In this paper, we propose a collaborative sensing scheme for source localization and imaging in an unmanned aerial vehicle (UAV) network. A two-stage image formation approach, which combines the robust adaptive beamforming technique and sparsity-based reconstruction strategy, is proposed to achieve accurate multi-source localization. In order to minimize the communication traffic in the UAV network, each UAV node only transmits the coarse-resolution image, in lieu of the large volume of raw sampled data. The proposed method maintains the robustness in the presence of model mismatch while providing a high-resolution image.
Modern radio telescopes commonly use antenna arrays, and high-resolution imaging techniques exploiting radio astronomical signals collected at these antenna arrays play a critical role to achieve their missions. Beamforming techniques have been developed in radio astronomy to generate dirty images with limited image resolutions for many years. Because the manifold of a radio telescope array varies over time due to the Earth rotation, beamformers are separately designed and implemented at each time epoch, and the resulting images are averaged to form enhanced dirty images. Considering the fact that astronomical scenes are typically sparse, we present a new method through sparse reconstruction to obtain clean astronomical images. Sparse reconstruction methods that fuse the measured data observed at multiple time epochs are examined and compared. Unlike beamforming techniques which require an additional deconvolution procedure for clean image formation, the proposed technique provides clean astronomical images with accurate estimation of the source position and a high dynamic range.
The essence of amplitude-modulation based dual-function radar-communications is to modulate the sidelobe of the transmit beampattern while keeping the main beam, where the radar function takes place, unchanged during the entire processing interval. The number of distinct sidelobe levels (SLL) required for information embedding grows exponentially with the number of bits being embedded. We propose a simple and computationally cheap method for transmit beampattern synthesis which requires designing and storing only two beamforming weight vectors. The proposed method first designs a principal transmit beamforming weight vector based on the requirements dictated by the radar function of the DFRC system. Then, a second weight vectors is obtained by enforcing a deep null towards the intended communication directions. Additional SLLs can be realized by simply taking weighted linear combinations of the two available weight vectors. The effectiveness of the proposed method for beampattern synthesis is verified using simulations examples.
Recently, dual-function radar-communications (DFRC) has been proposed as means to mitigate the spectrum congestion problem. Existing amplitude-shift keying (ASK) methods for information embedding do not take full advantage of the highest permissable sidelobe level. In this paper, a new ASK-based signaling strategy for enhancing the signal-to-noise ratio (SNR) at the communication receiver is proposed. The proposed method employs one reference waveform and simultaneously transmits a number of orthogonal waveforms equals to the number of 1's in the binary sequence being embedded. 3 dB SNR gain is achieved using the proposed method as compared to existing sidelobe ASK methods. The effectiveness of the proposed information embedding strategy is verified using simulations examples.
Frequency-hopping (FH) is one of the commonly used spread spectrum techniques that finds wide applications in communications and radar systems due to its capability of low probability of intercept, reduced interference, and desirable ambiguity property. In this paper, we consider the blind estimation of the instantaneous FH spectrum without the knowledge of hopping patterns. The FH signals are analyzed in the joint time-frequency domain, where FH signals manifest themselves as sparse entries, thus inviting compressive sensing and sparse reconstruction techniques for FH spectrum estimation. In particular, the signals' piecewise-constant frequency characteristics are exploited in the reconstruction of sparse quadratic time-frequency representations. The Bayesian compressive sensing methods are applied to provide high-resolution frequency estimation. The FH spectrum characteristics are used in the design of signal-dependent kernel within the framework of structure-aware sparse reconstruction.
In this paper, we propose a nonstationary jammer suppression method for GPS receivers when the signals are sparsely sampled. Missing data samples induce noise-like artifacts in the time-frequency (TF) distribution and ambiguity function of the received signals, which lead to reduced capability and degraded performance in jammer signature estimation and excision. In the proposed method, a data-dependent TF kernel is utilized to mitigate the artifacts and sparse reconstruction methods are then applied to obtain instantaneous frequency (IF) estimation of the jammers. In addition, an error tolerance of the IF estimate is applied is applied to achieve robust jammer suppression performance in the presence of IF estimation inaccuracy.
