CN115372922A - Sea surface target detection method based on statistical entropy - Google Patents

Sea surface target detection method based on statistical entropy Download PDF

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CN115372922A
CN115372922A CN202210842450.XA CN202210842450A CN115372922A CN 115372922 A CN115372922 A CN 115372922A CN 202210842450 A CN202210842450 A CN 202210842450A CN 115372922 A CN115372922 A CN 115372922A
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sea clutter
amplitude
statistical
sequence
sea
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范一飞
陈铎
陈士超
张翔
陶明亮
粟嘉
王伶
张兆林
李滔
谢坚
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Northwestern Polytechnical University
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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    • G—PHYSICS
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    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/28—Details of pulse systems
    • G01S7/285—Receivers
    • G01S7/292—Extracting wanted echo-signals
    • G01S7/2923—Extracting wanted echo-signals based on data belonging to a number of consecutive radar periods
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    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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    • G01S7/2927—Extracting wanted echo-signals based on data belonging to a number of consecutive radar periods by deriving and controlling a threshold value
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Abstract

The invention relates to a sea surface target detection method based on statistical entropy, which mainly solves the problem that the performance of the traditional Constant False Alarm Rate (CFAR) algorithm is reduced when the matching of a sea clutter model is inaccurate, and comprises the following implementation steps: 1. carrying out normalization processing on the sea clutter amplitude sequence; 2. calculating a statistical histogram of the sea clutter amplitude sequence, and analyzing the characteristic difference between the statistical histogram of the target distance unit and the statistical histogram of the pure sea clutter distance unit; 3. calculating an amplitude statistical entropy of the sea clutter model based on the sea clutter amplitude statistical histogram; 4. and performing target detection based on the nonparametric CFAR and in combination with the calculation result of the sea clutter model amplitude statistical entropy, and outputting a target detection result. The detection performance of the method is superior to that of the traditional CFAR target detection algorithm, and the detection performance of the radar on the small sea surface target is improved.

