Cognitive Sub-Nyquist Hardware Prototype of a Collocated MIMO Radar

We present the design and hardware implementation of a radar prototype that demonstrates the principle of a sub-Nyquist collocated multiple-input multiple-output (MIMO) radar. The setup allows sampling in both spatial and spectral domains at rates much lower than dictated by the Nyquist sampling theorem. Our prototype realizes an X-band MIMO radar that can be configured to have a maximum of 8 transmit and 10 receive antenna elements. We use frequency division multiplexing (FDM) to achieve the orthogonality of MIMO waveforms and apply the Xampling framework for signal recovery. The prototype also implements a cognitive transmission scheme where each transmit waveform is restricted to those pre-determined subbands of the full signal bandwidth that the receiver samples and processes. Real-time experiments show reasonable recovery performance while operating as a 4x5 thinned random array wherein the combined spatial and spectral sampling factor reduction is 87.5% of that of a filled 8x10 array.Comment: 5 pages, Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing (CoSeRa) 201

Efficient Transmit Beamspace Design for Search-free Based DOA Estimation in MIMO Radar

In this paper, we address the problem of transmit beamspace design for multiple-input multiple-output (MIMO) radar with colocated antennas in application to direction-of-arrival (DOA) estimation. A new method for designing the transmit beamspace matrix that enables the use of search-free DOA estimation techniques at the receiver is introduced. The essence of the proposed method is to design the transmit beamspace matrix based on minimizing the difference between a desired transmit beampattern and the actual one under the constraint of uniform power distribution across the transmit array elements. The desired transmit beampattern can be of arbitrary shape and is allowed to consist of one or more spatial sectors. The number of transmit waveforms is even but otherwise arbitrary. To allow for simple search-free DOA estimation algorithms at the receive array, the rotational invariance property is established at the transmit array by imposing a specific structure on the beamspace matrix. Semi-definite relaxation is used to transform the proposed formulation into a convex problem that can be solved efficiently. We also propose a spatial-division based design (SDD) by dividing the spatial domain into several subsectors and assigning a subset of the transmit beams to each subsector. The transmit beams associated with each subsector are designed separately. Simulation results demonstrate the improvement in the DOA estimation performance offered by using the proposed joint and SDD transmit beamspace design methods as compared to the traditional MIMO radar technique.Comment: 32 pages, 10 figures, submitted to the IEEE Trans. Signal Processing in May 201

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

Quasi-Orthogonal Wideband Radar Waveforms Based on Chaotic Systems

Many radar applications, such as those involving multiple-input, multiple-output (MIMO) radar, require sets of waveforms that are orthogonal, or nearly orthogonal. As shown in the work presented here, a set of nearly orthogonal waveforms with a high cardinality can be generated using chaotic systems, and this set performs comparably to other waveform sets used in pulse compression radar systems. Specifically, the nearly orthogonal waveforms from chaotic systems are shown to possess many desirable radar properties including a compact spectrum, low range sidelobes, and an average transmit power within a few dB of peak power. Moreover, these waveforms can be generated at essentially any practical time length and bandwidth. Since these waveforms are generated from a deterministic process, each waveform can be represented with a small number of system parameters. Additionally, assuming these waveforms possess a large time-bandwidth product, a high number of nearly orthogonal chaotic waveforms exist for a given time and bandwidth. Thus the proposed generation procedure can potentially be used to generate a new transmit waveform on each pulse.United States. Air Force (Contract FA8721-05-C-0002)Massachusetts Institute of Technology. Research Laboratory of ElectronicsBAE SystemsTexas Instruments Incorporated. Leadership University Consortium Progra

Nearly orthogonal, doppler tolerant waveforms and signal processing for multi-mode radar applications

In this research, we investigate the design and analysis of nearly orthogonal, Doppler tolerant waveforms for diversity waveform radar applications. We then present a signal processing framework for joint synthetic aperture radar (SAR) and ground moving target indication (GMTI) processing that is built upon our proposed waveforms. ^ To design nearly orthogonal and Doppler tolerant waveforms, we applied direct sequence spread spectrum (DSSS) coding techniques to linear frequency modulated (LFM) signals. The resulting transmitted waveforms are rendered orthogonal using a unique spread spectrum code. At the receiver, the echo signal can be decoded using its spreading code. In this manner, transmit orthogonal waveforms can be matched filtered only with the intended receive signals. ^ Our proposed waveforms enable efficient SAR and GMTI processing concurrently without reconfiguring a radar system. Usually, SAR processing requires transmit waveforms with a low pulse repetition frequency (PRF) rate to reduce range ambigu- ity; on the other hand, GMTI processing requires a high PRF rate to avoid Doppler aliasing and ambiguity. These competing requirements can be tackled by employing some waveforms (with low PRF) for the SAR mission and other waveforms (with high PRF) for the GMTI mission. Since the proposed waveforms allow separation of individual waveforms at the receiver, we can accomplish both SAR and GMTI processing jointl