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Passive position determination of the radiating source is widely applied in wireless sensor networks (WSN). Compared with traditional two-step methods, the direct position determination (DPD) methods estimate the position in a single step and exhibit conspicuous performance superiority at a low signal-to-noise ratio (SNR). Unfortunately, the computational load for the exhaustive maximum likelihood (ML) DPD search is unacceptable for real-time processing. Although the conventional gradient-based iterative DPD method has lower computational complexity, it easily converges to the local maxima. In this paper, for a stationary source, we aim to tackle the problem of DPD using Doppler frequency shifts in a computationally attractive way. The waveform of the transmitted signal is a priori knowledge, and the transmitted frequency offset exists as the nuisance parameter. Based on the expectation maximization (EM) concept, we propose an approximately globally-optimal iterative ML DPD algorithm. The proposed method regards the source-receiver vector as the unobserved latent variable, and then by exploiting the Laplace approximation technique and polar coordinate system, the original multi-dimensional grid search is transformed into multiple one-dimensional estimations for polar angle. To guarantee global optimality for the estimation of transmitted frequency offsets, a method different from its counterpart in the iterative cycle is utilized when the local maxima are achieved. Moreover, by correctly simplifying the estimation cost function in the expectation step, both the search of the polar angle and the design of the iteration parameter is avoided, which contributes to prominent computational savings. Simulation results demonstrate that the proposed modified EM-based DPD estimator (MEM-DPD) has high probability of converging to the global maximum. Meanwhile, the estimation performance is very close to the Cramér-Rao lower bound (CRLB) with minor iterations. IEEE

Target localization is widely applied in wireless sensor networks (WSNs). In this article, we tackle the problem of position estimation for multiple stationary targets using Doppler frequency shifts and angles by moving sensor arrays. The computational load for the exhaustive maximum likelihood (ML) direct position determination (DPD) search is insufferable. Based on Pincus' theorem and the importance sampling (IS) concept, we propose a novel noniterative ML DPD method, and the circular mean is used for superior position estimation performance; meanwhile, a concrete criterion is also proposed for the choice of the importance function. The computational complexity of the proposed method is modest, and the off-grid problem that most existing DPD techniques face is significantly alleviated. Moreover, it can be implemented in parallel separately. In addition, we present the closed-form Cramer-Rao lower bound (CRLB) for the DPD with moving sensor arrays and stationary targets. Simulation results demonstrate that the proposed ML DPD estimator can achieve better estimation accuracy than state-of-the-art DPD techniques. With a reasonable parameter choice, the estimation performance of the proposed technique is very close to the CRLB, even in the adverse conditions of low signal-to-noise ratio (SNR) levels.

We consider Global Navigation Satellite Systems (GNSS) spoofing attacks and devise a countermeasure appropriate for mobile GNSS receivers. Our approach is to design detectors that, operating after the signal acquisition, enable the victim receiver to determine with high probability whether it is under a spoofing attack or not. Namely, the binary hypothesis is that either the GNSS receiver tracks legitimate satellite signals, H-0, or spoofed signals, H-1. We assume that there exists an unknown number of multiple spoofers in the environment and the attack strategy (which legitimate signals are spoofed by which spoofers) is not known to the receiver. Based on these assumptions, we propose an algorithm that identifies the number of spoofers and clusters the spoofing data by using Bayesian information criterion (BIC) rule. Depending on the estimated and clustered data we propose a detector, called as generalized likelihood ratio (GLRT)-like detector. We compare the performance of the GLRT-like detector with a genie-aided detector in which the attack strategy and the number of spoofers is known by the receiver. In addition to this, we extend the GLRT-like detector for the case where the noise variance is also unknown and present the performance results., QC 20220110

We study the problem of estimating the overall mutual information in M independent parallel discrete-time memory-less Gaussian channels from N independent data sample pairs per channel (inputs and outputs). We focus on the case where the number of active channels L is sparse in comparison with the total number of channels (L ≪ M), for which the direct application of the maximum likelihood principle is problematic due to overfitting, especially for moderate to small N. For this regime, we show that the bias of the mutual information estimate is reduced by resorting to the minimum description length (MDL) principle. As a result, simple pre-processing based on a per-channel threshold on the empirical squared correlation coefficient is required with a fixed threshold that monotonically decreases with N as 1 - N -1/N , for N ≥ 4. The resulting improvement is shown in terms of the estimated information bias., This work is supported by projects TEC2016-76409-C2-1-R (WINTER), Ministerio de Economia y Competividad, Spanish National Research Plan, and 2017 SGR 578 - AGAUR, Catalan Government., Peer Reviewed, Postprint (author's final draft)

