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This communication presents an efficient iterative approach for locating the near-field (NF) coherent sources. In each iteration, the covariance matrices only containing single source information are constructed by using alternating oblique projection. Then based on the principle of vector dot products, a new iterative direction-of-arrival (DOA) estimator is proposed. After the DOA of each separated signal is estimated, the paired ranges are obtained from the 1-D maximum likelihood (ML) estimator. The proposed algorithm avoids high-dimensional spectral searches, subspace extraction, and any preprocessing such as spatial smoothing, leading to low computational complexities and high estimation accuracy. Comparative simulations show the efficiency and merits of the proposed method. [ABSTRACT FROM AUTHOR]

As an extension of spatial modulation (SM) technology, generalized spatial modulation (GSM) is a promising technique for high data-rates systems. In GSM system, multiple antennas are activated in each time slot, which can greatly enhance the band efficiency. However, the usage of multiple active antennas for transmission also brings difficulties of the demodulation at the receiver. The computational complexity of the traditional maximum likelihood (ML) detector is closely related to the modulation order. Based on this point, we propose an ordered block hard-limited maximum likelihood (OB-HLML) algorithm for MQAM constellation, which is independent of modulation order. OB-HLML algorithm first sorts the possibility of transmit antenna combinations (TACs), and then detects the possible signal vectors of each ordered TAC by HLML detector. Simulation results show that the proposed OB-HLML algorithm can provide almost the same bit error rate (BER) performance as the ML algorithm with greatly reduced computational complexity. [ABSTRACT FROM AUTHOR]

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This paper proposes a new method to get explicit expressions of some quantities associated with performance analysis of the maximum likelihood DOA algorithm in the presence of an additive Gaussian noise on the antenna elements. The motivation of the paper is to make a quantitative analysis of the ML DOA algorithm in the case of multiple incident signals. We present a simple method to derive a closed-form expression of the MSE of the DOA estimate based on the Taylor series expansion. Based on the Taylor series expansion and approximation, we get explicit expressions of the MSEs of estimates of azimuth angles of all incident signals. The validity of the derived expressions is shown by comparing the analytic results with the simulation results. [ABSTRACT FROM AUTHOR]

For InSAR topographic mapping, multi-baseline InSAR height estimation is known to be an effective way to facilitate phase unwrapping by significantly increasing the ambiguity intervals and maintaining good height measurement sensitivity, especially in mountainous areas. In this paper, an efficient multi-baseline SAR interferometry approach based on maximum likelihood estimation is developed for refined topographic mapping in mountainous areas. In the algorithm, maximum likelihood (ML) height estimation is used to measure the topographic details and avoid the complicated phase unwrapping process. In order to be well-adapted to the mountainous terrain conditions, the prior height probability is re-defined to take the local terrain conditions and neighboring height constraint into consideration in the algorithm. In addition, three strategies are used to optimize the maximum likelihood height estimation process to obtain higher computational efficiency, so that this method is more suitable for spaceborne InSAR data. The strategies include substituting a rational function model into the complicated conversion process from candidate height to interferometric phase, discretizing the continuous height likelihood probability, and searching for the maximum likelihood height with a flexible step length. The experiment with simulated data is designed to verify the improvement of the ML height estimation accuracy with the re-defined prior height distribution. Then the optimized processing procedure is tested with the multi-baseline L-band ALOS/PALSAR data covering the Mount Tai area in China. The height accuracy of the generated multi-baseline InSAR DEM can meet both standards of American DTED-2 and Chinese national 1:50,000 DEM (mountain) Level 2. [ABSTRACT FROM AUTHOR]

A low-complexity MIMO detection scheme is presented that decomposes a MIMO channel into multiple decoupled subsets of streams that can be detected separately. The scheme employs QL decomposition followed by elementary matrix operations to transform the channel matrix into a generalized elementary structure matching the subsets of streams to be detected. The proposed scheme avoids matrix inversion operations, and allows subsets to overlap thus achieving better diversity gain. Simulations demonstrate that this approach performs to within a few tenths of a dB from the optimum detection algorithm. [ABSTRACT FROM AUTHOR]

This paper is concerned with the maximum-likelihood (ML) calibration methods tailored to the antenna arrays whose spatial responses are perturbed by mutual coupling effects and unknown sensor gain/phase responses. Unlike the existing work, the proposed methods are capable of jointly exploiting the spatial-domain information and time-domain waveform information of the calibration sources. Two kinds of numerical optimization algorithm are devised dependent on different array geometries. One is suitable for arbitrary irregular array manifold, while the other applies to some particular uniform arrays. Additionally, based on the maximum a posteriori probability (MAP) criterion, we extend the two algorithms to the scenario where the true values of the calibration source azimuths deviate slightly from the nominal ones with a priori known Gaussian distribution. The Cramér-Rao bound (CRB) expressions for the unknowns are derived in the absence and presence of the azimuth deviations, respectively. Simulation results support that the performances of the proposed algorithms are preferable to the ones which merely employs the spatial-domain information of the calibration sources, and are able to attain the corresponding CRB. [ABSTRACT FROM AUTHOR]

