Frequency-Based Optimal Radar Waveform Design for Classification Performance Maximization Using Multiclass Fisher Analysis

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

doi:10.1109/tgrs.2020.3008562

Cited by 9 publications

(7 citation statements)

References 35 publications

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“…In this section, we present the simulation results generated using synthetic and real targets data to study the performance of the optimized waveforms presented in Sections III-B2 and III-B1 and the closed-form solution derived in Section III-B1 (from here on referred to as "the diagonal solution" and the waveform as "the diagonal waveform") against other radar waveforms found in the literature [25].…”

Section: Resultsmentioning

confidence: 99%

“…Here, as in [25], the classifier assigns the target to the class closest to the target return Y in terms of the Mahalanobis distance after projecting all the received data and the means by w. The projection vector w, depending on the scenario, is given by the following.…”

Section: Classifier Designmentioning

confidence: 99%

“…Adaptive waveform design strategies require more computations than nonadaptive strategies as waveforms are designed continuously until a decision is made. In [25], a nonadaptive waveform and classifier design strategy based on the Fisher analysis was presented. This addressed the scenarios where a target is to be assigned to one of two or multiple classes of targets based on its extended target frequency response (TFR) in the presence of noise and angular uncertainty.…”

Section: Introductionmentioning

confidence: 99%

“…In this article, we expand on our previous work in [25] with three main contributions. First, we introduce signal-dependent interference and clutter to the signal model for the two scenarios: 1) when the target classes share the same covariance matrix and 2) when they have different covariance matrices.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Target Classification Using Frequency-Based Radar Waveform Design

Alshirah

Gishkori²,

Mulgrew

2022

IEEE Trans. Geosci. Remote Sensing

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Classifier Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

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“…From the central limit theorem, it is often assumed that radar signals follow a zero-mean complex Gaussian distribution [35]- [37]. All signals in Fig.…”

Section: Problem Formulationmentioning

confidence: 99%