In this paper, we analyze the micro-Doppler signatures of elderly gait patterns in the presence of walking aids using radars. The signatures are based on real data experiments conducted in a laboratory environment using human subjects walking with a walking cane and a walker. Short-time Fourier transform is used to provide the local signal behavior over frequency and to detail the changes in the micro-Doppler signatures over time. Intrinsic differences in the Doppler and micro-Doppler signatures of the elderly gait observed with and without the use of a walking aid are highlighted. Features that capture these differences can be effective in discriminating gait with walking aids from normal human gait.
In this paper, we examine the role of high-resolution time-frequency distributions (TFDs) of radar micro-Doppler signatures for fall detection. The work supports the recent and rising interest in using emerging radar technology for elderly care and assisted living. Spectrograms have been the de facto joint-variable signal representation, depicting the signal power in both time and frequency. Although there have been major advances in designing quadratic TFDs which are superior to spectrograms in terms of detailing the local signal behavior, the contributions of these distributions in the area of human motion classifications and their offerings in enhanced feature extractions have not yet been properly evaluated. The main purpose of this paper is to show the effect of using high-resolution TFD kernels, in lieu of spectrogram, on fall detection. We focus on the extended modified B-distribution (EMBD) and exploit the level of details it provides as compared with the coarse and smoothed time-frequency signatures offered by spectrograms.
KEYWORDS: Statistical analysis, Thallium, Monte Carlo methods, Signal to noise ratio, Neodymium, Silicon, Super resolution, Surveillance, Sensors, Measurement devices
The coprime sampling scheme allows signal frequency estimation through two sub-Nyquist samplers where the down-sampling rates M and N are coprime integers. By considering the difference set of this pair of O(M + N ) physical samples, O(MN ) consecutive virtual samples can be generated. In this paper, a generalized coprime sampling technique is proposed by using O(M + pN ) samples to generate O(pMN ) virtual samples, where p is an integer argument. As such, the existing coprime sampling techniques are represented as a special case of a much broader and generalized scheme. The analytical expressions of the number of virtual samples, frequency resolution and the corresponding latency time are derived. The effectiveness of the proposed technique is verified using simulation results.
Two-dimensional (2D) transmit beamforming aims at focusing the transmitted energy within certain desired sector while minimizing the amount of energy in the out-of-sector regions. In this paper, we propose parsimonious formulations to the sidelobe control problem in 2D transmit beamforming with multidimensional arrays. The out-of-sector region is partitioned into a small number of subsectors where the subspace spanned by the steering vectors associated with the spatial directions within a certain subsecetor is approximated by the effective discrete- prolate spheroidal sequences associated with that subsector. Then, the sidelobe control is achieved by imposing constraints on the magnitude of the inner product between the 2D transmit beamforming weight vector and the discrete-prolate spheroidal sequences. Simulations examples are presented which show the effectiveness of the proposed formulations.
KEYWORDS: Antennas, Received signal strength, Data modeling, Distance measurement, Manufacturing, Telecommunications, Error analysis, Quantization, Electromagnetism, System identification
Radio frequency identification (RFID) systems present an incredibly cost-effective and easy-to-implement solution to
close-range localization. One of the important applications of a passive RFID system is to determine the reader position
through multilateration based on the estimated distances between the reader and multiple distributed reference tags
obtained from, e.g., the received signal strength indicator (RSSI) readings. In practice, the achievable accuracy of
passive RFID reader localization suffers from many factors, such as the distorted RSSI reading due to channel
impairments in terms of the susceptibility to reader antenna patterns and multipath propagation. Previous studies have
shown that the accuracy of passive RFID localization can be significantly improved by properly modeling and
compensating for such channel impairments. The objective of this paper is to report experimental study results that
validate the effectiveness of such approaches for high-accuracy RFID localization. We also examine a number of
practical issues arising in the underlying problem that limit the accuracy of reader-tag distance measurements and,
therefore, the estimated reader localization. These issues include the variations in tag radiation characteristics for similar
tags, effects of tag orientations, and reader RSS quantization and measurement errors. As such, this paper reveals
valuable insights of the issues and solutions toward achieving high-accuracy passive RFID localization.