Dans ce mémoire, nous abordons le problème de l’estimation conjointe des angles d’arrivée (AA) et des délais de propagation (ECAD) dans une configuration NDA (de l’anglais "Non Data Aided") où le signal transmis est inconnu à la réception. On applique le maximum de vraisemblance (MV) connu pour atteindre la meilleure performance possible. On commence par la dérivation de la fonction de vraisemblance logarithmique (FVL) dans un scénario totalement autodidacte. La technique d’échantillonnage préférentiel (EP) est utilisée pour réduire la multidimensionnalité du problème de maximisation sans recourir à une solution itérative. De plus, grâce à une approximation très utile, on est capable de réduire la complexité de la technique d’échantillonnage préférentiel originale sans aucune dégradation significative des performances. Les simulations montrent l’avantage indéniable de la nouvelle technique MV EP par rapport aux techniques ECAD existantes et la borne inférieure de Cramèr-Rao (BICR) pour des niveaux moyens et élevés du rapport signal sur bruit (RSB). In this work, we tackle the problem of joint angles and delays estimation (JADE) in a non-data aided (NDA) configuration where the transmitted signal is unknown at the receiver. We do so by applying the maximum likelihood (ML) in order to obtain the best performance achievable. To that end, we derive the log-likelihood function (LLF) in a totally blind scenario. The importance sampling (IS) technique is used to reduce the multi-dimensionality of the maximization problem without recurring to an iterative approach. Moreover, owing to a very useful approximation, we are able to further reduce the complexity of the original importance sampling technique without any significant performance degradation. Computer simulations show that the new ML IS technique approaches in estimation performance the DA techniques and the Cramér-Rao lower bound (CRLB) at medium and high SNR levels.

The problem of direction-of-arrival (DOA) estimation is important in array signal processing. To estimate the DOAs of uncorrelated, partly correlated and coherent signals, a new iterative DOA estimation algorithm, named AOP-DOA, is proposed by using alternating oblique projection (AOP). In each iteration, the oblique projection approach is employed to separate the received signals, then the DOA of each separated signal is estimated one after another. After theoretical analysis on the relationship between the proposed AOP-DOA and the conventional alternating projection based maximum likelihood estimator (AP-MLE), an AOP&AP-DOA algorithm, which is a combination of AOP-DOA and AP-MLE, is developed to reduce the computational complexity of AOP-DOA. Extensive experiments validate the effectiveness and complexity of the proposed two algorithms. Particularly, AOP&AP-DOA keeps the merits of AOP-DOA, but exhibits superiority over AOP-DOA in terms of computational complexity when proper adaptive grid refinement strategy is applied.

The problem of direction-of-arrival (DOA) estimation is important in array signal processing. To estimate the DOAs of uncorrelated, partly correlated and coherent signals, a new iterative DOA estimation algorithm, named AOP-DOA, is proposed by using alternating oblique projection (AOP). In each iteration, the oblique projection approach is employed to separate the received signals, then the DOA of each separated signal is estimated one after another. After theoretical analysis on the relationship between the proposed AOP-DOA and the conventional alternating projection based maximum likelihood estimator (AP-MLE), an AOP&AP-DOA algorithm, which is a combination of AOP-DOA and AP-MLE, is developed to reduce the computational complexity of AOP-DOA. Extensive experiments validate the effectiveness and complexity of the proposed two algorithms. Particularly, AOP&AP-DOA keeps the merits of AOP-DOA, but exhibits superiority over AOP-DOA in terms of computational complexity when proper adaptive grid refinement strategy is applied.

