Frequency-domain Based Waveform Design for Binary Extended-target Classification

Alshirah, Sultan Z.; Gishkori, Shahzad; Mulgrew, Bernard

doi:10.1109/icassp.2019.8682673

Cited by 4 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Assuming that the frequency snapshot model is used (where y(t) is Fourier-transformed into the frequency domain where the time-bandwidth product w t satisfies w t ≥ 16 [2], [34] where t is the length of the received signal in the time domain and 16 is the minimum time-bandwidth product in the frequency snapshot model), the vectorized radar return in the frequency domain Y can be calculated from (1) as in the following:…”

Section: A Signal Modelmentioning

confidence: 99%

“…Solving ( 14) to find a closed-form solution is a challenging problem. However, a numerical solution for the optimization problem in ( 14) can be computed using MATLAB optimization toolbox (e.g., [34]).…”

Section: B Waveform Designmentioning

confidence: 99%

“…We test the closed-form solution against flat spectrum wideband waveform, optimized waveforms obtained from the optimization algorithms and closed-form solutions optioned at low and high waveform energy levels. The initial results in this article were preliminary published and presented in [34].…”

Section: Introductionmentioning

confidence: 97%

See 2 more Smart Citations

Optimal Target Classification Using Frequency-Based Radar Waveform Design

Alshirah

Gishkori²,

Mulgrew

2022

IEEE Trans. Geosci. Remote Sensing

Self Cite

View full text Add to dashboard Cite

In this article, we introduce a frequency-based waveform design to maximize binary target classification. The optimization problems can be solved via optimal solvers available in the literature. The presented formulation is applicable to the classification scenarios where the extended target frequency responses (TFRs) are complex, random, and normally distributed with unequal mean vectors. However, their covariance matrices are either identical or different. For the former, i.e., identical covariance matrices, the optimization problem consists of a cost function that has been obtained by deriving a closed-form expression of the probability of misclassification. For the latter, i.e., different covariance matrices, we present an optimization problem where the cost function has been obtained via the Fisher separation function. For this, we also present a closed-form expression for an approximate solution to the optimization problem. We show that the proposed solution achieves performance levels comparable to the exact solution of the optimization problem obtained via state-of-the-art optimization solvers while incurring low computational complexity. We expand on the two main scenarios by introducing clutter (i.e., signal-dependent interference) in the signal model and studying possible closed-form designs in extreme waveform energy levels. Simulations are conducted using synthetically generated data in addition to the civilian vehicle data from the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset, in order to validate the proposed method.

show abstract

Section: A Signal Modelmentioning

confidence: 99%

Section: B Waveform Designmentioning

confidence: 99%