Description

Sea surface target detection method based on statistical entropy
Technical Field
The invention relates to a sea surface target detection method based on statistical entropy, belonging to the technical field of radar signal processing.
Background
The sea clutter is a back scattering echo generated by the sea surface under the irradiation of the radar, and the detection capability of the radar on a sea surface target can be seriously influenced by the presence of the sea clutter. The formation mechanism of the sea clutter is quite complex, and the characteristics of the sea clutter are not only dependent on the current sea surface environment parameters, but also influenced by the radar working parameters.
The traditional Constant False Alarm Rate (CFAR) target detection algorithm is based on the statistical characteristics of the sea clutter, a mathematical statistical model is used for carrying out statistical modeling on the sea clutter, and a corresponding detection threshold is calculated to realize target detection. For the sea clutter model, rayleigh distribution, lognormal distribution and Weibull distribution are firstly used for describing the amplitude statistical characteristics of the sea clutter, and then the statistical characteristics of fitting the sea clutter by using K distribution and Pareto distribution are provided in consideration of the time-space correlation and trailing characteristics of the sea clutter. However, when the sea state is high, the presence of a large number of sea spikes and anomalous scattering units may cause a mismatch of the sea clutter model, resulting in a decrease in target detection performance.
Disclosure of Invention
Technical problem to be solved
The target detection algorithm based on the sea clutter amplitude statistical entropy overcomes the defect that the target detection capability is reduced when the sea clutter amplitude models are not matched in the traditional CFAR target detection algorithm, the characteristic difference of different sea clutter amplitude histogram expansion degrees between the target unit and the sea clutter unit is described by using the characteristics of the entropy, the target detection algorithm based on the sea clutter amplitude statistical entropy is provided, and the target detection performance under the sea clutter background is improved.
Technical scheme
A sea surface target detection method based on statistical entropy is characterized by comprising the following steps:
step 1: sea clutter sequence normalization processing
The sea clutter amplitude sequence is as follows: x = { X k K =1,2,3.. N }, where N denotes the length of the sea clutter amplitude sequence, and the new sequence X' is obtained by performing normalization processing using the standard deviation of the sea clutter amplitude sequence:
X'＝X/std(X)
wherein std (X) is the standard deviation of the sea clutter amplitude sequence;
step 2: sea clutter amplitude histogram calculation
Step 2.1: calculating the number of statistics in a sea clutter amplitude histogram
Aiming at the normalized sea clutter sequence X' obtained in the step 1, calculating the number of all statistics in the sea clutter amplitude histogram by using the following formula:
Figure BDA0003751665330000021
wherein lx represents the number of all statistics, fix (-) represents rounding down, and m and n represent any positive integer;
step 2.2: calculating the amplitude of each statistic
Aiming at the number of statistics in the sea clutter amplitude histogram calculated in the step 2.1, the sea clutter sequence is placed in each affiliated statistical component according to the amplitude of the sea clutter sequence, the number nn of the sea clutter sequence in each statistic is calculated, namely the amplitude of each statistic is stored in a vector nn, and the calculation steps are as follows:
d＝(max(X')-min(X'))/lx
(id,(i+1)d),i＝0,1...,lx-1
wherein d represents the width of each statistic, namely the maximum value minus the minimum value of the amplitude of the sea clutter sequence contained in each statistic, (id, (i + 1) d), i =0,1 \8230, and lx-1 represents the amplitude interval which can contain the sea clutter sequence in the ith statistic; drawing a sea clutter amplitude statistical histogram according to the calculation result;
and step 3: computing statistical entropy of sea clutter model amplitude
Calculating the amplitude statistical entropy of the sea clutter model aiming at the statistical histogram obtained in the step 2; constructing a sea clutter model amplitude statistical entropy to describe the difference of a statistical histogram between a target distance unit and a sea clutter distance unit; the calculation process of the amplitude statistical entropy of the sea clutter model is as follows:
F＝nn/N
Figure BDA0003751665330000031
wherein F = nn/N represents the conversion of the frequency nn into the frequency F, E represents the calculation result of the statistical entropy of the amplitude of the sea clutter model, and F (i) represents the ith frequency component;
and 4, step 4: object detection
And (3) performing target detection by taking the result of the amplitude statistical entropy E calculated in the step (3) as the input of a feature detector, wherein the structure of the feature detector is as follows:
Figure BDA0003751665330000032
wherein Z represents a decision threshold, H 0 Unit for indicating distance of sea clutter H 1 Representing the target range bin.
A computer system, comprising: one or more processors, a computer readable storage medium, for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the above-described method.
A computer-readable storage medium having stored thereon computer-executable instructions for performing the above-described method when executed.
Advantageous effects
The sea surface target detection method based on the statistical entropy can be applied to a shore-based/missile-borne/airborne sea detection radar, the characteristic difference of echo signal statistical histograms of targets and clutter distance units is described by defining the amplitude statistical entropy, the target detection is further realized by utilizing the characteristic difference, and the detection capability of the radar on small sea surface targets is improved. Has the following beneficial effects:
1. according to the method, the amplitude statistical histogram of the sea clutter sequence is calculated, the echo signal statistical histogram of the target distance unit and the sea clutter distance unit is analyzed, and amplitude statistical entropy characteristics are extracted and used as the input of a target detector. Because the difference between the sea clutter and the target can be fully described by the amplitude statistical entropy characteristics, the target detection performance can be improved by the detection method based on the amplitude statistical entropy.
2. Because the target detection algorithm based on the amplitude statistical entropy does not depend on the sea clutter amplitude model, the defect that the target detection capability is reduced due to model mismatch in the traditional CFAR target detection method is overcome, and the detection performance of the radar on small targets on the sea is improved.
Drawings
The drawings are only for purposes of illustrating particular embodiments and are not to be construed as limiting the invention, wherein like reference numerals are used to designate like parts throughout.