The problem of direction-of-arrival (DOA) estimation is important in array signal processing. To estimate the DOAs of uncorrelated, partly correlated and coherent signals, a new iterative DOA estimation algorithm, named AOP-DOA, is proposed by using alternating oblique projection (AOP). In each iteration, the oblique projection approach is employed to separate the received signals, then the DOA of each separated signal is estimated one after another. After theoretical analysis on the relationship between the proposed AOP-DOA and the conventional alternating projection based maximum likelihood estimator (AP-MLE), an AOP&AP-DOA algorithm, which is a combination of AOP-DOA and AP-MLE, is developed to reduce the computational complexity of AOP-DOA. Extensive experiments validate the effectiveness and complexity of the proposed two algorithms. Particularly, AOP&AP-DOA keeps the merits of AOP-DOA, but exhibits superiority over AOP-DOA in terms of computational complexity when proper adaptive grid refinement strategy is applied.

Separation of savanna land cover components is challenging due to the high heterogeneity of this landscape and spectral similarity of compositionally different vegetation types. In this study, we tested the usability of very high spatial and spectral resolution WorldView-2 (WV-2) imagery to classify land cover components of African savanna in wet and dry season. We compared the performance of Object-Based Image Analysis (OBIA) and pixel-based approach with several algorithms: k-nearest neighbor (k-NN), maximum likelihood (ML), random forests (RF), classification and regression trees (CART) and support vector machines (SVM). Results showed that classifications of WV-2 imagery produce high accuracy results (> 77%) regardless of the applied classification approach. However, OBIA had a significantly higher accuracy for almost every classifier with the highest overall accuracy score of 93%. Amongst tested classifiers, SVM and RF provided highest accuracies. Overall classifications of the wet season image provided better results with 93% for RF. However, considering woody leaf-off conditions, the dry season classification also performed well with overall accuracy of 83% (SVM) and high producer accuracy for the tree cover (91%). Our findings demonstrate the potential of imagery like WorldView-2 with OBIA and advanced supervised machine-learning algorithms in seasonal fine-scale land cover classification of African savanna., SCOPUS: ar.j, info:eu-repo/semantics/published

We consider carrier frequency offset (CFO) estimation in the context of multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems over noisy frequency-selective wireless channels with both single- and multiuser scenarios. We conceived a new approach for parameter estimation by discretizing the continuous-valued CFO parameter into a discrete set of bins and then invoked detection theory, analogous to the minimum-bit-error-ratio optimization framework for detecting the finite-alphabet received signal. Using this radical approach, we propose a novel CFO estimation method and study its performance using both analytical results and Monte Carlo simulations. We obtain expressions for the variance of the CFO estimation error and the resultant BER degradation with the single- user scenario. Our simulations demonstrate that the overall BER performance of a MIMO-OFDM system using the proposed method is substantially improved for all the modulation schemes considered, albeit this is achieved at increased complexity., QC 20150817

We propose an expectation maximization (EM) strategy for improving the precision of time of flight (ToF) light detection and ranging (LiDAR) scanners. The novel algorithm statistically accounts not only for the bias induced by temperature changes in the laser diode, but also for the multi-modality of the measurement noises that is induced by mode-hopping effects. Instrumental to the proposed EM algorithm, we also describe a general thermal dynamics model that can be learned either from just input-output data or from a combination of simple temperature experiments and information from the laser’s datasheet. We test the strategy on a SICK LMS 200 device and improve its average absolute error by a factor of three., Funding Agency:DISIRENorrbottens Forskningsrad

We propose an expectation maximization (EM) strategy for improving the precision of time of flight (ToF) light detection and ranging (LiDAR) scanners. The novel algorithm statistically accounts not only for the bias induced by temperature changes in the laser diode, but also for the multi-modality of the measurement noises that is induced by mode-hopping effects. Instrumental to the proposed EM algorithm, we also describe a general thermal dynamics model that can be learned either from just input-output data or from a combination of simple temperature experiments and information from the laser’s datasheet. We test the strategy on a SICK LMS 200 device and improve its average absolute error by a factor of three., Funding Agency:DISIRENorrbottens Forskningsrad

