In this chapter, we presented an advanced sparse signal model referred to as two level block sparsity model and introduced its applications in multichannel radar signal processing such as TWRI and STAP. By enforcing both the clustered sparsity of each single -channel signal and the joint sparsity pattern of the signals across all the channels, the two -level block sparsity model can help in clustering the dominant components and suppressing the artifacts. In the case of TWRI, the two level block sparsity model was directly applied to radar image formation in free space and through -wall scenarios. In the case of STAP, the two -level block sparsity model was utilized to fi rst reconstruct the angle -Doppler domain and then estimate CCM. The experimental results on simulations and real radar data have demonstrated the positive effect of the two -level block sparsity model on improving the quality of TWRI and enhancing the detection performance of STAP. The applications of the two -level block sparsity model can also be extended to other multichannel radar systems, such as multiple -input multiple -output (MIMO) radar, multistatic radar, and distributed radar. To further develop the two -level block sparsity model in various applications, it is worth studying more accurate formulation of the prior knowledge about the clustered structure and more simplified approaches for selection of model parameters.

Chapter Contents:

• 3.1 Introduction
• 3.2 Formulation of the two-level block sparsity model
• 3.2.1 Clustered sparsity of single-channel data
• 3.2.2 Joint sparsity of the multichannel data
• 3.2.3 Two-level block sparsity
• 3.3 TWRI based on two-level block sparsity
• 3.3.1 Signal model and algorithm description
• 3.3.2 Model parameter selection
• 3.3.3 Computational complexity
• 3.3.4 Experimental results
• 3.3.4.1 Simulations
• 3.3.4.2 Experiments on real radar data
• 3.4 STAP based on two-level block sparsity
• 3.4.1 Signal model and algorithm description
• 3.4.2 Experimental results
• 3.4.2.1 Simulations
• 3.4.2.2 Experiments on real radar data
• 3.5 Conclusion
• References

Inspec keywords: Doppler radar; estimation theory; radar imaging; pattern clustering; image reconstruction; radar detection; space-time adaptive processing

Other keywords: STAP; multistatic radar; multiple-input multiple-output radar; CCM estimation; angle-Doppler domain reconstruction; MIMO radar; TWRI; two-level block sparsity model; multichannel radar signal processing; advanced sparse signal model; radar image formation; distributed radar

Subjects: Signal detection; Radar equipment, systems and applications; Optical, image and video signal processing; Other topics in statistics