Vehicle counting, time-of-travel analysis, and other traffic studies frequently require the classification and identification of vehicles in a roadway. Unfortunately, many current technologies for identifying vehicles, such as image-based methods that use cameras and machine vision, are not appropriate for studies that require low-power consumption and low cost. Additionally, privacy issues are becoming a larger concern with the increasing controversy surrounding the public collection of imagery. In this work we evaluate a multi-modal sensing approach to vehicle classification and identification using an ensemble of sensors measurements including electromagnetic emanations and acoustic signatures. A novel kernel regression method is also used for signal learning to classify and identify vehicles without the need of invasive images. Multi-mode sensing, as well as signal learning, is shown to significantly increase the classification rate of specific vehicle classes.

This paper proposes an architecture for using multiple-input multiple-output techniques for a multimode fiber broadcast channel, allowing simultaneous transmission of unique streams to different users on the same fiber while using square-law detection. The resulting system throughput scales nearly linearly with the number of transmitters and receivers. The paper also proposes a training scheme appropriate for use with square-law detection.

Linear precoding (beamforming) techniques exist that maximize the sum rate in multi-antenna broadcast channels. Such algorithms require accurate channel state information (CSI) at the transmitter and are performance sensitive to erroneous or outdated CSI. The current work focuses on adapting these algorithms to work on the statistics of the channel, rather than channel state, in order to provide more stable performance. This stable beamforming method is shown to require the full spatial correlation matrix thus suffering from high complexity in the feedback channel. Parameterization of the spatial correlation using popular channel models is also considered to make the proposed beamforming practical.

This work explores the performance of a multiple-input multiple-output broadcast channel where both the transmitter and receivers have outdated channel knowledge due to node motion or other time-variations in the communication channel. A performance analysis based on measured channel responses reveals significant throughput degradation for optimal linear and nonlinear precoding strategies unless the channel state information (CSI) is frequently fed back to the transmitter. The paper then develops a linear beamforming precoding strategy based on channel distribution information in the form of a full spatial correlation matrix for each user. This algorithm is shown to provide highly stable communication, with a throughput that is higher than that for optimal precoders operating on outdated CSI, in a time-variant environment, indicating that this approach can operate with significantly reduced feedback frequency. Furthermore, the paper demonstrates the use of the well-known Kronecker and Weichselberger models to parameterize the full correlation matrix to enable further reduction in the amount of feedback data required for implementation of the new beamforming technique.