CN113466798B - Sea surface small target detection method based on polarized signal multi-scale entropy characteristics - Google Patents
Sea surface small target detection method based on polarized signal multi-scale entropy characteristics Download PDFInfo
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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
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- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
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- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
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Abstract
According to the sea surface small target detection method based on the polarized signal multi-scale entropy characteristics, provided by the invention, the radar echo signal single characteristics are adopted, the algorithm structure is simple, and the calculated amount and complexity are small; the parallel architecture and algorithm can be adopted, so that the real-time processing requirement of the radar echo signals is further met; after using the polarized multi-scale entropy index, a unit containing a small target distance will be detected with the smallest multi-scale entropy index value. Moreover, under the polarized multi-scale entropy index, the multi-scale entropy index of sea clutter presents stable or unchanged characteristics in all test data, and part of sudden fluctuation can be removed by adopting a fitting method, so that the characteristics of the sea clutter can be better identified, and the target detection performance can be further improved.
Description
Technical Field
The invention relates to the technical field of radar signal processing, in particular to a sea surface small target detection method based on polarized signal multi-scale entropy characteristics.
Background
The application requirements of the radar detection of the small targets on the sea surface are huge in the fields of sea telemetry, ship traffic safety, sea emergency rescue and the like, but the radar detection of the small targets on the sea surface has great challenges. This is because small target radar echoes on the sea are usually small, and particularly under high sea conditions, the radar echoes of the target are usually "annihilated" by sea radar clutter, which results in a large false alarm probability (FAR, false Alarm Rate) for small target detection, and even cannot be detected at all.
The radar detection problem of the small sea surface target is essentially characterized by a classification problem, namely, sea surface clutter is accurately distinguished from target echoes by single or multiple characteristics of sea surface radar echo signals. Further, the current technical methods for solving the problem are mainly classified into single-feature and multi-feature methods. The single-feature method mainly comprises a scattering statistical method, a sea clutter fractal or chaotic feature method, an information entropy feature method and the like; the multi-feature method mainly comprises a time-frequency feature analysis method of radar echo signals, such as distance-Doppler features and the like; or a machine learning (SVM, support Vector Machine) method combining a plurality of single features such as information entropy, fractal, etc., but the above method has the following drawbacks:
Disadvantage 1:
The mathematical models established by single feature methods such as scatter statistics, fractal, etc. on sea clutter are all based on the assumption of independent scattering (INDEPENDENT SCATTERING), which, although good results are achieved in some applications, does not fit the radar scattering mechanism in all real natural scenarios.
Disadvantage 2:
Lack of decisive features; such as the statistical distribution of sea clutter cannot be described by a single distribution and morphological parameters, so that the false alarm probability exists when the method is adopted, and an adaptive threshold technology is required to maintain a Constant false alarm probability (CFAR, constant FALSE ALARM RATE); or further combining a plurality of single features to form a decisive feature for seeking stable or controllable false alarm probability.
Therefore, the sea surface small target detection method based on the polarized signal multi-scale entropy characteristics is needed, the requirement of radar echo signal real-time processing can be met, the method can be adapted to different sea condition conditions, and the target detection performance is improved.
Disclosure of Invention
First, the technical problem to be solved
In order to solve the problems in the prior art, the invention provides a sea surface small target detection method based on polarized signal multi-scale entropy characteristics, which can realize the requirement of radar echo signal real-time processing, can be suitable for different sea condition conditions, and improves the target detection performance.
(II) technical scheme
In order to achieve the above purpose, the invention adopts the following technical scheme:
a sea surface small target detection method based on polarized signal multi-scale entropy characteristics comprises the following steps:
s1, acquiring radar echo data, and determining the maximum sliding window length of the radar echo data;
s2, constructing a multi-scale entropy matrix according to the maximum sliding window length, and calculating a polarization multi-scale entropy index according to the constructed multi-scale entropy matrix;
And S3, detecting the radar echo data according to the polarization multi-scale entropy index.
(III) beneficial effects
The invention has the beneficial effects that: the radar echo signal single feature is adopted, the algorithm structure is simple, and the calculated amount and complexity are small; the parallel architecture and algorithm can be adopted, so that the real-time processing requirement of the radar echo signals is further met; after using the polarized multi-scale entropy index, a unit containing a small target distance will be detected with the smallest multi-scale entropy index value. Moreover, under the polarized multi-scale entropy index, the multi-scale entropy index of sea clutter presents stable or unchanged characteristics in all test data, and part of sudden fluctuation can be removed by adopting a fitting method, so that the characteristics of the sea clutter can be better identified, and the target detection performance can be further improved.
