Abstract The design of minimum phase finite impulse response (FIR) filters is considered. The study demonstrates that the residual errors achieved by current state‐of‐the‐art design methods are nowhere near the smallest error possible on a finite resolution digital computer. This is shown to be due to conceptual errors in the literature pertaining to what constitutes a factorable linear phase filter. This study shows that factorisation is possible with a zero residual error (in the absence of machine finite resolution error) if the linear operator or matrix representing the linear phase filter is positive definite . Methodology is proposed able to design a minimum phase filter that is optimal—in the sense that the residual error is limited only by the finite precision of the digital computer, with no systematic error. The study presents practical application of the proposed methodology by designing two minimum phase Chebyshev FIR filters. Results are compared to state‐of‐the‐art methods from the literature, and it is shown that the proposed methodology is able to reduce currently achievable residual errors by several orders of magnitude.

In this letter, we demonstrate the utility of estimating a probabilistic model of the underlying seasonal and interannual variations experienced by land cover time series in a given geographical region. Time series that deviate from these trajectories due to the human-induced change appear as outliers and can be detected using their Mahalanobis distance from the mean under the joint distribution of time samples. We apply this model to a collection of pixel time series acquired by the Moderate Resolution Imaging Spectroradiometer platform over Limpopo province, South Africa, for the task of identifying human settlement expansion. For estimation of the time of change, we present a hypothesis testing approach that tests for a decrease in correlation between samples before and after the change. This was found to be highly effective, yielding a mean absolute error of 52 days.

The well-being of the environment is one of the major factors that contributes to sustainability. Sustainable human settlements require local governance to plan, implement, develop, and manage human settlements expansions. This is important as the number anthropogenic activities is directly correlated to the increase in human population within a geographical region. Regional mapping of land cover conversion of natural vegetation to new human settlements is essential. In this paper we explore the effect which the length of a temporal sliding window has on the success of detecting land cover change. It is shown using a short Fourier transform as a feature extraction method provides meaningful robust input to a machine learning method. In theory, the performance is increased by improving the estimates on the features by increasing the length of the sliding window. Experiments were conducted in the Limpopo province of South Africa and were found that increasing the length of the sliding window beyond 12 months yield minor improves due to other seasonal and external factors.

In this paper, we propose a new method for extracting features from time-series satellite data to detect land cover change. We propose to make use of the behavior of a deterministic nonlinear system driven by a time-dependent force. The driving force comprises a set of concatenated model parameters regressed from fitting a model to a Moderate Resolution Imaging Spectroradiometer time series. The goal is to create behavior in the nonlinear deterministic system, which appears predictable for the time series undergoing no change, while erratic for the time series undergoing land cover change. The differential equation used for the deterministic nonlinear system is that of a large-amplitude pendulum, where the displacement angle is observed over time. If there has been no change in the land cover, the mean driving force will approximate zero, and hence the pendulum will behave as if in free motion under the influence of gravity only. If, however, there has been a change in the land cover, this will for a brief initial period introduce a nonzero mean driving force, which does work on the pendulum, changing its energy and future evolution, which we demonstrate is observable. This we show is sufficient to introduce an observable change to the state of the pendulum, thus enabling change detection. We extend this method to a higher dimensional differential equation to improve the false alarm rate in our experiments. Numerical results show a change detection accuracy of nearly 96% when detecting new human settlements, with a corresponding false alarm rate of 0.2% (omission error rate of 4%). This compares very favorably with other published methods, which achieved less than 90% detection but with false alarm rates all above 9% (omission error rate of 66%).

A probability graph model can effectively model spectral and spatial dependencies within remote sensing images for land cover classification. The most common structure used to unify this probabilistic information is a second order Markov network that encapsulate unary and pairwise potentials. In this paper we explore various heuristics to discover new graph structures that will assist with classifying land cover. Experiments were conducted to compare classification accuracies in two study areas; one homogeneous and one heterogeneous located in the Kwazulu-Natal province, South Africa.