This paper provides a quantitative analysis of the optimal accuracy and resolution in inverse imaging based on the Cram ́er-Rao lower bound. The imaging problem is characterized by the forward operator and its Jacobian. The Fisher information operator is defined for a deterministic parameter in a real Hilbert space and a stochastic measurement in a finite-dimensional complex Hilbert space with Gaussian measure. The connection between the Fisher information and the Singular Value Decomposition (SVD) based on the Maximum Likelihood (ML) criterion (the ML-based SVD) is established. It is shown that the eigenspaces of the Fisher information provide a suitable basis to quantify the trade-off between the accuracy and the resolution of the (non-linear) inverse problem. It is also shown that the truncated ML-based pseudo-inverse is a suitable regularization strategy for a linearized problem, which exploits a sufficient statistics for estimation within these subspaces. The statistical-based Cram ́er-Rao lower bound provides a complement to the deterministic upper bounds and the L-curve techniques that are employed with linearized inversion. To this end, the Electrical Impedance Tomography (EIT) provides an interesting example where the eigenvalues of the SVD usually do not exhibit a very sharp cut-off, and a trade-off between the accuracy and the resolution may be of practical importance. A numerical study of EIT is described, including a statistical analysis of the model errors due to the linearization.

This paper investigates the validity of the random-volume-over-ground (RVoG) scattering model assumption for forest scattering on polarimetric interferometric synthetic aperture radar (PolInSAR) data. The model makes some assumptions about the data and the structure of coherency matrices, namely, the equality of the polarimetric covariance matrices and the affine equivalence of the contracted polarimetric interferometric covariance matrix with a Hermitian matrix. The proposed methodology is divided into two main steps. First, invertible affine transforms (ATs) are studied and proposed as a tool to operate with coherence regions. Based on this analysis, the concept of the trace matrix is introduced as its rank depends on the RVoG model assumption validity. Then, with the objective to consider the effects of speckle noise, we consider a maximum-likelihood (ML) framework, on the hypothesis of data distributed according to the complex Gaussian distribution. Hence, we define the ML estimator (MLE) of the PolInSAR coherency matrix according to the RVoG model assumption and the generalized likelihood ratio test of the model. The validity tests and the MLE are analyzed in terms of simulated and real PolInSAR data, considering P-band and L-band data over tropical and boreal forests., Peer Reviewed, Postprint (published version)

Entomophthoromycota is one of six major phylogenetic lineages among the former phylum Zygomycota. These early terrestrial fungi share evolutionarily ancestral characters such as coenocytic mycelium and gametangiogamy as a sexual process resulting in zygospore formation. Previous molecular studies have shown the monophyly of Entomophthoromycota, thus justifying raising the taxonomic status of these fungi to a phylum. Multi-gene phylogenies have identified five major lineages of Entomophthoromycota. In this review we provide a detailed discussion about the biology and taxonomy of these lineages: I) Basidiobolus (Basidiobolomycetes: Basidiobolaceae; primarily saprobic); II) Conidiobolus (Entomophthoromycetes, Ancylistaceae; several clades of saprobes and invertebrate pathogens), as well as three rapidly evolving entomopathogenic lineages in the family Entomophthoraceae centering around; III) Batkoa; IV) Entomophthora and allied genera; and V) the subfamily Erynioideae which includes Zoophthora and allied genera. Molecular phylogenic analysis has recently determined the relationships of several taxa that were previously unresolved based on morphology alone: Eryniopsis, Macrobiotophthora, Massospora, Strongwellsea and two as yet undescribed genera of Basidiobolaceae.

Entomophthoromycota is one of six major phylogenetic lineages among the former phylum Zygomycota. These early terrestrial fungi share evolutionarily ancestral characters such as coenocytic mycelium and gametangiogamy as a sexual process resulting in zygospore formation. Previous molecular studies have shown the monophyly of Entomophthoromycota, thus justifying raising the taxonomic status of these fungi to a phylum. Multi-gene phylogenies have identified five major lineages of Entomophthoromycota. In this review we provide a detailed discussion about the biology and taxonomy of these lineages: I) Basidiobolus (Basidiobolomycetes: Basidiobolaceae; primarily saprobic); II) Conidiobolus (Entomophthoromycetes, Ancylistaceae; several clades of saprobes and invertebrate pathogens), as well as three rapidly evolving entomopathogenic lineages in the family Entomophthoraceae centering around; III) Batkoa; IV) Entomophthora and allied genera; and V) the subfamily Erynioideae which includes Zoophthora and allied genera. Molecular phylogenic analysis has recently determined the relationships of several taxa that were previously unresolved based on morphology alone: Eryniopsis, Macrobiotophthora, Massospora, Strongwellsea and two as yet undescribed genera of Basidiobolaceae.