KEYWORDS: Radar, Phased arrays, Signal to noise ratio, Antennas, Receivers, Statistical analysis, Sensors, Control systems, Target detection, Algorithm development
We consider the problem of single snapshot direction-of-arrival (DOA) estimation of multiple targets in monostatic multiple-input multiple-output (MIMO) radar. When only a single snapshot is used, the sample covariance matrix of the data becomes non-invertible and, therefore, does not permit application of Capon-based DOA estimation techniques. On the other hand, low-resolution techniques, such as the conventional beamformer, suffer from biased estimation and fail to resolve closely spaced sources. In this paper, we propose a new Capon-based method for DOA estimation in MIMO radar using a single radar pulse. Assuming that the angular locations of the sources are known a priori to be located within a certain spatial sector, we employ multiple transmit beams to focus the transmit energy of multiple orthogonal waveforms within the desired sector. The transmit weight vectors are carefully designed such that they have the same transmit power distribution pattern. As compared to the standard MIMO radar, the proposed approach enables transmitting an arbitrary number of orthogonal waveforms. By using matched-filtering at the receiver, the data associated with each beam is extracted yielding a virtual data snapshot. The total number of virtual snapshots is equal to the number of transmit beams. By choosing the number of transmit beams to be larger than the number of receive elements, it becomes possible to form a full-rank sample covariance matrix. The Capon beamformer is then applied to estimate the DOAs of the targets of interest. The proposed method is shown to have improved DOA estimation performance as compared to conventional single-snapshot DOA estimation methods.
KEYWORDS: Signal detection, Fourier transforms, Bismuth, Fermium, Frequency modulation, Time-frequency analysis, Signal processing, Compressed sensing, Spectrum analysis, Signal analyzers
Spectrum analysis of speech signals is important for their detection, recognition, and separation. Speech signals are nonstationary with time-varying frequencies which, when analyzed by Fourier analysis over a short time window, exhibit harmonic spectra, i.e., the fundamental frequencies are accompanied by multiple associated harmonic frequencies. With proper modeling, such harmonic signal components can be cast as group sparse and solved using group sparse signal reconstruction methods. In this case, all harmonic components contribute to effective signal detection and fundamental frequency estimation with improved reliability and spectrum resolution. The estimation of the fundamental frequency signature is implemented using the block sparse Bayesian learning technique, which is known to provide high-resolution spectrum estimations. Simulation results confirm the superiority of the proposed technique when compared to the conventional STFT-based methods.
Unattended catastrophic falls result in risk to the lives of elderly. There are growing efforts and rising interest in detecting falls of the aging population, especially those living alone. Radar serves as an effective non-intrusive sensor for detecting human activities. For radar to be effective, it is important to achieve low false alarms, i.e., the system can reliably differentiate between a fall and other human activities. In this paper, we discuss the time-scale based signal analysis of the radar returns from a human target. Reliable features are extracted from the scalogram and are used for fall classifications. The classification results and the advantages of using a wavelet transform are discussed.
Missing samples in the time domain introduce noise-like artifacts in the ambiguity domain due to their de facto
zero values assumed by the bilinear transform. These artifacts clutter the dual domain of the time-frequency
signal representation and obscures the time-frequency signature of single and multicomponent signals. In order
to suppress the artifacts influence, we formulate a problem based on the sparsity aware kernel. The proposed
kernel design is more robust to the artifacts caused by the missing samples.
Multi-window spectrograms offer higher energy concentration in contrast to the traditional single-window spec- trograms. However, these quadratic time-frequency distributions were not introduced to deal with randomly undersampled signals. This paper applies sparse reconstruction techniques to provide time-frequency represen- tations of nonstationary signals using the Hermite functions as multiple windows, under randomly sampled or missing data. The multi-window sparse reconstruction approach improves energy concentration by utilizing the common local sparse frequency support property across the different employed windows.
Passive radar systems, which utilize broadcast and navigation signals as sources of opportunity, have attracted significant interests in recent years due to their low cost, covertness, and the availability of different illuminator sources. In this paper, we propose a novel method for synthetic aperture imaging in multi-static passive radar systems based on a group sparse Bayesian learning technique. In particular, the problem of imaging sparse targets is formulated as a group sparse signal reconstruction problem, which is solved using a complex multi- task Bayesian compressive sensing (CMT-BCS) method to achieve a high resolution. The proposed approach significantly improves the imaging resolution beyond the range resolution. Compared to the other group sparse signal reconstruction methods, such as the block orthogonal matching pursuit (BOMP) and group Lasso, the CMT-BCS provides significant performance improvement for the reconstruction of sparse targets in the redundant dictionary with high coherence. Simulations results demonstrate the superior performance of the proposed approach.
