The linear sampling method (LSM) offers a qualitative image reconstruction approach, which is known as a viable alternative for obstacle support identification to the well-studied filtered backprojection (FBP), which depends on a linearized forward scattering model. Of practical interest is the imaging of obstacles from sparse aperture far-field data under a fixed single frequency mode of operation. Under this scenario, the Tikhonov regularization typically applied to LSM produces poor images that fail to capture the obstacle boundary. In this paper, we employ an alternative regularization strategy based on constraining the sparsity of the solution's spatial gradient. Two regularization approaches based on the spatial gradient are developed. A numerical comparison to the FBP demonstrates that the new method's ability to account for aspect-dependent scattering permits more accurate reconstruction of concave obstacles, whereas a comparison to Tikhonov-regularized LSM demonstrates that the proposed approach significantly improves obstacle recovery with sparse-aperture data.

One main drawback of using Time Difference of Arrival (TDOA) methods for source localization and navigation is that they require centralization of multiple copies of a signal. This paper considers blindly estimating the location of a Cyclic Prefix (CP) in an Orthogonal Frequency Division Multiplexing (OFDM) signal, enabling distributed TDOA computation up to an integer ambiguity. This ambiguity can be resolved using integer least-squares methods, if enough TDOAs are available. The contributions of this paper are derivation of the Cramer-Rao Lower Bound (CRLB) on locating the CP, and hence on the underlying source localization problem.

A technique that consists of correlation followed by thresholding is widely used to mitigate multipath effects, especially in the presence of non-line-of-sight (NLoS) effects, for TOA estimation. In practice, an accurate threshold that the technique relies on is difficult to obtain, since optimizing the threshold requires prior knowledge of channel statistics and signal/noise power, which are not always known a priori nor are easy to estimate. In this paper, we propose a new thresholding method that does not rely on such prior knowledge and is robust against an inaccurate threshold.

Non-GPS navigation techniques that use signals of opportunity (e.g. TV/AM broadcast signals with known source locations) without aid of satellite-based location systems (e.g. GPS) are promising since they use readily available strong signals that are not susceptible to blockage or jamming. However, problems such as large asynchronous clock offsets among receivers and large broadcast signal frequency offsets arise because broadcast signals are not dedicated for ranging purposes. In this paper, we propose an asynchronous differential time-difference-of-arrival (TDOA) positioning method to overcome the problems of biased signal frequencies and asynchronous receiver clocks. The proposed method eliminates the need for synchronous receiver clocks in the TDOA method and relaxes the tight timing constraint between receivers as required by the double-difference method in the differential GPS (dGPS).

Most methods for sensor self-localization require either synchronization or round trip communications among the networked sensors. A differential time-difference-of-arrival (dTDOA) method is proposed in this paper that does not require such time synchronization nor round trip communications, thus greatly reduces demand on sensor communications resources and makes implementation much easier. Theoretical results are presented for the principle of the new method and simulations are also given that confirm the effectiveness of the proposed method.

Large clock offsets and drifts impose many difficulties on sensor self-localization. We have proposed a differential time-difference-of-arrival (dTDOA) localization method in [1] to overcome the problem caused by asynchronous clock offsets. In this paper, the dTDOA method is extended to combat clock drifts by a two-stage clock-drift cancellation method. Furthermore, based on the clock-drift cancellation, a maximum likelihood estimation (MLE) method that is theoretically optimal for sensor locations is derived. The method can achieve sensor localization using a single round of time-of-arrival (TOA) measurements, and thus greatly reduces the communications burden and timing constraint.

Cyclostationarity has been exploited in direction of arrival (DOA) estimation due to its signal selectivity and immunity against noise and interference. For coherent signals, e.g., those arising from a multipath propagation environment, the resulting rank deficiency of the signal cyclic correlation matrix hampers proper DOA estimation. Spatial smoothing (SS) is an effective tool to cope with this problem. However, SS reduces effective array span, thus also reduces the DOA resolution as well as the number of detectable sources. In this paper, we propose an instantaneous DOA estimation method for moving wideband cyclostationary sources under a multipath environment. Based on the movement of the sources, our method is able to decorrelate wideband coherent signals caused by multipath without SS. Effectiveness of our method is demonstrated by simulations.