In this paper, theoretical lower bounds on performance of linear least-squares (LLS) position estimators are obtained, and performance differences between LLS and nonlinear least-squares (NLS) position estimators are quantified. In addition, two techniques are proposed in order to improve the performance of the LLS approach. First, a reference selection algorithm is proposed to optimally select the measurement that is used for linearizing the other measurements in an LLS estimator. Then, a maximum likelihood approach is proposed, which takes correlations between different measurements into account in order to reduce average position estimation errors. Simulations are performed to evaluate the theoretical limits and to compare performance of various LLS estimators. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A companding-based transformation method is introduced to quasi-maximum-likelihood (ML) detectors, such as the QR-decomposition-based M-algorithm (QRD-M) and list sphere decoding, for coded multiple-input–multiple-output (MIMO) systems in this paper. The key idea of the proposed companding technique is to compress (i.e., down-weight) the dubious observation of the accumulated branch metric by taking into account its statistical characteristics so that, after companding, the estimation error of the unreliable detected information bits due to insufficient candidate size and/or channel estimation error is significantly mitigated without disproportionate compromise of the reliable information bits. By employing the proposed companding method, the original leptokurtically distributed log-likelihood ratio of the detected information bits becomes more Gaussian distributed. As an illustrative example, the QRD-M detector is employed in this paper. Numerical results show that the QRD-M detector based on the proposed companding paradigm achieves significant performance gain over the conventional method and approaches the performance of the ML detector for a 16-ary quadrature-amplitude-modulated (16-QAM) \4 \times \4 MIMO multiplexing system with lower-than-linear-detector computational complexity. [ABSTRACT FROM PUBLISHER]

We derive a class of algorithms for independent component analysis (ICA) based on maximum likelihood (ML) estimation and perform stability analysis of natural gradient ML ICA with and without the constraint for unitary demixing matrix. In the process, we demonstrate how Wirtinger calculus facilitates derivations, and most importantly, performing second-order analysis in the complex domain and eliminates the need for making simplifying assumptions. We derive natural gradient complex ML ICA update rule and its variant with a unitary constraint, as well as a Newton algorithm for better convergence behavior. The conditions for local stability are derived and studied using a generalized Gaussian density (GGD) source model. When the sources are circular and non-Gaussian, we show analytically that both the ML and ML-unitary ICA update rules converge to the inverse of mixing matrix subject to a phase shift. When the sources are noncircular and non-Gaussian, we show that the nonunitary ML ICA update rule is more stable than the ML-unitary ICA update rule. When the sources are noncircular Gaussians, both update rules are stable only when the sources have distinct noncircularity indices. Simulation results are given to support these results. [ABSTRACT FROM AUTHOR]

In a MIMO multiple-access channel where users employ Space-Time Block Codes (STBC), interference cancellation can be used to suppress co-channel interference and recover the desired signal of each user at the receiver. Leveraging the special properties of Alamouti matrices, we first show that spatial multiplexing of Alamouti signals retains the space-time diversity gain of Alamouti signaling using our proposed low-complexity Alamouti BLAST-MMSE (A-BLAST) Algorithm. Next, in contrast to traditional transmit diversity that focuses on STBC construction at the transmitter, this paper looks at transmit diversity from the perspective of the receiver. In other words, the receiver gets to choose the STBC's, which are favourable to the channel assuming a fixed BLAST receive algorithm. In a multiuser MAC setting, we first present a systematic methodology to exploit different decomposition structure in Alamouti matrices, each with different tradeoff between performance and decoding complexity using possibly different MIMO receive algorithms. We then demonstrate that the notion of angles (the inner product of two quaternionic vectors) between multiuser channels determines the performance of MIMO receive algorithms. As an application of the general theory, we transform the decoding problem for several types of Quasi-Orthogonal STBC (QOSTBC) into multiuser detection of virtual Alamouti users. Building upon our A-BLAST Algorithm, we propose new algorithms for decoding single-user and multiuser QOSTBC. In particular, we show that bit error probability is a function of the quaternionic angle between virtual users (for a single user) or multiple users. This angle varies with the type of QOSTBC and leads to a new form of adaptive modulation called code diversity, where feedback instructs the transmitter how to choose from a plurality of codes. [ABSTRACT FROM AUTHOR]

