CN102621535B - High-efficiency method for estimating covariance matrix structures - Google Patents

Abstract

The invention discloses an efficient covariance matrix structure estimation method, belongs to the field of radar signal processing, and mainly solves the problem of covariance matrix structure estimation of radar target self-adaptive detection under the background of non-Gaussian clutter waves. Aiming at the problem that neither the traditional sampling covariance matrix nor the normalized sampling covariance matrix can make the corresponding adaptive detector have complete CFAR characteristics, the present invention uses the method of dividing the real part and imaginary part data to calculate the sampling covariance after preprocessing. The variance matrix ensures that the obtained initialization matrix has complete CFAR characteristics for the clutter, and then makes full use of the real and imaginary parts of the auxiliary data to iterate, which not only reduces the computational complexity in the iterative process, but also helps to improve the estimation accuracy. The invention has popularization and application value in radar target adaptive CFAR detector.

Description

An efficient method for estimating the structure of covariance matrix

1. Technical field

The invention belongs to the field of radar target self-adaptive detection, and in particular relates to an efficient covariance matrix structure estimation method.

2. Background technology

Adaptive constant false alarm ratio (CFAR) detection of radar targets is an important part of radar target detection. For single-array radar target detection, it is only necessary to estimate the clutter power level of the detected unit to achieve adaptive CFAR detection of the target. However, when the radar is a multi-element radar, the target detection probability can be effectively improved by coherent accumulation of the echo signals of each array element, but at the same time, more clutter parameters need to be estimated.

In the Gaussian clutter background, the sample covariance matrix SCM (sample covariance matrix) is the maximum likelihood estimate (MLE) of the covariance matrix, and the radar target can be obtained by directly using the SCM instead of the real value of the covariance matrix. Adaptive CFAR detector. However, in the case of high resolution or low angle of incidence, the radar clutter is more in the form of non-Gaussian, at this time, the Gaussian model can no longer accurately simulate the radar clutter.

For non-Gaussian clutter, spherically invariant random vector (SIR V) is usually used for modeling, such as the classic K distribution clutter, Weibull distribution clutter, etc. can be modeled by SIRV. SIRV is composed of the product of the texture component representing the clutter power level and the Gaussian random vector representing the clutter speckle component. It is generally believed that the texture component values of different distance units are different, but have the same speckle component covariance matrix. The covariance moment of the speckle component is also called the covariance matrix structure of the clutter. Under the background of non-Gaussian clutter modeled by SIRV, Conte E et al. assumed that the clutter covariance matrix was known, and proposed a normalized matched filter (NMF), and further pointed out that only need The corresponding adaptive detector ANMF (adaptive NMF) can be obtained by replacing the covariance matrix in the NMF with the appropriate estimated value of the co-avoidance matrix. Since the ANMF detector itself is invariant to the clutter power level, only the structure of the clutter covariance matrix needs to be estimated. Since it is difficult to obtain the maximum likelihood estimation of the clutter covariance matrix structure under the non-Gaussian clutter background of SIRV modeling, the estimation problem of the covariance matrix structure becomes the The key to adaptive testing.

One method is to use SCM as an estimate of the covariance matrix structure, and the resulting adaptive detector is called SCM-ANMF. SCM-ANMF has CFAR properties for clutter covariance matrix structure, but cannot guarantee CFAR properties for clutter texture components, therefore, SCM-ANMF is not a complete CFAR detector. Another method is to use a normalized method for each distance unit clutter to eliminate the influence of texture components, that is, to use normalized SCM (normalized SCM, NSCM) as the estimate of the covariance matrix structure, and the obtained adaptive detection The device is called NSCM-ANMF. Although NSCM-ANMF guarantees the CFAR characteristics of the clutter texture components, it cannot guarantee the CFAR characteristics of the clutter covariance matrix structure. Therefore, SCM-ANMF is not a complete CFAR detector. Considering the CFAR characteristics of adaptive detectors, neither SCM nor NSCM is an effective estimation method.

Gini F proposed an iterative estimation method using NSCM as the initialization matrix, referred to as NSCM-RE. Under the finite number of iterations of NSCM-RE, the corresponding ANMF does not have complete CFAR characteristics theoretically, but after multiple iterations of NSCM-RE, the corresponding ANMF has approximate CFAR characteristics. At present, NSCM-RE has been widely used in the adaptive detection of non-Gaussian clutter. However, NSCM-RE needs multiple iterations to make the corresponding adaptive detector have approximate CFAR characteristics, which obviously increases the computational complexity.

3. Contents of the invention

1. Technical problems to be solved

The purpose of the present invention is to provide a kind of efficient clutter covariance matrix structure estimation method for multi-element radar target adaptive CFAR detection under the non-Gaussian clutter background, wherein the main technical problems to be solved include:

(1) The initialization estimation of the covariance matrix structure, so that the adaptive detector ANMF obtained after the estimation matrix is substituted into the NMF detector has CFAR characteristics for both the clutter covariance matrix structure and the clutter texture component;

(2) Efficient iterative estimation, which leads to higher estimation accuracy and lower computational complexity after iteration.

