPerforming data mapping to obtain mapped sea clutter data { D (k) }, wherein k is 1.
The result of sea clutter suppression of one piece of scan data of the radar is shown in fig. 3, and it can be seen that the sea clutter near the radar is well estimated, so that the target submerged by the sea clutter is effectively detected.
S3, reading 100 scanning line data and performing sea clutter suppression processing, namely, sea clutter data { T } on 100 scanning line datai(k) 1, 2.. 100, k ═ 1,. 50}, and the sea clutter data { d (k) } estimated in step S2, k ═ 1,. 50} are respectively subjected to subtraction, and the result is subjected to binarization processing according to a threshold 60. When the suppression is performed to 85 pieces, the average value of the scanning line data of the first 50 points of the last 15 pieces is stored while the clutter suppression is performed, and is recorded as
S4, judging whether the detection distance of the radar is changed or not, if so, executing a step S13, otherwise, executing a step S5;
s5, updating sample data if the sample data meets the requirement
Then step S3 is executed without re-estimating the sea clutter data; if not, the average value C' (k) of the scan line data of the first 50 points of 15 stored in step S3 is usedAnd (k) executing step S2 to re-estimate the parameters.
The scanning data of one frame of radar is shown in fig. 4, and the suppression result is shown in fig. 5, so that it can be seen that the sea clutter near the radar is effectively suppressed, and meanwhile, the target submerged by the sea clutter is effectively detected.
In the present embodiment, weibull distribution is used, but the present invention is not limited to weibull distribution, and in actual processing, other three distributions, i.e., rayleigh distribution, log-normal distribution, and K distribution, may be selected according to different situations.
The Rayleigh distribution is a classical function for describing the amplitude distribution of the sea clutter, is suitable for describing the sea clutter of the low-resolution radar, and when a sea clutter unit contains a large number of mutually independent scattering sources and does not contribute obviously to individuals, the envelope amplitude of the radar sea clutter obeys the Rayleigh distribution; the lognormal distribution is suitable for some flat area high-resolution sea clutter data; under the conditions of high-resolution radar and low incidence angle, the wave clutter of the general sea situation can be accurately described by using Weibull distribution; the K distribution has good fitting effect on sea clutter envelope data obtained when the radar works at a low incidence complementary angle, can simulate the long tail characteristic of sea clutter amplitude distribution and can also correctly simulate the time correlation of the sea clutter amplitude distribution, the characteristic is very important for accurately predicting the detection performance after echo pulse accumulation, and meanwhile, the K distribution also has a wide application range and is suitable for different types of radar sea clutter.
According to the sea clutter suppression method provided by the embodiment of the invention, a user can select different sea clutter distributions to carry out sea clutter estimation according to different sea conditions, and whether sample data needs to be updated to carry out re-estimation is judged according to the suppression effect, so that the influence on a target is reduced to the minimum while the sea clutter is suppressed as much as possible, and the problem of great attenuation on the amplitude of a near target signal is solved.
It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. All such possible equivalents and modifications are deemed to fall within the scope of the invention as defined in the claims.
Claims (7)
1. A method for suppressing sea clutter of a marine radar comprises the following steps:
s1, initializing, namely completing the initialization setting of parameters and the initialization of data, and specifically comprising the following steps:
s11, selecting the distribution of sea clutter according to the actual sea situation and the performance of the radar;
s12, setting parameters of a suppression method, N: number of scan lines processed in a sample update period, M: number of scanning lines required for sample estimation, α: re-estimated threshold, β: a threshold value of binarization;
s13, reading M scanning line data, and marking as { Ti(k) M, k 1, 2, Num, represented by the formula
Calculating the data point number of the sea clutter on one scanning line, wherein n is the data point number of the sea clutter on one scanning line, D is the farthest distance between the sea clutter near the radar and the radar, Range is the detection distance of the radar, and Num is the number of the data points on one scanning line, and obtaining the first n scanning line data points { T } of M scanning linesi(k) M, k is 1, 2.. n }, and the average value is recorded as
And storing as sample data;
s2, parameter estimation and data mapping, wherein sample data C (k) and step S11 are used for selecting the distribution of the sea clutter and estimating the parameters of the distribution to obtain sea clutter data { B (k) }, wherein k is 1Carrying out data mapping to obtain mapped sea clutter data D (k);
s3, reading N scanning line data and carrying out sea clutter suppression treatment, namely carrying out subtraction operation on the N scanning line data and the sea clutter data D (k) estimated in the step S2, carrying out binarization treatment on the result according to a threshold value beta, reading the first N point data of the N-M +1 to N scanning data while carrying out sea clutter suppression when the suppression is carried out to N-M, and taking the average value of the data and recording the average value as the average value
And storing;
s4, judging whether the detection distance of the radar is changed or not, if so, executing a step S13, otherwise, executing a step S5;
s5, updating sample data if the sample data meets the requirement
Then step S3 is executed without re-estimating the sea clutter data; if not, the sample data C (k) is updated with the average value C' (k) of the M previous n scan line data points saved in step S3, and step S2 is executed to re-estimate the parameters.
