Widely-distributed Radar Imaging Based on Consensus ADMM
| Autor: | Mohammad Alaee-Kerahroodi, Ruizhi Hu, Bjorn Ottersten, Ahmed Murtada, Bhavani Shankar Mysore Rama Rao |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Signal Processing (eess.SP)
Optimization problem Computer science Computation ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 0211 other engineering and technologies 020206 networking & telecommunications 02 engineering and technology Antenna diversity law.invention Noise law Radar imaging FOS: Electrical engineering electronic engineering information engineering 0202 electrical engineering electronic engineering information engineering Electrical Engineering and Systems Science - Signal Processing Antenna (radio) Radar Cluster analysis Algorithm 021101 geological & geomatics engineering |
| Zdroj: | BASE-Bielefeld Academic Search Engine |
| DOI: | 10.1109/radarconf2147009.2021.9454975 |
| Popis: | A widely-distributed radar system is a promising architecture to enhance radar imaging performance. However, most existing algorithms rely on isotropic scattering assumption, which is only satisfied in collocated radar systems. Moreover, due to noise and imaging model imperfections, artifacts such as layovers are common in radar images. In this paper, a novel $l_1$-regularized, consensus alternating direction method of multipliers (CADMM) based algorithm is proposed to mitigate artifacts by exploiting a widely-distributed radar system's spatial diversity. By imposing the consensus constraints on the local images formed by distributed antenna clusters and solving the resulting distributed optimization problem, the scenario's spatial-invariant common features are retained. Simultaneously, the spatial-variant artifacts are mitigated, and it will finally converge to a high-quality global image in the consensus of all distributed measurements. The proposed algorithm outperforms the joint sparsity-based composite imaging (JSC) algorithm in terms of artifacts mitigation. It can also reduce the computation and storage burden of large-scale imaging problems through its distributed and parallelizable optimization scheme. Submitted to IEEE Radarconf 21 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|