Cyclostationarity based direction of arrival (DOA) estimation is of interest due to its immunity to interference and noise. Recently the use of cyclic methods with a conjugate minimum-redundancy linear array (MRLA) and an appropriate matrix augmentation technique was exploited by Gelli and Izzo (1997) to improve the performance of cyclic MUSIC for narrowband signals. In this paper, we propose a cyclostationarity based wideband signal DOA estimation method with a conjugate MRLA. Exploiting the similarity of our problem to DOA estimation for coherent signals, we propose to utilize the forward/backward spatial smoothing (FBSS) technique in our method instead of using the matrix augmentation technique. It is shown that our new method, in addition to generalizing the previous method to wideband signals, is able to detect more signals with improved performance to the previous method.

For direction of arrival (DOA) estimation under a multipath environment, algorithms exist to estimate the DOAs of all paths associated with different sources, by employing various schemes to cope with coherent signals induced by multipath. Most of these methods do not provide any information on which source a detected path belongs to, i.e., source association. However, information on source association is useful for identifying, locating, and tracking the sources. In this letter, we propose a novel method to associate all paths to each source based on the assumption that the DOAs of all paths have been detected by an existing method. The description of our source association algorithm and the simulation are based on wideband cyclostationary signals. However, our algorithm can be easily modified to work for both stationary signals and narrowband cyclostationary signals.

Many man-made signals encountered in communications, such as BPSK, FSK, AM signals, exhibit cyclostationarity. By exploiting the cyclostationarity, cyclic MUSIC has been shown to be able to separate signals with different cycle frequencies, thus, to be able to perform signal selective direction of arrival (DOA) estimation. However, both cyclic MUSIC and conventional MUSIC suffer performance deterioration when the signals are either wideband or coherent. We propose a new method that combines the spatial smoothing (SS) method with a revised cyclic MUSIC named averaged cyclic MUSIC. Simulation results show the effectiveness of the proposed method in signal selective DOA estimation for wideband signals under a multipath environment.

The advantages of spatial diversity are investigated with respect to two metrics. i.e., near-far resistance and the rank of the channel matrix. The impact of spatial diversity on the rank of the channel matrix and its significance is first studied. Then the near-far resistance of the minimum mean square error (MMSE) detector is derived for wireless code division multiple access (CDMA) communication systems with spatial diversity. The derived near-far resistance under different transmit-receive antenna configurations is analyzed and compared. It is shown that near-far resistance is enhanced when spatial diversity is introduced.

In practical OFDM systems, cyclic prefix insertion and channel estimation are indispensable to achieve coherent demodulation. In this paper, a linear smoothing based blind channel estimation method for OFDM systems over frequency-selective channels is presented. The proposed method does not require a cyclic prefix and thus leading to higher spectral efficiency. Compared with some existing blind methods, our newly proposed method is computationally efficient and results in good performance.

In this letter, the near-far resistance of the minimum mean square error (MMSE) detector is derived for multicarrier code division multiple access (MC-CDMA) communication systems both with cyclic prefix (CP) and without CP. It is shown that the near-far resistance of MC-CDMA without CP is the same as that of the direct sequence CDMA (DS-CDMA) while MC-CDMA with CP has better performance on near-far resistance at the expense of reduced bandwidth efficiency. Computer simulations confirm theoretical findings.

The near-far problem in a multiuser setting places fundamental limitations on the performance of CDMA communication systems. In the past, significant effort has focused on designing practical, and hence suboptimum, detectors to combat multiple access interference (MAI). However, the optimality in near-far resistance is generally lost with suboptimum detectors. One method which can enhance the near-far resistance of suboptimum detectors under multipath channels is presented. The proposed method is able to overcome the shortcomings of existing methods. Computer simulations confirm the theoretical findings.