FIG. 1 is a flow chart of an algorithmic implementation of the present invention;
FIG. 2 is a range-time-intensity image of an X-band radar echo;
FIG. 3 is a statistical histogram of target range unit radar echo amplitudes and pure sea clutter range unit sea clutter amplitudes: a. a sea clutter distance unit; b. a target distance unit;
FIG. 4 is a target detection result based on the statistical entropy of the measured echo and the sea clutter amplitude of the X-band radar.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
Referring to fig. 1, the specific implementation process of the present invention is as follows:
step 1, sea clutter amplitude sequence normalization processing. The sea clutter amplitude sequence is as follows: x = { X k K =1,2,3.. N } is normalized by the standard deviation of the sea clutter amplitude sequence to obtain a new sequence X'.
X'＝X/std(X)
And 2, calculating a sea clutter amplitude histogram.
a) Calculating the number of each statistic of the sea clutter amplitude histogram
And (3) calculating the number of each statistic of the sea clutter amplitude histogram by using the following formula according to the normalized sea clutter sequence X' obtained in the step 1.
Figure BDA0003751665330000051
Wherein lx represents the sum of all statistics, fix (·) represents rounding-down, m and n represent any positive integer, but the selection of m and n should be adapted to the length and amplitude of the sea clutter sequence as much as possible, so that the drawn statistical histogram is more beautiful.
b) Calculating the amplitude of each statistic
According to the number of statistics in the sea clutter amplitude histogram calculated in the step a and the amplitude of the sea clutter sequence, the sea clutter sequence is placed in each corresponding statistical unit, the number nn of the sea clutter sequences in the statistical unit, namely the amplitude of each column is stored in a vector nn, and the calculation steps are as follows:
d＝(max(X')-min(X'))/lx
(id,(i+1)d),i＝0,1…,lx-1
wherein d represents the width of any one statistic, namely the maximum value minus the minimum value of the amplitude of the sea clutter sequences contained in any one statistic, (id, (i + 1) d), i =0,1 \8230, and lx-1 represents the amplitude interval in which the ith statistic can contain the sea clutter sequences. And drawing a statistical histogram of the sea clutter amplitude according to the calculation result.
And 3, calculating the amplitude statistical entropy of the sea clutter model according to the statistical histogram obtained in the step 2. The calculation process of the amplitude statistical entropy of the sea clutter model is as follows:
F＝nn/N
Figure BDA0003751665330000052
wherein F = nn/N represents the conversion of the frequency nn into the frequency F, E represents the calculation result of the statistical entropy of the amplitude of the sea clutter model, and F (i) represents the ith frequency component.
Step 4, target detection
And (3) performing target detection by taking the result of the amplitude statistical entropy E obtained by calculation in the step (3) as the input of a feature detector, wherein the structure of the feature detector is as follows:
Figure BDA0003751665330000061
wherein Z represents a decision threshold, and the values of the statistical entropy E of the amplitudes of different distance units are independently and identically distributed, so that the value of the decision threshold Z can be determined by a nonparametric CFAR method, and H is 0 Indicates that there is no target in the range bin, H 1 Indicating the presence of a target at that range bin. The reason for setting the decision threshold in this way is: when the target appears, the amplitude of the echo signal is relatively increased, so that the distribution of the pillars in the statistical histogram is expanded, and the value of the statistical entropy E of the amplitude of the sea clutter is increased.
The effects of the present invention can be further illustrated by the following measured data tests:
the experimental conditions are as follows: the experiment adopts the actual measurement of an X-band radar to carry out the analysis of a statistical histogram of the amplitude of the sea clutter and the calculation of the amplitude entropy of the sea clutter, the height of the X-band radar is 30m, the experiment adopts data collected by a radar polarization mode under HH, and the SCR is about 0-6 dB. The main parameter indexes of the X-band radar are shown in the table 1, and the distance-time-intensity image of the echo of the X-band radar is shown in the figure 2.
Table 1X-band radar parameter index table
Figure BDA0003751665330000062
Test contents and results:
the method is characterized in that entropy is utilized to describe the data dispersion degree in an information theory, the difference between the target distance unit and the sea clutter amplitude statistical histogram of the sea clutter distance unit is focused, the amplitude entropy of the target distance unit and the amplitude entropy of the sea clutter distance unit are respectively calculated, and non-parametric CFAR is utilized to detect the target.
Fig. 3 is a statistical histogram of the amplitude of the target distance unit and the sea clutter distance unit, respectively, where fig. 3.a is a statistical histogram of the sea clutter distance unit and fig. 3.b is a statistical histogram of the target distance unit. From a comparison, it can be seen that the distribution of the statistics in fig. 3.B is more dispersed than in fig. 3.A, which also corroborates the conclusion that the amplitude entropy increases when the target appears.
Fig. 4 shows the calculation results of the amplitude entropies of different range bins, wherein the dotted line represents the calculated threshold value under the non-parametric CFAR, and the circle represents the amplitude entropies of different range bins. It can be seen from the figure that the magnitude entropy of the distance units is smaller than the threshold value only if the magnitude entropy of the seventh distance unit is larger than the threshold value, and the magnitude entropies of the other surrounding distance units are smaller than the threshold value, which indicates that the target only appears in the seventh distance unit.
It can thus be concluded that: the sea clutter and the targets can be distinguished in the sea target detection by using the difference of the target distance unit and the sea clutter distance unit in the amplitude entropy value.
Analyzing the target detection performance: in this section, the detection performance of the proposed method was analyzed. Table 2 shows the detection probability comparison between the target detection method provided by the present invention and the conventional CFAR target detection algorithm when the false alarm rate is one thousandth.
TABLE 2 comparison of the test performances of the different methods
Figure BDA0003751665330000071
As can be seen from Table 2, the detection performance of the method is superior to that of the traditional CFAR target detection algorithm, and the detection performance of the method for detecting the small targets on the sea under the condition of low signal-to-noise ratio is improved.
While the invention has been described with reference to specific embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention.

