3,736 research outputs found
Spatial Compressive Sensing for MIMO Radar
We study compressive sensing in the spatial domain to achieve target
localization, specifically direction of arrival (DOA), using multiple-input
multiple-output (MIMO) radar. A sparse localization framework is proposed for a
MIMO array in which transmit and receive elements are placed at random. This
allows for a dramatic reduction in the number of elements needed, while still
attaining performance comparable to that of a filled (Nyquist) array. By
leveraging properties of structured random matrices, we develop a bound on the
coherence of the resulting measurement matrix, and obtain conditions under
which the measurement matrix satisfies the so-called isotropy property. The
coherence and isotropy concepts are used to establish uniform and non-uniform
recovery guarantees within the proposed spatial compressive sensing framework.
In particular, we show that non-uniform recovery is guaranteed if the product
of the number of transmit and receive elements, MN (which is also the number of
degrees of freedom), scales with K(log(G))^2, where K is the number of targets
and G is proportional to the array aperture and determines the angle
resolution. In contrast with a filled virtual MIMO array where the product MN
scales linearly with G, the logarithmic dependence on G in the proposed
framework supports the high-resolution provided by the virtual array aperture
while using a small number of MIMO radar elements. In the numerical results we
show that, in the proposed framework, compressive sensing recovery algorithms
are capable of better performance than classical methods, such as beamforming
and MUSIC.Comment: To appear in IEEE Transactions on Signal Processin
Maximizing the Number of Spatial Nulls with Minimum Sensors
In this paper, we attempt to unify two array processing frameworks viz, Acoustic Vector Sensor (AVS) and two level nested array to enhance the Degrees of Freedom (DoF) significantly beyond the limit that is attained by a Uniform Linear Hydrophone Array (ULA) with specified number of sensors. The major focus is to design a line array architecture which provides high resolution unambiguous bearing estimation with increased number of spatial nulls to mitigate the multiple interferences in a deep ocean scenario. AVS can provide more information about the propagating acoustic field intensity vector by simultaneously measuring the acoustic pressure along with tri-axial particle velocity components. In this work, we have developed Nested AVS array (NAVS) ocean data model to demonstrate the performance enhancement. Conventional and MVDR spatial filters are used as the response function to evaluate the performance of the proposed architecture. Simulation results show significant improvement in performance viz, increase of DoF, and localization of more number of acoustic sources and high resolution bearing estimation with reduced side lobe level
Broadband modified-circle-shape patch antenna with H-aperture feeding for a passive radar array
In this paper, the design of a broadband modified-circle-shape patch antenna with H-aperture feeding is presented, to be used as single radiating element in the array of the surveillance channel of an UHF passive radar. Different techniques are proposed to achieve a relative bandwidth of more than 30%, and challenging radiation pattern characteristics for the defined application. The achievement of these requirements is proved through measurements in anechoic chamber. A NULA is designed using optimization techniques and considering coupling effects between elements. The NULA was integrated in IDEPAR, the passive radar demonstrator developed in the University of Alcalá, and validated through measurement campaigns. Results prove a significant improvement of the passive radar target detection and bearing estimation capabilities
A New Heterogeneous Hybrid MIMO Receive Structure of Rapidly Eliminating DOA Ambiguity
Massive multiple input multiple output(MIMO)-based fully-digital receive
antenna arrays eventuate a huge amount of circuit costs and complexity to
direction of arrival(DOA) estimation, which is hard to satisfy the needs of
high precision and low cost in future green wireless communication. To address
this challenge, a novel heterogeneous hybrid MIMO receiver is proposed in this
paper and a high performance DOA estimator called heterogeneous cross-minimum
distance (HCMD) is developed based on the structure. The antenna arrays are
first divided into multiple groups, and each group adopts a different hybrid
structure. The virtual antenna arrays of these groups are then used for DOA
estimation to generate multiple candidate angle sets, where each set contains a
unique true solution and multiple pseudo-solutions. Finally, the cross-distance
minimization method is applied to the multiple candidate angle sets to select
the corresponding true solution for each group, and the final DOA estimation is
given by combining the multiple true solutions. Simulation results show that as
the number of antennas tends to large-scale, the proposed method can rapidly
find the true solution for each group and achieve excellent estimation
performance
Biologically Inspired Sensing and MIMO Radar Array Processing