Near-far resistance is one of the most important performance measures in a multiuser communication system, especially in a CDMA system. The optimum multiuser detector can achieve the optimum near-far resistance. But due to its high computational complexity, the optimum detector is impractical to implement. Cost-effective suboptimum detectors are more practical to implement. In this paper, we derive the near-far resistance of the optimum detector and some linear detectors, and compare them each other. Computer simulations are also performed to compare with the theoretical results.

Wavelet packets have been found a promising candidate for user signature waveforms in code division multiple access communication systems due to their good orthogonality property. In this paper we investigate the use of wavelet packets in multicarrier CDMA. In conventional multicarrier CDMA systems, the multi channel signals are modulated on sub carriers of sinusoid waveforms, using FFT to achieve fast computation. To combat multipath channel effect, a guard interval is inserted between consecutive data symbols. On the other hand, wavelet packet waveforms have the property of localization in both time and frequency domains. This property enables the time domain diversity of the multi carrier signal so that the need for guard intervals is eliminated by time domain diversity combining. Simulations are shown to compare the performance of the wavelet packet receiver design with the DFT based multi carrier CDMA system.

Traditional subspace methods (SS) for blind channel identification require accurate rank estimation with a computational complexity of O(m/sup 3/), where m is the data vector length. We introduce new adaptive subspace algorithms using ULV updating and successive cancellation techniques. In addition to reducing the computational complexity to O(m/sup 2/), the new algorithms do not need to estimate the subspace rank. Channel length can be overestimated during the subspace tracking and channel vector optimization steps. It can then be recovered at the end by a successive cancellation procedure. Simulation shows that the new algorithms outperform the traditional SS methods for the case that the subspace rank is difficult to estimate.

One significant difference between CDMA systems and FDMA/TDMA systems is that the number of users supported in a CDMA system is not hard-limited by the number of frequency bands or time slots. Whenever a new user is added to a CDMA system, the existing users will suffer a slightly higher interference level. The interference level highly depends on the orthogonality of user waveforms. We present an algorithm to construct a set of wavelet packet based user waveforms for CDMA systems. This algorithm provides more signature waveforms than previously proposed wavelet packet based waveform division systems. The basic waveform set is constructed utilizing double orthogonality based on wavelet packets and binary Walsh codes. This double orthogonality produces auto- and cross-correlations that are much better than the conventional wavelet packet sets and are comparable to the pseudo random binary codes. Under system overload, the waveforms introduce a tolerable interference level to the users.

In this paper we consider the blind multiuser detection problem for asynchronous DS-CDMA systems operating in a multipath environment. Using only the spreading code of the desired user, we first estimate the column vector subspace of the channel matrix by multiple linear prediction. Then zero-forcing detectors and MMSE detectors with arbitrary delay can be obtained without explicit channel estimation. This avoids any channel estimation error and the resulting methods are therefore more robust and more accurate. The new algorithms are robust against timing estimation errors, and are extremely near-far resistant. Simulations show the effectiveness of these methods.

A linearly constrained constant modulus algorithm (CMA) for CDMA detection and equalization is proposed in this paper. Multipath propagation is assumed and only the code sequence of a particular desired user is known. The new algorithm overcomes the shortcoming that the unconstrained CMA cannot lock to the desired user, and yields better performance compared to the existing constrained minimum output energy method.

We propose a blind MMSE equalization algorithm to compute fractionally spaced blind equalizers with arbitrary delay directly from second order statistics of the observations. The algorithm is based on multi-channel linear prediction. Performance analysis demonstrates that our algorithm has higher signal-to-interference-plus-noise-ratio (SINR) than its zero-forcing counter part. Simulations show that our algorithms outperform some typical existing algorithms.

Most eigenstructure-based blind channel identification and equalization algorithms with second-order statistics need SVD or EVD of the correlation matrix of the output signal. We show new algorithms based on QR factorization of the output data directly. A recursive algorithm is developed by updating a rank-revealing ULV decomposition. Compared with existing algorithms in the same category, our algorithm is computationally more efficient and numerically (potentially) more robust. The computation in each recursion of the recursive algorithm can be reduced to the order of O(m/sup 2/) under some simplifications, where m is the dimension of the received signal vector. Numerical simulations demonstrate the performance of the proposed algorithm.