2. Technical solution

The efficient covariance matrix structure estimation method of the present invention comprises the following technical measures: first, utilize the real part (I channel data) of auxiliary data to be divided by the first element of imaginary part (Q channel data) to carry out data preprocessing, then Calculate the SCM for the preprocessed data, then divide the imaginary part (Q channel data) of the auxiliary data by the first element of the real part (I channel data) for data preprocessing, and then calculate the SCM for the preprocessed data , and then add the obtained two SCMs and divide by the number of auxiliary units to obtain the initialization estimation matrix of the covariance matrix structure. Finally, use the initialization estimation matrix to iterate to obtain the estimated value of the covariance matrix structure.

In the above technical solution, since the real part and the imaginary part of the auxiliary data contain the same texture component value, dividing the real part of the auxiliary data by the first element of the imaginary part can effectively eliminate the texture estimation of the covariance matrix structure on the one hand. On the other hand, the correlation between speckle components will not be introduced, so as to ensure that the ANMF corresponding to the obtained initial estimation matrix has CFAR characteristics for both the clutter covariance matrix structure and the clutter texture component.

In the above technical solution, since one complex number operation is equivalent to four real number operations, compared with NSCM-RE, the present invention only needs to perform real number operations, which can not only reduce the calculation amount by half, but also help to improve the estimation accuracy.

3. Beneficial effect

Compared with NSCM-RE, the present invention has the following advantages:

(1) The adaptive detector ANMF corresponding to this method has CFAR characteristics for both the clutter covariance matrix structure and the clutter texture component;

(2) This method has higher estimation accuracy than NSCM-RE under the same auxiliary data and the same number of iterations;

(3) The calculation complexity of this method is lower than that of NSCM-RE under the same auxiliary data and the same number of iterations;

4. Description of drawings

Fig. 1 in the supplementary figure of the specification is a block diagram of the adaptive detector ANMF corresponding to the present invention, and Fig. 2 is a flow chart of covariance matrix structure estimation in the present invention. The function of device 1 in Fig. 1 is realized by Fig. 2, device 2 calculates detection statistics, and device 3 is a decision device.

In Fig. 2, devices 1 and 2 are data preprocessors, devices 3 and 4 are sampling covariance matrix calculators, and device 5 is an iterator.

5. Specific implementation

再利用被检测单元数据z₀计算通过装置2计算检测统计量λ，FIG. 1 is a block diagram of an adaptive detector ANMF corresponding to the present invention. First, use the auxiliary unit data z _t (t = 1, 2, ..., K) to estimate the structure estimation value of the clutter covariance matrix through the device 1

Utilize the detected unit data _z0 to calculate and calculate the detection statistic λ by the device 2,

$\mathrm{<span\; class="notranslate">\; \&lambda;</span>}<span\; class="notranslate">\; =</span>\frac{{<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; p</span>}}^{\mathrm{<span\; class="notranslate">\; h</span>}}{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; z</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; |</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; p</span>}}^{\mathrm{<span\; class="notranslate">\; h</span>}}{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}\mathrm{<span\; class="notranslate">\; p</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; z</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}^{\mathrm{<span\; class="notranslate">\; h</span>}}{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; z</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$

In the formula, p is the known direction vector. Finally, through the device 3, the detection statistic λ is compared with the detection threshold T corresponding to the given false alarm probability to determine whether the target exists or not. If λ≥T, it is judged that the target exists, otherwise it is judged that the target does not exist.

The present invention will be further described in detail below in conjunction with accompanying drawing 2 of the specification (ie, device 2 in Fig. 1 ). With reference to accompanying drawing 2 of specification sheet, the specific implementation mode of the present invention is divided into the following several steps:

Step 1: Determine the auxiliary distance unit data z _t , t=1,..., K, where, $z_t^{\left(1\right)}={[z_t^{\left(1\right)}\left(1\right),z_t^{\left(1\right)}\left(2\right),...,z_t^{\left(1\right)}\left(N\right)]}^T$ Indicates the echo data from the I channel, $z_t^{\left(2\right)}={[z_t^{\left(2\right)}\left(1\right),z_t^{\left(2\right)}\left(2\right),...,z_t^{\left(2\right)}\left(N\right)]}^T$ Indicates the echo data from the Q channel, and N indicates the number of received signal radar array elements.

