Jill K. Nelson

Signal processing for underwater acoustic communications

The performance and complexity of signal processing systems for underwater acoustic communications has dramatically increased over the last two decades. With its origins in noncoherent modulation and detection for communication at rates under 100 b/s, phase-coherent digital communication systems employing multichannel adaptive equalization with explicit symbol-timing and phase tracking are being deployed in commercial and military systems, enabling rates in excess of 10 kb/s. Research systems have been shown to further dramatically increase performance through the use of spatial multiplexing. Iterative equalization and decoding has also proven to be an enabling technology for dramatically enhancing the robustness of such systems. This article provides a brief overview of signal processing methods and advances in underwater acoustic communications, discussing both single carrier and emerging multicarrier methods, along with iterative decoding and spatial multiplexing methods.

A Quasi EM Method for Estimating Multiple Transmitter Locations

We consider estimating multiple transmitter locations based on received signal strength measurements by a sensor network of randomly located receivers. This problem is motivated by the search for available spectrum in cognitive radio applications. We create a quasi expectation maximization (EM) algorithm for localization under lognormal shadowing. Simulated performance is compared to random guessing and to global optimization using constriction particle swarm (CPSO). Results show that the proposed quasi EM algorithm outperforms both alternatives given a fixed number of guesses, and the performance gap grows as the number of transmitters increases.

Multi-directional decision feedback for 2D equalization

We propose an equalization algorithm that employs multiple decision-feedback equalizers (DFE)s operating in different directions and arbitration among the outputs of these equalizers to mitigate the effects of two-dimensional intersymbol interference (ISI). The multi-directional arbitrated DFE (MAD) exploits directional diversity to reduce the effects of error-propagation while maintaining complexity on the same order as a DFE. Simulation results show that, when four DFEs are used, the MAD algorithm can achieve substantial gains over a single DFE, including gains of over 10 dB at 10/sup -2/ BER for simulations in this paper.

Comparing student understanding of signals and systems using a concept inventory, a traditional exam and interviews

Concept inventories play a growing role in assessing student understanding in engineering curricula. A common application of concept inventories is a pre/post- test assessment in a course. For this reason, it is important to confirm the validity of any new concept inventory, i.e., to verify that the inventory measures what it is designed to assess. The signals and systems concept inventory (SSCI) is a 25-question multiple-choice exam assessing core concepts in undergraduate signals and systems courses. This paper presents two analyses supporting the validity of the SSCI. The first analysis compares the responses of 40 students to final exam questions with their responses to related SSCI questions. This analysis finds statistically-significant correlations between the SSCI and the final exam for questions on convolution and Fourier transform properties. The second analysis examines the interview responses of 18 students to SSCI questions on frequency-selective filtering and convolution. The interviews suggest students have a strong understanding of high and low frequency, have some understanding of the relationship between time and frequency domains, but struggle to interpret frequency responses. The interviews also suggest that many students retain some conceptual understanding of convolution after their memory of the convolution integral has faded.

Bayesian ML Sequence Detection for ISI Channels

We propose a Bayesian technique for blind detection of coded data transmitted over a dispersive channel. The Bayesian maximum likelihood sequence detector views the channel taps as stochastic quantities drawn from a known distribution and computes the probability of any transmitted sequence by averaging over the tap values. The resulting path metric requires memory of all previous symbols, and hence a tree-based algorithm is employed to find the most likely transmitted sequence. Simulation results show that the Bayesian detector can achieve bit error rates within 1/4 dB of the conventional known-channel maximum likelihood (ML) sequence detector.

Students' interpretation of the importance and difficulty of concepts in signals and systems

Two ongoing challenges facing instructors when designing courses are (1) do students identify/understand important concepts in the course, and (2) what makes concepts difficult for students to understand? In particular, do students see the relationship between the procedures taught and the fundamental concepts they support? In this study, we use interviews with 39 undergraduate engineering students to address these questions in the context of a sophomore-level continuous-time signals and systems course. Each student interviewed was asked which concept in the course was most difficult, which was most important, and why. Student responses regarding the concepts and the reasons were qualitatively analyzed, and a codebook was developed. The results of the coding provide broad insight into what factors make a particular concept difficult and/or important from the student perspective. We conjecture that general elements drawn from the results obtained here can be applied beyond signals and systems and across the engineering curriculum.

Linear turbo equalization for parallel ISI channels

We propose a method for exploiting transmit diversity using parallel independent intersymbol interference channels together with an iterative equalizing receiver. Linear iterative turbo equalization (LITE) employs an interleaver in the transmitter and passes a priori information on the transmitted symbols between multiple soft-input/soft-output minimum mean-square error linear equalizers in the receiver. We describe the LITE algorithm, present simulations for both stationary and fading channels, and develop a framework for analyzing the evolution of the a priori information as the algorithm iterates.

Linear iterative turbo-equalization (LITE) for dual channels

We examine a point-to-point communications scenario in which two or more separate, but known, channels are available for data transmission. While sending the same data across multiple channels provides channel diversity, we introduce additional temporal diversity by permuting the order of the data prior to transmission over one or more of the channels. As a receiver, we introduce a low complexity iterative equalization algorithm, inspired by iterative decoders for turbo-codes, which we call linear iterative turbo-equalization (LITE). The LITE algorithm contains one minimum mean square error linear equalizer for each channel and passes soft-information between the different equalizers in the form of a prior over the transmitted data. The linear equalizers differ from conventional equalizers by incorporating this prior in the minimization. Through simulations, we compare the empirical performance of the LITE algorithm to that of conventional linear and decision feedback equalizers, as well as maximum likelihood decoding for the set of channels. Our simulations demonstrate that the LITE algorithm can achieve equalization performance comparable to maximum likelihood decoding with computational complexity comparable to that of linear equalization.

Estimating multiple transmitter locations from power measurements at multiple receivers

We consider the estimation of the locations of multiple transmitters based on received signal strength measurements at a network of randomly-placed receivers. We generalize the expectation-maximization (EM) method to create a quasi EM algorithm for localization under lognormal shadowing. Simulated performance is compared to a state-of-the-art global optimizer and to random guessing. Results reveal that the proposed quasi EM algorithm outperforms both alternatives in median and ninety-fifth percentile error, especially as the number of receivers increases.

Target tracking via a sampling stack-based approach

We propose a novel approach to tracking a target in clutter based on the stack algorithm for tree search. The proposed tracking approach reduces the size of the search tree by employing a coarse discretization of the target state space. To reduce the quantization error that results from coarse discretization, the representative value of each quantized region is sampled from an estimated importance sampling function. A forgetting factor is included in the likelihood metric to control the effect of previous decisions and to reduce algorithm complexity. Simulations reveal that the proposed algorithm provides significantly reduced complexity while suffering no performance degradation relative to stack-based tracking with finer quantization.