2. The method for suppressing sea clutter of a marine radar according to claim 1, wherein said sea clutter is distributed in a rayleigh distribution, a lognormal distribution, a weibull distribution, and a K distribution.
3. The method for suppressing sea clutter according to claim 2, wherein the specific process of the parameter estimation of the rayleigh distribution is: the rayleigh distribution probability density function is as follows:
wherein σ2For average power, { zi1, L n, which is a sample data sequence used for estimating sea clutter distribution on a scanning line of the radar, and n is the number of data points, then a parameter σ of rayleigh distribution can be estimated according to the sample data;
4. the method for suppressing marine radar sea clutter according to claim 2, wherein the specific process of the lognormal distribution parameter estimation is as follows: the probability density function of the lognormal distribution is as follows
Wherein σ is a shape parameter and is a lognormal distribution ln ziThe standard deviation of (a), the tailing of the probability density distribution of which becomes longer as σ increases; μ is a scale parameter and is lognormal distribution ln ziMean value of { z }iI is 1, L n, which is a sample data sequence used for estimating sea clutter distribution on a scanning line of the radar, n is the number of data points, and the parameters sigma and mu of the lognormal distribution can be estimated according to the sample data by taking the number between 0.5 and 1.2 according to the difference of sea conditions;
5. the method for suppressing marine radar sea clutter according to claim 2, wherein the specific process of the parameter estimation of the weibull distribution is as follows: the weibull distribution probability density function is as follows,
wherein q is a scale parameter, p is a shape parameter, the value range is more than 0 and less than or equal to 2, and zi1, L n } for a sample on a scan line of the radar used to estimate the distribution of sea clutterThe data sequence, n being the number of data points, the distribution parameters (p, q) are estimated as follows: the Weibull distribution of sea clutter is empirical and is set to (p)0，q0) Designing an iteration interval (p) of p, q0-δp，p0+δp)，(q0-δq，q0+δq) Wherein δp，δqDetermining the size of p and q iteration intervals according to values preset according to actual conditions, wherein q is used as an outer loop and p is used as an inner loop, performing curve fitting, recording the difference value between curve data and sample data of each fitting, and taking the p and q values of the iteration as parameters when the difference value is smaller than a given threshold value.
6. The method for suppressing marine radar sea clutter according to claim 2, wherein the specific process of the parameter estimation of the weibull distribution is as follows: the weibull distribution probability density function is as follows,
wherein q is a scale parameter, p is a shape parameter, the value range is more than 0 and less than or equal to 2, and Z is { Z ═i1, L n, for a sample data sequence on a scan line of the radar used for estimating the distribution of the sea clutter, n is the number of data points, and the distribution parameters (p, q) are estimated as follows:
wherein<.>The moment estimate is represented.
7. The method for suppressing marine radar sea clutter according to claim 2, wherein the specific process of the parameter estimation of the K-distribution is: the K-distribution probability density function is as follows,
wherein, Kv(.) is a modified Bessel function of the second type, c is a scale parameter which influences the mean power of the clutter, and the shape parameter v reflects the degree of bias of the K distribution, { z }iI is 1, L n, which is a sample data sequence used for estimating the distribution of the sea clutter on one scanning line of the radar, n is the number of data points,
and (3) estimating parameters of the K distribution by adopting a moment estimation method, and for known samples, estimating the overall corresponding origin moment of each order by adopting the origin moment of the samples:
then the empirical estimate of the moment estimate of v, c is as follows:
wherein,the second order and the fourth order origin moments of the sample data are respectively.
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