Hsien-Sen Hung

3-D MUSIC with polynomial rooting for near-field source localization

The bearing, elevation, and range estimation of near-field narrow-band sources from data observed across an array of sensors is considered. For high-resolution source localization, the multiple signal classification (MUSIC) algorithm is modified and extended to its 3-D version by accounting for the spherical curvature and spreading factor in the array manifold. However, the simultaneous estimation of bearing, elevation, and range from the 3-D MUSIC spectrum requires exhaustive multidimensional search. In order to alleviate the computational load, an alternative algorithm is proposed. The proposed algorithm involves a search in the range domain combined with polynomial rooting, which replaces the search in the azimuth-elevation domain, for bearing and elevation estimation.

Fabrication and Characterization of High-Sensitivity Underwater Acoustic Multimedia Communication Devices with Thick Composite PZT Films

This paper presents a high-sensitivity hydrophone fabricated with a Microelectromechanical Systems (MEMS) process using epitaxial thin films grown on silicon wafers. The evaluated resonant frequency was calculated through finite-element analysis (FEA). The hydrophone was designed, fabricated, and characterized by different measurements performed in a water tank, by using a pulsed sound technique with a sensitivity of −190 dB ± 2 dB for frequencies in the range 50–500 Hz. These results indicate the high-performance miniaturized acoustic devices, which can impact a variety of technological applications.

Predictive Angle Tracking Algorithm Based on Extended Kalman Filter

In the case of moving sources, various target angle tracking algorithms have been proposed and reported in the literature for multiple narrow-band targets. Yang and Kaveh proposed an iterative adaptive eigen-subspace method in conjunction with the multiple signal classification (MUSIC) algorithm to track the DOA angles of multiple targets (Yang & Kaveh, 1988). Due to the data association problem caused by multi-target tracking, the adaptive MUSIC method fails to track targets when they are moving close to each other. Although the method proposed by Sword, et al. (1990) can avoid the data association problem, errors are accumulated in each iteration, making it unable to track targets that are mutually close. Due to the nature of prediction-correction filtering process, Kalman filter (KF) can reduce estimation errors and avoid the data association problem when applied to angle tracking, as stated in several references (Javier & Sylvie, 1999; Yang, 1995; Park, et al. 1994). Rao, et al. (1994) proposed to estimate DOA angles of targets using the maximum likelihood method and feeding the results to a KF. However, it is assumed that the signal powers of the targets are all different, making the algorithm impractical. Javier and Sylvie (1999) suggested to estimate target angles using the projection approximation subspace tracking algorithm with deflation (PASTd) (Yang, 1995) and a Newton-type method (for MUSIC spectrum) for the use in the KF. It has lower computational load and better tracking performance than Rao’s algorithm, but still exhibits poor tracking success rate at low signalto-noise ratios (SNRs). Park, et al. (1994) proposed an approach, which utilizes predicted angles obtained from Sword’s method. The approach also uses the constrained least-squares criterion to confine the dynamic range of angles. The choice of relevant parameters is empirical and is not suitable for various scenarios of different moving speeds and SNRs. Besides, the tracking performance degrades seriously with an increasing number of crossing targets. Later on, to improve Park’s method, Ryu, et al. (1999, 2002) suggested to obtain the angle innovations of the targets from a signal subspace, instead of the sensor output covariance matrix, via projection approximation subspace tracking (PAST) algorithm (Yang, 1995). Chang, et al. (2005) modified Park’s algorithm by incorporating a spatial smoothing (Shan et al., 1985) technique to overcome multipath interference, and also coherent signalsubspace (CSS) (Wang & Kaveh, 1985) processing for tracking wideband targets. All of the above algorithms are based on the sample covariance matrix or signal subspace made with

Direct Mapping OFDM-Based Transmission Scheme for Underwater Acoustic Multimedia

In this paper, we propose a direct mapping (DM) orthogonal frequency division multiplexing (OFDM) based transmission scheme for underwater acoustic multimedia (UAM). We adopt DM multi-input multi-output (MIMO), power assignment mechanism, OFDM, adaptive modulation, and unequal error protection in the UAM system. The proposed underwater acoustic multimedia system employs high power, low speed modulation, and schemes providing significant error protection for the transmission of sensor data messages requiring a stringent bit-error rate (BER). In contrast, low power, high speed modulation, and less capable error protection schemes are provided for messages that can tolerate a high BER. Simulation results show that the proposed scheme can not only fulfill the quality of services (QoS) required by a UAM system, but also achieve the mission of maximum transmission bit rates or minimum transmission power.

