A systematic analysis of signal to interference plus noise ratio (SINR) at each step of the signal processing chain of frequency modulated continuous wave based automotive radar is presented for the first time. Using the real-time measured data, SINR improvement after each processing stage of the victim receiver is quantified for various automotive sensor parameters and scenarios.

A systematic end to end modelling of millimetre wave automotive radar is presented that can be used to analyse the performance of MIMO radar chipsets in realistic road conditions. The radar - environment interaction is demonstrated for extended targets, clutter, and transmissions from nearby sensors - radar interferences. Furthermore, various aspects of sensor configuration and signal processing according to the hardware and software capabilities of automotive radars are investigated. The modelled radar performance in different domains is validated through real time measurements using automotive MIMO radars.

With the current push towards increased levels of autonomy in the automotive sector, accurate sensing of the vehicle surroundings is critical, even under challenging environmental conditions. Automotive radar is well suited to this application. This paper describes a novel approach which uses a combination of Doppler beam sharpening, for high resolution imaging of the stationary environment, and a multi-target tracker for estimating the positions and velocities of dynamic targets. Experimental results recorded from a moving vehicle are presented.

There is an increasing demand for detailed sensing of the region surrounding a vehicle in order to develop advanced autonomous driving technology that remains robust even under challenging weather conditions. Radar is capable of meeting propagation requirements but is traditionally limited by poor resolution and high sidelobes compared to optical techniques. In this paper we build on our previous work, which pairs traditional multiple-input multiple-output (MIMO) processing with Doppler beam sharpening (DBS). This combined MIMO-DBS approach significantly improves cross-range resolution while also reducing sidelobe levels. We provide detailed analysis of expected performance based on system parameters. We also propose an alternative approach to provide a wide field-of-view (FOV) even in the presence of Doppler ambiguities, where our previously published work suffered a reduced field-of-view below that of the underlying array at higher vehicle speeds. We provide experimental verification of the technique presented.

A universal image segmentation framework, which can be applied to various high-resolution automotive radar imagery produced by different beamforming strategies, is expected in the radar community to provide robust support to the development of autonomous driving. This paper estimates the universality of the segmentation framework, which is developed based on radar data produced by the mechanical steer beamforming, by directly implementing it onto another high-resolution radar imagery produced by the beamforming strategy of MIMO Doppler beam sharpening (DBS). The comparison of the distribution features of two parts of data shows that the return power level shift caused by the resolution difference is the major factor that needs to be compensated for the framework transfer implementation. The details of the universal segmentation framework are given to show that this can significantly simplify the complicated manual labelling and feature extraction. The segmentation results are discussed with the analysis of the performance and the potential future work.

Image segmentation on automotive radar imagery is the key technique for identifying the passable and impassable regions for path planning in autonomous or assistive driving. The availability of consecutive frames which measure the driving scene shifted along with the timeline enables improved segmentation on radar imagery. The frame fusion on automotive radar map is implemented as a two-step procedure: 1) The pixel-to-pixel mapping between consecutive frames is achieved based on an inertial measurement unit (IMU); 2) The information fusion of consecutive frames is achieved based on the Kalman filter. The frame fusion operation leads to correct classification of the initially “unknown” regions and overall improves the confidence of classification compared to single frame segmentation. The segmentation results with frame fusion are presented and compared with the results of single frame segmentation to demonstrate the segmentation improvement.

The radar signatures of automotive targets were measured at 24, 150 and 300 GHz in an anechoic controlled environment using three radar systems. Radar cross section results for a wheelchair, pushchair, pedestrian and bicycle are presented. The potential of low TeraHertz (low-THz) sensors to measure additional characteristic features of the targets and enhancing their recognition is also shown.

This paper presents a brief case study which illustrates the role that internal reflections can play in degrading the point response, and hence effective range resolution, of a frequency modulated continuous wave (FMCW) radar, using a high-resolution multi-receiver 300 GHz radar as a test case. A compensation technique is presented which corrects for this effect by calibrating against a reference target, allowing for the bandwidth-limited range resolution to be restored.

In this paper we apply the Doppler beam sharpening technique of azimuth refinement to the emerging area of low-THz radar. This improves the image quality and thus aids object classification in low-THz radar imaging systems, for example for autonomous platforms. The paper briefly explains the theory behind the process of Doppler beam sharpening; this is then experimentally tested and verified against two closely space corner reflector reference targets.

Communication satellites in geosynchronous orbit are increasingly broadcasting digital signals with high bandwidth and high power. These signals are in principle well-suited to radar imaging and the study presented here is an initial feasibility study for a passive bistatic synthetic aperture radar using satellites in geosynchronous orbit (GEO). The persistent viewing possible from GEO could enable important new applications. The mission concept is outlined and studies of the available signal formats identify digital TV broadcasts in Ku-band as most suitable for radar imaging. The additional space hardware required is a dedicated receive channel, which could be implemented as a hosted payload at modest cost. Our findings so far suggest that the mission concept is feasible for coarse spatial resolution images and that it could therefore provide a low-cost technology demonstration of geosynchronous radar.