Drawings
FIG. 1 is a flowchart of a sea surface small target detection method based on polarized signal multi-scale entropy characteristics in an embodiment of the invention;
FIG. 2 is a schematic diagram illustrating convergence of multi-scale entropy under VV polarization according to an embodiment of the present invention;
FIG. 3 is a schematic diagram showing convergence of multi-scale entropy under HH polarization in accordance with an embodiment of the present invention;
FIG. 4 is a PCI schematic diagram of the IPIX radar data of embodiment 17 of the present invention under the common sliding window length;
FIG. 5 is a PCI schematic diagram of the IPIX radar data of embodiment 18 of the present invention under the common sliding window length;
FIG. 6 is a PCI schematic diagram of the IPIX radar data of embodiment 19 of the present invention under the common sliding window length;
FIG. 7 is a PCI schematic diagram of the IPIX radar data of embodiment 25 of the present invention under the common sliding window length;
FIG. 8 is a PCI schematic diagram of example 26 IPIX radar data of the present invention at a common sliding window length;
FIG. 9 is a PCI schematic diagram of the IPIX radar data of embodiment 30 of the present invention under the common sliding window length;
FIG. 10 is a PCI schematic diagram of the IPIX radar data of embodiment 31 of the present invention under the common sliding window length;
FIG. 11 is a PCI schematic diagram of the IPIX radar data of embodiment 40 of the present invention under the common sliding window length;
FIG. 12 is a PCI schematic diagram of embodiment 54 of the present invention with common sliding window length for IPIX radar data;
FIG. 13 is a PCI schematic diagram of the IPIX radar data No. 280 of the embodiment of the present invention under the common sliding window length;
FIG. 14 is a PCI schematic diagram of IPIX radar data of embodiment 283 of the present invention at a common sliding window length;
FIG. 15 is a PCI schematic diagram of IPIX radar data of embodiment 310 of the present invention at a common sliding window length;
FIG. 16 is a PCI schematic diagram of embodiment 311 of the present invention with common sliding window length for IPIX radar data;
FIG. 17 is a PCI schematic diagram of embodiment 320 of the present invention with common sliding window length for IPIX radar data;
FIG. 18 is a PCI schematic diagram of example 17 IPIX radar data of the present invention at maximum sliding window length;
FIG. 19 is a PCI schematic diagram of example 18 IPIX radar data of the present invention at maximum sliding window length;
FIG. 20 is a PCI schematic diagram of example 19 IPIX radar data of the present invention at maximum sliding window length;
FIG. 21 is a PCI schematic diagram of example 25 IPIX radar data of the present invention at maximum sliding window length;
FIG. 22 is a PCI schematic diagram of example 26 IPIX radar data of the present invention at maximum sliding window length;
FIG. 23 is a PCI schematic diagram of example 30 IPIX radar data of the present invention at maximum sliding window length;
FIG. 24 is a PCI schematic diagram of example 31 IPIX radar data of the present invention at maximum sliding window length;
FIG. 25 is a PCI schematic diagram of example 40 IPIX radar data of the present invention at maximum sliding window length;
FIG. 26 is a PCI schematic diagram of example 54 IPIX radar data of the present invention at maximum sliding window length;
FIG. 27 is a PCI schematic diagram of embodiment 280 of the present invention with the maximum sliding window length of IPIX radar data;
FIG. 28 is a PCI schematic diagram of IPIX radar data under maximum sliding window length in embodiment 283 of the present invention;
FIG. 29 is a PCI schematic diagram of embodiment 310 of the present invention with the maximum sliding window length of IPIX radar data;
FIG. 30 is a PCI schematic diagram of embodiment 311 of the present invention with the maximum sliding window length of IPIX radar data;
fig. 31 is a schematic diagram of PCI of the IPIX radar data No. 320 of the present invention at maximum sliding window length.
Detailed Description
The invention will be better explained by the following detailed description of the embodiments with reference to the drawings.