Source-localization techniques are crucial in transportation applications such as navigation or Global Positioning Systems (GPS). A computationally efficient technique for multiple wideband source localization is presented in this paper using the expectation-maximization (EM) algorithm in the near field of a sensor array/area. Our basic idea is to decompose the recorded sensor data, which is a superimposition of multiple sources, into individual components in the frequency domain and then separately estimate the corresponding location parameters associated with each source. Instead of the conventional alternating projection (AP) method, we propose to adopt the EM algorithm in this paper; our method involves two steps, namely, Expectation (E-step) and Maximization (M-step). In the E-step, the individual incident source waveforms are estimated. Then, in the M-step, the maximum-likelihood (ML) estimates of the source-location parameters are obtained. These two steps are iteratively and alternatively executed until the user's predefined convergence is reached. The computational complexity comparison between our proposed EM algorithm and the existing AP scheme is also investigated. Provably, it is shown through Monte Carlo simulations that the computational complexity of the proposed EM algorithm is significantly lower than that of the conventional AP scheme. We also derive the Cramer-Rao lower bound (CRLB) for the source-location estimates. [ABSTRACT FROM AUTHOR]

The jamming robustness of frequency hopping (FH) systems with M-ary frequency shift keying (MFSK) modulation may be potentially neutralized by a follower partial-band jammer. In this paper, a maximum likelihood (ML)-based algorithm that uses a two-element array is proposed for joint follower jamming rejection and symbol detection in slow FHJMFSK systems over quasi-static flat Rayleigh fading channels. The algorithm is derived by treating both the received jamming components and the unknown data symbols as deterministic quantities to be jointly estimated in an integrated ML operation. In addition, an approximate expression for the symbol error rate (SER) of the proposed scheme is derived when BFSK signaling is employed in a jamming dominant scenario. Analytical and simulated results show that the proposed approach is able to remove jamming and outperform the conventional and sample matrix inversion (SMI)-based beam-formers in the presence of a follower partial-band jammer. [ABSTRACT FROM AUTHOR]

In this paper, we develop a maximum-likelihood (ML) spatio--temporal blind source separation (BSS) algorithm, where the temporal dependencies are explained by assuming that each source is an autoregressive (AR) process and the distribution of the associated independent identically distributed (i.i.d.) innovations process is described using a mixture of Gaussians. Unlike most ML methods, the proposed algorithm takes into account both spatial and temporal information, optimization is performed using the expectation-maximization (EM) method, the source model is adapted to maximize the likelihood, and the update equations have a simple, analytical form. The proposed method, which we refer to as autoregressive mixture of Gaussians (AR-MOG), outperforms nine other methods for artificial mixtures of real audio. We also show results for using AR-MOG to extract the fetal cardiac signal from real magnetocardiographic (MCG) data. [ABSTRACT FROM AUTHOR]

Sensor networks are widely used in monitoring and tracking a large number of objects. Without prior knowledge on the dynamics of object distribution, their density estimation could be learned in an adaptive manner to support effective sensor placement. After sensors observe the "current" locations of objects, the estimates of object distribution are updated with these new obser- vations through a recursive distributed expectation-maximization algorithm. Based on the real-time estimates of object distribution, an adaptive sensor placement algorithm could be designed to achieve stable and high accuracy in tracking mass objects. This paper constructs a Gaussian mixture model to characterize the mixture distribution of object locations and proposes a novel methodology to adaptively update sensor placement. Our simulation results demonstrate the effectiveness of the proposed algorithm for adaptive sensor placement and boundary estimation nfm~c nhipi~t~ [ABSTRACT FROM AUTHOR]

Parallel imaging is a powerful technique to speed up magnetic resonance (MR) image acquisition via multiple coils. Both the received signal of each coil and its sensitivity map, which describes its spatial response, are needed during reconstruction. Widely used schemes such as SENSE assume that sensitivity maps of the coils are noiseless while the only errors are in coil outputs. In practice, however, sensitivity maps are subject to a wide variety of errors. At first glance, sensitivity noise appears to result in an errors-in-variables problem of the kind that is typically solved using total least squares (TLSs). However, existing TLS algorithms are in general inappropriate for the specific type of block structure that arises in parallel imaging. In this paper, we take a maximum likelihood approach to the problem of parallel imaging in the presence of independent Gaussian sensitivity noise. This results in a quasi-quadratic objective function, which can be efficiently minimized. Experimental evidence suggests substantial gains over conventional SENSE, especially in nonideal imaging conditions like low signal-to-noise ratio (SNR), high g-factors and large acceleration, using sensitivity maps suffering from misalignment, ringing, and random noise. [ABSTRACT FROM AUTHOR]

