CN110221265B - Distance extension target detection method based on strong scattering point self-adaptive estimation - Google Patents
Distance extension target detection method based on strong scattering point self-adaptive estimation Download PDFInfo
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Abstract
The invention provides a distance extension target detection method based on self-adaptive estimation of strong scattering points, aiming at the problem that the detection performance of a traditional extension target detector is not stable when the information of the scattering points of a target is unknown. Compared with the traditional algorithm, the algorithm provided by the invention has higher robustness, and does not need any prior information.
Description
Technical Field
The invention relates to the field of radar target detection, in particular to range expansion target detection, and provides a novel range expansion target detection method based on strong scattering point self-adaptive estimation.
Background
The range high-resolution radar can reduce the clutter power of each range unit and more accurately characterize the structural characteristics of the target, thereby beneficially affecting target detection, classification, identification and the like, and therefore, the range high-resolution radar is widely applied. When the distance resolution is far smaller than the target, the target energy is dispersed to the plurality of distance resolution units to form a distance extended target, and the detection performance is greatly lost by adopting the traditional point target constant false alarm detection algorithm, so that the constant false alarm detection algorithm for researching the distance extended target has great significance.
Two classic extended target detection algorithms proposed in the eighties of the last century are an energy accumulation detector (integrator) and a binary detector (M/N), respectively, wherein the energy accumulation detector accumulates all scattering points in a window to be detected, and has better detection performance when signal capacity is uniformly distributed in the detection window, and has larger detection loss when the scattering points of the target are sparse; the binary detector is an extension of a narrow-band constant false alarm detector, is easy to understand and realize, and has two disadvantages, namely, the number of strong scattering centers needing a priori target is large in limitation in practical application, and the detection performance is reduced by only using the information of the number of the scattering centers without using energy information during the second detection.
There are several researchers studying the optimal detector when the scattering point density is a priori. There is a document that proposes a Scattering point Density prior maximum likelihood estimation detector (SDD-GLRT), which can effectively detect extended targets with different sparsity/Scattering point densities, and is a statistically optimal detection, and does not fully utilize Scattering point information. In the literature, a double-Threshold Constant False Alarm detector (DT-CFAR) is proposed by using density and amplitude information of scattering points, and double-Threshold detection is performed by using double thresholds, and a Constant False Alarm is used for each detection, so that the detection performance of a sparse target is greatly improved, but the density information of prior scattering points is also required.
In recent years, many scholars have attempted to design extended target detectors that do not rely on a priori information on scattering points. Some researchers have proposed a distance extended Target detector (OS-RSTD) based on Order statistics, which first performs descending Order arrangement on the scattering point capability in the detection window, and then performs accumulation detection on all possible scattering point numbers exhaustively based on the sorted scattering points, until making a decision, and solves the problem that the traditional method depends on the Target scattering point information. However, in order to maintain constant false alarm, the false alarm rate of each detection channel is higher than that of the detector, and when the number of scattering points is small, the loss is large compared with that of a single-point detector. An improved dual-threshold maximum likelihood estimation detector (DT-GLRT) is proposed by the scholars, which does not use prior information of scattering points to estimate a first threshold, but uses maximum likelihood estimation and AIC criteria, and uses noise power as the first threshold, and the second threshold is calculated according to a false alarm rate and the number of strong scattering points. Compared with the traditional method, the performance of the method in different scattering point distribution environments is greatly improved, but the target scattering point information is not utilized in the first threshold calculation, so that the false alarm rate of the first detection is high, and the detection performance of the system is reduced because the false alarm point participates in the second detection.
In summary, the main problem of the conventional extended target detection method is that firstly, prior scattering point information is required, which cannot be satisfied in many application occasions; secondly, when the prior information is unknown, the detection performance of the traditional method is not stable when different scattering points are distributed.
Disclosure of Invention
Technical problem to be solved
The invention provides a distance extension target detection method based on strong scattering point self-adaptive estimation, aiming at the problems that the traditional extension target detection method needs scattering point prior information or the detection performance is not stable enough when different scattering points are distributed.
Technical scheme
A distance extension target detection method based on strong scattering point self-adaptive estimation is characterized by comprising the following steps:
step 1:the echo signal after square rate detection is input to the detector, and is recorded as Y = { Y = 1 ,y 2 ,...y J }；
Step 2: performing k-means clustering with the cluster of 2 on Y to obtain two cluster sets, and marking the cluster with the large mean value as C 1 And another cluster is marked as C 2 ；
And step 3: estimating the value of the number of strong scattering centers K: k = card (C) 1 ) Where card represents the number of elements in the matrix;
and 4, step 4: determining the value of a first threshold gamma as set C 2 The maximum value of the medium element, i.e.;
where max (, is taken to be the maximum value;
and 5: determining the value of the second threshold η:
the relationship among the number K of strong scattering points, the primary threshold gamma and the second threshold eta:
wherein, P fa Is the false alarm probability, σ, of the system 2 The power is noise power, and J is the number of input detection points;
will P fa 、σ 2 J and K are used as known quantities, a gamma value can be obtained by calculation for each eta, and the relation between gamma and eta is stored into a table for table lookup; inquiring corresponding eta according to the gamma obtained in the step 4 by adopting a lookup table method, and if the gamma in the table is not accurate, inquiring the eta corresponding to the closest gamma in the table to be used as a second threshold;
and 6: carrying out non-coherent accumulation on the strong scattering points and obtaining the quantity to be detected
And 7: and when D is larger than eta, judging that the target is present, otherwise, judging that the target is absent.
