Clutter suppression for airborne phased radar with conformal arrays by least squares estimation

Xie, Wu; Duan, Keqing; Gao, Fei; Wang, Yongliang; Zhang, Zenghui

doi:10.1016/j.sigpro.2011.01.009

Cited by 11 publications

(24 citation statements)

References 5 publications

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“…redIn addition, it usually ignores range ambiguities, i.e., N a = 1, where the justification can be seen in [42]. Then, the parameter σ redin (5) can be estimated using the observation data by solving the LS problem as follows [42], [43],σ…”

Section: A Ccm Estimated By Lsmentioning

confidence: 99%

“…From this point of view, the impact of prior knowledge uncertainty is mitigated. Because the methods in [43], [44] and [45] do not consider any strategies to mitigate the impact of prior knowledge uncertainty, the performance will depend highly on the accuracy of the prior knowledge. The KAPE approach in [42] also adopts the CMT and can mitigate the impact of prior knowledge uncertainty in a sense.…”

Section: Select a Group Of Space-time Steering Vectorsmentioning

confidence: 99%

“…To provide further investigation about the performance of our proposed algorithms, we compare the SINR performance versus the snapshots of our proposed LRGP KA-STAP and LRGP RD-KA-STAP algorithms with the Loaded SMI (LSMI), the EFA algorithm (3 Doppler bins), the 3 × 3 JDL algorithm, redStoica's scheme in [36] (the prior knowledge covariance matrix is computed in the same way as the CSMIECC bluealgorithm), and the CSMIECC algorithm (the combination parameter is set to 0.6) in [43], where the simulation results are shown in Fig. 6.…”

Section: Comparison With Conventional Stap Algorithmsmentioning

confidence: 99%

“…Furthermore, the computational complexity caused by sparse recovery is expensive. A data independent STAP method based on prolate spheroidal wave functions (PSWF) has been considered in MIMO radar by incorporating the clutter ridge [45], where the computational complexity is significantly reduced compared with the approaches in [42] and [43]. However, it is highly dependent on the ideal clutter subspace and is not robust against clutter subspace mismatches.…”

Section: Introductionmentioning

confidence: 99%

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Knowledge-Aided STAP Using Low Rank and Geometry Properties

Yang

Lamare

et al. 2014

International Journal of Antennas and Propagation

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This paper presents knowledge-aided space-time adaptive processing (KA-STAP) algorithms that exploit the lowrank dominant clutter and the array geometry properties (LRGP) for airborne radar applications. The core idea is to exploit the fact that the clutter subspace is only determined by the spacetime steering vectors, redwhere the Gram-Schmidt orthogonalization approach is employed to compute the clutter subspace. Specifically, for a side-looking uniformly spaced linear array, the algorithm firstly selects a group of linearly independent spacetime steering vectors using LRGP that can represent the clutter subspace. By performing the Gram-Schmidt orthogonalization procedure, the orthogonal bases of the clutter subspace are obtained, followed by two approaches to compute the STAP filter weights. To overcome the performance degradation caused by the non-ideal effects, a KA-STAP algorithm that combines the covariance matrix taper (CMT) is proposed. For practical applications, a reduced-dimension version of the proposed KA-STAP algorithm is also developed. The simulation results illustrate the effectiveness of our proposed algorithms, and show that the proposed algorithms converge rapidly and provide a SINR improvement over existing methods when using a very small number of snapshots.

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Section: A Ccm Estimated By Lsmentioning

confidence: 99%

Section: Select a Group Of Space-time Steering Vectorsmentioning

confidence: 99%

Section: Comparison With Conventional Stap Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Knowledge-Aided STAP Using Low Rank and Geometry Properties

Yang

Lamare

et al. 2014

International Journal of Antennas and Propagation

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“…Knowledge-aided (KA) STAP approaches using digital land classification data and digital elevation data were proposed to select training data to obtain improved STAP performance [11]. Another KA-STAP method, called model-based approach (see [12][13][14][15][16][17][18][19] and the references therein), basically employs some prior knowledge to form the simplified general clutter model (GCM) and then blends the GCM with the measured observations to design the STAP filter or directly uses it to design the STAP filter. This method can obtain good performance in a small-samplesupport condition with accurate prior knowledge.…”

Section: Introductionmentioning

confidence: 99%

Direct Data Domain Sparsity-Based STAP Utilizing Subaperture Smoothing Techniques

Yang

Qin

et al. 2015

International Journal of Antennas and Propagation

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We propose a novel direct data domain (D3) sparsity-based space-time adaptive processing (STAP) algorithm utilizing subaperture smoothing techniques for airborne radar applications. Different from either normal sparsity-based STAP or D3 sparsity-based STAP, the proposed algorithm firstly uses only the snapshot in the cell under test (CUT) to generate multiple subsnapshots by exploiting the space-time structure of the steering vector and the uncorrelated nature of the components of the interference covariance matrix. Since the interference spectrum is sparse in the whole angle-Doppler plane, by employing a sparse regularization, the generated multiple subsnapshots are jointly used to recover the interference spectrum. The interference covariance matrix is then estimated from the interference spectrum, followed by the space-time filtering and the target detection. Simulation results illustrate that the proposed algorithm outperforms the generalized forward/backward method, the conventional D3 least squares STAP algorithm, and the existing D3 sparsity-based STAP algorithm. Furthermore, compared with the normal sparsity-based STAP algorithm using multiple snapshots, the proposed algorithm can also avoid the performance degradation caused by discrete interferers merely appearing in the CUT.

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Joint magnitude and phase constrained STAP approach

Zhu

Liao

2015

Digital Signal Processing

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Clutter suppression for airborne phased radar with conformal arrays by least squares estimation

Cited by 11 publications

References 5 publications

Knowledge-Aided STAP Using Low Rank and Geometry Properties

Knowledge-Aided STAP Using Low Rank and Geometry Properties

Direct Data Domain Sparsity-Based STAP Utilizing Subaperture Smoothing Techniques

Joint magnitude and phase constrained STAP approach

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