The contributions of this dissertation are in the fields of biologically inspired sensing and multi-input multi-output: MIMO) radar array processing. In our research on biologically inspired sensing, we focus on the mechanically coupled ears of the female Ormia ochracea. Despite the small distance between its ears, the Ormia has a remarkable localization ability. We statistically analyze the localization accuracy of the Ormia\u27s coupled ears, and illustrate the improvement in the localization performance due to the mechanical coupling. Inspired by the Ormia\u27s ears, we analytically design coupled small-sized antenna arrays with high localization accuracy and radiation performance. Such arrays are essential for sensing systems in military and civil applications, which are confined to small spaces. We quantitatively demonstrate the improvement in the antenna array\u27s radiation and localization performance due to the biologically inspired coupling. On MIMO radar, we first propose a statistical target detection method in the presence of realistic clutter. We use a compound-Gaussian distribution to model the heavy tailed characteristics of sea and foliage clutter. We show that MIMO radars are useful to discriminate a target from clutter using the spatial diversity of the illuminated area, and hence MIMO radar outperforms conventional phased-array radar in terms of target-detection capability. Next, we develop a robust target detector for MIMO radar in the presence of a phase synchronization mismatch between transmitter and receiver pairs. Such mismatch often occurs due to imperfect knowledge of the locations as well as local oscillator characteristics of the antennas, but this fact has been ignored by most researchers. Considering such errors, we demonstrate the degradation in detection performance. Finally, we analyze the sensitivity of MIMO radar target detection to changes in the cross-correlation levels: CCLs) of the received signals. Prior research about MIMO radar assumes orthogonality among the received signals for all delay and Doppler pairs. However, due to the use of antennas which are widely separated in space, it is impossible to maintain this orthogonality in practice. We develop a target-detection method considering the non-orthogonality of the received data. In contrast to the common assumption, we observe that the effect of non-orthogonality is significant on detection performance
A Cramér-Rao bounds based analysis of 3D antenna array geometries made from ULA branches
International audienceIn the context of passive sources localization using antenna array, the estimation accuracy of elevation, and azimuth are related not only to the kind of estimator which is used, but also to the geometry of the considered antenna array. Although there are several available results on the linear array, and also for planar arrays, other geometries existing in the literature, such as 3D arrays, have been less studied. In this paper, we study the impact of the geometry of a family of 3D models of antenna array on the estimation performance of elevation, and azimuth. The Cramer-Rao Bound (CRB), which is widely spread in signal processing to characterize the estimation performance will be used here as a useful tool to find the optimal configuration. In particular, we give closed-form expressions of CRB for a 3D antenna array under both conditional, and unconditional observation models. Thanks to these explicit expressions, the impact of the third dimension to the estimation performance is analyzed. Particularly, we give criterions to design an isotropic 3D array depending on the considered observation model. Several 3D particular geometry antennas made from uniform linear array (ULA) are analyzed, and compared with 2D antenna arrays. The isotropy condition of such arrays is analyzed. The presented framework can be used for further studies of other types of arrays
A phase-based technique for localization of uhf-rfid tags moving on a conveyor belt: Performance analysis and test-case measurements
A new phase-based technique for localization and
tracking of items moving along a conveyor belt and equipped with
ultrahigh frequency-radio frequency identification (UHF-RFID)
tags is described and validated here. The technique is based on
a synthetic-array approach that takes advantage of the fact that
the tagged items move along a conveyor belt whose speed and
path are known apriori. In this framework, a joint use is done
of synthetic-array radar principles, knowledge-based processing,
and efficient exploitation of the reader-tag communication signal.
The technique can be easily implemented in any conventional
reader based on an in-phase and quadrature receiver and it does
not require any modification of the reader antenna configurations
usually adopted in UHF-RFID portals. Numerical results are used
to investigate the performance analysis of such methods, and
also to furnish system design guidelines. Finally, the localization
capability is also demonstrated through a measurement campaign
in a real conveyor belt scenario, showing that a centimeter-order
accuracy in the tag position estimation can be achieved even in
a rich multipath environment
Wireless capsule gastrointestinal endoscopy: direction of arrival estimation based localization survey
One of the significant challenges in Capsule Endoscopy (CE) is to precisely determine the pathologies location. The localization process is primarily estimated using the received signal strength from sensors in the capsule system through its movement in the gastrointestinal (GI) tract. Consequently, the wireless capsule endoscope (WCE) system requires improvement to handle the lack of the capsule instantaneous localization information and to solve the relatively low transmission data rate challenges. Furthermore, the association between the capsule’s transmitter position, capsule location, signal reduction and the capsule direction should be assessed. These measurements deliver significant information for the instantaneous capsule localization systems based on TOA (time of arrival) approach, PDOA (phase difference of arrival), RSS (received signal strength), electromagnetic, DOA (direction of arrival) and video tracking approaches are developed to locate the WCE precisely. The current article introduces the acquisition concept of the GI medical images using the endoscopy with a comprehensive description of the endoscopy system components. Capsule localization and tracking are considered to be the most important features of the WCE system, thus the current article emphasizes the most common localization systems generally, highlighting the DOA-based localization systems and discusses the required significant research challenges to be addressed
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