Advantageous effects
The invention provides a distance extended target detection method based on strong scattering point self-adaptive estimation, which can self-adaptively estimate the number of strong scattering points of a target without any prior information, and solves the problem that the traditional extended target method needs the prior scattering point information; the method has high accuracy in estimating the strong scattering points, so that the detection performance is stable when different scattering points are distributed, and the problem that the detection performance is not stable when the traditional method is distributed at different scattering points is solved.
Compared with the traditional group target identification method, the method has the advantages that:
1) In the threshold design, not only noise information but also target information are utilized, and the strong scattering point can be estimated in a self-adaptive manner, so that the detection performance under different scattering point distribution environments is improved;
2) The prior information of scattering points is not relied on, so that the application range of the scattering point is greatly expanded;
3) The number of the threshold and the strong scattering points is estimated by adopting a kmeans clustering algorithm, the algorithm operation amount is small, and the engineering realization is easy.
Drawings
FIG. 1 is a flow chart of an extended target detection method proposed by the present invention
Detailed Description
The invention will now be further described with reference to the following examples and drawings:
the method adopts a double-threshold idea, and firstly self-adaptively estimates the number of strong scattering points and a first threshold by using a kmeans clustering algorithm; then, a second threshold is determined according to the first threshold and the number of scattering points, so that the constant false alarm performance is ensured; and finally, completing detection through two judgments. The method comprises the following specific steps:
1) The input to the detector is an echo signal after square rate detection, and is denoted as Y = { Y = 1 ,y 2 ,...y J }；
2) Performing k-means clustering (cluster is 2) on Y to obtain two cluster sets, and marking the cluster with large mean value as C 1 And another cluster is marked as C 2 ；
3) Estimate of the number of strong scattering centers K, K = card (C) 1 ) Card stands for momentThe number of elements in the array;
4) The value of the first threshold gamma is set C 2 The maximum value of the medium element, i.e.;
where max (, is taken to be the maximum value;
5) The formula (1) is a relation among the number K of strong scattering points, a primary threshold gamma and a second threshold eta:
in the formula, P fa The false alarm probability of the system is preset. Sigma 2 The noise power can be estimated in real time, J is the number of input detection points, K is the number of strong scattering points estimated in step 3), and γ is the value of the first threshold estimated in step 4), so that the variable in the formula (1) only has a quadratic threshold η. But since eta cannot contain known quantity P fa 、σ 2 J, K and gamma, and using the method of lookup table to express P fa 、σ 2 J and K are known quantities, a gamma value can be calculated for each eta, and the relation between gamma and eta is stored into a table for table lookup. In the table lookup process, for a given P fa 、σ 2 And J, manufacturing a plurality of tables according to different K values for different K values estimated in the step 3). When the gamma value corresponding to which eta cannot be accurately corresponded, the estimated first threshold is used
Closest to the gamma value in the table
As a second threshold η;
6) Carrying out non-coherent accumulation on the strong scattering points and obtaining the quantity to be detected
7) And finishing detection, judging that the target is present if D is larger than eta, and otherwise, judging that the target is absent.
Claims (1)
1. A distance extension target detection method based on strong scattering point self-adaptive estimation is characterized by comprising the following steps:
step 1: the echo signal after square rate detection is input to the detector, and is recorded as Y = { Y = 1 ,y 2 ,...y J }；
And 2, step: performing k-means clustering with the cluster of 2 on Y to obtain two cluster sets, and recording the cluster with large mean value as C 1 And another cluster is marked as C 2 ；
And 3, step 3: estimating the value of the number of strong scattering centers K: k = card (C) 1 ) Where card represents the number of elements in the matrix;
and 4, step 4: determining the value of a first threshold gamma as set C 2 The maximum value of the medium element, i.e.;
where max (, is taken to be the maximum value;
and 5: determining a value of a second threshold η:
the relationship among the number K of strong scattering points, the first threshold gamma and the second threshold eta:
wherein, P fa Is the false alarm probability, σ, of the system 2 The power is noise power, and J is the number of input detection points;
will P fa 、σ 2 J and K are used as known quantities, a gamma value can be obtained by calculation for each eta, and the relation between gamma and eta is stored into a table for table lookup; when the gamma value corresponding to which eta can not be accurately corresponded, the estimated first threshold is used
With gamma in the tableThe values being closest
As a second threshold η;
step 6: carrying out non-coherent accumulation on the strong scattering points and obtaining the quantity to be detected
And 7: and if D is larger than eta, judging that the target is present, otherwise, judging that the target is absent.
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