Jun Ling

Missing Data Recovery Via a Nonparametric Iterative Adaptive Approach

We introduce a missing data recovery methodology based on a weighted least squares iterative adaptive approach (IAA). The proposed method is referred to as the missing-data IAA (MIAA) and it can be used for uniform or nonuniform sampling as well as for arbitrary data missing patterns. MIAA uses the IAA spectrum estimates to retrieve the missing data, by means of either a frequency domain or a time domain approach. Numerical examples are presented to show the effectiveness of MIAA for missing data reconstruction. In particular, we show that MIAA can outperform an existing competitive approach, and this at a much lower computational cost.

Spectral estimation of irregularly sampled exponentially decaying signals with applications to RF spectroscopy

The problem of estimating the spectral content of exponentially decaying signals from a set of irregularly sampled data is of considerable interest in several applications, for example in various forms of radio frequency spectroscopy. In this paper, we propose a new nonparametric iterative adaptive approach that provides a solution to this estimation problem. As opposed to commonly used methods in the field, the damping coefficient, or linewidth, is explicitly modeled, which allows for an improved estimation performance. Numerical examples using both simulated data and data from NQR experiments illustrate the benefits of the proposed estimator as compared to currently available nonparametric methods.

Enhanced channel estimation and efficient symbol detection in MIMO underwater acoustic communications

Effective training sequences and reliable channel estimation algorithms are essential for enhancing the performance of multi-input multi-output (MIMO) underwater acoustic communications (UAC). Also, effective interference cancellation schemes are crucial for reliable symbol detection. In this paper, the problem of designing MIMO training sequences is considered. Moreover, we present a sparse learning via iterative minimization (SLIM) algorithm for enhanced channel estimation and reduced computational complexity. Furthermore, RELAX-BLAST, a linear minimum mean-squared error based symbol detection scheme, is implemented efficiently by exploiting the conjugate gradient method and diagonalization properties of circulant matrices. The proposed MIMO UAC techniques are evaluated using both simulated and experimental examples.

Enhanced Channel Estimation and Symbol Detection for High Speed MIMO Underwater Acoustic Communications

This paper focuses on the channel estimation and symbol detection problems in multi-input multi-output (MIMO) underwater acoustic (UWA) communications. To enhance channel estimation performance, a cyclic approach for designing training sequences and an iterative adaptive approach (IAA) for estimating the channel taps are presented. Sparse channel estimates can be obtained. by combining IAA with the Bayesian information criterion (BIC). Moreover, the parametric RELAX algorithm can be used to improve the IAA with BIG estimates further. Regarding symbol detection, a minimum mean-squared error (MMSE) based detection scheme, called RELAX-BLAST, is presented. Both simulated and experimental results are provided to validate the proposed MIMO scheme.

On Bayesian Channel Estimation and FFT-Based Symbol Detection in MIMO Underwater Acoustic Communications

Reliable channel estimation and effective interference cancellation are essential for enhancing the performance of multiple-input-multiple-output (MIMO) underwater acoustic communication (UAC) systems. In this paper, an efficient user-parameter-free Bayesian approach, referred to as sparse learning via iterative minimization (SLIM), is presented. SLIM provides good channel estimation performance along with reduced computational complexity compared to iterative adaptive approach (IAA). Moreover, RELAX-BLAST, which is a linear minimum mean-squared error (MMSE)-based symbol detection scheme, is implemented efficiently by making use of the conjugate gradient (CG) method and diagonalization properties of circulant matrices. The proposed algorithm requires only simple fast Fourier transform (FFT) operations and facilitates parallel implementations. These MIMO UAC techniques are evaluated using both simulated and in-water experimental examples. The 2008 Surface Processes and Acoustic Communications Experiment (SPACE08) experimental results show that the proposed MIMO UAC schemes can enjoy almost error-free performance even under severe ocean environments.