Regional monitoring of land cover conversion of natural vegetation to new informal human settlements is essential when investigating the migration of people to urbanized cities. Detecting these new settlements require reliable change detection methods. A robust change detection metric can be derived by analyzing the area under the autocorrelation function for a time series. The time dependence on the first and second moment causes a non-stationary event within the time series which results in non-symmetrical variations. In this work we explore the behavior of the autocorrelation function using new integration, differentiation and windowing approaches. Experiments were conducted in the Gauteng province of South Africa and we found a proper windowing function improved the overall detection accuracy.

Creating the optimal bipartite graph and decoding of aLow Density Parity Check (LDPC) codeword on it is deemedan NP-complete problem. Much work in the last decade hasgone into proper construction of LDPC codes with maximumgirth and optimized stopping sets; to ensure the BERapproaches channel capacity. When an edge alteration to thegraph is proposed, it completely changes the performance ofthe graph and usually leads to the re-analysis of the entiregraphs properties. In this work we propose a method oflowering the error floor experienced in an LDPC code by intelligentlyinserting a set of known bits in the message frame.This deactivates paths in the graph located around trappingsets without modification of edges in the graph, and provideshigh log-likelihood information to the induced sub-graph forimproved performance.

We present a method based on Markov Random Fields (MRFs) for conducting decision level fusion of segments derived from multiple images of the same region. These images are not required to share the same resolution or sensor characteristics. By working at the segment level we preserve the advantages of segment based image classification while also incorporating the benefits of using multiple image sources. Segment fusion is achieved by constructing a MRF graph over the segments with an edges connecting overlapping segments from different images. These edges penalize connected segments for taking different labels as a function of the degree of overlap. Experimentation on the fusion of Land-sat and SPOT5 imagery for classification of different forest types shows the ability of this method to deliver substantial improvements in classification accuracy.

In this paper we present a 2-tier higher order Conditional Random Field which is used for land cover classification. The Conditional Random Field is based on probabilistic messages being passed along a graph to compute efficiently the conditional probability for a land cover class. Conventionally the information is passed among direct spatial neighbors to improve classification accuracy. The inclusion of higher order descriptive structures in the graphs allow for more information to be pass along to further improve classification accuracy. Unfortunately this increases the computational cost beyond what is feasible to classify a large geographical area. In this work we investigate a spatially based cluster potential to improve classification accuracy while keeping the computational costs tractable. We also expand the typical 1-tier proto-graph used in conventional CRFs to a 2-tier graph to encapsulate the temporal dimension. This further improves the classification accuracy by modeling the seasonal variations experienced throughout the year. The conventional and higher order CRF are compared to a Random Forest on monthly composited Landsat images. These two CRFs are then compared to the same CRFs expanded to a 2-tier graph. An overall improvement between 2-4% is observed in our study area which is located near the city of Vryheid, South Africa.

An important aspect of research in the remote sensing field is to objectively compare different classifiers. This is the foundation of hundreds of research projects and in this paper we will address some raising concerns when evaluating solutions for classification of data sets with skewed class distributions. The quality of assessment is based on the problem specified by the user and the corresponding hypothesis defined. This hypothesis will determine how two or more classifiers are scored to determine which one is better for a particular application. In this paper we present two experiments that illustrate how, if unaware and misunderstood, statistical measurements can be misleading. One experiment is based on a Synthetic Aperture Radar image with a highly skewed class distribution and the second experiment is based on a Landsat image with a minor skewed distribution. From both experiments it can be seen that ill-defining the problem, can lead to false statements and the reporting of statistically invalid conclusions.