KEYWORDS: Sensors, Signal detection, Antennas, Wave propagation, Point spread functions, Signal to noise ratio, Silicon, Array processing, Electromagnetic radiation
Coprime array, which utilizes a coprime pair of uniform linear subarrays, is an attractive structure to achieve sparse array configurations. Alternatively, effective coprime array configurations can be implemented using a uniform linear array with two coprime sensing frequencies. This enables the integration of the coprime array and filter concepts to achieve high capabilities in meeting system performance and complexity constraints. This paper examines its performance for direction-of-arrival estimations. In particular, we analyze the number of detectable signals and the estimation accuracy as related to the array configurations and sensing frequencies.
This paper proposes efficient target localization methods for a passive radar system using bistatic time-of-arrival
(TOA) information measured at multiple synthetic array locations, where the position of these synthetic array
locations is subject to random errors. Since maximum likelihood (ML) formulation of this target localization
problem is a non-convex optimization problem, semi-definite relaxation (SDR)-based optimization methods in
general do not provide satisfactory performance. As a result, approximated ML optimization problems are
proposed and solved with SDR plus bisection methods. For the case without position errors, it is shown that the
relaxation guarantees a rank-one solution. The optimization problem for the case with position errors involves
only a relaxation of a scalar quadratic term. Simulation results show that the proposed algorithms outperform
existing methods and provide mean square position error performance very close to the Cramer-Rao lower bound
even for larger values of noise and position estimation errors.
KEYWORDS: Antennas, Reflection, Wave propagation, Radiation effects, Error analysis, Received signal strength, Wireless communications, Phase measurement, Radio propagation, Signal attenuation
Radio frequency identification (RFID) is a rapidly developing wireless communication technology for electronically
identifying, locating, and tracking products, assets, and personnel. RFID has become one of the most
important means to construct real-time locating systems (RTLS) that track and identify the location of objects
in real time using simple, inexpensive tags and readers. The applicability and usefulness of RTLS techniques
depend on their achievable accuracy. In particular, when multilateration-based localization techniques are exploited,
the achievable accuracy primarily relies on the precision of the range estimates between a reader and the
tags. Such range information can be obtained by using the received signal strength indicator (RSSI) and/or the
phase difference of arrival (PDOA). In both cases, however, the accuracy is significantly compromised when the
operation environment is impaired. In particular, multipath propagation significantly affects the measurement
accuracy of both RSSI and phase information. In addition, because RFID systems are typically operated in short
distances, RSSI and phase measurements are also coupled with the reader and tag antenna patterns, making
accurate RFID localization very complicated and challenging. In this paper, we develop new methods to localize
RFID tags or readers by exploiting sparse signal recovery techniques. The proposed method allows the channel
environment and antenna patterns to be taken into account and be properly compensated at a low computational
cost. As such, the proposed technique yields superior performance in challenging operation environments with
the above-mentioned impairments.
The joint optimization of source beamformer and distributed relay coefficients is considered for a cooperative
network that uses the output mean-square error (MSE) as the performance metric. Two methods are proposed for
solving this nonconvex optimization problem. The first approach iteratively optimizes the source beamformer and
relay coefficients, whereas the second method optimizes the relay coefficients keeping the source beamformer as
the maximum-ratio-transmitter (MRT). Both approaches are reformulated as relaxed semidefinite programming
problems. The optimality of MRT is proven analytically under some conditions. The superiority of the proposed
methods is verified using numerical examples.
In this paper, we consider resolving over-the-horizon radar (OTHR) Doppler returns. A high-resolution time-frequency
(TF) representation of the received signal is obtained by using the local polynomial Fourier transform
(LPFT). From the optimally concentrated LPFT, multicomponent Doppler signatures, which are only several
frequency bins apart, are extracted using an instantaneous frequency estimation method based on the Viterbi
algorithm. The performance of the proposed method is validated using real data.
