Cognitive multichannel ISAR imaging for maritime coastal surveillance and ground border control

Giusti, Elisa; Bacci, Alessio; Stinco, Pietro; Saverino, Anna Lisa; Gini, Fulvio; Berizzi, F.; Greco, Maria

doi:10.1109/camsap.2015.7383790

Cited by 4 publications

(5 citation statements)

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“…In Ref. [22], a hierarchy is used to manage short‐term and long‐term goals. Hierarchical Fully Adaptive Radar (HFAR) framework was subsequently developed as an extension of the existing FAR framework [23] to allow multiple PACs to operate in a hierarchical fashion so that multiple levels of task inference and management are achieved.…”

Section: Introductionmentioning

confidence: 99%

Multi‐task tracking and classification with an adaptive radar

John-Baptiste

Brandewie

Vinci

et al. 2021

IET Radar Sonar & Navi

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This study investigates the use of adaptive radar waveforms in performing a multi-task operation involving the tracking and classification of a single target. The work applies a previously reported hierarchical fully adaptive radar framework and considers a situation in which the target tracking function is performed using a transmit waveform having adaptive pulse repetition frequency, pulse width, bandwidth, and coherent processing interval parameters, while the classification function can be performed using only a fixed radar waveform. Two alternate strategies for selecting the next waveform are then described. The first strategy is focussed on maintaining fixed measurement performance goals in terms of target range and velocity and classification entropy, while the second approach varies these performance goals depending on the target state. Both approaches are examined for simulated datasets at W-band representing a moving human target, as well as for laboratory measurements of this scenario. The results for both simulation and experiment show the benefits of including adaptive waveforms in performing multiple radar tasks.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Introductionmentioning

confidence: 99%

Multi‐task tracking and classification with an adaptive radar

John-Baptiste

Brandewie

Vinci

et al. 2021

IET Radar Sonar & Navi

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“…These two metrics can be used to optimize radar signal transmission parameters to determine the pulse repetition frequency (PRF) and associated time interval that match the environment. Giusti et al [16] analyzed a cognitive multi-platform synthetic aperture radar system design problem, and discussed a method based on cognitive and autonomous learning from observation to implement autonomous waveform diversity system to maximize its performance over time. Aittomaki et al [17] proposed an efficient and low computational complexity method for optimizing the power allocation of MIMO radars to achieve maximum discovery probability.…”

Section: Introductionmentioning

confidence: 99%

Research on Cognitive Marine Radar Based on LFM Waveform Control

Liu

Zhang

Suo

et al. 2019

Sensors

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In this paper, the method of applying cognitive radar technology to marine radar is studied, and the cognitive marine radar structure and transmitted signal model with three control parameters are constructed. The selection method of waveform control parameters, which is based on the target spatial distribution and the reference target detection effect with the minimum emission energy as the criterion, is given. The transmission signal control selection method given in this paper can flexibly realize different emission signal groups of m × n × p groups by independently setting the values m, n and p of three control parameters. It does not require radar hardware circuit reconstruction to meet the radar waveform changes. This is more convenient for the technical realization of cognitive marine radar. According to the method of this paper, a cognitive marine radar test system was constructed. The experimental results show that the proposed radar could reduce the emission energy by 15.9 dB compared with the traditional fixed-parameter pulse compression marine radar under the experimental conditions.

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“…While he forgoes an explicit hierarchical construction, Guerci allows for multiple levels of processing as well, simultaneously considering mission planning, radar task scheduling, and adaptive transmit and receive [2]. In [25], a two‐tier hierarchy is used to divide two scales of tasks, what Giusti et al refer to as cognitive and fully adaptive processes. The fully adaptive processing allows the cognitive inverse synthetic aperture radar system to actuate the power and bandwidth in two spectral bands, while the cognitive processing adapts the actuating function itself over larger time scales based on changes in the environment [25].…”

Section: Introductionmentioning

confidence: 99%

“…In [25], a two‐tier hierarchy is used to divide two scales of tasks, what Giusti et al refer to as cognitive and fully adaptive processes. The fully adaptive processing allows the cognitive inverse synthetic aperture radar system to actuate the power and bandwidth in two spectral bands, while the cognitive processing adapts the actuating function itself over larger time scales based on changes in the environment [25]. Bruggenwirth et al .…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we extend the FAR framework in [5] to provide a general approach for designing hierarchical cognitive processors. The first contribution of this work is to develop a general mathematical formulation of an HFAR system which provides a mathematical formalism for descriptive solutions, such as in [19, 20, 25, 26], and unifies detailed application‐specific algorithms, such as in [18, 21, 22]. The HFAR framework thus provides a common mathematical basis for describing, relating, and comparing existing algorithms, as well as for designing new ones.…”

Section: Introductionmentioning

confidence: 99%

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