226 research outputs found
Target localization in MIMO radar systems
MIMO (Multiple-Input Multiple-Output) radar systems employ multiple antennas to transmit multiple waveforms and engage in joint processing of the received echoes from the target. MIMO radar has been receiving increasing attention in recent years from researchers, practitioners, and funding agencies. Elements of MIMO radar have the ability to transmit diverse waveforms ranging from independent to fully correlated. MIMO radar offers a new paradigm for signal processing research. In this dissertation, target localization accuracy performance, attainable by the use of MIMO radar systems, configured with multiple transmit and receive sensors, widely distributed over an area, are studied. The Cramer-Rao lower bound (CRLB) for target localization accuracy is developed for both coherent and noncoherent processing. The CRLB is shown to be inversely proportional to the signal effective bandwidth in the noncoherent case, but is approximately inversely proportional to the carrier frequency in the coherent case. It is shown that optimization over the sensors\u27 positions lowers the CRLB by a factor equal to the product of the number of transmitting and receiving sensors. The best linear unbiased estimator (BLUE) is derived for the MIMO target localization problem. The BLUE\u27s utility is in providing a closed-form localization estimate that facilitates the analysis of the relations between sensors locations, target location, and localization accuracy. Geometric dilution of precision (GDOP) contours are used to map the relative performance accuracy for a given layout of radars over a given geographic area. Coherent processing advantage for target localization relies on time and phase synchronization between transmitting and receiving radars. An analysis of the sensitivity of the localization performance with respect to the variance of phase synchronization error is provided by deriving the hybrid CRLB. The single target case is extended to the evaluation of multiple target localization performance. Thus far, the analysis assumes a stationary target. Study of moving target tracking capabilities is offered through the use of the Bayesian CRLB for the estimation of both target location and velocity. Centralized and decentralized tracking algorithms, inherit to distributed MIMO radar architecture, are proposed and evaluated. It is shown that communication requirements and processing load may be reduced at a relatively low performance cost
Target Localization Accuracy Gain in MIMO Radar Based Systems
This paper presents an analysis of target localization accuracy, attainable
by the use of MIMO (Multiple-Input Multiple-Output) radar systems, configured
with multiple transmit and receive sensors, widely distributed over a given
area. The Cramer-Rao lower bound (CRLB) for target localization accuracy is
developed for both coherent and non-coherent processing. Coherent processing
requires a common phase reference for all transmit and receive sensors. The
CRLB is shown to be inversely proportional to the signal effective bandwidth in
the non-coherent case, but is approximately inversely proportional to the
carrier frequency in the coherent case. We further prove that optimization over
the sensors' positions lowers the CRLB by a factor equal to the product of the
number of transmitting and receiving sensors. The best linear unbiased
estimator (BLUE) is derived for the MIMO target localization problem. The
BLUE's utility is in providing a closed form localization estimate that
facilitates the analysis of the relations between sensors locations, target
location, and localization accuracy. Geometric dilution of precision (GDOP)
contours are used to map the relative performance accuracy for a given layout
of radars over a given geographic area.Comment: 36 pages, 5 figures, submitted to IEEE Transaction on Information
Theor
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
A comparison of processing approaches for distributed radar sensing
Radar networks received increasing attention in recent years as they can outperform
single monostatic or bistatic systems. Further attention is being dedicated
to these systems as an application of the MIMO concept, well know
in communications for increasing the capacity of the channel and improving
the overall quality of the connection. However, it is here shown that radar
network can take advantage not only from the angular diversity in observing
the target, but also from a variety of ways of processing the received signals. The
number of devices comprising the network has also been taken into the analysis.
Detection and false alarm are evaluated in noise only and clutter from a theoretical
and simulated point of view. Particular attention is dedicated to the statistics
behind the processing. Experiments have been performed to evaluate practical
applications of the proposed processing approaches and to validate assumptions
made in the theoretical analysis. In particular, the radar network used for
gathering real data is made up of two transmitters and three receivers. More than
two transmitters are well known to generate mutual interference and therefore
require additional e�fforts to mitigate the system self-interference. However,
this allowed studying aspects of multistatic clutter, such as correlation, which
represent a first and novel insight in this topic. Moreover, two approaches for
localizing targets have been developed. Whilst the first is a graphic approach, the
second is hybrid numerical (partially decentralized, partially centralized) which
is clearly shown to improve dramatically the single radar accuracy. Finally the
e�ects of exchanging angular with frequency diversity are shown as well in some
particular cases. This led to develop the Frequency MIMO and the Frequency
Diverse Array, according to the separation of two consecutive frequencies. The
latter is a brand new topic in technical literature, which is attracting the interest
of the technical community because of its potential to generate range-dependant
patterns. Both the latter systems can be used in radar-designing to improve the
agility and the effciency of the radar
Multistatic radar optimization for radar sensor network applications
The design of radar sensor networks (RSN) has undergone great advancements in recent years. In fact, this kind of system is characterized by a high degree of design flexibility due to the multiplicity of radar nodes and data fusion approaches. This thesis focuses on the development and analysis of RSN architectures to optimize target detection and positioning performances. A special focus is placed upon distributed (statistical) multiple-input multipleoutput (MIMO) RSN systems, where spatial diversity could be leveraged to enhance radar target detection capabilities.
In the first part of this thesis, the spatial diversity is leveraged in conjunction with cognitive waveform selection and design techniques to quickly adapt to target scene variations in real time. In the second part, we investigate the impact of RSN geometry, particularly the placement of multistatic radar receivers, on target positioning accuracy. We develop a framework based on cognitive waveform selection in conjunction with adaptive receiver placement strategy to cope with time-varying target scattering characteristics and clutter distribution parameters in the dynamic radar scene. The proposed approach yields better target detection performance and positioning accuracy as compared with conventional methods based on static transmission or stationary multistatic radar topology.
The third part of this thesis examines joint radar and communication systems coexistence and operation via two possible architectures. In the first one, several communication nodes in a network operate separately in frequency. Each node leverages the multi-look diversity of the distributed system by activating radar processing on multiple received bistatic streams at each node level in addition to the pre-existing monostatic processing. This architecture is based on the fact that the communication signal, such as the Orthogonal Frequency Division Multiplexing (OFDM) waveform, could be well-suited for radar tasks if the proper waveform parameters are chosen so as to simultaneously perform communication and radar tasks. The advantage of using a joint waveform for both applications is a permanent availability of radar and communication functions via a better use of the occupied spectrum inside the same joint hardware platform. We then examine the second main architecture, which is more complex and deals with separate radar and communication entities with a partial or total spectrum sharing constraint. We investigate the optimum placement of radar receivers for better target positioning accuracy while reducing the radar measurement errors by minimizing the interference caused by simultaneous operation of the communication system. Better performance in terms of communication interference handling and suppression at the radar level, were obtained with the proposed placement approach of radar receivers compared to the geometric dilution of precision (GDOP)-only minimization metric
- …