Waveform design for target classification in MIMO radar systems

Amiri, Reza; Olamaei, Seyed Ahmad; Alaie, M. Bagher

doi:10.1016/j.dsp.2022.103728

Cited by 3 publications

(2 citation statements)

References 37 publications

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“…Update the parameter estimate θi (t) through (23). 6. Compute the auxiliary model outputs xi1 (t), xi2 (t), … , xir (t) by (28).…”

Section: Lemma 1 (Matrix Inversion Lemma)mentioning

confidence: 99%

“…Recently, the identification of multivariable systems has become the focus of research and many identification algorithms have been proposed for multivariable systems 5 . Multiple‐input multiple‐output (MIMO) systems (i.e., multivariable systems) widely exist in modern industrial fields such as radar control systems 6 and motion control systems 7 . The MIMO systems are called the multivariable systems, which have the characteristics of numerous input and output variables, complex structures, high dimensions, and uncertain interference signals.…”

Section: Introductionmentioning

confidence: 99%

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Hierarchical recursive least squares parameter estimation methods for multiple‐input multiple‐output systems by using the auxiliary models

Xing,

Ding,

Pan

et al. 2023

Adaptive Control & Signal

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SummaryMultiple‐input multiple‐output (MIMO) models are widely used in practical engineering. This article derives a new identification model of the MIMO system by decomposing the MIMO system into several multiple‐input single‐output subsystems. By means of the auxiliary model identification idea, an auxiliary model‐based recursive least squares (AM‐RLS) algorithm is derived for identifying the MIMO systems. In order to reduce the computational burden for identifying MIMO systems, this article presents a hierarchical identification model for the MIMO systems. By applying the hierarchical identification principle, an auxiliary model‐based hierarchical least squares (AM‐HLS) algorithm is proposed for improving the computational efficiency. The computational efficiency analysis indicates that the AM‐HLS algorithm is effective in reducing the calculation amount compared with the AM‐RLS algorithm. Moreover, this article analyzes the convergence of the AM‐HLS algorithm. The simulation example shows that the AM‐RLS and AM‐HLS algorithms studied in this article are effective.

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“…Update the parameter estimate θi (t) through (23). 6. Compute the auxiliary model outputs xi1 (t), xi2 (t), … , xir (t) by (28).…”

Section: Lemma 1 (Matrix Inversion Lemma)mentioning

confidence: 99%