Example 1
Referring to fig. 1, the sea surface small target detection method based on the polarized signal multi-scale entropy features comprises the following steps:
s1, acquiring radar echo data, and determining the maximum sliding window length of the radar echo data;
The step S1 specifically comprises the following steps:
Acquiring radar echo data { s i } (i=1, 2,3., rb), and determining the maximum sliding window length corresponding to the radar echo data when the multi-scale entropy converges.
The multi-scale entropy convergence judgment basis is as follows:
Wherein d represents the Euclidean distance of the two vectors;
MSE represents multi-scale entropy;
delta represents a threshold value, and the value is 0.01;
τ represents a scale factor, and the value is 20;
r=0.15 x std { s }, where std { s } represents the standard deviation of the random sequence s;
If the above determination is established, the maximum sliding window length m max,i =m, otherwise, setting m=m+1, and repeating the above determination until the determination is established;
After determining the longest sliding window lengths of the radar echo data of the rb distance units, respectively, the maximum sliding window lengths of the set of radar data are defined as: m max,i＝max{mmax,1,mmax,2,mmax,3,...,mmax,i,...,mmax,rb }.
S2, constructing a multi-scale entropy matrix according to the maximum sliding window length, and calculating a polarization multi-scale entropy index according to the constructed multi-scale entropy matrix;
The step S2 specifically comprises the following steps:
S21, according to the maximum sliding window length, respectively adopting a multi-scale entropy algorithm for radar echo data containing rb distance units under VV and HH polarization to construct multi-scale entropy matrixes, wherein MSE VV,MSEHH is respectively adopted, and the dimension of each multi-scale entropy matrix is tau x rb;
S22, calculating a polarized multi-scale entropy index matrix PMSE according to the multi-scale entropy matrix MSE VV,MSEHH;
S23, calculating a polarized multi-scale entropy index PCI according to the polarized multi-scale entropy index matrix PMSE.
The method for calculating the polarized multi-scale entropy index matrix PMSE comprises the following steps:
or is equivalent to
The method for calculating the polarized multi-scale entropy index PCI comprises the following steps:
Where column represents the column summation over the PMSE matrix.
The dimension of the obtained polarized multi-scale entropy index PCI is 1' rb, and the elements respectively represent the polarized multi-scale entropy indexes of rb distance units.
And S3, detecting the radar echo data according to the polarization multi-scale entropy index.
The step S3 specifically comprises the following steps:
S31, searching a minimum value in the polarization multi-scale entropy index;
s32, acquiring a sequence number of a distance unit where the minimum value is located, wherein the distance unit is the unit where the target is located.
(1) The algorithm is efficient and real-time;
the multi-scale sample entropy of radar echo signal single feature-radar echo amplitude is adopted, the algorithm structure is simple, and the calculated amount and complexity are small; the parallel architecture and algorithm can be adopted, so that the real-time processing requirement of the radar echo signals is further met;
(2) Excellent sea condition suitability;
Previous methods, such as sea clutter radar echo statistical modeling, are difficult to realize stable and controllable false alarm probability under all sea conditions, and therefore cannot be a good decisive feature; the polarized multi-scale entropy index provided by the invention shows the same detection effect under all sea conditions of the prior IPIX radar data (14 groups in total) in 1993, namely, a distance unit containing a target always has the minimum polarized multi-scale entropy multi-scale characteristic compared with sea clutter, which proves that the polarized multi-scale entropy index has excellent sea condition adaptability;
(3) The sea clutter identification capability is good;
18-31, after the polarized multi-scale entropy index is adopted, the multi-scale entropy index of sea clutter presents stable or unchanged characteristics in all test data, and part of sudden fluctuation can also be removed by adopting a fitting method, so that the characteristics of the sea clutter can be better identified, and the target detection performance can be further improved.
In addition, in the invention, when a multi-scale entropy algorithm is adopted as a basic algorithm for analyzing the echo signals of the sea surface radar target, the maximum sliding window length under multi-scale entropy convergence is found and pointed out to be used as a parameter input for multi-scale entropy estimation, so that the polarization multi-scale entropy index provided by the invention is effective for sea surface target detection; creatively proposes to combine multiscale entropy features of VV and HH polarization signals to construct a polarization multiscale entropy index, and to detect sea surface targets by the multi-scale entropy index, so that all separation of targets and sea surface clutter can be realized in a test data set.
