52 research outputs found
Two-Channel Passive Detection Exploiting Cyclostationarity
This paper addresses a two-channel passive detection problem exploiting
cyclostationarity. Given a reference channel (RC) and a surveillance channel
(SC), the goal is to detect a target echo present at the surveillance array
transmitted by an illuminator of opportunity equipped with multiple antennas.
Since common transmission signals are cyclostationary, we exploit this
information at the detector. Specifically, we derive an asymptotic generalized
likelihood ratio test (GLRT) to detect the presence of a cyclostationary signal
at the SC given observations from RC and SC. This detector tests for different
covariance structures. Simulation results show good performance of the proposed
detector compared to competing techniques that do not exploit
cyclostationarity
Wireless Localization Systems: Statistical Modeling and Algorithm Design
Wireless localization systems are essential for emerging applications that rely on
context-awareness, especially in civil, logistic, and security sectors. Accurate localization in indoor environments is still a challenge and triggers a fervent research
activity worldwide. The performance of such systems relies on the quality of range
measurements gathered by processing wireless signals within the sensors composing
the localization system. Such range estimates serve as observations for the target
position inference. The quality of range estimates depends on the network intrinsic
properties and signal processing techniques. Therefore, the system design and analysis call for the statistical modeling of range information and the algorithm design
for ranging, localization and tracking. The main objectives of this thesis are: (i) the
derivation of statistical models and (ii) the design of algorithms for different wire-
less localization systems, with particular regard to passive and semi-passive systems
(i.e., active radar systems, passive radar systems, and radio frequency identification
systems). Statistical models for the range information are derived, low-complexity
algorithms with soft-decision and hard-decision are proposed, and several wideband
localization systems have been analyzed. The research activity has been conducted
also within the framework of different projects in collaboration with companies and
other universities, and within a one-year-long research period at Massachusetts Institute of Technology, Cambridge, MA, USA. The analysis of system performance,
the derived models, and the proposed algorithms are validated considering different case studies in realistic scenarios and also using the results obtained under the
aforementioned projects
First-Order Statistical Framework for Multi-Channel Passive Detection
In this paper we establish a general first-order statistical framework for
the detection of a common signal impinging on spatially distributed receivers.
We consider three types of channel models: 1) the propagation channel is
completely known, 2) the propagation is known but channel gains are unknown,
and 3) the propagation channel is unknown. For each problem, we address the
cases of a) known noise variances, b) common but unknown noise variances, and
c) different and unknown noise variances. For all 9 cases, we establish
generalized-likelihood-ratio (GLR) detectors, and show that each one can be
decomposed into two terms. The first term is a weighted combination of the GLR
detectors that arise from considering each channel separately. This result is
then modified by a fusion or cross-validation term, which expresses the level
of confidence that the single-channel detectors have detected a common source.
Of particular note are the constant false-alarm rate (CFAR) detectors that
allow for scale-invariant detection in multiple channels with different noise
powers.Comment: 26 pages, 1 tabl
OFDM passive radar employing compressive processing in MIMO configurations
A key advantage of passive radar is that it provides a means of performing position detection and tracking without the need for transmission of energy pulses. In this respect, passive radar systems utilising (receiving) orthogonal frequency division multiplexing (OFDM) communications signals from transmitters using OFDM standards such as long term evolution (LTE), WiMax or WiFi, are considered. Receiving a stronger reference signal for the matched filtering, detecting a lower target signature is one of the challenges in the passive radar. Impinging at the receiver, the OFDM waveforms supply two-dimensional virtual uniform rectangul ararray with the first and second dimensions refer to time delays and Doppler frequencies respectively. A subspace method, multiple signals classification (MUSIC) algorithm, demonstrated the signal extraction using multiple time samples. Apply normal measurements, this problem requires high computational resources regarding the number of OFDM subcarriers. For sub-Nyquist sampling, compressive sensing (CS) becomes attractive. A single snap shot measurement can be applied with Basis Pursuit (BP), whereas l1-singular value decomposition (l1-SVD) is applied for the multiple snapshots. Employing multiple transmitters, the diversity in the detection process can be achieved. While a passive means of attaining three-dimensional large-set measurements is provided by co-located receivers, there is a significant computational burden in terms of the on-line analysis of such data sets. In this thesis, the passive radar problem is presented as a mathematically sparse problem and interesting solutions, BP and l1-SVD as well as Bayesian compressive sensing, fast-Besselk, are considered. To increase the possibility of target signal detection, beamforming in the compressive domain is also introduced with the application of conve xoptimization and subspace orthogonality. An interference study is also another problem when reconstructing the target signal. The networks of passive radars are employed using stochastic geometry in order to understand the characteristics of interference, and the effect of signal to interference plus noise ratio (SINR). The results demonstrate the outstanding performance of l1-SVD over MUSIC when employing multiple snapshots. The single snapshot problem along with fast-BesselK multiple-input multiple-output configuration can be solved using fast-BesselK and this allows the compressive beamforming for detection capability
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