Chiao-En Chen

Stochastic Maximum-Likelihood DOA Estimation in the Presence of Unknown Nonuniform Noise

This correspondence investigates the direction-of-arrival (DOA) estimation of multiple narrowband sources in the presence of nonuniform white noise with an arbitrary diagonal covariance matrix. While both the deterministic and stochastic Cramer-Rao bound (CRB) and the deterministic maximum-likelihood (ML) DOA estimator under this model have been derived by Pesavento and Gershman, the stochastic ML DOA estimator under the same setting is still not available in the literature. In this correspondence, a new stochastic ML DOA estimator is derived. Its implementation is based on an iterative procedure which concentrates the log-likelihood function with respect to the signal and noise nuisance parameters in a stepwise fashion. A modified inverse iteration algorithm is also presented for the estimation of the noise parameters. Simulation results have shown that the proposed algorithm is able to provide significant performance improvement over the conventional uniform ML estimator in nonuniform noise environments and require only a few iterations to converge to the nonuniform stochastic CRB.

An Iterative Hybrid Transceiver Design Algorithm for Millimeter Wave MIMO Systems

In this letter, a new algorithm for millimeter wave multiple-input-multiple-output hybrid (mixed RF and baseband) transceiver design is proposed. The proposed algorithm iteratively updates the phases of the phase-shifters in the RF precoder (or RF combiner) to minimize the weighted sum of squared residuals between the optimal full-baseband design and the hybrid design, and is guaranteed to converge to at least a local optimal solution. Simulation results show that the proposed iterative design can achieve almost the same performance as the optimal full-baseband design, in spite of using a much smaller number of RF chains.

Maximum likelihood DOA estimation of multiple wideband sources in the presence of nonuniform sensor noise

We investigate the maximum likelihood (ML) direction-of-arrival (DOA) estimation of multiple wideband sources in the presence of unknown nonuniform sensor noise. New closed-form expression for the direction estimation Cramér-Rao-Bound (CRB) has been derived. The performance of the conventional wideband uniform ML estimator under nonuniform noise has been studied. In order to mitigate the performance degradation caused by the nonuniformity of the noise, a new deterministic wideband nonuniform ML DOA estimator is derived and two associated processing algorithms are proposed. The first algorithm is based on an iterative procedure which stepwise concentrates the log-likelihood function with respect to the DOAs and the noise nuisance parameters, while the second is a noniterative algorithm that maximizes the derived approximately concentrated log-likelihood function. The performance of the proposed algorithms is tested through extensive computer simulations. Simulation results show the stepwise-concentrated ML algorithm (SC-ML) requires only a few iterations to converge and both the SC-ML and the approximately-concentrated ML algorithm (AC-ML) attain a solution close to the derived CRB at high signal-to-noise ratio.

An Iterative Minmax Per-Stream MSE Transceiver Design for MIMO Interference Channel

This article presents a new MSE-based transceiver design for the MIMO interference channel (IC). The proposed design monotonically decreases the maximum per-stream MSE of all users by alternatively optimizing between a minimum MSE solution for the equalizers and a second-order-cone-programming (SOCP) solution for the precoders. As the systems error rate performance at high signal-to-noise ratio (SNR) is dominated by the spatial stream with the largest MSE, minimizing the maximum per-stream MSE not only improves the overall system performance but also ensures the fairness in error rate among users. The performance advantage of the proposed algorithm is verified by the numerical simulations.

Design and testing of robust acoustic arrays for localization and enhancement of several bird sources

Sensor network technology can revolutionize the study of animal ecology by providing a means of non-intrusive, simultaneous, unmanned monitoring. In this paper, we investigate the design, analysis, and testing of acoustic arrays for localizing bird vocalizations of different species. The spectra of the bird waveforms affect the desired dimension of the array. Microphones are placed in a uniform circular array and are finely synchronized within a few microseconds. We apply the approximate maximum likelihood (AML) method to estimate the source direction-of-arrival (DOA) and perform beamforming for signal enhancement. The crossing of the distributed DOA estimated bearings is used to localize the birds, and the enhanced signals axe used for training and estimation for the classification of the birds. The experimental results demonstrate the practicality and robustness of our array design

A computationally efficient near-optimal algorithm for capacity-maximization based joint transmit and receive antenna selection

This letter presents a computationally efficient joint transmit and receive antenna selection (EJTRAS) algorithm based on a modification of the selection criterion in [1]. We show that the modification allows us to reduce the algorithms computational complexity by a factor of L, where L is the number of selected antennas without sacrificing the performance. Nearoptimal outage and ergodic capacity can therefore be attained with significantly lower complexity as verified by the extensive computer simulations.

An Improved Ordered-Block MMSE Detector for Generalized Spatial Modulation

In this letter, an improved ordered-block minimum-mean-squared-error (OB-MMSE) detector for generalized spatial modulation systems is presented. We first propose to use the concentrated distance metric derived from the conditional maximum likelihood estimator as the ordering metric for the OB-MMSE and then design a computationally-efficient algorithm for computing this metric. The improved ordering performance of the proposed algorithm allows the early-termination of the OB-MMSE detector without noticeable performance loss which can be exploited to further reduce its complexity. Simulation results show that the proposed algorithm can achieve better performance-complexity tradeoff compared to the existing OB-MMSE detector.

Detection and estimation for communication and radar systems

1. Introduction and motivation to detection and estimation 2. Review of probability and random processes 3. Statistical hypothesis testing theory 4. Detection of deterministic binary signals in Gaussian noises 5. M-ary detection and classification of deterministic signals 6. Non-coherent detection 7. Parameter estimation 8. Analytical and simulation methods for system performance analysis and design.

A New Lattice Reduction Algorithm for LR-Aided MIMO Linear Detection

Lattice reduction (LR) has recently emerged as a promising technique for improving the performance of suboptimal multiple-input-multiple-output (MIMO) detectors. For LR-aided MIMO detection, the Lenstra-Lenstra-Lovász (LLL) and Seysens algorithm (SA) have been considered almost exclusively to date. In this paper, we introduced a new LR algorithm for LR-aided linear detection (LD). In contrast to the LLL and SA, which are targeted to search for bases with relatively short basis vectors, the proposed algorithm has been designed to improve the minimum Euclidean distance of the LR-aided linear detector, thus exhibiting improved error rate at high SNR. The error-rate performance of the proposed algorithm as well as the required complexity has been demonstrated through extensive computer simulations.

Acoustic sensor networks for woodpecker localization

Sensor network technology can revolutionize the study of animal ecology by providing a means of non-intrusive, simultaneous monitoring of interaction among multiple animals. In this paper, we investigate design, analysis, and testing of acoustic arrays for localizing acorn woodpeckers using their vocalizations. Each acoustic array consists of four microphones arranged in a square. All four audio channels within the same acoustic array are finely synchronized within a few micro seconds. We apply the approximate maximum likelihood (AML) method to synchronized audio channels of each acoustic array for estimating the direction-of-arrival (DOA) of woodpecker vocalizations. The woodpecker location is estimated by applying least square (LS) methods to DOA bearing crossings of multiple acoustic arrays. We have revealed the critical relation between microphone spacing of acoustic arrays and robustness of beamforming of woodpecker vocalizations. Woodpecker localization experiments using robust array element spacing in different types of environments are conducted and compared. Practical issues about calibration of acoustic array orientation are also discussed.