Foroohar Foroozan

Time Reversal Based Active Array Source Localization

Source localization especially direction-of-arrival (DOA) estimation using sensor arrays is of considerable interest in both classical array signal processing and radar applications. Most radar systems are designed under the line-of-sight (LOS) assumption with multipath echos treated as undesired clutter noise. Strong multipath, therefore, has a negative impact on the resolution of the radar systems and their ability in accurately localizing the target. Rather than treating multipath as a detrimental effect, the paper introduces time reversal (TR) to exploit spatial/multipath diversity in improving the capability of the existing localization algorithms. In particular, we design TR based range and DOA estimators that adaptively adjust the probing radar waveforms to the multipath characteristics of the environment. The benefits of the spatial/multipath diversity in the proposed DOA and range estimators are quantified by deriving the respective Cramér-Rao bounds (CRB) and comparing them with the analytical expressions for their conventional counterparts. Numerical simulations also confirm the benefit of applying TR to source localization algorithms especially at low signal-to-noise ratios below -5 dB.

A light-weight contention-based clustering algorithm for wireless ad hoc networks

In this paper a new distributed clustering algorithm based on sending beacons has been proposed. In this algorithm, mobile nodes compete with each other to become cluster-head based on the number of neighbors. A simulation study has been conducted in a fully mobile network to investigate the performance of this algorithm. The results show scalability advantage of this algorithm in networks with a large number of mobile nodes based on the average number of generated clusters and stability of the created clusters. The comparison with other algorithms shows the flexibility and suitability of the new algorithm.

Time-Reversal Ground-Penetrating Radar: Range Estimation With Cramér–Rao Lower Bounds

In this paper, first, a new range-estimation technique using time reversal (TR) for ground-penetrating-radar (GPR) applications is presented. The estimator is referred to as the TR/GPR range estimator. The motivation for this paper comes from the need of accurately estimating the location of underground objects such as landmines or unexploded ordinance for safe clearance. Second, the Cramér-Rao lower bound (CRLB) for the performance of the TR/GPR range estimator is derived and compared with the CRLB for the conventional matched filter (MF). The CRLB analysis shows that the TR/GPR range estimator has the potential to achieve higher accuracy in estimating the location of the target than that of the conventional MF estimator. Third, the proposed TR/GPR estimator is tested using finite-difference time-domain simulations, where the surface-based reflection GPR is modeled using an electromagnetic transverse-magnetic (TM) mode formulation. In our simulations, the TR/GPR estimator outperforms the conventional MF approach by up to 5-dB reduction in mean square error at signal-to-noise ratios ranging from -20 to 20 dB for dry-soil environments.

Breast cancer detection using time reversal signal processing

Breast cancer is the second leading cause of cancer death after lung cancer among women. The value of advanced imaging platforms such as magnetic resonance imaging (MRI) for early breast cancer detection remains unclear. The paper applies a time reversal beamforming imager [15] for detecting and locating early stage breast cancer tumours from MRI data. In our simulations based on the finite difference, time domain (FDTD) electromagnetic model, the proposed detector estimates the locations of breast cancer tumours with a higher accuracy than some of the current state of art signal processing estimation algorithms tested by us.

Direction finding algorithms for time reversal MIMO radars

A time reversal (TR) based direction of arrival (DOA) estimation framework for multiple-input/multiple-output (MIMO) radars is presented. We develop minimum variance distortionless response (MVDR) and multiple signal classification (MUSIC) based DOA estimators for the TR/MIMO setup. The TR/MIMO estimation algorithms outperform their conventional counterparts in: (i) analytical Cramér Rao Bounds (CRB) comparisons, and; (ii) numerical Monte Carlo simulations for a range of signal to noise ratios that we tested.

MUSIC-based array imaging in multi-modal ultrasonic non-destructive testing

We propose a MUSIC based array imaging method for ultrasonic non-destructive testing (NDT) applications. We take the mode conversion phenomenon into account and develop a MUSIC-based imaging technique which exploits the additional information that is present in all propagating modes to the advantage of the imaging process- a problem not previously addressed in the context of NDT applications. Both the numerical simulations as well as data validation show that our proposed approach performs better than the traditional MUSIC based and delay-and-sum (DAS) based imaging algorithms in terms of root mean square error (RMSE) and also provides higher resolution and better side lobe suppression capabilities.

Cramér-Rao bound for time reversal active array direction of arrival estimators in multipath environments

In this paper, we study the Cramér-Rao bound (CRB) for time reversal (TR) based direction of arrival (DOA) estimators operating in a rich multipath environment. Our setup is based on an array of active antennas capable of estimating the range and DOA of a passive target. We derive an analytical expression for the CRB of the TR/DOA estimator and compare it with that of the conventional DOA estimator by expressing the two CRBs in terms of the multipath parameters (multipaths attenuations and delays). Our analytical results are verified by running Ground Penetrating Radar (GPR) simulations using the electromagnetic Finite Difference Time Domain (FDTD) models. Our simulations illustrate the potential of superior performance with gains of up to 15 dB possible with the TR/DOA estimator over the conventional approach.

Effect of connectivity and mobility on the performance of stability-based clustering algorithm for ad hoc wireless networks

A new distributed clustering algorithm for wireless ad hoc networks has been proposed. The main motivation in the proposed algorithm is improvement of stability and scalability simulation study has been conducted in a fully mobile network to investigate the performance of this algorithm. Also the effect of mobility and connectivity on the performance parameters has been evaluated. The comparison with other algorithms shows improvement of stability and scalability of the proposed algorithm in comparison with other algorithms.

Time reversal: Algorithms for M-ARY target classification using array signal processing

An M-ary time reversal (TR) maximum likelihood classifier for a single pair of transmitting and receiving transducer element was derived in for underwater acoustic target detection applications. This paper considers a more general TR setup consisting of a P-element transmitting array and an N element receiving array and derives the M-ary conventional and TR classifiers for the multielement case in an electromagnetic communication environment. We show that the TR algorithm provides a classification gain of over 3 dB at low signal to noise ratios as compared to the conventional classifiers.

Time reversal MIMO radar: Improved CRB and Angular Resolution Limit

The paper derives closed form (nonmatrix) expression for the deterministic Cramér-Rao bound (CRB) for the direction-of-arrival associated with a target embedded in a noisy, multipath channel using the time reversal (TR) MIMO system. By incorporating the TR built-in adaptive waveform processing feature to reshape the MIMO probing signals, we prove that the CRBs for the direction of arrival can be improved in ways not foreseen with the conventional MIMO radars. A second contribution of the paper is the analytical derivation of the Angular Resolution Limit (ARL) defined as the minimal separation between two targets to be separately resolved by the MIMO radar. At high signal-to-noise ratio, we show that the TR ARL inherits the properties of the TR CRB and is superior to its conventional counterpart by a factor proportional to the order of the channel multipath.