The wide-scale deployment of radars, distributed across a platform and across multiple platforms for reliable 360° situational awareness (SA), introduces the challenge of radar interference. Interference can broadly be categorised as self-interference (between radars mounted on the same platform) and mutual interference (signals received from radars on other platforms). Both types of interference impede the reliability of SA delivered by such systems, particularly in dense environments where numerous radars operate simultaneously within the same frequency band. This work presents a comprehensive evaluation of a multi-modal beamforming approach that combines unfocused synthetic aperture radar with the traditional Multiple-Input, Multiple-Output beamformer to enhance radar resolution and suppress interference. Additionally, various aspects of sensor configurations defining hardware and software capabilities of state-of-the-art radars are discussed, and a systematic analysis of signal-to-interference-plus-noise ratio at each step of the processing is presented. Extensive simulations and experimental results in both automotive and maritime environments are shown to validate the effectiveness of the proposed approach.

The fusion of measurements from multiple sensing nodes in a distributed radar network is essential for reliable 360° situational awareness, but it depends on precise inter-sensor synchronisation. This work analyses how timing, carrier-frequency, and sampling-rate offsets map into structured inter-sensor phase distortions that reduce coherent gain and defocus images. We develop a unified error-to-phase model to derive mitigation strategies that maintain coherence under representative timing, jitter, and intermittent decorrelation due to occlusion and shadowing.

Autonomous systems require sensors that provide high-resolution imagery in adverse lighting and weather conditions for advanced situational awareness. In this regard, radars are a mandatory component of autonomous systems. Although Multiple-Input Multiple-Output (MIMO) radars provide high angular resolution beyond that of their actual physical dimension, much higher cross-range resolutions are required, especially in traffic congested areas, to differentiate and recognize closely positioned targets. The motion of the MIMO radar platform can be exploited to obtain higher cross-range resolution in the off-boresight direction, using Synthetic Aperture Radar (SAR) and Doppler Beam Sharpening (DBS) techniques, but improvements in the boresight direction, the most crucial direction for path planning, require the use of super-resolution techniques. This paper proposes a technique that combines the Burg algorithm with MIMO-SAR and MIMO-DBS radar data to enhance the cross-range resolution in the boresight direction and to achieve further enhanced cross-range resolution in off-boresight directions. The proposed technique is applied to both frequency domain and time domain data in back-projection (BP) and DBS image formation processing. A comprehensive comparison is made, with evaluation of corresponding performance and operational complexity. The performance of the technique is validated through simulation, lab-based and real-world experiments at a frequency of 77 GHz.

ABSTRACT The detection and tracking of individuals in fire environments remain challenging because smoke, flames and time‐varying clutter can mask or distort radar returns. This paper investigates radar‐based recognition of a moving person in a real fire‐test environment using 79 and 300 GHz radar measurements, with emphasis on how frequency, propagation and scene dynamics affect target observability. Range‐Doppler analysis is used to separate moving human returns from stationary fire‐scene reflections, and a region‐based tracking stage is applied to estimate the firefighter trajectory under severe clutter. Experimental results show that 79 GHz radar provides more reliable target visibility and track continuity in the tested scenarios, whereas 300 GHz radar is more limited by lower transmit power and reduced practical detection robustness in the same environment. The study supports the development of radar sensing strategies for firefighter safety and rescue operations in visually degraded environments.

In this paper, the extrapolation of a 2D multiple-input multiple-output (MIMO) array is proposed using the Burg algorithm to achieve higher angular resolution beyond that of the corresponding 2D MIMO virtual array. The main advantage of such an approach is that it allows us to dramatically decrease both the physical size and the number of antenna elements of the MIMO array. The performance and limitations of the Burg algorithm are examined through both simulation and experimentation at 77 GHz. The experimental methodology used to acquire 3D data of range, azimuth and elevation information with the 1D MIMO off-the-shelf radar is described. Using this method, the performance of the proposed array can be tested experimentally, especially at frequencies where it is desired to assess the antenna response prior to fabricating the antenna.

The degradation of radar performance due to interference depends on both the interference power and the relationship between the victim and interference waveform parameters. This paper introduces a novel, generalized approach for analyzing the performance of FMCW radar in the presence of interference using a universal graphical tool - the Heatmap. This tool enables: (i) a comprehensive assessment of interference effects on radar performance based on waveform parameters, helping to identify critical cases; (ii) an evaluation of mitigation algorithm effectiveness across a wide range of interference scenarios; and (iii) a straightforward method for estimating signal-to-interference ratio in diverse scenarios. These applications are explored in detail with examples. Furthermore, the proposed calculation method of signal-to-interference ratio is tested through simulations and real experimental data.