In this paper, two novel algorithms for blind separation of noiseless instantaneous linear mixture of independent sources are presented. The proposed algorithms exploit non-Gaussianity of the independent sources by modeling their distribution using the Gaussian mixture model (GMM). The first proposed method is based on the maximum-likelihood (ML) estimator. According to this method, the sensors distribution parameters are estimated via the expectation-maximization (EM) algorithm for GMM parameter estimation and the separation matrix is estimated by applying nonorthogonal joint diagonalization of the estimated GMM covariance matrices. The second proposed method is also a ML-based approach. According to this method, the distribution parameters of the prewhitened sensors are estimated via the EM algorithm for GMM parameter estimation and a unitary separation matrix is estimated by applying orthogonal joint diagonaliization of the estimated GMM covariance matrices. It is shown that estimation of the sensors distribution parameters amounts to obtaining a tight lower bound on the log-likelihood of the separation matrix, and that the joint diagonalization of the estimated GMM covariance matrices amounts to maximization of the obtained tight lower bound. Simulations demonstrate that the proposed methods outperform state-of-the-art blind source separation techniques in terms of interference-to-signal ratio. [ABSTRACT FROM AUTHOR]

In this paper, a new channel-estimation algorithm based on maximum-likelihood (ML) algorithm for estimation and tracking of the multiple-input-multiple-output (MIMO) channels is presented. The ML algorithm presents the optimum estimation when the exact channel model is known. The derived channel-estimation algorithm is very efficient, with a computational complexity comparable to the least mean square and much lower than the recursive least squares and the Kalman algorithms. The proposed algorithm is analyzed, and the effect of the channel-tracking error is applied as a modifying component for the derived algorithm. The proposed algorithm is simulated for half- and full-rank flat-fading time-varying MIMO channels for the different values of fDT, Eb/No, and training lengths via Monte Carlo simulation technique. The minimum mean-square-error (mmse) joint detector is considered as the detection algorithm. The output of the mmse receiver is considered as the virtual training data in the blind mode of operation: the same as in the decision-directed algorithm. By various simulations, the bit error rate and the mse of tracking the proposed algorithm for different values of fDT, presenting the speed of channel variations, are evaluated and compared with the Kalman filtering approach. By simulating the proposed algorithm for different values of the training length, the minimum training length required for different channel conditions is extracted. [ABSTRACT FROM AUTHOR]

We present two procedures for validating track estimates obtained using the maximum-likelihood probabilistic data association (ML-PDA) algorithm. The ML-PDA, developed for very low observable (VLO) target tracking, always provides a track estimate that must then be tested for target existence by comparing the value of the log likelihood ratio (LLR) at the track estimate to a threshold. Using extreme value theory, we show that in the absence of a target the LLR at the track estimate obeys approximately a Gumbel distribution rather than the Gaussian distribution previously ascribed to it in the literature. The offline track validation procedure relies on extensive offline simulations to obtain a set of track validation thresholds that are then used by the tracking system. The real-time procedure uses the data set that produced the track estimate to also determine the track validation threshold. The performance of these two procedures is investigated through simulation of two active sonar tracking scenarios by comparing the false and true track acceptance probabilities. These techniques have potential for use in a broader class of maximum likelihood estimation problems with similar structure [ABSTRACT FROM PUBLISHER]

This paper applies a robust statistical scheme to the problem of unsupervised learning of high-dimensional data. We develop, analyze, and apply a new finite mixture model based on a generalization of the Dirichlet distribution. The generalized Dirichlet distribution has a more general covariance structure than the Dirichlet distribution and offers high flexibility and ease of use for the approximation of both symmetric and asymmetric distributions. We show that the mathematical properties of this distribution allow high-dimensional modeling without requiring dimensionality reduction and, thus, without a loss of information. This makes the generalized Dirichlet distribution more practical and useful. We propose a hybrid stochastic expectation maximization algorithm (HSEM) to estimate the parameters of the generalized Dirichlet mixture. The algorithm is called stochastic because it contains a step in which the data elements are assigned randomly to components in order to avoid convergence to a saddle point. The adjective "hybrid" is justified by the introduction of a Newton-Raphson step. Moreover, the HSEM algorithm autonomously selects the number of components by the introduction of an agglomerative term. The performance of our method is tested by the classification of several pattern-recognition data sets. The generalized Dirichlet mixture is also applied to the problems of image restoration, image object recognition and texture image database summarization for efficient retrieval. For the texture image summarization problem, results are reported for the Vistex texture image database from the MIT Media Lab. [ABSTRACT FROM AUTHOR]