Example two
The difference between the embodiment and the first embodiment is that the embodiment will further explain how the sea surface small target detection method based on the polarized signal multi-scale entropy features of the present invention is implemented with reference to a specific application scenario:
1. determining the length of a maximum sliding window;
Acquiring radar echo data { s i } (i=1, 2,3., rb), and determining the maximum sliding window length corresponding to the radar echo data when the multi-scale entropy converges.
The multi-scale entropy convergence judgment basis is as follows:
Wherein d represents the Euclidean distance of the two vectors;
MSE represents multi-scale entropy;
delta represents a threshold value, and the value is 0.01;
τ represents a scale factor, and the value is 20;
r=0.15 x std { s }, where std { s } represents the standard deviation of the random sequence s;
If the above determination is established, the maximum sliding window length m max,i =m, otherwise, setting m=m+1, and repeating the above determination until the determination is established;
After determining the longest sliding window lengths of the radar echo data of the rb distance units, respectively, the maximum sliding window lengths of the set of radar data are defined as: m max,i＝max{mmax,1,mmax,2,mmax,3,...,mmax,i,...,mmax,rb }.
2. Constructing a polarization multi-scale entropy index;
According to the maximum sliding window length, respectively adopting a multi-scale entropy algorithm to radar echo data containing rb distance units under VV and HH polarization to construct multi-scale entropy matrixes, namely MSE VV,MSEHH and tau x rb in the dimension;
calculating a polarized multi-scale entropy index matrix PMSE according to the multi-scale entropy matrix MSE VV,MSEHH;
And calculating a polarized multi-scale entropy index PCI according to the polarized multi-scale entropy index matrix PMSE.
The method for calculating the polarized multi-scale entropy index matrix PMSE comprises the following steps:
or is equivalent to
The method for calculating the polarized multi-scale entropy index PCI comprises the following steps:
Where column represents the column summation over the PMSE matrix.
The dimension of the obtained polarized multi-scale entropy index PCI is 1' rb, and the elements respectively represent the polarized multi-scale entropy indexes of rb distance units.
3. Extracting a target unit;
searching a minimum value in the polarized multi-scale entropy index;
And obtaining the sequence number of the distance unit with the minimum value, wherein the distance unit is the unit with the target.
FIGS. 2 and 3 show the convergence of multiscale entropy with sliding window length m for each of VV and HH polarizations for a range bin in a set of radar sea echoes, respectively; at a given scale factor, the sample entropy of the random sequence is affected by the sliding window length and the threshold. In the multi-scale entropy algorithm, the sample entropy of the sequence tends to zero along with the continuous increase of the threshold value; conversely, as the threshold value is continuously reduced, the sample entropy of the sequence tends to infinity; it is furthermore meaningless to discuss the convergence of the sample entropy with the threshold value. According to the sample entropy algorithm, the sample entropy is presumed to be converged to a stable value along with the increase of the sliding window length m; by specifying the convergence condition, the multi-scale entropy in fig. 2 and 3 will converge at m=6; the sliding window length at this time is also named maximum sliding window length.
The significance of fig. 2 and 3 is that in the past multi-scale entropy applications, the sliding window length takes on a value of m=2; but it is clear that the multi-scale entropy has not converged at m=2 in this example; it will be seen hereinafter that the present method must then require a converging multiscale entropy, i.e. effective at a sliding window length of m=6;
Fig. 4-17 are 17 sets of data using the IPIX radar in canada of 1993 (14 sets of test data are provided by the IPIX radar in canada, and the numbers are 17, 18, 19, 25, 26, 30, 31, 40, 54, 280, 283, 310, 311, 320 respectively), and the test performance of the proposed polarized multi-scale entropy indexes on targets under the common sliding window length m=2 is shown representatively. The polarized multi-scale entropy index of the target echo displayed in the data with the reference number of 17 and 19 has no obvious characteristic, and the target characteristic cannot be effectively distinguished from the sea echo characteristic; the polarization multi-scale entropy indexes of the target echo in the rest 12 groups of data are smaller than Sea echo, so that the target can be effectively detected, and the polarization multi-scale entropy indexes are proposed after the research discovers that the simple multi-scale entropy indexes utilizing VV or HH polarization cannot realize target detection under lower and moderate Sea background Conditions (Sea Conditions), integrate the multi-scale entropy characteristics of the VV and HH polarization, and even if the performance of the detected target is greatly improved in the condition of m=2 compared with the prior art, the target and Sea clutter perfect separation is not realized in the test data set.