A new transform domain array signal processing technique is proposed for identification of multipath communication channels. The received array element outputs are transformed to delay-Doppler domain by using the cross-ambiguity function (CAF) for efficient exploitation of the delay-Doppler diversity of the multipath components. Clusters of multipath components can be identified by using a simple amplitude thresholding in the delay-Doppler domain. Particle swarm optimization (PSO) can be used to identify parameters of the multipath components in each cluster. The performance of the proposed PSO-CAF technique is compared with the space alternating generalized expectation maximization (SAGE) technique and with a recently proposed PSO based technique at various SNR levels. Simulation results clearly quantify the superior performance of the PSO-CAF technique over the alternative techniques at all practically significant SNR levels.

A data-aided frequency offset estimation algorithm suited for burst-mode communications is described and analyzed. The proposed algorithm is based on a Pilot symbol assisted modulation (PSAM) frame structure, which consists of regularly spaced pilot symbols and a group of consecutive pilot symbols embedded in. Firstly, an initial estimate based on consecutive pilot symbols is generated for reducing the phase ambiguity. Secondly, based on both spaced and consecutive pilot symbols, fine preliminary estimates are generated to improve estimation accuracy. Lastly, the final estimate is obtained by combining the fine preliminary estimates with the initial. This three-step algorithm is shown to provide high estimation accuracy close to those of FFT-based algorithm at low Signal-to-noise ratio (SNR). In addition, it is suitable for high carrier frequency offset cases.

We consider frame synchronization for fixed frame length communication system with periodically embedded sync words. In the past few decades, numerous researchers have made significant contributions in various aspects. However, it is almost surprising that a scrutinization over the literature reveals to us an important common assumption: frame synchronization decision-making through single-frame observation. Intuition tells us that an enlarged multiple-frame observation set bears the potential to reduce sync word identification ambiguities and also to combat multi-path fading through "frame diversity". The objective of this work is thus to characterize the performance improvement of using multiple-frame decision rules rather than the single-frame decision rules considered in the previous work. We select the work of Lui and Tan as our standing point, and have conducted a comparative study in a very comprehensive manner. In particular, we shall show that the benefits of multiple-frame based decision rules can be substantial. For example, in many cases, the performance of the correlation rule based on double-frame observation is superior to that of the optimum maximum likelihood rule with single-frame observation. While further enlarging the observation period beyond double frames was found to yield diminishing returns.

The received signal strength (RSS)-based approach to wireless localization offers the advantage of low cost and easy implementability. To circumvent the nonconvexity of the conventional maximum likelihood (ML) estimator, in this paper, we propose convex estimators specifically for the RSS-based localization problems. Both noncooperative and cooperative schemes are considered. We start with the noncooperative RSS-based localization problem and derive a nonconvex estimator that approximates the ML estimator but has no logarithm in the residual. Next, we apply the semidefinite relaxation technique to the derived nonconvex estimator and develop a convex estimator. To further improve the estimation performance, we append the ML estimator to the convex estimator with the result by the convex estimator as the initial point. We then extend these techniques to the cooperative localization problem. The corresponding Cramer-Rao lower bounds (CRLB) are derived as performance benchmarks. Our proposed convex estimators comply well with the RSS measurement model, and simulation results clearly demonstrate their superior performance for RSS-based wireless localization.

A data-aided frequency offset estimation algorithm suited for burst-mode communications is described and analyzed. The proposed algorithm is based on a Pilot symbol assisted modulation (PSAM) frame structure, which consists of regularly spaced pilot symbols and a group of consecutive pilot symbols embedded in. Firstly, an initial estimate based on consecutive pilot symbols is generated for reducing the phase ambiguity. Secondly, based on both spaced and consecutive pilot symbols, fine preliminary estimates are generated to improve estimation accuracy. Lastly, the final estimate is obtained by combining the fine preliminary estimates with the initial. This three-step algorithm is shown to provide high estimation accuracy close to those of FFT-based algorithm at low Signal-to-noise ratio (SNR). In addition, it is suitable for high carrier frequency offset cases.

