This paper reports the advancements on a recently presented silicon photonic chip implementing a photonics-based coherent multiband radar transceiver. In this work, the chip has been packaged and engineered for its use in a complete radar system capable of operating up to the X band. The electronic RF frontend and the digital baseband equipment have been developed in order to guarantee performance aligned with the state of the art of conventional radars. A first indoor field trial has been carried out in X band. Range/Velocity acquisitions allow to confirm the radar system effectiveness, posing the base for the next step: the multiband field trial and coherent data fusion.

Distributed multiple-input multiple-output (MIMO) radar systems deployed on mobile platforms are gaining strategic importance across a wide range of civil and military applications. These systems offer high sensitivity, robustness to shadowing, and improved target localization capabilities. In recent years, photonic technologies have demonstrated the capability to coherently distribute the radar signals via optical fiber or free space optical links, thereby enabling the development of coherent distributed radar configurations. This coherence enables effective data fusion and centralized processing from the spatially distributed radars. Thereby, coherent MIMO processing can be achieved across widely separated radars, as in the case of radar mounted on a swarm of drones. The design of a distributed MIMO radar system is inherently challenging due to the large number of design parameters involved, especially for mobile sensor platforms. Stepping from our recent experimental demonstrations of coherent photonics-assisted distributed MIMO radar, this paper analyzes the impact of the main system design parameters, i.e., radar signal bandwidth, number of radar peripherals, and baseline geometry and dimension, on the key performance indicators, i.e., spatial resolution and sidelobe levels of the MIMO ambiguity function. It also proposes an innovative approach to improve system performance. This approach aims to minimize hardware complexity by exploiting a combination of meta-heuristic algorithms for architecture optimization and signal post-processing. The former step allows optimization of spatial resolution, while the latter reduces the detrimental effects of sidelobes, which are typical in distributed MIMO radars. The proposed analysis focuses on a specific application of a photonics-assisted radar constellation mounted on a swarm of drones operating in the X and Ka bands. In particular, the proposed optimization method is applied to 3 × 3 MIMO system, demonstrating the capability to achieve 2 cm resolution in both range and cross-range. The detection capability of the enhanced system is validated when the target is located 50 m away from baseline, using cell-averaging constant false alarm rate detection criterion. A target detection probability of 0.996 is achieved at false alarm probability of 10⁻⁶. These results confirm the effectiveness of the proposed approach in enabling high-performance, low-resource drone-based MIMO radar surveillance systems.

This paper deals with the imaging problem from data collected by means of a microwave photonics-based distributed radar network. The radar network is leveraged on a centralized architecture, which is composed of one central unit (CU) and two transmitting and receiving dual-band remote radar peripherals (RPs), it is capable of collecting monostatic and multistatic phase-coherent data. The imaging is herein formulated as a linear inverse scattering problem and solved in a regularized way through the truncated singular value decomposition inversion scheme. Specifically, two different imaging schemes based on an incoherent fusion of the tomographic images or a fully coherent data processing are herein developed and compared. Experimental tests carried out in a port scenario for imaging both a stationary and a moving target are reported to validate the imaging approach.

Multiple-input multiple-output (MIMO) radars offer significant advantages over conventional standalone radars in terms of target detection and localization capabilities. However, to fully exploit their potential, such systems require excellent time and phase synchronization among the central unit and the radar heads. Only recently, thanks to microwave photonics (MWP) techniques, it has been possible to develop the first coherent MIMO radar demonstrators. In this paper, a simulation tool in MATLAB programming language is proposed to model coherent MIMO radars based on MWP techniques in maritime surveillance applications. Moreover, a novel approach for estimating the radar cross section of extended maritime targets is presented. The system performance in co-located and distributed MIMO configuration, as well as in single- and multi-band operations, is evaluated by means of relevant key performance indicators (KPIs). Simulations are carried out in two close-to-reality scenarios. In the first, a co-located MIMO radar is mounted on top of a patrolling vessel. In the second, a distributed MIMO radar is deployed inside a port. The proposed KPIs are analyzed in terms of both geometric and frequency diversities of the system, laying the foundations for general system optimization criteria valid in any given surveillance application.

The recent advancements in hyperspectral satellite missions have significantly expanded Earth Observation capabilities. Availability of high-resolution hyperspectral data and products thereof, such as those delivered by the PRISMA mission of the Italian Space Agency (ASI), provides unprecedented opportunities for monitoring Earth processes in a wide range of applications. Enhancement of hyperspectral image products plays a crucial role in further increasing data exploitation possibilities. In this work, we propose a unified, physics-based APproAch for joint detection of Clouds, water, and sHadows in hypErspectral images (APACHE), focusing on PRISMA L1 product enhancement. Unlike existing approaches that typically address detection of one individual surface type at a time, our methodology jointly extracts the three surfaces of interest in an automated, stepwise, and unsupervised manner. More importantly, there is no need for parameter tuning, labeled data, or multi-source images. APACHE only leverages spectral properties of clouds, water, and shadows combined with a non-parametric clustering technique. Experimental results on a variety of PRISMA images from diverse datasets demonstrate the effectiveness of the proposed methodology in accurately detecting the surfaces of interest, both with respect to state-of-the-art methods and enhancing PRISMA products. On average, APACHE achieves accuracies of 96.42% for cloud detection, 99.23% for water detection, and 97.55% for shadow detection, with improvements ranging from 2% to 30% over the best performing comparison method. These results highlight APACHE potential to improve hyperspectral satellite data exploitation across a wide range of remote sensing applications.