In this paper, we consider ultrasonic imaging for the visualization of flaws in a material. Ultrasonic imaging is a
powerful nondestructive testing (NDT) tool which assesses material conditions via the detection, localization, and
classification of flaws inside a structure. Multipath exploitations provide extended virtual array apertures and, in turn,
enhance imaging capability beyond the limitation of traditional multisensor approaches. We utilize reflections of
ultrasonic signals which occur when encountering different media and interior discontinuities. The waveforms observed
at the physical as well as virtual sensors yield additional measurements corresponding to different aspect angles.
Exploitation of multipath information addresses unique issues observed in ultrasonic imaging. (1) Utilization of physical
and virtual sensors significantly extends the array aperture for image enhancement. (2) Multipath signals extend the
angle of view of the narrow beamwidth of the ultrasound transducers, allowing improved visibility and array design
flexibility. (3) Ultrasonic signals experience difficulty in penetrating a flaw, thus the aspect angle of the observation is
limited unless access to other sides is available. The significant extension of the aperture makes it possible to yield flaw
observation from multiple aspect angles. We show that data fusion of physical and virtual sensor data significantly
improves the detection and localization performance. The effectiveness of the proposed multipath exploitation approach
is demonstrated through experimental studies.
KEYWORDS: Relays, Probability theory, Receivers, Signal to noise ratio, Wireless communications, Interference (communication), Palladium, Systems modeling, Yield improvement, Information theory
In this paper, we consider the use of cooperative communications for broadcast wireless networks where the
signal transmitted from a source node is relayed by a relay to the destination users, which are distributed within
a circular area. The objective of this paper is, given the available resources, to properly determine the position
of the relay so that the worst-case outage probability performance within the service area is minimized. The
problem is solved through the use of semidefinite relaxation.
Ultrasonic sensor arrays continue to be broadly applied for nondestructive material testing. Generally, conventional
beamforming techniques have been the favorite approach to generate images from the sensor array data. In this paper,
we examine the use of multiple-input multiple-output (MIMO) ultrasonic processing technique for imaging internal
structures of materials. The goal is to identify and locate potential defects and anomalies. The imaging technique is
comprised of excitation of transmitting sensors with sequential or orthogonal wideband signals, matched filtering, and
adaptive weighting. The weighting of the signals at the receiver takes into account the transducer ultrasound radiation
patterns. The MIMO technique is particularly attractive for ultrasonic imaging, as the different bistatic combinations of
transmit and receive sensor pairs allows effective and simple formations of virtual arrays with extended apertures and
denser spatial sampling. As such, high-resolution images can be generated with fewer or available transducers. The
performance of this technique is experimentally examined using test specimens with artificially drilled small size flat
bottom holes that simulate defects. One-dimensional and two-dimensional array configurations are used to form desired
virtual arrays and their respective imaging capabilities are evaluated and compared.
Moving radar platforms form synthetic apertures for effective target localization. One of the important target
localization techniques is to multilaterate the position of a target based on the own positions of the radar and
the range estimates obtained at each radar position. In practical applications, the radar positions as well as the
range estimates are subject to error due to maneuvering, timing error, as well as measurement noise. Previous
works have shown that, by incorporating the semidefinite relaxation techniques which permit the use of convex
optimization approaches to solve a large class of nonconvex estimation problems, improved target location
estimates can be achieved over those obtained from conventional techniques, such as least square methods. In
some radar applications, on the other hand, it may be advantageous to incorporate the Doppler measurements.
Doppler frequency information is often complementary to range measurements in target localization and is
particularly helpful when range information alone does not provide satisfactory target localization performance.
In this paper, we consider the problem of target localization based on both range and Doppler estimates obtained
at multiple radar locations, where such information as well as the radar locations are subject to certain random
errors. Semidefinite relaxation is applied to formulate convex solutions for this problem. Simulation results are
provided to demonstrate performance improvement by utilizing both range and Doppler estimates.
In conventional RFID systems, only one tag can be identified at a time. Tag collisions occur if more than one
tag simultaneously occupies the shared RF channel, resulting in low identification efficiency and long delay,
particularly when the population of tags is large. In this paper, we propose two RFID anti-collision protocols,
both are based on framed slotted ALOHA (FSA), to concurrently identify multiple tags by using a multi-antenna
reader. The first one is Blind Identification Protocol, which relies on blind estimation of the channel between the
activated tags and the reader and, as such, does not require redesign of the existing RFID tags. The second one
is Orthogonal ID-aided Identification Protocol which estimates the channels with the use of temporary orthogonal
IDs, which are randomly selected by the tags and are inserted at the head of each tag's reply signal. The use of
orthogonal IDs facilitates both the detection of activated tags and the channel estimation. Unlike code division
multiple access (CDMA) techniques which rely on the use of excessive bandwidth and require redesign of tags,
the proposed protocols use multiple antennas at the reader to support concurrent identification of multiple tags
without the requirement of additional bandwidth and no or minimal modifications to the existing tags. As a
result, the proposed techniques yield significant improvement of the identification efficiency and reduction of
the identification delay. We analyze, in an analytical framework, the identification efficiency of the proposed
protocols, and the optimum frame size is derived.