We consider frame synchronization for fixed frame length communication system with periodically embedded sync words. In the past few decades, numerous researchers have made significant contributions in various aspects. However, it is almost surprising that a scrutinization over the literature reveals to us an important common assumption: frame synchronization decision-making through single-frame observation. Intuition tells us that an enlarged multiple-frame observation set bears the potential to reduce sync word identification ambiguities and also to combat multi-path fading through "frame diversity". The objective of this work is thus to characterize the performance improvement of using multiple-frame decision rules rather than the single-frame decision rules considered in the previous work. We select the work of Lui and Tan as our standing point, and have conducted a comparative study in a very comprehensive manner. In particular, we shall show that the benefits of multiple-frame based decision rules can be substantial. For example, in many cases, the performance of the correlation rule based on double-frame observation is superior to that of the optimum maximum likelihood rule with single-frame observation. While further enlarging the observation period beyond double frames was found to yield diminishing returns.

The received signal strength (RSS)-based approach to wireless localization offers the advantage of low cost and easy implementability. To circumvent the nonconvexity of the conventional maximum likelihood (ML) estimator, in this paper, we propose convex estimators specifically for the RSS-based localization problems. Both noncooperative and cooperative schemes are considered. We start with the noncooperative RSS-based localization problem and derive a nonconvex estimator that approximates the ML estimator but has no logarithm in the residual. Next, we apply the semidefinite relaxation technique to the derived nonconvex estimator and develop a convex estimator. To further improve the estimation performance, we append the ML estimator to the convex estimator with the result by the convex estimator as the initial point. We then extend these techniques to the cooperative localization problem. The corresponding Cramer-Rao lower bounds (CRLB) are derived as performance benchmarks. Our proposed convex estimators comply well with the RSS measurement model, and simulation results clearly demonstrate their superior performance for RSS-based wireless localization.

The received signal strength (RSS)-based approach to wireless localization offers the advantage of low cost and easy implementability. To circumvent the nonconvexity of the conventional maximum likelihood (ML) estimator, in this paper, we propose convex estimators specifically for the RSS-based localization problems. Both noncooperative and cooperative schemes are considered. We start with the noncooperative RSS-based localization problem and derive a nonconvex estimator that approximates the ML estimator but has no logarithm in the residual. Next, we apply the semidefinite relaxation technique to the derived nonconvex estimator and develop a convex estimator. To further improve the estimation performance, we append the ML estimator to the convex estimator with the result by the convex estimator as the initial point. We then extend these techniques to the cooperative localization problem. The corresponding Cramer-Rao lower bounds (CRLB) are derived as performance benchmarks. Our proposed convex estimators comply well with the RSS measurement model, and simulation results clearly demonstrate their superior performance for RSS-based wireless localization.

A data-aided frequency offset estimation algorithm suited for burst-mode communications is described and analyzed. The proposed algorithm is based on a Pilot symbol assisted modulation (PSAM) frame structure, which consists of regularly spaced pilot symbols and a group of consecutive pilot symbols embedded in. Firstly, an initial estimate based on consecutive pilot symbols is generated for reducing the phase ambiguity. Secondly, based on both spaced and consecutive pilot symbols, fine preliminary estimates are generated to improve estimation accuracy. Lastly, the final estimate is obtained by combining the fine preliminary estimates with the initial. This three-step algorithm is shown to provide high estimation accuracy close to those of FFT-based algorithm at low Signal-to-noise ratio (SNR). In addition, it is suitable for high carrier frequency offset cases.