Claims (3)

1. A sea surface target detection method based on statistical entropy is characterized by comprising the following steps:
step 1: sea clutter sequence normalization processing
The sea clutter amplitude sequence is as follows: x = { X k K =1,2,3.. N }, where N denotes the length of the sea clutter amplitude sequence, and the new sequence X' is obtained by performing normalization processing using the standard deviation of the sea clutter amplitude sequence:
X'＝X/std(X)
wherein std (X) is the standard deviation of the sea clutter amplitude sequence;
step 2: sea clutter amplitude histogram calculation
Step 2.1: calculating the number of statistics in a sea clutter amplitude histogram
Aiming at the normalized sea clutter sequence X' obtained in the step 1, calculating the number of all statistics in the sea clutter amplitude histogram by using the following formula:
Figure FDA0003751665320000011
wherein lx represents the number of all statistics, fix (-) represents rounding down, and m and n represent any positive integer;
step 2.2: calculating the amplitude of each statistic
Aiming at the number of the statistics in the sea clutter amplitude histogram calculated in the step 2.1, the sea clutter sequence is placed in each statistical component according to the amplitude of the sea clutter sequence, the number nn of the sea clutter sequence in each statistic is calculated, namely the amplitude of each statistic is stored in a vector nn, and the calculation steps are as follows:
d＝(max(X')-min(X'))/lx
(id,(i+1)d),i＝0,1...,lx-1
wherein d represents the width of each statistic, namely the maximum value minus the minimum value of the amplitude of the sea clutter sequence contained in each statistic, (id, (i + 1) d), i =0,1, lx-1 represents the amplitude interval which can contain the sea clutter sequence in the ith statistic; drawing a sea clutter amplitude statistical histogram according to the calculation result;
and step 3: computing statistical entropy of sea clutter model amplitude
Calculating the amplitude statistical entropy of the sea clutter model aiming at the statistical histogram obtained in the step 2; constructing a sea clutter model amplitude statistical entropy to describe the difference of a statistical histogram between a target distance unit and a sea clutter distance unit; the calculation process of the sea clutter model amplitude statistical entropy is as follows:
F＝nn/N
Figure FDA0003751665320000021
wherein F = nn/N represents the conversion of the frequency nn into the frequency F, E represents the calculation result of the statistical entropy of the amplitude of the sea clutter model, and F (i) represents the ith frequency component;
and 4, step 4: object detection
And (3) performing target detection by taking the result of the amplitude statistical entropy E calculated in the step (3) as the input of a feature detector, wherein the structure of the feature detector is as follows:
Figure FDA0003751665320000022
wherein Z represents a decision threshold, H 0 Unit for indicating distance of sea clutter H 1 Representing the target range bin.
2. A computer system, comprising: one or more processors, a computer readable storage medium, for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of claim 1.
3.A computer-readable storage medium having stored thereon computer-executable instructions, which when executed, perform the method of claim 1.
CN202210842450.XA 2022-07-18 2022-07-18 Sea surface target detection method based on statistical entropy Pending CN115372922A (en)

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