Frequency-domain channel estimation for nonlinear multicarrier underwater communication systems

Underwater acoustic (UWA) communications have drawn attention to many researchers in recent years. The major obstacles to reliable UWA communications include limited bandwidth, long multipath delay and large Doppler shifts of the UWA channel. Due to the hostile UWA environment, implementation of a high-speed UWA communication system often requires advanced modulation and signal processing techniques. The multicarrier modulation in the form of orthogonal frequency-division multiplexing (OFDM) has many attractive features which make it a potential candidate for UWA communications. However, the OFDM signal has a high peak-to-average-power ratio (PAPR) and hence is highly susceptible to nonlinearities in the communication link. For combating channel nonlinearities, knowledge of the nonlinear channel is essential. In this paper, we propose a novel method for the estimation of nonlinear channels in OFDM UWA communications. Compared to conventional methods, the proposed method requires a significantly smaller amount of data to achieve the channel estimation. This makes the proposed method suitable for time-varying UWA channels. The effectiveness of the proposed method is demonstrated by applying it to estimate the nonlinear channel of a simulated OFDM UWA communication system.

Joint Bearing and Range Estimation of Multiple Objects from Time-Frequency Analysis

Direction-of-arrival (DOA) and range estimation is an important issue of sonar signal processing. In this paper, a novel approach using Hilbert-Huang transform (HHT) is proposed for joint bearing and range estimation of multiple targets based on a uniform linear array (ULA) of hydrophones. The structure of this ULA based on micro-electro-mechanical systems (MEMS) technology, and thus has attractive features of small size, high sensitivity and low cost, and is suitable for Autonomous Underwater Vehicle (AUV) operations. This proposed target localization method has the following advantages: only a single snapshot of data is needed and real-time processing is feasible. The proposed algorithm transforms a very complicated nonlinear estimation problem to a simple nearly linear one via time-frequency distribution (TFD) theory and is verified with HHT. Theoretical discussions of resolution issue are also provided to facilitate the design of a MEMS sensor with high sensitivity. Simulation results are shown to verify the effectiveness of the proposed method.

Low-complexity spatial-frequency-time-domain MMSE multiuser detectors for MC DS-CDMA

This paper presents three multiuser detectors (MUDs) for the uplinks of spatial-frequency-time-domain (SFT-domain) spread multi-carrier (MC) direct sequence code division multiple access (DS-CDMA) systems. First, aiming at achieving significant performance gain, we propose a joint optimum SFT-domain MUD based on minimum mean square error (MMSE) decision rules. Further, to reduce the computational complexity, we decompose the SFT-domain received signal to low-dimension signal components and propose the corresponding separate MMSE MUDs. Complexity analysis and computer simulations show that the joint optimum SFT-domain MMSE MUD performs the best among the proposed schemes. Meanwhile, the separate MMSE MUDs can provide effective performance close to that of the joint optimum SFT-domain MMSE MUD but with a modest computational overhead.

Application of Fast Subspace ECM Algorithm in Cyclostationary Signal Estimation

In this paper, a fast subspace extended cyclic MUSIC algorithm is presented for DOA estimation of cyclostationary signals. The proposed method replaces the singular value decomposition required in the extended cyclic MUSIC algorithm with the matrix inverse-free version of the fast rational approximation algorithm. It has comparable resolution power, less accuracy, and lower computational complexity than the extended cyclic MUSIC algorithm.