Aims and Scope

IET Radar, Sonar & Navigation covers the theory and practice of systems and signals for radar, sonar, radiolocation, navigation and surveillance purposes, in aerospace and terrestrial applications.

Examples include advances in:

    Waveform design
    Clutter and detection
    Electronic warfare
    Adaptive array and superresolution methods
    Tracking algorithms
    Synthetic aperture
    Target recognition techniques

Special Issue on Radar Research in India: History and Recent Advances
提出日: 2024-10-01

Recently, there has been significant expansion of the radar research and development ecosystem in India. This is driven by several new planetary missions that are being successfully deployed; investment in the research and development of indigenous defence infrastructure; the increased engagement of radar researchers from academia, industry and research labs in reputed national and international conferences, and publications in radar related journals; and in several new start-ups in aerospace and defence sectors in the last five years. This Special Issue of IET Radar, Sonar & Navigation aims to raise awareness of the emerging research on radar from India, and to showcase their state-of-the-art work on radar systems, sub-systems, components, signal processing algorithms, and studies and analyses of electromagnetic phenomenology and models for civilian, remote sensing and military applications. International collaborations among the authors are encouraged. However, the first author of the manuscript should be based in India. The topics of interest for this special issue include, but are not limited to those provided below.

Papers submitted to the Special Issue should contain at least 40% new material, in the form of new results and discussion, compared to any prior publications. They will undergo the full process of peer-review in line with the journal's policies and editorial standards.

Topics for this call for papers include but are not restricted to:

    Radar for military/dual-use applications;
    Radar for civilian applications;
    Radar for remote sensing applications;
    Integrated sensing and communications systems and signal processing;
    Radar systems, subsystems, and components;
    Radar signal processing algorithms;
    Machine learning and Artificial Intelligence for radar;
    Radar phenomenology studies;
    Radar modelling of targets, propagation, and clutter;
    Radar theory;
    Historical perspectives on radar research and development in India.

Guest Editors:

Shobha Sundar Ram
IIIT Delhi,
India

Kumar Vijay Mishra
US DEVCOM Army Research Laboratory,
United States

Kumar Vaibhav Srivastava
IIT Kanpur,
India

Special Issue on Trust through eXplAInability (XAI), Robustness and Verification of Autonomous Systems
提出日: 2024-12-01

The large success of AI based solutions for autonomous systems applications has strongly impacted people's minds to get prepared and ready for the use of these systems in our daily life.

Indeed, the booming development of machine learning techniques and their embeddings in suitable, small size computation hardware has made some unrealistic applications of autonomous systems just few years back the reality of today. To effectively adopt those intelligent autonomous systems solutions, the end users need to feel safe in engaging with those machines through the level of trust, under different conditions of operations, they have in the outputs of those solutions.

Interpreting the autonomous systems, and more specifically, autonomous vehicles' decisions through the explainability of the machine learning techniques composing their solutions is key. The other way to secure the adoption of those techniques onboard autonomous vehicles is to go through rigorous model verification and robustness processes including dealing with anomalies and adversarial attacks.

The focus of this Special Issue is on modern XAI, Robustness and Verification of AI based autonomous vehicles' decision-making in terms of planning, navigation and guidance. Quantum software stacks and libraries facilitate the link from a high-level description of algorithms to a low-level implementation with quantum gates, for solving concrete problems and applications expected to demonstrate quantum advantage.

Topics for this call for papers include but are not restricted to:

    XAI for autonomous vehicle planning and guidance;
    Validation and Verification (V&V) of AI based solution for autonomous vehicle navigation;
    Pruning of deep learning solutions for autonomous vehicles application;
    Embedding of safe deep learning solutions for autonomous vehicles;
    Robustness of AI-based autonomous vehicle navigation and guidance through anomaly detection;
    Robustness of AI-based autonomous vehicle navigation and guidance through adversarial attacks.

Guest Editors:

Nabil Aouf
University of London;
United Kingdom

Xiaowei Huang
University of Liverpool;
United Kingdom

Abdelhafid Zenati
University of London;
United Kingdom

Daniele De Martini
University of Oxford;
United Kingdom