The fusion of data from different sensing nodes, operating within a distributed sensing suite, is essential for reliable 360◦ imagery around a platform to provide enhanced situational awareness. This work presents a methodology for synchronising data between different sensors and generating multi-modal imagery from a multi-perspective sensing suite, comprising both proprioceptive and exteroceptive sensors.

This paper presents experimental results of the application of Doppler Beam Sharpening (DBS) to enhance the resolution and detectability of maritime targets, with a particular focus on marine infrastructure. Two approaches are investigated: (i) 77 GHz multi-modal sensing based on combined Multiple-Input Multiple-Output (MIMO) and DBS processing, and (ii) 150 GHz real-aperture radar with DBS beamforming. The performance of these beamformers is evaluated and compared with a LiDAR point cloud to highlight the advantages of higher frequencies for next-generation radar sensors.

Advancement toward fully autonomous systems requires enhanced sensing and perception, particularly a 360 degrees vision for safe maneuvering. One approach to achieving this is through a distributed network of radar sensors, operating in homogeneous or heterogeneous configurations, strategically positioned to provide increased coverage and visibility in otherwise blind regions. Such a multiperspective sensing network, complemented with multimodal signal processing, can significantly improve the angular resolution of the radar, delivering high-fidelity scene imagery essential for region classification and path planning. This study presents a methodology for multimodal and multiperspective sensing using heterogeneous radar sensors, utilizing Doppler beam sharpening (DBS) within multiple-input-multiple-output (MIMO) radars to enhance the resolution and coverage. Traditional frequency-modulated continuous wave (FMCW)-MIMO radars, currently the most widely used configuration, are prone to Doppler aliasing, limiting the field of view (FoV) in DBS and MIMO-DBS processing. To address this limitation, the effective FoV in multiperspective image is extended to that provided by the radar's physical aperture. The proposed framework is validated using 77-GHz radar chipsets in both automotive and maritime conditions, with sensors mounted in front-looking, corner-looking, and side-looking orientations.

This paper reports some measurement of objects in water at 77, 150 and 207 GHz. The objects were a hollow metal sphere, a pallet and a plastic ball, which was a surrogate for the head of a swimmer. The measurements were made using vertical polarization in the fresh water wave tank at FloWave in Edinburgh. The radar cross section (RCS) values of the pallet and the plastic ball were approximately -22 dBm 2 and -25 dBm 2 respectively, with no clear dependence on frequency. These values are lower than might have been expected. The return from the sphere was also reduced because it was wet, in agreement with the expectations from previous work. The RCS values were enhanced to of the order of -15 dBm 2 by the wakes when the targets were towed through the water at about 1 ms -1 . The paper also contains an estimate of the upper limit of the RCS of the distributed reflection from the water surface in the measurement conditions, which were equivalent to sea state one.

This paper describes a methodology of detecting and tracking targets in maritime conditions from a moving radar platform using a combination of MIMO beamformer, CFAR detection, clustering, and a multi-target tracker based on an extended Kalman filter. For validation, experiments have been conducted using compact 77-GHz automotive MIMO radar on a lake with an approaching paddler as the target of interest. The results show a reliable performance with a mean error of 0.32 m in the range and 1.39 degrees the angle, estimated within a 20 s observation interval.

High resolution radar sensing is essential to provide situational awareness to small and medium sized marine platforms. However, detecting small targets on the sea surface is a challenging task for the marine surveillance radars because of the weak echoes and relatively low velocity. While there is a similarity and significant body of research on high resolution radar sensing in automotive environment, the direct translation of such techniques to marine sensing is difficult due to fundamentally dynamic underlaying sea surface. This paper addresses the need of developing novel radar sensing capabilities to image and, potentially, classify small marine targets, such as paddlers, buoys, flotsam and jetsam, or the incoming large waves. Our proposed approach combines Multiple Input, Multiple Output (MIMO) and Doppler Beam Sharpening (DBS) beamforming techniques with the Ordered Statistics – Cell Averaging Constant False Alarm Rate (OSCA-CFAR) for robust target detection, Density Based Spatial Clustering of Applications with Noise (DBSCAN) for clustering, and an adaptive focusing technique. With the developed methodology, multiple small ‘dynamic’ targets within the marine scene have been imaged and detected against substantially suppressed sea background.