RFID is an increasingly valuable business and technology tool for electronically identifying, locating, and tracking
products, assets, and personnel. As a result, precise positioning and tracking of RFID tags and readers have
received considerable attention from both academic and industrial communities. Finding the position of RFID
tags is considered an important task in various real-time locating systems (RTLS). As such, numerous RFID
localization products have been developed for various applications. The majority of RFID positioning systems is
based on the fusion of pieces of relevant information, such as the range and the direction-of-arrival (DOA). For
example, trilateration can determine the tag position by using the range information of the tag estimated from
three or more spatially separated reader antennas. Triangulation is another method to locate RFID tags that
use the direction-of-arrival (DOA) information estimated at multiple spatially separated locations. The RFID
tag positions can also be determined through hybrid techniques that combine the range and DOA information.
The focus of this paper to study the design and performance of the localization of passive RFID tags using
array processing techniques in a multipath environment, and exploiting multi-frequency CW signals. The latter
are used to decorrelate the coherent multipath signals for effective DOA estimation and for the purpose of
accurate range estimation. Accordingly, the spatial and frequency dimensionalities are fully utilized for robust
and accurate positioning of RFID tags.
Gunshot detection, sniper localization, and bullet trajectory prediction are of significant importance in military
and homeland security applications. While the majority of existing work is based on acoustic and electro-optical
sensors, this paper develops a framework of networked radar systems that uses distributed radar sensor networks
to achieve the aforementioned objectives. The use of radio frequency radar systems allows the achievement of subtime-
of-flight tracking response, enabling to response before the bullet reaches its target and, as such, effectively
leading to the reduction of injuries and casualties in military and homeland security operations. The focus of
this paper is to examine the MIMO radar concept with concurrent transmission of low-correlation waveforms
from multiple radar sets to ensure wide surveillance coverage and maintain a high waveform repetition frequency
for long coherent time interval required to achieve return signal concentration.
An important task in urban sensing operation is accurate estimation and positioning tracking of moving targets. Equally
important is target identifications and classifications based on distinctions in the respective motion signatures, such as
targets encountering translation versus rotation or vibration motions or targets exhibiting linear versus nonlinear
motions. Dual-frequency radars, which estimate the range of a target based on the phase difference between two closely
spaced frequencies, have been shown to be a cost-effective method for this purpose. In particular, when time-frequency
signal representation techniques are incorporated, Doppler signature enhancement and localization in the time-frequency
domain enables robust range estimation of multiple targets with different motion profiles and small radar cross-sections.
In this paper, the dual-frequency radar concept is extended to develop a narrowband (NB) frequency-hopping (FH) radar.
The proposed NB-FH radar provides multifold advantages such as low probability of intercept, low interference to/from
other systems, and flexible bandwidth usage for the improvement of range accuracy and phase unwrapping.
Directional antennas have shown to be advantageous in an ad hoc wireless network over traditional omnidirectional
antennas to provide higher network throughput because they can reduce the power required for the service
coverage and packet transmission, mitigate interference in the directions away from that of the desired users, and
reduce the number of hops between distant source and sink nodes. Recently, multibeam antennas (MBAs) have
been developed to further increase the network throughput. MBAs can be implemented using either multiple
fixed-beam directional antennas or multi-channel smart antennas. The difference between them lies in whether
the beams are predefined or adaptively controlled. MBAs not only inherit the advantages of directional antennas,
but also support concurrent communications with multiple neighboring nodes. Such advantages are achieved,
however, only with sophisticated scheduling schemes. For example, it is crucial to appropriately allocate the
available beams to the neighboring users that attempt to transmit packets. Previous results have shown the
importance of utilizing appropriate scheduling in avoiding collision. Specifically, in a multipath propagation
environment, signals transmitted from a neighboring node may fall into multiple beams at the receiving node
and thus result in more frequent contentions. In this paper, we examine the feasibility and node throughput
performance of relevant scheduling algorithms, such as those based on packet priority and throughput maximization,
and investigate the effect of multipath propagation on the exploitation of the scheduling schemes as well as
on the throughput performance.
