Guanghan Xu

Direction-of-arrival estimation via exploitation of cyclostationary-a combination of temporal and spatial processing

Many modulated communication signals exhibit a cyclostationarity (or periodic correlation) property, corresponding to the underlying periodicity arising from carrier frequencies or band rates. By exploiting cyclostationarity, i.e. evaluating the cyclic correlations of the received data at certain cycle frequencies, one can extract the cyclic correlations of only signals with the same cycle frequency and null out the cyclic correlations of stationary additive noise and all other cochannel interferences with different cycle frequencies. Thus, the signal detection capability can be significantly improved. An approach for exploiting cyclostationarity that is asymptotically exact for either narrowband or broadband sources, unlike previous methods, is proposed. The method also has significant implementational advantages over the earlier techniques. The simulation results indicate a significantly better performance in some environments. >

Fast recursive identification of state space models via exploitation of displacement structure

Abstract The seemingly computational burden of state space model identification has limited its real-time application though it offers some important advantages over methods based on input-output transfer functions. It has been shown recently that ideas from the theory of displacement structure can be used in state space identification to reduce the computational burden of batch processing from O ( MN 2 ) to O ( MN ) flops when the data matrix is of size M × N , where N > M . However, in many on-line identification scenarios with slowly time-varying systems, it is desirable to update the model as time goes on with the minimal computational burden. In this paper, we extend our results of the batch processing algorithm to allow updating of the identified state space model with O ( M 2 ) flops. Again, the theories of displacement structure and of the fast subspace decomposition (FSD) technique play crucial roles in the realization of the fast updating algorithm. Some computer simulation results are also presented.

Blind identification and equalization of multipath channels

A fractionally spaced blind identification and equalization method for a multipath channel is proposed and analyzed. Unlike most of the adaptive blind equalization methods which rely on higher-order statistics, the proposed method utilizes the second-order statistical information of the process. Identification and equalization of a possible nonminimum phase channel are achieved by exploiting cyclostationarities of the received communication signals. The proposed identification algorithm needs much less data than existing techniques and is asymptotically exact. Simulations demonstrated the promising performance of the proposed algorithm for the blind equalization of a three-ray multipath channel.<<ETX>>

Maximum likelihood detection of co-channel communication signals via exploitation of spatial diversity

A maximum-likelihood method is used to jointly detect multiple cochannel communication signals. The joint detection idea is explained by considering the simple case in which all the signals are synchronized. Extensions to more complicated cases for asynchronized and convolutionally coded signals are discussed. Computer simulations have been conducted to compare the performance of the proposed detection method with that of independent detection methods in some typical scenarios. The simulation results show that the performance gain is significant if some cochannel signals are closely spaced. Some suboptimal methods that require much less complexity than the full joint detection methods but have marginal performance loss are also discussed.<<ETX>>

A fast algorithm for signal subspace decomposition and its performance analysis

A fast signal-subspace decomposition (FSD) algorithm is presented for sample covariance matrices, which only needs O(M/sup 2/d) flops, where d(<<M) denotes the signal subspace dimension. A theoretical performance analysis was conducted, and it shows the strong consistency of the estimation of d and the asymptotic equivalence between the FSD estimate and the one obtained from an eigendecomposition. The approach can be easily implemented in parallel to further reduce the computation time to as little as O(Md) or O(log Md) by using O(M) or O(M/sup 2/) multipliers, respectively.<<ETX>>

Detection of number of sources via exploitation of centro-symmetry property

Forward/backward (FB) averaging is employed when possible in sensor array processing to improve parameter estimation accuracy by exploiting a centro-symmetry of the signal subspace. While FB averaging effectively doubles the number of data samples, it also introduces intersample correlation. This correlation complicates not only the performance analysis, but the detection of the number of sources as well. It is shown how existing detection procedures such as sequential hypothesis (or likelihood ratio) tests, minimum description length criterion (MDL), and AIC (Akaike information criterion) can be modified when FB averaging is used.<<ETX>>

Parallel implementation and performance analysis of beamspace ESPRIT

Most high-resolution algorithms for sensor array processing require an eigendecomposition which is difficult to implement in parallel and costs at least O(M/sup 3/) multiplications for an M*M matrix, corresponding to M sensors. Recently, S.D. Silverstein et al, (1991) presented a subband parallel scheme, which reduced the computation time from O(M/sup 3/) to O(L/sup 3/), where L(<M) is the subband length or the beamspace dimension. However, the potential problem is that a uniform linear array commonly used in direction finding loses its displacement invariance structure after the beamspace transformation. Thus, the DOA estimation may be more computationally intensive since the computationally efficient ESPRIT algorithm cannot be applied directly. The authors show a way of restoring the lost structure, which results in a new beamspace ESPRIT algorithm. The asymptotic performance analysis and simulation results of the beamspace ESPRIT method are presented.<<ETX>>

A new array signal processing method via exploitation of cyclostationarity

The authors consider the problem of direction finding of radar or sonar communication signals based on the data received by sensor arrays. Most communication (modulated) signals exhibit cyclostationarity corresponding to the underlying periodicity which may be carrier frequency or baud rate. By exploiting cyclostationarity, one can significantly improve the signal detection capability, i.e. null out other co-channel interferences and stationary background noise. They propose a new approach for array signal processing via exploitation of cyclostationarity, which is asymptotically exact for either narrow-band or broad-band sources. Moreover, the new technique has implementation advantages over the existing techniques. The simulation results indicate a significantly better performance than that of the prior algorithms.<<ETX>>

Numerical aspects of temperature profile reconstruction using acoustic tomography in RTP

Precise wafer temperature control is crucial to the viability of the emerging technology of rapid thermal processing (RTP) for semiconductor manufacturing. The authors examine the problem of accurate noninvasive measurement of wafer temperature, which is required for precise temperature control. The paper extends the work of Khuri-Yakub et al. (1993) on acoustic techniques for noninvasive wafer temperature measurement. The authors propose a method for estimation of wafer temperatures via regularized tomographic inversion using a priori knowledge of properties of the temperature distribution and data obtained by their technique. Results of simulation studies of the methods proposed are described.<<ETX>>

Fast subspace decomposition of data matrices

The authors present a fast subspace decomposition method (Bi-FSD) for (rectangular) data matrices, employing the bidiagonalization Lanczos algorithm. It only requires O(NMd) flops for a N*M data matrix and achieves almost an order of magnitude computational reduction over the O(NM/sup 2/+M/sup 3/) SVD (singular value decomposition) or ED (eigendecomposition) approach. A novel detection scheme is also presented that can be implemented at each intermediate step of estimating the signal subspace. Unlike many fast algorithms that trade performance for speed, rigorous performance analysis shows that Bi-FSD has the same asymptotic performance as the more costly SVD, and the Bi-FSD detection scheme is strongly consistent. Also, the most computationally intensive part (i.e., O(NM) operations) is O(d) matrix-vector products, which can be easily implemented in parallel for even faster computation. All these features of the Bi-FSD algorithm make it easier to implement a class of high-resolution array signal processing algorithms in real time.<<ETX>>