It has been shown that time series containing reflectance values from the first two spectral bands of the MODerate-resolution Imaging Spectroradiometer (MODIS) land surface reflectance product can be modelled as a triply modulated cosine function. A meta-optimization approach has been proposed in the literature for setting the parameters of the non-linear Extended Kalman Filter (EKF) to rapidly and efficiently estimate the features for these triply modulated cosine functions using spatial information. In this paper we modify this approach to utilize temporal information instead of spatial information to greatly reduce the processing time and storage requirements to process each time series. The parameters derived from the newly proposed method is classified with a support vector machine and compared to the original approach. Performance of the methods is tested on the Limpopo province in South Africa.

We propose to modulate a deterministic non-linear system with state variables that were derived from an Extended Kalman Filter in order to detect change in a MODIS time series. The deterministic non-linear system used in this work is a gravity pendulum, where the displacement angle is observed over time. The analysis shows a change detection accuracy better than 99% obtained with this deterministic non-linear system when detecting new human settlements, with a corresponding false alarm rate lower than 3%.

Most research on Distributed Space-Time Block Coding focus on utilizing 2 relaying nodes that are assumed to be permanently available. In the case where higher diversity order is available for utilization, maintaining full-rate become troublesome or the complexity of decoding will be increased significantly. In this paper a distributed full-rate encoding scheme is proposed that will achieve any order of diversity, while maintaining lower complexity. The idea is to adapt an existing code, which is based on a low complexity approach used in conventional Multiple Input Multiple Output systems, to work on an amplify-and-forward relaying network. This will ensure the ability of the system to scale with ease by accommodating any number of relays with minimal overhead. In addition, the method does not require any control-information overhead for the codegeneration coordination process among relay nodes. Simulation results show the effectiveness of this code which retains its full-diversity, full-rate and linear decoding complexity.

Two-way Wireless Relaying Networks (TWRNs) have attracted interest recently due to the substantial bandwidth efficiency and performance they can achieve. So far, most research has assumed perfect timing synchronization within the network. Unfortunately, perfect synchronization is difficult (or impossible) to achieve as a timing misalignment between the received versions of the two-way transmitted signals will occur at both transmission phases. In this paper, an enhanced Parallel Interference Cancellation (PIC) detector for TWRNs employing a Space-time block code is proposed to mitigate efficiently the performance degradation caused by imperfect synchronization. In addition, a change in the transmission scheme of the TWRN is also introduced that will enable a symbol-wise decoding process at the receiving node. Unlike the previous PIC detector, numerical simulations show that the proposed detection scheme can achieve performance close to that of perfect synchronization even under severe imperfect synchronization conditions in just two iterations, while maintaining a low-level of computational complexity.

Several existing direct‐sequence spread spectrum (DSSS) detection and estimation algorithms assume prior knowledge of the symbol period or sequence length, although very few sequence‐length estimation techniques are available in the literature. This study presents two techniques to estimate the sequence length of a baseband DSSS signal affected by additive white Gaussian noise. The first technique is based on a known autocorrelation technique which is used as reference, and the second technique is based on principal component analysis. Theoretical analysis and computer simulation show that the second technique can correctly estimate the sequence length at a lower signal‐to‐noise ratio than the first technique. The techniques presented in this study can estimate the sequence length blindly which can then be fed to semi‐blind detection and estimation algorithms.

The extraction of information on land cover classes using unsupervised methods has always been of relevance to the remote sensing community. In this paper, a novel criterion is proposed, which extracts the inherent information in an unsupervised fashion from a time series. The criterion is used to fit a parametric model to a time series, derive the corresponding covariance matrices of the parameters for the model, and estimate the additive noise on the time series. The proposed criterion uses both spatial and temporal information when estimating the covariance matrices and can be extended to incorporate spectral information. The algorithm used to estimate the parameters for the model is the extended Kalman filter (EKF). An unsupervised search algorithm, specifically designed for this criterion, is proposed in conjunction with the criterion that is used to rapidly and efficiently estimate the variables. The search algorithm attempts to satisfy the criterion by employing density adaptation to the current candidate system. The application in this paper is the use of an EKF to model Moderate Resolution Imaging Spectroradiometer time series with a triply modulated cosine function as the underlying model. The results show that the criterion improved the fit of the triply modulated cosine function by an order of magnitude on the time series over all seven spectral bands when compared with the other methods. The state space variables derived from the EKF are then used for both land cover classification and land cover change detection. The method was evaluated in the Gauteng province of South Africa where it was found to significantly improve on land cover classification and change detection accuracies when compared with other methods.

