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Content Provider | IET Digital Library |
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Author | Addabbo, Pia Aubry, Augusto Maio, Antonio De Pallotta, Luca Ullo, Silvia Liberata |
Abstract | In this study, authors address high-range-resolution (HRR) profile reconstruction, when stepped-frequency waveforms are eventually used to maintain a narrow instantaneous bandwidth, resorting to the sparse learning via iterative minimisation (SLIM) paradigm, a regularised minimisation approach with an l q -norm constraint (for 0 < q ≤ 1 ), providing a variant to the original method. Particularly, the proposed method resorts to the regularised maximum-likelihood estimation paradigm including a term promoting the sparsity of the profile and related to the l q -norm of the vector containing the scatterers’ reflectivities. A priori information on the interference power level is also accounted for, at the design stage, and, assuming that each range cell under test contains at most one scatterer, the actual active scatterers composing the target are determined by exploiting the Bayesian information criterion (BIC). BIC is also used to automatically select the optimised q, so as to make the procedure adaptive with respect to q. Once the location of the active scatterers has been determined, a least-squares approach is also used to obtain even more precise HRR reconstruction. Furthermore, an efficient algorithm to define optimised frequency hopping patterns, in the presence of a continuous and coordinated feedback between the transmitter and receiver, is presented and assessed. The carried out analysis shows that the SLIM-based procedure presents higher accuracy in the HRR profile recovery than other widely used techniques, i.e. the iterative adaptive approach (IAA). Moreover, results demonstrate that the target range profile estimation capabilities are enhanced, both for SLIM and IAA, when the cognitive paradigm is employed. |
Starting Page | 512 |
Ending Page | 521 |
Page Count | 10 |
ISSN | 17518784 |
Volume Number | 13 |
e-ISSN | 17518792 |
Issue Number | Issue 4, Apr (2019) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-rsn/13/4 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-rsn.2018.5102 |
Journal | IET Radar, Sonar & Navigation |
Publisher Date | 2018-11-09 |
Access Restriction | Open |
Rights Holder | © The Institution of Engineering and Technology |
Subject Keyword | Actual Active Scatterers Bayes Method Bayesian Information Criterion BIC Cognitive Paradigm Continuous Feedback Coordinated Feedback Design Stage HRR Profile Recovery Interference Power Level Interpolation And Function Approximation Iterative Adaptive Approach Iterative Method Iterative Minimisation Paradigm Learning in AI Least Squares Approximation Least-squares Approach Lq-norm Constraint Maximum Likelihood Estimation Minimisation Narrow Instantaneous Bandwidth Numerical Analysis Optimisation Technique Optimised Frequency Hopping Pattern Precise HRR Reconstruction Radar High-range-resolution Profile Reconstruction Radar Resolution Radar Theory Range Cell Receiver Regularised Maximum-likelihood Estimation Paradigm Regularised Minimisation Approach Signal Processing And Detection SLIM-based Procedure Sparse Learning Statistics Stepped-frequency Waveform Target Range Profile Estimation Capabilities Transmitter |
Content Type | Text |
Resource Type | Article |
Subject | Electrical and Electronic Engineering |
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