Mohammad T. Alkhodary

Practical application of compressive sensing to ultra-wideband channels

Compressive sensing (CS) is proposed by many researchers as a solution to the formidable sampling requirements of promising ultra-wideband (UWB) technology. Previous research was evaluated by simulating the IEEE UWB channel model. This study examines the behaviour of compressive sensing for practical UWB signals and proposes approaches to enhance signal reconstruction performance. Four practical dictionaries are proposed to increase the sparsity of the realistic UWB signals. Those dictionaries account for the practical effects of channel-like pulse dispersion and the unavoidable effects of antenna directivity. Both measured data and a more practical directional model are used for evaluation. It is shown that CS based on the new dictionaries is able to reconstruct the practical UWB signals more efficiently with reasonable complexity. Performance improvement of the proposed practical dictionaries in channel estimation for two different receivers is quantified.

Through-the-wall radar imaging exploiting Pythagorean apertures with sparse reconstruction

Through-the-wall radar imaging (TWRI) is receiving a considerable attention recently due to its diverse applications. One of the impinging challenges is the multipath propagation from the surrounding environment and even targets themselves. Multipath propagation produces ghost targets which populate the scene and not only create confusion with genuine targets but deteriorate the performance of compressive sensing (CS) algorithms. Unlike genuine targets, ghost locations are aspect dependent. Successful exploitation of this feature is dictated by the subarray selection modality. Up to this far, random multiple subarrays selection is the practice in exploiting aspect dependence. This paper suggests new subarray configurations based on Pythagorean triple which is made of pairwise coprime numbers that can enhance ghost suppression process and improve image resolution. The sensing matrices of the proposed subarrays are developed and analyzed. The paper investigates the effectiveness of generating the images from all the elements in the array as opposed to generating the images by processing designed subarrays individually and then combining the results. This comparison is done in view of multipath ghost suppression exploiting aspect dependence feature. Results based on synthesized data and electromagnetic propagation simulator show the effectiveness of the proposed arrays.

Developing Electrical Engineering Course in an Active Cooperative Learning (ACL) Platform

The Department of Electrical Engineering plans to run one of its basic courses, Introduction to Electrical Systems and Computation (EE 206), in Active Collaborative Learning (ACL) format. We have surveyed and examined several alternatives and flavors of running ACL, in an attempt to customize a platform that best achieves the course objectives. Issues related to team formation, team management and team assessment were debated. A survey of similar courses in other leading universities was conducted. The authors had the opportunity to test parts of the proposal in their running courses during the phase of the new course development. The feedback and lessons learned were incorporated to make adjustment and fine tuning to the proposal. The course is divided into four modules: Introducing EE fields, Project I: Research Activity, Matlab, and Project II: Robotics. In drafting the pilot course material, we did every effort to provide the fine details so that instructors feel comfortable with the approach. A complete set of documents has been developed to run the course in a productive and engaging manner.

Experimental evaluation of coprime sampler in direction of arrival estimation

Direction of arrival (DOA) estimation has many applications in beam-steering to improve signal reception and interference suppression. The number of sources in the scene of interest is usually very small. Thus, sparse reconstruction becomes a good candidate to work with reduced data sets. Recently, coprime arrays have been proposed for source localization. In this paper, a moving coprime array configuration is implemented for DOA estimation under sparse reconstruction framework. The proposed array uses only one antenna element. The antenna moves along the array axis to cover certain locations specified by the conventional coprime array. A stepped frequency continuous wave (SFCW) signal over ultra-wideband (UWB) is used. A microcontroller is used to control the movement and the data acquisition from the vector network analyzer to the computer. Two main advantages arise out of this approach. First, the complexity in terms of the total number of antenna elements and receivers needed to implement the array is reduced. Second, the mutual coupling effect is eliminated since only one antenna is active at a time. Experimental results in real scenarios were conducted to validate the proposed configuration. It is shown that coprime sampling is superior to uniform sampling with the same number of elements. The impact of the antenna directivity is highlighted.

Low-sampling-rate M-ary multiple access UWB communications in multipath channels

The desirable characteristics of ultra-wideband (UWB) technology are challenged by formidable sampling frequency, performance degradation in the presence of multi-user interference, and complexity of the receiver due to the channel estimation process. In this paper, a low-rate-sampling technique is used to implement M-ary multiple access UWB communications, in both the detection and channel estimation stages. A novel approach is used for multiple-access-interference (MAI) cancelation for the purpose of channel estimation. Results show reasonable performance of the proposed receiver for different number of users operating many times below Nyquist rate.

Practical implementation of compressive sensing to UWB signals

Compressive sensing (CS) is proposed by many researchers as a solution to the formidable sampling requirements of the promising Ultra wideband technology (UWB). Previous research was evaluated by simulating the IEEE UWB channel model. This paper studies the behavior of compressive sensing for practical UWB signals and proposes approaches to enhance the signal reconstruction performance. Four practically-based dictionaries are proposed to increase the sparsity of the realistic UWB signals. Those dictionaries accounts for the practical effects of the channel like pulse dispersion and the unavoidable effects of the antenna directivity. Both measured data and a more practical directional model are used for evaluation. It is shown that CS based on the new dictionaries is able to reconstruct the practical UWB signals more efficiently with reasonable complexity.