We consider frame synchronization for fixed frame length communication system with periodically embedded sync words. In the past few decades, numerous researchers have made significant contributions in various aspects. However, it is almost surprising that a scrutinization over the literature reveals to us an important common assumption: frame synchronization decision-making through single-frame observation. Intuition tells us that an enlarged multiple-frame observation set bears the potential to reduce sync word identification ambiguities and also to combat multi-path fading through "frame diversity". The objective of this work is thus to characterize the performance improvement of using multiple-frame decision rules rather than the single-frame decision rules considered in the previous work. We select the work of Lui and Tan as our standing point, and have conducted a comparative study in a very comprehensive manner. In particular, we shall show that the benefits of multiple-frame based decision rules can be substantial. For example, in many cases, the performance of the correlation rule based on double-frame observation is superior to that of the optimum maximum likelihood rule with single-frame observation. While further enlarging the observation period beyond double frames was found to yield diminishing returns.

Wireless localization has drawn significant attention over the past decade and the received signal strength (RSS) based localization scheme provides a low-cost, low-complexity and easy-implementation solution. When the statistics of the RSS measurement error is known, the Maximum Likelihood (ML) estimator is asymptotically optimal. However, due to the nature of the localization problem itself, the formed ML estimator is nonconvex, causing the search for the global minimum very difficult. In addition, its performance highly depends on the initial point provided if a local optimization method is applied to find the solution. To circumvent this problem, we apply the Semidefinite Programming (SDP) relaxation technique to the RSS-based localization problem. After reformulation and relaxation, we finally form a convex SDP estimator. A superior property of a convex estimator is that the solution is not affected by the initial point provided since any local minimum is also its global minimum. The Cramer-Rao Lower Bound (CRLB) is then derived as a benchmark for the performance comparison. Simulation results show that the proposed SDP estimator exhibit excellent performance in the RSS-based localization system and it is very suitable for the case when there are only very limited base stations hearable.

Wireless localization has drawn significant attention over the past decade and the received signal strength (RSS) based localization scheme provides a low-cost, low-complexity and easy-implementation solution. When the statistics of the RSS measurement error is known, the Maximum Likelihood (ML) estimator is asymptotically optimal. However, due to the nature of the localization problem itself, the formed ML estimator is nonconvex, causing the search for the global minimum very difficult. In addition, its performance highly depends on the initial point provided if a local optimization method is applied to find the solution. To circumvent this problem, we apply the Semidefinite Programming (SDP) relaxation technique to the RSS-based localization problem. After reformulation and relaxation, we finally form a convex SDP estimator. A superior property of a convex estimator is that the solution is not affected by the initial point provided since any local minimum is also its global minimum. The Cramer-Rao Lower Bound (CRLB) is then derived as a benchmark for the performance comparison. Simulation results show that the proposed SDP estimator exhibit excellent performance in the RSS-based localization system and it is very suitable for the case when there are only very limited base stations hearable.

This paper describes an online maximum likelihood estimator for the transition probabilities associated with a jump Markov linear system (JMLS). The maximum likelihood estimator is derived using the reference probability method, which exploits an hypothetical probability measure to find recursions for complex expectations. Expectation maximization (EM) procedure is utilized for maximizing the likelihood function. In order to avoid the exponential increase in the number of statistics of the optimal EM algorithm, we make interacting multiple model (IMM)-type approximations. The resulting method needs the mode weights of an IMM filter with N3 components, where N is the number of models in the JMLS. The algorithm can also supply base-state estimates and covariances as a by-product. The performance of the estimator is illustrated on two simulated examples and compared to a recently proposed alternative., © 2008 IEEE.

To address the variation of noise level in non-stationary noise signals, we study the noise gain estimation for speech enhancement using hidden Markov models (HMM). We consider the noise gain as a stochastic process and we approximate the probability density function (PDF) to be log-normal distributed. The PDF parameters are estimated for every signal block using the past noisy signal blocks. The approximated PDF is then used in a Bayesian speech estimator minimizing the Bayes risk for a novel cost function, that allows for an adjustable level of residual noise. As a more computationally efficient alternative, we also derive the maximum likelihood (ML) estimator, assuming the noise gain to be a deterministic parameter. The performance of the proposed gain-adaptive methods are evaluated and compared to two reference methods. The experimental results show significant improvement under noise conditions with time-varying noise energy., QC 20110711

