Reduced-rank STAP for target detection in heterogeneous environments

Degurse, Jean-François; Savy, Laurent; Marcos, Sylvie

doi:10.1109/taes.2014.120414

Cited by 34 publications

(16 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Consider that the location of the target is close to the look direction 0 u , we evaluate the monopulse ratio at the look direction by a first-order Taylor series [2]   r is the bias of the monopulse ratio. However, this adaptive monopulse processor may be limited in real-time application for a high degrees of freedom (DOF) system with large number of elements, due to the heavy computation load and slow convergence speed, which motivates the following investigation of implementing a subspace-based approach [7,8] in conjunction with the adaptive monopulse algorithm.…”

Section: Figure 1 Sum and Difference Beampatterns And The Resulting mentioning

confidence: 99%

See 1 more Smart Citation

An Efficient Adaptive Monopulse Estimation Algorithm Based on MPASTd Approach

Yu¹,

Shen²,

Wu³

et al. 2015

IJCA

View full text Add to dashboard Cite

In this paper, an improved adaptive monopulse scheme based on modified projection approximation subspace tracking deflation (MPASTd) approach is proposed. Owing to the fast convergence speed of MPASTd for recursively estimating the interference subspace, the proposed algorithm can obtain the formula for adaptive monopulse ratio calculation by a subspace projection. Therefore, it is able to accurately estimate the source location using less training data and reduce the computational complexity significantly. The effectiveness and fast convergence speed of the proposed method is verified by simulation results.The rest of this paper is organized as follows. Section 2 summarizes the principle of adaptive monopulse algorithm. The proposed MPASTd-based subspace method for adaptive monopulse is presented in Section 3. First, we briefly introduce the eigencanceler, and give the weight vector based on eigen-decomposition (ED). Then the improved adaptive monopulse scheme based on MPASTd is introduced in detail. In Section 4, the computer simulation results are given to demonstrate that the proposed approach outperforms the original adaptive monopulse while reducing the computational complexity and the convergence time. A brief conclusion is given in Section 5.  

show abstract

Section: Figure 1 Sum and Difference Beampatterns And The Resulting mentioning

confidence: 99%

“…But in an operational situation, this condition is difficult to satisfy. One way to overcome this is to reduce the dimension, which means that the subspace projection methods [7,8] can be introduced into the adaptive monopulse technique to improve the performance.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Adaptive Monopulse Estimation Algorithm Based on MPASTd Approach

Yu¹,

Shen²,

Wu³

et al. 2015

IJCA

View full text Add to dashboard Cite

show abstract

“…Obviously, the clutter suppression performance of TDS-STAP is largely limited by the number and statistic characteristics of the training samples. Furthermore, to lower the limit on the training sample, several single dataset STAP (SDS-STAP) algorithms have been proposed [32][33][34][35][36][37]. They only use CUT data for CCM estimation or clutter suppression.…”

Section: Introductionmentioning

confidence: 99%

“…They only use CUT data for CCM estimation or clutter suppression. The direct data domain STAP (D3-STAP) [33][34][35] and the maximum likelihood estimation detector (MLED) algorithm [36,37] are two typical SDS-STAP algorithms, but their benefits are available at the expense of system DOFs loss. Another direct data domain STAP approach using sparse recovery (D3SR-STAP) was proposed in [32].…”

Section: Introductionmentioning

confidence: 99%

Deterministic-aided single dataset STAP method based on sparse recovery in heterogeneous clutter environments

Wang

Zou

Wang

et al. 2018

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

Traditional space-time adaptive processing (STAP) usually needs many independent and identically distributed (i.i.d) training datasets for estimating clutter covariance matrix (CCM). But this requirement is hardly satisfied in the heterogeneous clutter environments, which lead to an inaccurate estimation of CCM and accordingly degrade the performance of STAP significantly. To improve the performance of STAP in heterogeneous environments, a novel deterministic-aided (DA) single dataset STAP method based on sparse recovery technique (SR) is proposed in this paper. This presented algorithm exploits the property that the clutter components of side-looking airborne or spaceborne radar are distributed along the clutter ridge to estimate the CCM of the cell under test (CUT) without any secondary training data. The new method only uses a single CUT data to acquire a high-resolution angle-Doppler power spectrum using sparse recovery (SR) approach and then employs a new adaptive deterministic-aided generalized inner product (GIP) algorithm to recognize and select the clutter components in the CUT angle-Doppler power spectrum automatically. Subsequently, the CCM, which is used to construct the weights of STAP filter, can be effectively estimated by the selected clutter components to fulfill the final STAP filter processing. Simulation results verify the effectiveness of the proposed detection method.

show abstract

“…The Siegel also appears in statistical mechanics, see [7] and was recently used in image processing (see [8]). Information geometry is now a standard framework in radar processing (see [4][5][6][9][10][11][12][13]). The information geometry on positive definite Teoplitz block Teoplitz matrices is directly related to the metric on the Siegel space (see [14]).…”

Section: Introductionmentioning

confidence: 99%

Kernel Density Estimation on the Siegel Space with an Application to Radar Processing

Chevallier

Forget

Barbaresco

et al. 2016

Entropy

View full text Add to dashboard Cite

This paper studies probability density estimation on the Siegel space. The Siegel space is a generalization of the hyperbolic space. Its Riemannian metric provides an interesting structure to the Toeplitz block Toeplitz matrices that appear in the covariance estimation of radar signals. The main techniques of probability density estimation on Riemannian manifolds are reviewed. For computational reasons, we chose to focus on the kernel density estimation. The main result of the paper is the expression of Pelletier's kernel density estimator. The computation of the kernels is made possible by the symmetric structure of the Siegel space. The method is applied to density estimation of reflection coefficients from radar observations.

show abstract

Reduced-rank STAP for target detection in heterogeneous environments

Cited by 34 publications

References 16 publications

An Efficient Adaptive Monopulse Estimation Algorithm Based on MPASTd Approach

An Efficient Adaptive Monopulse Estimation Algorithm Based on MPASTd Approach

Deterministic-aided single dataset STAP method based on sparse recovery in heterogeneous clutter environments

Kernel Density Estimation on the Siegel Space with an Application to Radar Processing

Contact Info

Product

Resources

About