Clutter Subspace Characteristics-Aided Space-Time Adaptive Outlier Sample Selection Method.

Area of Science:

• Radar Signal Processing
• Statistical Signal Processing

Background:

• Accurate clutter covariance matrix (CCM) estimation is crucial for statistical space-time adaptive processing (STAP) in airborne radar.
• Heterogeneous environments in airborne radar hinder obtaining independent and identically distributed (IID) training samples for CCM estimation.
• Contaminated training samples must be identified and removed before CCM estimation.

Purpose of the Study:

• To address the limitations of traditional methods like the generalized inner product (GIP) in CCM estimation.
• To develop a novel method for selecting training samples that is computationally efficient and robust to outliers.
• To improve the accuracy and reliability of CCM estimation in non-ideal airborne radar scenarios.

Main Methods:

• A clutter subspace-based training sample selection method is proposed.
• The method leverages specific distribution characteristics within the space-time plane of the clutter spectrum.
• This approach aims to identify and utilize only relevant and clean training data for CCM construction.

Main Results:

• The proposed method simplifies the construction of the clutter covariance matrix (CCM).
• It demonstrates significantly lower computational complexity compared to traditional approaches.
• The method exhibits reduced sensitivity to outliers in the training data, enhancing robustness.

Conclusions:

• The clutter subspace-based training sample selection method offers an effective solution for CCM estimation in STAP.
• It overcomes the challenges posed by heterogeneous environments and contaminated training data.
• The method provides a computationally efficient and outlier-robust alternative for airborne radar systems.