This dissertation is devoted to the design and analysis of synchronization and channel parameter estimation schemes in wireless communications. Intrigued by the observation that the information is conveyed through wireless channels by uniformly spaced pulses that are some kind of "distorted" convolution of data symbols and a shaping pulse, we try to set up a framework to study synchronization and channel parameter estimation problems in the frequency domain. The dissertation consists of four major parts. Many issues in digital communications and signal processing involve the analysis of the inverse of Toeplitz matrices. In the first part, the convergence of the inverse of Toeplitz matrices and its application are presented. Under the condition that the $z$-transform of the sequence with which the Toeplitz matrices are associated has no zero on the unit circle, we show that the inverse converges to a circular matrix in the weak sense. Furthermore, for the finite boundary quadratic form, a sufficient condition under which the convergence can be strengthened into the strong sense and an upper bound of the approximation residue error are derived. It is well known that a circular matrix can be eigendecomposed by the discrete Fourier transform (DFT) which provides the desired frequency domain approach. In practical systems, synchronization parameters such as timing and carrier phase offsets, and channel response in fading channels are acquired with the help of a training sequence (TS) that is known to the receiver, which is called the data-aided (DA) estimation. In the second part, the performance limit that is the Cramer-Rao lower Bound (CRB) for the DA joint carrier phase and timing offsets estimation with an arbitrary TS is derived using the properties of Toeplitz matrices. Unlike the CRB derived in the literature, the bound derived in this dissertation reveals the close relation between a TS and its resultant performance limit, therefore it provides a quantitati

This dissertation is devoted to the design and analysis of synchronization and channel parameter estimation schemes in wireless communications. Intrigued by the observation that the information is conveyed through wireless channels by uniformly spaced pulses that are some kind of "distorted" convolution of data symbols and a shaping pulse, we try to set up a framework to study synchronization and channel parameter estimation problems in the frequency domain. The dissertation consists of four major parts. Many issues in digital communications and signal processing involve the analysis of the inverse of Toeplitz matrices. In the first part, the convergence of the inverse of Toeplitz matrices and its application are presented. Under the condition that the $z$-transform of the sequence with which the Toeplitz matrices are associated has no zero on the unit circle, we show that the inverse converges to a circular matrix in the weak sense. Furthermore, for the finite boundary quadratic form, a sufficient condition under which the convergence can be strengthened into the strong sense and an upper bound of the approximation residue error are derived. It is well known that a circular matrix can be eigendecomposed by the discrete Fourier transform (DFT) which provides the desired frequency domain approach. In practical systems, synchronization parameters such as timing and carrier phase offsets, and channel response in fading channels are acquired with the help of a training sequence (TS) that is known to the receiver, which is called the data-aided (DA) estimation. In the second part, the performance limit that is the Cramer-Rao lower Bound (CRB) for the DA joint carrier phase and timing offsets estimation with an arbitrary TS is derived using the properties of Toeplitz matrices. Unlike the CRB derived in the literature, the bound derived in this dissertation reveals the close relation between a TS and its resultant performance limit, therefore it provides a quantitati

A high performance Universal Modem ASIC that supports several modulation types and burst mode frame formats is under development. The ASIC is designed to work under stringent conditions such as large carrier frequency offset (up to 13 percent symbol rate) and low signal-to-noise ratio (SNR). Powerful and generic data-aided (DA) parameter estimators are necessary to accommodate many modes. In this paper we present an approximated maximum likelihood (ML) carrier frequency offset estimator, ML joint carrier phase and timing offsets estimator and their systolic VLSI implementations for PSK burst modems. The performances are close to the Cramer-Rao lower bounds (CRLB) at low SNRs. Compared with theoretical solutions the estimators proposed here are much simpler and easier to implement by the current VLSI technology.VTC'99

A high performance Universal Modem ASIC that supports several modulation types and burst mode frame formats is under development. Powerful and generic data-aided (DA) parameter estimators are necessary to accommodate many modes. In this paper we present an approximated maximum likelihood (ML) carrier frequency offset estimator, ML joint carrier phase and timing offsets estimator and their systolic VLSI implementations for PSK burst modems. The performances are close to the Cramer-Rao lower bounds (CRLB) at low SNRs. Compared with theoretical solutions, the estimators proposed here are much simpler and easier to implement by the current VLSI technology. The CRLB for DA estimations is discussed in some depth, some issues on training sequence design is also addressed in this work.Globecomm99