The paper considers the detection of beetle infestations in North American pine forests using high temporal resolution, coarse spatial resolution MODIS remotely sensed satellite images. Two methods are proposed to detect beetle infestation, both applying a triply modulated cosine model. The first method uses an Extended Kalman Filter (EKF) for estimating model parameters, and the second a Least Squares estimator. When beetles infest a forest, the changes in the affect large geographical area. Therefore, the change detection metrics are based on the time series of each pixel, and do not utilize information from neighboring pixels. Using data from the Rocky Mountain region of the United States and of British Columbia in Canada, we show that our methods are highly effective at detecting beetle infestations.

There has been recent developments in the use of hyper-temporal satellite time series data for land cover change detection and classification in South Africa and in particular, the monitoring of human settlement expansion is of relevance as it is the most pervasive form of land-cover change in the country. In this paper, a spatio-temporal change detection method is proposed that is applicable over large regions. This is achieved by adjusting the change threshold based on the change properties of a neighbourhood of pixels. Results indicate that the addition of spatial information increase change detection accuracy when compared to a pixel based approach.

Substantial performance gain and a full coding rate can be achieved by utilizing a Closed-Loop Extended-Orthogonal Space-Time Block Coding (CL-EO-STBC) scheme in wireless relaying networks, referred to as D-CL-EO-STBC. So far, most research has focused on the perfect synchronization case and for only two-nodes wireless relaying networks. Unfortunately, perfect synchronization is difficult (or impossible) to achieve in wireless relaying networks, as a timing misalignment between the received versions of the transmitted signals from the relaying nodes will occur. In this paper, an improved detection scheme, based on a PIC detector which was previously introduced, is proposed to mitigate the performance degradation due to imperfect synchronization in four-nodes wireless relaying networks employing CL-EO-STBC. Numerical simulations show that the proposed detection scheme can achieve performance close to that of perfect synchronization even under severe imperfect synchronization conditions in just two iterations while maintaining a low-level of computational complexity.

Human settlement expansion is one of the most pervasive forms of land-cover change in South Africa. The use of Page's cumulative sum (CUSUM) test is proposed as a method to detect new settlement developments in areas that were previously covered by natural vegetation using 500-m Moderate Resolution Imaging Spectroradiometer time-series satellite data. The method is a sequential per-pixel change alarm algorithm that can take into account positive detection delay, probability of detection, and false-alarm probability to construct a threshold. Simulated change data were generated to determine a threshold during a preliminary offline optimization phase. After optimization, the method was evaluated on examples of known land-cover change in the Gauteng and Limpopo provinces of South Africa. The experimental results indicated that CUSUM performs better than band differencing in the before-mentioned study areas.

In this letter, a first-order Moderate Resolution Imaging Spectroradiometer time-series simulator, which uses a colored simple harmonic oscillator, is proposed. The simulated data can be used to augment data sets so that data intensive classification and change detection algorithms can be applied without enlarging the available ground truth data sets. The simulator's validity is tested by simulating data sets of natural vegetation and human settlement areas and comparing it with the ground truth data in Gauteng province located in South Africa. The difference found between the real and simulated data sets, which is reported in the experiments, is negligent. The simulated and real-world data sets are compared by using a wide selection of class and pixel metrics. In particular, the average temporal Hellinger distance between the real and simulated data sets is 0.2364 and 0.2269 for the vegetation and settlement classes, respectively, whereas the average parameter Hellinger distance is 0.1835 and 0.2554, respectively.

