Radar target profiling and recognition based on TSI-optimized compressive sensing kernel

Yujie Gu, Nathan A. Goodman, Amit Ashok

Research output: Contribution to journalArticle

27 Citations (Scopus)

Abstract

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 highresolution 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.

Original languageEnglish (US)
Article number6819874
Pages (from-to)3194-3207
Number of pages14
JournalIEEE Transactions on Signal Processing
Volume62
Issue number12
DOIs
StatePublished - Jun 15 2014

Fingerprint

Radar
Radar systems
Taylor series
Digital to analog conversion
Probability distributions
Electric power utilization
Sampling
Bandwidth
Computer simulation
Costs

Keywords

  • Compressive sensing (CS)
  • Gaussian mixture (GM)
  • optimal sensing matrix
  • radar profiling
  • task-specific information (TSI)

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Signal Processing

Cite this

Radar target profiling and recognition based on TSI-optimized compressive sensing kernel. / Gu, Yujie; Goodman, Nathan A.; Ashok, Amit.

In: IEEE Transactions on Signal Processing, Vol. 62, No. 12, 6819874, 15.06.2014, p. 3194-3207.

Research output: Contribution to journalArticle

@article{8f1f5425dbd54888bbda5d00df8ae8f2,
title = "Radar target profiling and recognition based on TSI-optimized compressive sensing kernel",
abstract = "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 highresolution 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.",
keywords = "Compressive sensing (CS), Gaussian mixture (GM), optimal sensing matrix, radar profiling, task-specific information (TSI)",
author = "Yujie Gu and Goodman, {Nathan A.} and Amit Ashok",
year = "2014",
month = "6",
day = "15",
doi = "10.1109/TSP.2014.2323022",
language = "English (US)",
volume = "62",
pages = "3194--3207",
journal = "IEEE Transactions on Signal Processing",
issn = "1053-587X",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "12",

}

TY - JOUR

T1 - Radar target profiling and recognition based on TSI-optimized compressive sensing kernel

AU - Gu, Yujie

AU - Goodman, Nathan A.

AU - Ashok, Amit

PY - 2014/6/15

Y1 - 2014/6/15

N2 - 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 highresolution 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.

AB - 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 highresolution 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.

KW - Compressive sensing (CS)

KW - Gaussian mixture (GM)

KW - optimal sensing matrix

KW - radar profiling

KW - task-specific information (TSI)

UR - http://www.scopus.com/inward/record.url?scp=84901809362&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84901809362&partnerID=8YFLogxK

U2 - 10.1109/TSP.2014.2323022

DO - 10.1109/TSP.2014.2323022

M3 - Article

VL - 62

SP - 3194

EP - 3207

JO - IEEE Transactions on Signal Processing

JF - IEEE Transactions on Signal Processing

SN - 1053-587X

IS - 12

M1 - 6819874

ER -