Radio Frequency Identification (RFID) has recently attracted much attention in both the technical and business communities. It has found wide applications in, for example, toll collection, supply-chain management, access control, localization tracking, real-time monitoring, and object identification. Situations may arise where the movement directions of the tagged RFID items through a portal is of interest and must be determined. Doppler estimation may prove complicated or impractical to perform by RFID readers. Several alternative approaches, including the use of an array of sensors with arbitrary geometry, can be applied. In this paper, we consider direction-of-arrival (DOA) estimation techniques for application to near-field narrowband RFID problems. Particularly, we examine the use of a pair of RFID antennas to track moving RFID tagged items through a portal. With two antennas, the near-field DOA estimation problem can be simplified to a far-field problem, yielding a simple way for identifying the direction of the tag movement, where only one parameter, the angle, needs to be considered. In this case, tracking of the moving direction of the tag simply amounts to computing the spatial cross-correlation between the data samples received at the two antennas. It is pointed out that the radiation patterns of the reader and tag antennas, particularly their phase characteristics, have a significant effect on the performance of DOA estimation. Indoor experiments are conducted in the Radar Imaging and RFID Labs at Villanova University for validating the proposed technique for target movement direction estimations.
Coded space-time cooperation is an efficient approach in delivering information over a relay network. Multiple
cooperative terminals (nodes) form a distributed multiple-input-multiple-output (MIMO) systems, thus
providing high data rates and high diversity gains. However, unlike conventional co-located MIMO systems,
it is impractical for distributed MIMO networks to maintain perfect timing synchronization between different
transmit terminals. In particular, the presence of a fractional-symbol delay difference between the signals transmitted
from different terminals can cause erroneous sampling positions and yield highly dispersive channels even
at a memoryless channel environment. Existing methods solve such problem based on time-domain approaches
where adaptive equalization is required at the receivers for combining the information transmitted from distributed
sources. In this paper, we propose the use of OFDM-based approaches using distributed space-frequency
codes. The proposed schemes are insensitive to fractional-symbol delays and lead to higher data rate transmission
and simplified implementation. In addition, the proposed schemes permit the use of relatively simple
amplify-and-forward algorithm in multi-hop wireless networks without delay accumulations. The time delay in
each relaying hop by reconstructing the cyclic prefix and, as such, improve the spectral efficiency, while keeping
a simple relaying structure.
Radio frequency identification (RFID) is poised for growth as businesses and governments explore applications implementing RFID. The RFID technology will continue to evolve to meet new demands for human and target location and tracking. In particular, there are increasing needs to find and track the positions of multiple RFID tagged items that are closely spaced. As a result, localization and tracking techniques with higher accuracy, yet low implementation complexity are required. This paper examines the applicability of
direction-of-arrival (DOA) estimation methods to the localization and tracking problems of passive RFID tags. Different scenarios of stationary and moving targets are considered. It is shown through performance analysis and simulations that simple DOA estimation
methods can be used to provide satisfactory localization performance.
Over the horizon radar (OTHR) is a well developed sensor technology in established use for long-range air and surface surveillance. More detailed information about the targets can be achieved by using simultaneous operation of multiple OTHRs. However, a key limitation with HF radar is the conflict between selection of an appropriate operating frequency and the demand for radar waveform bandwidth commensurate with the range resolution requirement of the radar. In this paper, we consider the simultaneous operation of two over-the-horizon radar systems that use the same frequency band with different chirp waveforms to respond the advanced wide-area surveillance needs without reducing the pulse repetitive frequency. A cross-radar interference cancellation technique is proposed and shown to be effective.
