Tensor-Based Sparse Recovery Space-Time Adaptive Processing for Large Size Data Clutter Suppression in Airborne Radar

Cui, Ning; Xing, Kun; Yu, Zhongjun; Duan, Keqing

doi:10.1109/taes.2022.3192223

Cited by 10 publications

(13 citation statements)

References 46 publications

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“…By utilizing intrinsic sparsity and compressive sensing techniques to recover the clutter, estimating the accurate clutter spectrum with minor training support is achievable. Similar conclusions were also obtained in other works [28][29][30][31][32]. The deep learning technique provides a new idea for STAP design.…”

Section: Related Worksupporting

confidence: 89%

Clutter Suppression Algorithm with Joint Intrinsic Clutter Motion Errors Calibration and Off-Grid Effects Mitigation in Airborne Passive Radars

Deng

Pei

et al. 2023

Applied Sciences

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In an airborne passive radar, multipath (MP) clutter, which is caused by MP signals contained in the contaminated reference signal, degrades the space-time adaptive processing (STAP) performance. The MP clutter suppression algorithm before STAP can mitigate the influence of impure reference signals. However, the performances of the existing MP clutter suppression methods deteriorate when the intrinsic clutter motion (ICM) exists because the sparse model of MP clutter is disturbed. To eliminate the impacts of ICM on MP clutter suppression, a joint optimization algorithm is developed for airborne passive radar. Firstly, the sparse model of MP clutter is modified by taking ICM fluctuation into account. Subsequently, the joint optimization function of the ICM fluctuation and MP clutter profile is derived. Finally, based on the local search technique, MP clutter is suppressed with ICM error calibration and off-grid effects mitigation. A range of simulations verify the reliability and superiority of the proposed method.

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Section: Related Worksupporting

confidence: 89%

Clutter Suppression Algorithm with Joint Intrinsic Clutter Motion Errors Calibration and Off-Grid Effects Mitigation in Airborne Passive Radars

Deng

Pei

et al. 2023

Applied Sciences

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“…In other words, clutter suppression needs to be achieved using space–time adaptive processing (STAP) technology. Due to the limited computing resources on the airborne platform, an advanced subarray‐level sparse recovery STAP (SR‐STAP) processing framework can be adopted to reduce the data dimensionality and achieve fast processing [12].…”

Section: The Proposed Radar Data Processingmentioning

confidence: 99%

A radar extended target angle estimation method based on effective phase clustering

Zhang,

Luo,

Wang

et al. 2023

The Journal of Engineering

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For high‐resolution radar, traditional direction of arrival (DOA) angle measuring algorithms for point‐like targets cannot be directly applied to extended targets. Instead, pixel‐based detection and clustering must be performed first, followed by angle estimation. However, the presence of factors such as clutter and system noise makes it impossible to guarantee a one‐to‐one correspondence between the multiple scattering points of an extended target and the real target, which may result in significant discrepancies between the angle estimation and localization results and the real target. To address this issue, this paper proposes and discusses a radar extended target arrival angle estimation method based on effective phase clustering. The proposed method selects effective target scatterers through the interferometric phase information between channels, clusters these points, and then estimates the DOA angle of the extended target, effectively reducing the impact of interference scatterers on DOA angle estimation and improving the angular measurement accuracy. The effectiveness of the proposed method was validated by the measured data obtained from an airborne digital beamforming radar system.

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“…A tensor is a multidimensional array, which can represent data and extract the characteristic of data in each dimension easily. Tucker decomposition [10] is a widely used method for obtaining principal components of a tensor, and can be calculated via higher order singular value decomposition (HOSVD) [11]. Therefore, in this paper, we utilize Tucker decomposition to extract the characteristics of clutter in the spatial and Doppler domains.…”

Section: Extraction Of Clutter Subspace Based On Tensor Tucker Decomp...mentioning

confidence: 99%

“…is called the core tensor. For a detailed discussion on the above content, please refer to reference [11]. Since the factor matrices A , B and C are orthogonal to each other, G can be obtained using the following equation.…”

Section: Extraction Of Clutter Subspace Based On Tensor Tucker Decomp...mentioning

confidence: 99%

“…This paper employs a third-order tensor to represent clutter data, where mode-1, mode-2, and mode-3 [11] correspond to spatial channels, samples, and Doppler channels, respectively. Subsequently, structured features of the clutter in the spatial and Doppler domains can be extracted using tensor Tucker decomposition.…”

Section: Tucker Decomposition Of a Third-order Tensormentioning

confidence: 99%

See 1 more Smart Citation

A novel reduced-dimension clutter suppression method based on tensor decomposition

Yang,

Xu,

et al. 2023

Fourth International Conference on Signal Processing and Computer Science (SPCS 2023)

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Tensor-Based Sparse Recovery Space-Time Adaptive Processing for Large Size Data Clutter Suppression in Airborne Radar

Cited by 10 publications

References 46 publications

Clutter Suppression Algorithm with Joint Intrinsic Clutter Motion Errors Calibration and Off-Grid Effects Mitigation in Airborne Passive Radars

Clutter Suppression Algorithm with Joint Intrinsic Clutter Motion Errors Calibration and Off-Grid Effects Mitigation in Airborne Passive Radars

A radar extended target angle estimation method based on effective phase clustering

A novel reduced-dimension clutter suppression method based on tensor decomposition

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