A new multicarrier system, termed multisymbol encapsulated orthogonal frequency division multiplexing (MSE-OFDM), is proposed, in which one cyclic prefix (CP) is used for multiple OFDM symbols. The original motivation for the MSE-OFDM proposal is to reduce the redundancy due to the CP in static-channel environments. The authors then found that an alternative implementation of the system can be used to improve the robustness to frequency offset and reduce the peak-to-average power ratio (PAPR). Equalization and demodulation algorithms for the MSE-OFDM system are proposed. A new preamble and the corresponding frequency-offset and channel estimation techniques are studied for the MSE-OFDM system. Using the proposed MSE-OFDM preamble, the joint maximum likelihood (ML) estimation of the frequency offset and the channel impulse response (CIR) is investigated in this paper. Possible ways to reduce the joint estimation complexity, including exploitation of the preamble structure, approximation of the joint ML estimator, and fast Fourier transform (FFT) pruning, are discussed. The performance of the proposed MSE-OFDM systems and channel estimators are analyzed and verified through numerical simulations. An analysis on the bandwidth efficiency, the performance with frequency offset, and the PAPR of the MSE-OFDM system are also presented. [ABSTRACT FROM AUTHOR]

In this letter, we develop a family of approximate maximum-likelihood (ML) detectors for multiple-input multiple-output systems by relaxing the ML detection problem using constellation-specific polynomial constraints. The resulting constrained optimization problem is solved using a penalty function approach. Moreover, to escape from the local minima, which improves the detection performance, a differential equation algorithm using classical mechanics is proposed. Simulation results show that the polynomial constrained detector performs better than least-squares (LS) detector. [ABSTRACT FROM AUTHOR]

This work addresses the derivation of the phase difference-based maximum likelihood (ML) phase unwrapping algorithm. To this end, we derive the joint statistics of the phase differences on a two-dimensional grid for the multichannel case, where several scaled wrapped phase values are available. Subsequently, we determine and study the structure of the phase difference-based ML estimator and compare it to known phase unwrapping techniques. This work allows us to frame single and multichannel algorithms in a common formulation. Moreover, among the known single-channel phase difference-based procedures, we identify those attaining an ML solution. We also show that multichannel phase difference-based and, recently proposed, phase-based ML algorithms achieve equivalent solutions. [ABSTRACT FROM AUTHOR]

Multiple-input multiple-output (MIMO) techniques are a key enabling technology for high-rate wireless communications. This paper discusses two ASIC implementations of MIMO sphere decoders. The first ASIC attains maximum-likelihood performance with an average throughput of 73 Mb/s at a signal-to-noise ratio (SNR) of 20 dB; the second ASIC shows only a negligible bit-error-rate degradation and achieves a throughput of 170 Mb/s at the same SNR. The three key contributing factors to high throughput and low complexity are: depth-first tree traversal with radius reduction, implemented in a one-node-per-cycle architecture, the use of the l∞-instead of l²-norm, and, finally, the efficient implementation of the enumeration approach recently proposed in [1]. The resulting ASICs currently rank among the fastest reported MIMO detector implementations. [ABSTRACT FROM AUTHOR]

This study discusses the localization problem based on time delay and Doppler shift for a far-field scenario. The conventional location methods employ two steps that first extract intermediate parameters from the received signals and then determine the source position from the measured parameters. As opposed to the traditional two-step methods, the direct position determination (DPD) methods accomplish the localization in a single step without computing intermediate parameters. However, the DPD cost function often remains non-convex, thereby it will cost a high amount of computational resources to find the estimated position through traversal search. Weiss proposed a DPD estimator to mitigate the computational complexity via eigenvalue decomposition. Unfortunately, when the computational resources are rather limited, Weiss's method fails to satisfy the timeliness. To solve this problem, this paper develops a DPD estimator using expectation maximization (EM) algorithm based on time delay and Doppler shift. The proposed method starts from choosing the transmitter-receiver range vector as the hidden variable. Then, the cost function is separated and simplified via the hidden variable, accomplishing the transformation from the high dimensional nonlinear search problem into a few one dimensional search subproblems. Finally, the expressions of EM repetition are obtained through Laplace approximation. In addition, we derive the Cramér–Rao bound to evaluate the best localization performance in this paper. Simulation results confirm that, on the basis of guaranteeing high accuracy, the proposed algorithm makes a good compromise in localization performance and computational complexity. [ABSTRACT FROM AUTHOR]