451 research outputs found
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
MIMO Radar Ambiguity Properties and Optimization Using Frequency-Hopping Waveforms
The concept of multiple-input multiple-output (MIMO) radars has drawn considerable attention recently. Unlike the traditional single-input multiple-output (SIMO) radar which emits coherent waveforms to form a focused beam, the MIMO radar can transmit orthogonal (or incoherent) waveforms. These waveforms can be used to increase the system spatial resolution. The waveforms also affect the range and Doppler resolution. In traditional (SIMO) radars, the ambiguity function of the transmitted pulse characterizes the compromise between range and Doppler resolutions. It is a major tool for studying and analyzing radar signals. Recently, the idea of ambiguity function has been extended to the case of MIMO radar. In this paper, some mathematical properties of the MIMO radar ambiguity function are first derived. These properties provide some insights into the MIMO radar waveform design. Then a new algorithm for designing the orthogonal frequency-hopping waveforms is proposed. This algorithm reduces the sidelobes in the corresponding MIMO radar ambiguity function and makes the energy of the ambiguity function spread evenly in the range and angular dimensions
Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays
Massive MIMO (multiple-input multiple-output) is no longer a "wild" or
"promising" concept for future cellular networks - in 2018 it became a reality.
Base stations (BSs) with 64 fully digital transceiver chains were commercially
deployed in several countries, the key ingredients of Massive MIMO have made it
into the 5G standard, the signal processing methods required to achieve
unprecedented spectral efficiency have been developed, and the limitation due
to pilot contamination has been resolved. Even the development of fully digital
Massive MIMO arrays for mmWave frequencies - once viewed prohibitively
complicated and costly - is well underway. In a few years, Massive MIMO with
fully digital transceivers will be a mainstream feature at both sub-6 GHz and
mmWave frequencies. In this paper, we explain how the first chapter of the
Massive MIMO research saga has come to an end, while the story has just begun.
The coming wide-scale deployment of BSs with massive antenna arrays opens the
door to a brand new world where spatial processing capabilities are
omnipresent. In addition to mobile broadband services, the antennas can be used
for other communication applications, such as low-power machine-type or
ultra-reliable communications, as well as non-communication applications such
as radar, sensing and positioning. We outline five new Massive MIMO related
research directions: Extremely large aperture arrays, Holographic Massive MIMO,
Six-dimensional positioning, Large-scale MIMO radar, and Intelligent Massive
MIMO.Comment: 20 pages, 9 figures, submitted to Digital Signal Processin
Radar Signal Processing for Interference Mitigation
It is necessary for radars to suppress interferences to near the noise level to achieve the best performance in target detection and measurements. In this dissertation work, innovative signal processing approaches are proposed to effectively mitigate two of the most common types of interferences: jammers and clutter. Two types of radar systems are considered for developing new signal processing algorithms: phased-array radar and multiple-input multiple-output (MIMO) radar. For phased-array radar, an innovative target-clutter feature-based recognition approach termed as Beam-Doppler Image Feature Recognition (BDIFR) is proposed to detect moving targets in inhomogeneous clutter. Moreover, a new ground moving target detection algorithm is proposed for airborne radar. The essence of this algorithm is to compensate for the ground clutter Doppler shift caused by the moving platform and then to cancel the Doppler-compensated clutter using MTI filters that are commonly used in ground-based radar systems. Without the need of clutter estimation, the new algorithms outperform the conventional Space-Time Adaptive Processing (STAP) algorithm in ground moving target detection in inhomogeneous clutter.
For MIMO radar, a time-efficient reduced-dimensional clutter suppression algorithm termed as Reduced-dimension Space-time Adaptive Processing (RSTAP) is proposed to minimize the number of the training samples required for clutter estimation. To deal with highly heterogeneous clutter more effectively, we also proposed a robust deterministic STAP algorithm operating on snapshot-to-snapshot basis. For cancelling jammers in the radar mainlobe direction, an innovative jamming elimination approach is proposed based on coherent MIMO radar adaptive beamforming. When combined with mutual information (MI) based cognitive radar transmit waveform design, this new approach can be used to enable spectrum sharing effectively between radar and wireless communication systems.
The proposed interference mitigation approaches are validated by carrying out simulations for typical radar operation scenarios. The advantages of the proposed interference mitigation methods over the existing signal processing techniques are demonstrated both analytically and empirically
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
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