Mutual interference between radars is one of the major obstacles currently faced by automotive industry towards the attainment of full vehicle autonomy. This paper presents an analysis and mitigation of interference in the spatial domain and evaluates its efficiency compared to the traditional mitigation techniques for various road scenarios and radar configurations. The performance metrics, including peak-to-highest side lobe levels and signal to interference plus noise ratio, have been assessed based on the angular separations between the target and interferers to highlight the cases where spatial domain mitigation might be suited.

This paper details the use of the Radon transform for improving small target detection in a marine environment by radar. The data used were collected using a 150 GHz radar that stared at anchored targets in a wave tank which simulated maritime conditions. After transforming range-time profiles of the data, a method was developed in Radon space to easily distinguish between the normative form of the sea clutter and the anomalous forms of the targets, after which the normative form is reduced. The proposed processing technique shows consistent improvements of 10-12 dB in the signal-to-clutter ratio.

This paper describes a novel approach based on a combination of MIMO and adaptive Doppler beam sharpening beamforming techniques to enhance radar resolution in dynamic maritime conditions followed by an implementation of a multi-target tracker based on extended Kalman filter to track dynamic targets, thereby, providing higher levels of autonomy to small and medium sized marine crafts. The experiments have been conducted using compact 77-GHz automotive radars in open-sea conditions (sea state 3) and results show reliable performance in detecting and tracking approaching wave using developed signal processing scheme despite relative platform and sea motion.

This paper presents an application of Doppler beam sharpening (DBS) to focus and enhance the resolution of multiple ‘dynamic' targets in maritime environment. The current limitations in radar-based maritime sensing have been analysed followed by adaptive DBS approach to detect and focus extended targets using traditional DBS, adaptive thresholding, and density-based clustering. Based on this, targets have been simultaneously focused with lower sidelobes and removal of Doppler sidebands inherent to DBS. Demonstrated experimental results have shown feasibility of proposed technique in improving target detection.

This paper presents a first example of experimental results of the application of Doppler beam sharpening (DBS) to enhance the resolution and detectability of maritime targets at a) 77 GHz using multiple-input multiple-output (MIMO) radar and b) 150 GHz using a real-aperture radar. The performance of DBS and MIMO-DBS beamforming have been evaluated with varied platform dynamics and test conditions to highlight the advantages of higher frequencies for the next generation of radar sensors. The applicability of DBS in maritime conditions has also been assessed.

The application of the Doppler Beam Sharpening (DBS) technique is shown for a free moving maritime target using a 150 GHz FMCW radar equipped with 10° horn antenna. The methodology of synchronizing the radar data with the spatial and inertial data is presented with the resulting DBS imagery from the scene at Coniston Water, UK. DBS with sub-THz (above 0.1 THz) radar was found to be an effective tool for producing images with range resolutions on the order of cm and azimuth resolutions of near 0.1°. This resolution was found to be more than 100 times more refined than the 150 GHz horn antenna azimuth beamwidth.

We present an investigation of the use of a repeatable wave tank experiment to replicate maritime conditions for the measurement of electromagnetic scattering at sub-THz frequencies. The goal is to use the experiment in the development of marine radar detection algorithms by detecting anomalies in the returns from the waves. This experimental technique can also be used for situations where experimental measurements are logistically challenging. The data obtained from the wave tank experiments are presented. We present a method of digitally recreating the wave profile for simulation purposes. We also present results for a simple anomaly detection algorithm to demonstrate the type of algorithms that can be applied to the data, and the direction of future wave tank experiments.

The drive towards higher levels of autonomy requires accurate perception of vehicle surroundings that can be achieved through distributed sensor network. This paper presents a technique of multi-modal sensing with heterogeneous radar sensors using multiple-input, multiple-output and Doppler beam sharpening (DBS) based beamforming. This provides an enhanced azimuth resolution and wide coverage around the vehicle. DBS has a fundamental limitation of reduced field of view (FoV) that depends on platform velocity. Based on radar data replication, unambiguous FoV has been extended to provide higher level of detail about the vehicle. Presented approach has been validated with experimental data gathered using 77 GHz radar chipsets mounted in forward-looking and corner-looking orientations with respect to the platform trajectory.