Fig. 18-31 are 17 sets of data using the IPIX radar in canada of 1993, and fig. 18-31 respectively show the detection performance of the proposed polarized multi-scale entropy index for the target at the maximum sliding window length (m=6 in this example) of each set of data. Fig. 18-31 show that the polarized multi-scale entropy index of the target echo in each set of test data is smaller than the sea echo, so that the polarized multi-scale entropy index can be used as a decisive feature to distinguish the target from the sea echo, and the minimum polarized multi-scale entropy index difference (310 # dataset, 0.1289) between the target and the sea clutter is used as a threshold, so that the multi-scale entropy index can realize a hundred percent of target detection performance in each set of data in the test dataset.
The foregoing description is only illustrative of the present invention and is not intended to limit the scope of the invention, and all equivalent changes made by the specification and drawings of the present invention, or direct or indirect application in the relevant art, are included in the scope of the present invention.
Claims (3)
1. The sea surface small target detection method based on the polarized signal multi-scale entropy characteristics is characterized by comprising the following steps:
s1, acquiring radar echo data, and determining the maximum sliding window length of the radar echo data;
s2, constructing a multi-scale entropy matrix according to the maximum sliding window length, and calculating a polarization multi-scale entropy index according to the constructed multi-scale entropy matrix;
s3, detecting the radar echo data according to the polarized multi-scale entropy index;
The step S2 specifically comprises the following steps:
S21, according to the maximum sliding window length, respectively adopting a multi-scale entropy algorithm for radar echo data containing rb distance units under VV and HH polarization to construct multi-scale entropy matrixes, wherein MSE VV,MSEHH is respectively adopted, and the dimension of each multi-scale entropy matrix is tau x rb;
The multi-scale entropy matrix is multi-scale entropy expressed by a matrix with dimension tau x rb, the MSE VV represents the multi-scale entropy matrix under the VV polarization, and the MSE HH represents the multi-scale entropy matrix under the HH polarization;
rb represents the number of range bins of the radar echo data; τ represents a scale factor;
S22, calculating a polarized multi-scale entropy index matrix PMSE according to the multi-scale entropy matrix MSE VV,MSEHH;
The polarized multi-scale entropy index matrix PMSE represents multi-scale entropy matrix MSE VV and MSE HH which are expressed in a matrix form, and the multi-scale entropy characteristics are combined;
S23, calculating a polarized multi-scale entropy index PCI according to the polarized multi-scale entropy index matrix PMSE;
the polarized multi-scale entropy index is the column summation of a polarized multi-scale entropy index matrix PMSE;
The method for calculating the polarized multi-scale entropy index matrix PMSE comprises the following steps:
or is equivalent to
The method for calculating the polarized multi-scale entropy index PCI comprises the following steps:
where column represents the column summation over the PMSE matrix;
The step S3 specifically comprises the following steps:
S31, searching a minimum value in the polarization multi-scale entropy index;
s32, acquiring a sequence number of a distance unit where the minimum value is located, wherein the distance unit is the unit where the target is located.
2. The sea surface small target detection method based on the polarized signal multi-scale entropy characteristics according to claim 1, wherein step S1 specifically comprises:
Radar echo data { s i } i=1, 2,3 …, rb of rb distance units are acquired, and a maximum sliding window length corresponding to the radar echo data is determined when multi-scale entropy converges.
3. The sea surface small target detection method based on the polarized signal multi-scale entropy characteristics according to claim 2, wherein the multi-scale entropy convergence judgment basis is:
Wherein d represents the Euclidean distance of the two vectors;
MSE represents multi-scale entropy;
delta represents a threshold value, and the value is 0.01;
τ represents a scale factor, and the value is 20;
r=0.15 x std { s }, where std { s } represents the standard deviation of the random sequence s;
if the above determination is established, the maximum sliding window length m max,i =m, otherwise, setting m=m+1, and repeating the above determination until the determination is established;
After determining the longest sliding window lengths of the radar echo data of the rb distance units, respectively, the maximum sliding window lengths of the set of radar data are defined as: m max,i＝max{mmax,1,mmax,2,mmax,3,...,mmax,i,...,mmax,rb }.
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