Most shift operator-based adaptive algorithms exhibit poor numerical behavior when the input discrete time process is obtained from a continuous time process by fast sampling. This includes the shift operator based least squares lattice algorithm. In this paper, we develop a delta least squares lattice algorithm. This algorithm has low computational complexity compared to the delta Levinson RLS algorithm and shows better numerical properties compared to the shift least squares lattice algorithm. Computer simulations show that the new algorithm also outperforms an existing delta least squares lattice algorithm.

The problem of estimating continuous-time autoregressive process parameters from discrete-time data is considered. The basic approach used is based on replacing the derivatives in the model by discrete-time differences, forming a linear regression and using the least squares method. It is known, however, that all standard approximations of the highest order derivative give a biased least squares estimate even as the sampling interval tends to zero. Some of our previous approaches to overcome this problem are reviewed. Then two new methods are presented. One of them, termed bias compensation, can be easily implemented efficiently in an order recursive manner. Comparative simulation results are also presented.

Most filters, adaptive or not, formulated using the delay operator, have no limit when sampling becomes fast and therefore they will have numerical problems. We will show that one reason that the normalized lattice filter has less numerical problems is because that it has a limit as the sampling period tends to zero. The transfer function in the s-domain obtained as a limit of the normalized lattice filter will, however, have only every other power in the denominator polynomial. We propose a modified normalized lattice filter that can realize any arbitrary transfer function in the discrete (z) domain and its order-recursive limit, as the sampling period tends to zero, can realize any arbitrary transfer function in the s-domain. Various stability properties of the new lattice are also studied.

The use of gradient descent recursive algorithms in blind adaptive equalization requires a cost function with a unique minimum such that the FIR equalizer setup removes sufficient intersymbol interference (ISI). A cost function based on minimizing the difference between the second and the fourth norms of the joint channel-equalizer impulse response, each raised to the fourth power, i.e., /spl par//spl middot//spl par//sub 2//sup 4/-/spl par//spl middot//spl par//sub 4//sup 4/ is proposed. An implementable recursive on-line algorithm using the above cost function is also derived for QAM inputs. A sizable array of examples shows that the above class is unimodal in equalizer weights. Extensive simulations show that the performance of the newly proposed algorithms is comparable to the CMA algorithms' performance without the misconvergences.

The use of gradient descent recursive identification in blind adaptive equalization requires a cost function with a unique minimum so that the FIR equalizer setup is guaranteed to remove sufficient ISI. We propose such a cost function which satisfies this requirement. The newly proposed cost function is the difference between the largest (l/sub 1/) and the smallest (l/sub /spl infin//) norms of the joint channel and equalizer impulse response. The global convergence for arbitrary linear channels holds regardless of the initial ISI and the equalizer length (i.e. for finite length equalizers). A globally convergent blind recursive equalizer is obtained based on an implementable approximation of such a cost function. Computer simulations compare favorably with existing algorithms of a similar kind.

Long-distance telephone communications of good quality require absence of echoes. The physical characteristics of an echo path are time varying and the impulse responses measured at different times can differ from each other. This means that an echo canceller must track the changes. Using a small number of coefficients in Haar-wavelet-based models we can efficiently identify echo paths which have certain typical impulse response shapes. The modeling error energy obtained is low (less than 2%). A simple wavelet-based LMS adaptive filter can be used for on-line estimation of the coefficients. A low number of time-consuming computations is obtained per input sample due to the usage of Haar wavelets. This number is less than the ones obtained by the FIR of DFT domain based modeling.

It is shown how time-varying systems can be modeled in several different ways by discrete-time wavelets. Interpretation of physical meanings, possible efficiency and other characteristics of the modeling are considered. System identification minimizing the mean square output error is studied. Optimal coefficients and the corresponding minimum MS error are found and they are time-varying. A least-mean-square adaptive filtering algorithm is derived for on-line filtering and system identification. Theoretically and by simulations the advantages of using wavelet-based filtering are shown: separation of adaptation effects from unknown time-varying system behavior and fast convergence. Adaptive coefficients estimated by a recursive-least-square algorithm can tend towards constants.>