In this paper a sequential time-varying maximum likelihood classifier is applied on coarse resolution MODIS surface reflectance data. It is shown that good class separability can be obtained after considering only one year of data. Finally it is shown that after NDVI, band 2 has the highest separability of all the MODIS land bands, and band 6 has the lowest separability for the presented case study.

There has been recent developments in the use of hyper-temporal satellite time series data for land cover change detection and classification. Recently, an Autocorrelation function (ACF) change detection method was proposed to detect the development of new human settlements in South Africa. In this paper, an extension to this change detection method is proposed that produces an estimate of the change date in addition to the change metric. Preliminary results indicate that comparable accuracy is achievable relative to the original formulation, with the added advantage of providing an estimate of the change date.

In this paper the Bias Variance Search Algorithm is proposed as an algorithm to optimize a candidate set of initial parameters for an Extended Kalman filter (EKF). The search algorithm operates on a Bias Variance Equilibrium Point criterion to determine how to set the initial parameters. The candidate set is then used by the EKF to estimate state parameters to fit a triply modulated cosine function to time series of the first two spectral bands of the MODerate-resolution Imaging Spectroradiometer (MODIS) land product. The state parameters are then used for land cover classification. The results of the search algorithm was tested on classifying land cover in the Limpopo province, South Africa. An improvement in land cover classification was observed when the method was compared to a robust regression method.

In this paper, the internal operations of an Extended Kalman Filter is investigated to see if any useful information can be derived to detect land cover change in a MODIS time series. The Extended Kalman Filter expands its internal covariance if a significant change in reflectance value is observed, followed by adapting the state parameters to compensate for this change. The analysis shows a change detection accuracy above 90% can be attained when evaluating the elements within the internal covariance matrix to detect new human settlements, with a corresponding false alarm rate below 11%.

Eigenvalue distributions of Wishart matrices are given in the literature as functions or distributions defined in terms of matrix arguments requiring numerical evaluation. As a result the relationship between parameter values and statistics is not available analytically and the complexity of the numerical evaluation involved may limit the implementation, evaluation and use of eigenvalue techniques using Wishart matrices. This study presents analytic expressions that approximate the distribution of the largest eigenvalue of white Wishart matrices and the corresponding sample covariance matrices. It is shown that the desired expression follows from an approximation to the Tracy–Widom distribution in terms of the Gamma distribution. The approximation offers largely simplified computation and provides statistics such as the mean value and region of support of the largest eigenvalue distribution. Numeric results from the literature are compared with the approximation and Monte Carlo simulation results are presented to illustrate the accuracy of the proposed analytic approximation.

A semi-blind method based on principal component analysis and sequence synchronisation is proposed to detect direct sequence spread-spectrum signals in a non-cooperative setting under low signal-to-noise ratio conditions. The intercepted signal is segmented to form a detection matrix from which a feature is extracted through cyclic shifting. Signal detection is then performed using a test statistic based on this feature. The carrier frequency and sequence duration of the signal to be detected are assumed known. Theoretical analysis and a computer simulation study show that the performance of the new detection method is superior to classic energy detection in an additive white Gaussian noise channel.

Three minimum-error land cover classifiers are compared on coarse resolution MODerate-resolution Imaging Spectroradiometer (MODIS) surface reflectance data for discerning between vegetation and settlement classes. It is shown that good class separability can be achieved using only the seasonal component of Normalized Difference Vegetation Index (NDVI) data, or the mean component of several other MODIS land bands. It is also shown why particular classifiers fail in certain spectral bands. Finally it is shown that after NDVI, band 2 has the highest separability of all the MODIS land bands, and band 5 has the lowest separability.