即Step 2: Calculate z _t real part data by device 1 and divide the first component of z _t imaginary part data to get

Right now

${\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; z</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; /</span>{\mathrm{<span\; class="notranslate">\; z</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>$

即Step 3: Calculate z _t imaginary part data by device 2 and divide the first component of z _t real part data to get

Right now

${\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; z</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; /</span>{\mathrm{<span\; class="notranslate">\; z</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; )</span>$

的采样协方差矩阵，即Step 4: Calculation by device 3

The sampling covariance matrix of

${\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; K</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; T</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 4</span>}<span\; class="notranslate">\; )</span>$

的采样协方差矩阵，即Step 5: Calculation by device 4

The sampling covariance matrix of

${\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; K</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; T</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 5</span>}<span\; class="notranslate">\; )</span>$

Step 6: Sum the sampling covariance matrices obtained in Step 3 and Step 4 and divide by the number of distance units to obtain an initial estimate of the covariance matrix structure, namely

${\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; K</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 6</span>}<span\; class="notranslate">\; )</span>$

利用迭代器进行迭代，对于初始化估计矩阵，m＝0，迭代后m＝m+1，迭代过程为Step 7: Put

Use the iterator to iterate, for the initialization estimation matrix, m=0, after iteration m=m+1, the iterative process is

${\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; K</span>}}\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; K</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; (</span>\frac{{\mathrm{<span\; class="notranslate">\; z</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; z</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; T</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; z</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; z</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; T</span>}}}{{\mathrm{<span\; class="notranslate">\; z</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; T</span>}}{<span\; class="notranslate">\; (</span>{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; )</span>}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; z</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; z</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; T</span>}}{<span\; class="notranslate">\; (</span>{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; )</span>}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; z</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 7</span>}<span\; class="notranslate">\; )</span>$

为本发明得到的协方差矩阵结构估计值，否则，将

作为输入值利用步骤7继续迭代。Step 8: After iteration, if m=M (M is the set number of iterations), then

is the estimated value of the covariance matrix structure obtained by the present invention, otherwise, it will be

The iteration continues with step 7 as an input value.

Claims (9)

1. an efficient covariance matrix structure method of estimation is characterized in that comprising the following steps:

Step 1: be identified for receiving signal radar array number, be detected unit and auxiliary data unit, record the echo data of each range unit;

Step 2: the data to each range unit of I passage are carried out pre-service;

Step 3: the data to each range unit of Q passage are carried out pre-service;

Step 4: the pretreated data of I passage are asked to sample covariance matrix;

Step 5: the pretreated data of Q passage are asked to sample covariance matrix;

Step 6: after the sample covariance matrix summation that step 4 and step 5 are obtained, divided by auxiliary range unit number, obtain the initialization estimated value of covariance matrix structure;

Step 7: utilize initialization estimated value and the auxiliary data of the covariance matrix structure obtained to carry out iteration, its iterative process is

In formula, N means the radar array number, and K means auxiliary range unit number, and t means the range unit numbering,

Expression is from the echo data of I passage,

Expression is from the echo data of Q passage,

Mean the covariance matrix structure estimated value after iteration m time, wherein, when m=0,

The initialization estimated value that means the covariance matrix structure;

Step 8: judge that whether iterations meets and should require, if meet, stop iteration, the estimated value of output covariance matrix structure, if do not meet, utilize estimated value that step 7 obtains to turn back to step 7 and continue iteration.

2. a kind of efficient covariance matrix structure method of estimation according to claim 1, it is characterized in that, described step 1 is specially: first be identified for receiving the radar array number N of echoed signal, adopt plural form to record the echo complex data z of each range unit corresponding to each array element of radar _t, z _t=[z _t(1), z _t(2) ..., z _t(N)] ^T, t is the range unit numbering, t=0 means detected unit, and t=1,2 ..., K means auxiliary data range unit, z _tReal part mean the data of I passage, imaginary part means the data of Q passage, z _t(n), n=1,2 ..., N means that range unit that n array element receives is numbered the echoed signal of t.

3. a kind of efficient covariance matrix structure method of estimation according to claim 1, is characterized in that, described step 2 is specially: for each auxiliary range unit t, to vectorial z _tThe real part data

All divided by

Imaginary data

(1) obtain

4. a kind of efficient covariance matrix structure method of estimation according to claim 1, is characterized in that, described step 3 is specially: for each auxiliary range unit t, to vectorial z _tImaginary data All divided by z _t(1) real part data

(1) obtain

5. a kind of efficient covariance matrix structure method of estimation according to claim 1, is characterized in that, described step 4 is specially: ask

The sample covariance matrix ∑ ⁽¹⁾.

6. a kind of efficient covariance matrix structure method of estimation according to claim 1, is characterized in that, described step 5 is specially: ask

The sample covariance matrix ∑ ⁽²⁾.

7. a kind of efficient covariance matrix structure method of estimation according to claim 1, is characterized in that, described step 6 is specially: ask ∑ ⁽¹⁾With ∑ ⁽²⁾And ∑, then ∑ is obtained to the initialization estimated matrix divided by auxiliary unit number K

8. a kind of efficient covariance matrix structure method of estimation according to claim 1, is characterized in that, described step 7 is specially: will

As initialized estimated matrix, utilize z _tReal part data and imaginary data carry out iteration, obtain the iterative estimate value

The initialization value of m is 0, and once, the value of m adds 1 to every iteration.

9. a kind of efficient covariance matrix structure method of estimation according to claim 1, is characterized in that, described step 8 is specially: the value to m is judged, if m reaches the iterations M of setting, be m=M, iteration stops, the estimated value of output covariance matrix structure

If m<M, continue step 8.

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