In this paper, we propose two-dimensional (2-D) frequency
modulated (FM) signals for digital watermarking. The hidden
information is embedded into an image using the binary phase
information of a 2-D FM prototype. The original image is properly
partitioned into several blocks. In each block, a 2-D FM watermark
waveform is used and the watermark information is embedded using
the binary phase. The parameters of the FM watermark are selected
in order to achieve low bit error rate (BER) detection of the
watermark. Detailed study of performance analysis and parameter
optimizations is performed for 2-D chirp signals as an example of
2-D FM waveforms. Experimental results compare the proposed
methods and support their effectiveness.
KEYWORDS: Filtering (signal processing), Signal processing, Convolution, Mobile communications, Error analysis, Sensors, Array processing, Electronic filtering, Fourier transforms, Signal to noise ratio
Inter-symbol interference (ISI) and co-channel interference (CCI)
are two major sources to signal impairment in mobile
communications. To suppress both ISI and CCI, space-time adaptive
processing (STAP) systems are shown to be effective, leading to
increased capacity and improved quality of service. The high
complexity and slow convergence, however, are often the hurdles in
practical implementation of the STAP systems. Several subband
array implementations have been proposed for STAP over the past
few years. These methods are to provide optimal or sub-optimal
steady state performance with reduced implementation complexity
and improved convergence performance. The purpose of this paper is
to investigate the steady state performance of subband arrays with
different decimation rates and to derive analytical expressions of
the minimum mean square error (MMSE). The discrete Fourier
transform (DFT) based subband arrays and both unconstrained and
constrained weight adaptations are considered.
KEYWORDS: Polarization, Polarimetry, Time-frequency analysis, Sensors, Signal processing, Matrices, Interference (communication), Array processing, Antennas, Signal to noise ratio
Time-frequency distributions (TFDs) have evolved to be a powerful
technique for nonstationary signal analysis and synthesis. With
the use of a multi-sensor array, spatial time-frequency
distributions (STFDs) have been developed and successfully applied
to high-resolution direction-of-arrival (DOA) estimation and blind
recovery of the source waveforms. In this paper, the polarimetric
dimension is introduced to the STFDs resulting in the spatial
polarimetric time-frequency distributions (SPTFDs) as a platform
for the processing of non-stationary polarized signals. In the
SPTFD platform, polarized signals are decomposed (projected) into
two orthogonal polarization components, such as horizontal and
vertical, and later processed in a manner where their polarization
characteristics are exploited. This empowers the STFDs with
additional degrees of freedom and improves the robustness of the
signal and noise subspaces, and therefore, serving to enhance DOA
estimation, signal recovery, and source separation performance. To
demonstrate the advantages of the SPTFDs, the polarimetric
time-frequency MUSIC (PTF-MUSIC) method for DOA estimation is
proposed based on the SPTFD platform and is shown to outperform
the time-frequency, polarimetric, and conventional MUSIC methods.
KEYWORDS: Signal to noise ratio, Time-frequency analysis, Sensors, Fermium, Frequency modulation, Receivers, Array processing, Interference (communication), Iterated function systems, Signal processing
This paper proposes a novel blind beamforming method for interference mitigation in direct-sequence spread-spectrum (DS/SS) communications based on subspace projection. Instantaneously narrowband interference signals are considered. The interference signals are highly localized in the time-frequency domain, and their characteristics are different from those of the DS/SS signal. The instantaneous frequencies of the nonstationary interference signals are estimated from the time-frequency domain and used in the temporal domain for interference nulling. We derive the receiver SNRs, which shows improved performance in strong interference environments.
KEYWORDS: Time-frequency analysis, Signal to noise ratio, Interference (communication), Matrices, Array processing, Fermium, Frequency modulation, Signal processing, Sensors, Data modeling
This paper proposes a novel time-frequency maximum likelihood (t-f ML) method for direction-of-arrival (DOA) estimation for non- stationary signals, and compares this method with conventional maximum likelihood DOA estimation techniques. Time-frequency distributions localize the signal power in the time-frequency domain, and as such enhance the effective SNR, leading to improved DOA estimation. The localization of signals with different t-f signatures permits the division of the time-frequency domain into smaller regions, each contains fewer signals than those incident on the array. The reduction of the number of signals within different time-frequency regions not only reduces the required number of sensors, but also decreases the computational load in multi- dimensional optimizations. Compared to the recently proposed time- frequency MUSIC (t-f MUSIC), the proposed t-f ML method can be applied in coherent environments, without the need to perform any type of preprocessing that is subject to both array geometry and array aperture.
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