Multiple-input Multiple-output Radar Waveform Design Methodologies

Multiple-input multiple-output (MIMO) radar is currently an active area of research. The MIMO techniques have been well studied for communications applications where they offer benefits in multipath fading environments. Partly inspired by these benefits, MIMO techniques are applied to radar and they offer a number of advantages such as improved resolution and sensitivity. It allows the use of transmitting multiple simultaneous waveforms from different phase centers. The employed radar waveform plays a key role in determining the accuracy, resolution, and ambiguity in performing tasks such as determining the target range, velocity, shape, and so on. The excellent performance promised by MIMO radar can be unleashed only by proper waveform design. In this article, a survey on MIMO radar waveform design is presented. The goal of this paper is to elucidate the key concepts of waveform design to encourage further research on this emerging technology.Defence Science Journal, 2013, 63(4), pp.393-401, DOI:http://dx.doi.org/10.14429/dsj.63.253

Mathematical optimization techniques for cognitive radar networks

This thesis discusses mathematical optimization techniques for waveform design in cognitive radars. These techniques have been designed with an increasing level of sophistication, starting from a bistatic model (i.e. two transmitters and a single receiver) and ending with a cognitive network (i.e. multiple transmitting and multiple receiving radars). The environment under investigation always features strong signal-dependent clutter and noise. All algorithms are based on an iterative waveform-filter optimization. The waveform optimization is based on convex optimization techniques and the exploitation of initial radar waveforms characterized by desired auto and cross-correlation properties. Finally, robust optimization techniques are introduced to account for the assumptions made by cognitive radars on certain second order statistics such as the covariance matrix of the clutter. More specifically, initial optimization techniques were proposed for the case of bistatic radars. By maximizing the signal to interference and noise ratio (SINR) under certain constraints on the transmitted signals, it was possible to iteratively optimize both the orthogonal transmission waveforms and the receiver filter. Subsequently, the above work was extended to a convex optimization framework for a waveform design technique for bistatic radars where both radars transmit and receive to detect targets. The method exploited prior knowledge of the environment to maximize the accumulated target return signal power while keeping the disturbance power to unity at both radar receivers. The thesis further proposes convex optimization based waveform designs for multiple input multiple output (MIMO) based cognitive radars. All radars within the system are able to both transmit and receive signals for detecting targets. The proposed model investigated two complementary optimization techniques. The first one aims at optimizing the signal to interference and noise ratio (SINR) of a specific radar while keeping the SINR of the remaining radars at desired levels. The second approach optimizes the SINR of all radars using a max-min optimization criterion. To account for possible mismatches between actual parameters and estimated ones, this thesis includes robust optimization techniques. Initially, the multistatic, signal-dependent model was tested against existing worst-case and probabilistic methods. These methods appeared to be over conservative and generic for the considered signal-dependent clutter scenario. Therefore a new approach was derived where uncertainty was assumed directly on the radar cross-section and Doppler parameters of the clutters. Approximations based on Taylor series were invoked to make the optimization problem convex and {subsequently} determine robust waveforms with specific SINR outage constraints. Finally, this thesis introduces robust optimization techniques for through-the-wall radars. These are also cognitive but rely on different optimization techniques than the ones previously discussed. By noticing the similarities between the minimum variance distortionless response (MVDR) problem and the matched-illumination one, this thesis introduces robust optimization techniques that consider uncertainty on environment-related parameters. Various performance analyses demonstrate the effectiveness of all the above algorithms in providing a significant increase in SINR in an environment affected by very strong clutter and noise

The Bi-directional Spatial Spectrum for MIMO Radar and Its Applications

<p>Radar systems have long applied electronically-steered phased arrays to discriminate returns in azimuth angle and elevation angle. On receiver arrays, beamforming is performed after reception of the data, allowing for many adaptive array processing algorithms to be employed. However, on transmitter arrays, up until recently pre-determined phase shifts had to applied to each transmitter element before transmission, precluding adaptive transmit array processing schemes. Recent advances in multiple-input multiple-output radar techniques have allowed for transmitter channels to separated after data reception, allowing for virtual non-causal "after-the-fact" transmit beamforming. The ability to discriminate in both direction-of-arrival and direction-of-departure allows for the novel ability to discriminate line-of-sight returns from multipath returns. This works extends the concept of virtual non-causal transmit beamforming to the broader concept of a bi-directional spatial spectrum, and describes application of such a spectrum to applications such as spread-Doppler multipath clutter mitigation in ground-vehicle radar, and calibration of a receiver array of a MIMO system with ground clutter only. Additionally, for this work, a low-power MIMO radar testbed was developed for lab testing of MIMO radar concepts.</p>Dissertatio

Forward scatter radar: innovative configurations and studies

This thesis is dedicated to the study of innovative forward scatter radar (FSR) configurations and techniques. FSR is a specific kind of bistatic radar having bistatic angle equal or close to 180˚. The goal of this PhD project is to investigate techniques and configurations which would improve FSR performance, making it a more appealing system. This thesis proposes an initial radar overview with deep focus on forward scatter capabilities. FSR principles, radar cross section and target signature are widely discussed. Thus, numerous innovative studies done during this PhD project are presented. FSR passive mode, MIMO geometry and moving transmitter/ moving receiver configurations are here investigated for the first time. Numerous experimental campaigns have been undertaken and a big quantity of data has been collected. Comprehensive analyses on measured and simulated results are presented. Moreover, various novel techniques to estimate target motion parameters have been developed and tested on real and simulated data. Results show a good match between measured and estimated kinematic information. Finally, clutter in moving ends FSR is discussed. In fact, the innovative moving ends configuration is affected by Doppler shift and clutter Doppler spread. Thus, it is important to understand how this issue limits the system performance