Radar Target Profiling and Recognition Based on TSI-Optimized Compressive Sensing Kernel

• Published in: IEEE transactions on signal processing Vol. 62; no. 12; pp. 3194 - 3207
• Main Authors: Gu, Yujie, Goodman, Nathan A., Ashok, Amit
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 15.06.2014; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applied sciences; Compressive sensing (CS); Computer simulation; Design engineering; Detection; Detection, estimation, filtering, equalization, prediction; Economic models; Entropy; Exact sciences and technology; Gaussian mixture (GM); Information, signal and communications theory; Kernel; Kernels; Mathematical models; Measurement; Noise; optimal sensing matrix; Optimization; Pattern recognition; Profiling; Radar; radar profiling; Radar systems; Sampling, quantization; Sensors; Signal and communications theory; Signal processing; Signal, noise; task-specific information (TSI); Telecommunications and information theory; State of the art; Nuisance parameter; Compressive sensing (CS); Mixture theory; Gaussian mixture (GM); Probability distribution; Optimization; Learning; AD converter; Wide band; Radar; Electric power consumption; Series expansion; radar profiling; optimal sensing matrix; Target detection; Probabilistic approach; Logarithmic function; Taylor series; Pattern recognition; Object recognition; Kernel method; Radar target; Gaussian process; Sampling rate; Object detection; Signal processing; task-specific information (TSI); Compressed sensing
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2014.2323022

The design of wideband radar systems is often limited by existing analog-to-digital (A/D) converter technology. State-of-the-art A/D rates and high effective number of bits result in rapidly increasing cost and power consumption for the radar system. Therefore, it is useful to consider compressive sensing methods that enable reduced sampling rate, and in many applications, prior knowledge of signals of interest can be learned from training data and used to design better compressive measurement kernels. In this paper, we use a task-specific information-based approach to optimizing sensing kernels for high-resolution radar range profiling of man-made targets. We employ a Gaussian mixture (GM) model for the targets and use a Taylor series expansion of the logarithm of the GM probability distribution to enable a closed-form gradient of information with respect to the sensing kernel. The GM model admits nuisance parameters such as target pose angle and range translation. The gradient is then used in a gradient-based approach to search for the optimal sensing kernel. In numerical simulations, we compare the performance of the proposed sensing kernel design to random projections and to lower-bandwidth waveforms that can be sampled at the Nyquist rate. Simulation results demonstrate that the proposed technique for sensing kernel design can significantly improve performance.