Gibbs-Sampler-Based Semiblind Equalizer in Underwater Acoustic Communications

The acoustic communication channel is frequency selective with long memory, leading to severe intersymbol interference (ISI). To mitigate ISI, equalizer becomes an indispensable module in the receiver structure. However, the time-varying nature of the underwater acoustic environment imposes unique challenges to the design of an effective equalizer. First, the equalization process needs to be performed on a block basis and the block length could be short. Second, concerning that the dynamic acoustic medium makes the newly acquired channel information readily outdated, it is desirable that the equalizer performance is robust against the inaccuracy of the channel information when the transmission scheme involves cross-block reference. In this paper, we consider a statistical semiblind equalizer implemented by the Gibbs sampler techniques. The proposed equalizer con- ducts channel estimation and symbol detection in a joint manner, and it is robust to the accuracy of the channel information. The effectiveness of the proposed semiblind equalizer is demonstrated using both simulated and the 2008 Surface Processes and Communications Experiment (SPACE08, Marthas Vineyard, MA) in-water experimentation examples.

Covert underwater acoustic communications: Transceiver structures, waveform designs and associated performances

Covert communications are conducted at a low received signal-to-noise ratio (SNR) to prevent interception or detection by an eavesdropper, and successful detection in this particular area heavily relies on the processing gain achieved by employing the direct-sequence spread-spectrum (DSSS) technique. If covert communications take place in underwater acoustic (UWA) environments, then additional challenges are present. UWA channels are time-varying in nature, which could preclude an accurate channel estimation at low SNR. Furthermore, UWA environments are frequency-selective with long-memory channels, which imposes challenges to the design of the spreading waveform. In this paper, we investigate covert UWA communications from a noncoherent perspective. Two modulation schemes are addressed, namely, binary orthogonal modulation and binary differential phase-shift keying (DPSK). Both schemes are coupled with the DSSS technique and a RAKE receiver. The employed spreading waveforms not only account for the transceiver structure and frequency-selective nature of the UWA channel, but also serve to protect the privacy of the transmitted information. The effectiveness of the proposed methods is verified by numerical examples.

Multi-input multi-output underwater communications over sparse and frequency modulated acoustic channels

This paper addresses multi-input multi-output (MIMO) communications over sparse acoustic channels suffering from frequency modulations. An extension of the recently introduced SLIM algorithm, which stands for sparse learning via iterative minimization, is presented to estimate the sparse and frequency modulated acoustic channels. The extended algorithm is referred to as generalization of SLIM (GoSLIM). The sparseness is exploited through a hierarchical Bayesian model, and because GoSLIM is user parameter free, it is easy to use in practical applications. Moreover this paper considers channel equalization and symbol detection for various MIMO transmission schemes, including both space-time block coding and spatial multiplexing, under the challenging channel conditions. The effectiveness of the proposed approaches is demonstrated using in-water experimental measurements recently acquired during WHOI09 and ACOMM10 experiments.

Probing waveforms and adaptive receivers for active sonar.

Active sonar systems involve the transmission and reception of one or more sequences, which provide a basis for extraction of the information on targets in the region of interest. The probing sequences at the transmitter and signal processing at the receiver play crucial roles in the overall system performance. We consider herein using CAN (cyclic algorithm-new) to synthesize probing sequences with good aperiodic autocorrelation properties. The performance of the CAN sequences will be compared with that of pseudo random noise (PRN) and random phase (RP) sequences, which often find uses in the active sonar systems. We will also consider two adaptive receiver designs, namely the iterative adaptive approach (IAA) and sparse learning via iterative minimization (SLIM) method. We will illustrate the performance of the algorithms via numerical examples, by comparing IAA and SLIM with the conventional matched filter (MF) method. Experimental results show that CAN, IAA and SLIM can contribute to the overall performance improvement of the active sonar systems.

Adaptive Range-Doppler Imaging and Target Parameter Estimation in Multistatic Active Sonar Systems

Multistatic active sonar systems involve the transmission and reception of multiple probing sequences. Since the multiple simultaneously transmitted probing sequences act as interferences to one another, adaptive receiver filters are needed for interference suppression and for target range-Doppler imaging. Two adaptive receiver designs, namely, the iterative adaptive approach (IAA) and the sparse learning via iterative minimization (SLIM) method, are considered for range-Doppler imaging via multistatic active sonar. The so-obtained range-Doppler images allow us to further estimate the target parameters. Specifically, we use the popular quasi-Newton method for target position estimation and the least squares (LS) fitting approach for target velocity determination. The effectiveness of the proposed multistatic active sonar signal processing techniques is verified using numerical examples.