Human settlement expansion is one of the most pervasive forms of land cover change in the Gauteng province of South Africa. A method for detecting new settlement developments in areas that are typically covered by natural vegetation using 500 m MODIS time-series satellite data is proposed. The method is a per pixel change alarm that uses the temporal autocorrelation to infer a change metric which yields a change or no-change decision after thresholding. Simulated change data was generated and used to determine a threshold during a preliminary off-line optimization phase. After optimization the method was evaluated on examples of known land cover change in the study area and experimental results indicate a 92% change detection accuracy with a 15% false alarm rate.

In this paper, a change detection accuracy comparison is made between a recently proposed EKF method and a sliding window Fast Fourier Transform (FFT) alternative within a spatio temporal change detection framework. Both methods produce a mean and amplitude parameter sequence which is then used to determine a change metric which yield a change of no-change decision after thresholding. The objective is to determine which of these methods produces a change metric value that is able to best discriminate between change and no-change.

In this paper a neural network is used to perform a mapping between Synthetic Aperture Radar (SAR) backscatter information and LiDAR measurements, and the performance of the neural network model is evaluated against that of a multiple linear regression model. Our aim is to find a relationship between SAR backscatter information and the LiDAR tree volume measurements on a number of land uses in South Africa's Kruger National Park, using a linear as well as a non-linear model. We also seek to find the optimal grid cell size as well as the best combination of SAR polarisation-and decomposition parameters. Our findings suggest that there exists a linear or at least a near-linear relationship between the SAR backscatter information and the LiDAR measurements in South African savannas and that the addition of polarisation-and decomposition parameters to the input of the models aid in improving the Root Mean Squared Error (RMSE) performance.

Time series derived from the first two spectral bands of the MODerate-resolution Imaging Spectroradiometer (MODIS) land surface reflectance product can be modelled as a pair of triply (mean, phase and amplitude) modulated cosine functions. This paper proposes a meta-optimization approach for setting the parameters of the non-linear Extended Kalman Filter to rapidly and efficiently estimate the features for the pair of triply modulated cosine functions. The approach is based on a unsupervised search algorithm over an appropriately defined manifold using spatial and temporal information. Performance of the new method is compared to other applicable methods and is tested on the Gauteng province which is South Africa's province with the fastest growing economy.

A method for detecting land cover change using NDVI time-series data derived from 500-m MODIS satellite data is proposed. The algorithm acts as a per-pixel change alarm and takes the NDVI time series of a 3 × 3 grid of MODIS pixels as the input. The NDVI time series for each of these pixels was modeled as a triply (mean, phase, and amplitude) modulated cosine function, and an extended Kalman filter was used to estimate the parameters of the modulated cosine function through time. A spatial comparison between the center pixel of the 3 × 3 grid and each of its neighboring pixel's mean and amplitude parameter sequence was done to calculate a change metric which yields a change or no-change decision after thresholding. Although the development of new settlements is the most prevalent form of land cover change in South Africa, it is rarely mapped, and known examples amount to a limited number of changed MODIS pixels. Therefore, simulated change data were generated and used for the preliminary optimization of the change detection method. After optimization, the method was evaluated on examples of known land cover change in the study area, and experimental results indicate an 89% change detection accuracy while a traditional annual NDVI differencing method could only achieve a 63% change detection accuracy.

A method for detecting land cover change using NDVI timeseries data derived from MODerate-resolution Imaging Spectroradiometer (MODIS) satellite data is proposed. The algorithm acts as a per pixel change alarm and takes as input the NDVI time-series of a 3 × 3 grid of MODIS pixels. An Extended Kalman Filter was used to estimate a series of parameters related to each NDVI signal. A spatial comparison between the center pixel of the the 3 × 3 grid and each of its neighboring pixels' parameters was done to calculate a change metric which compared to a threshold yielded a change or no-change decision. The method was tested on real change examples in the study area and results indicate 90% detection of new settlements occurring in naturally vegetated areas.

An automated land cover change detection method is proposed that uses coarse resolution hyper-temporal satellite time series data. The study compared two different unsupervised clustering approaches that operate on the short term Fourier transform coefficients of subsequences of 8-day composite MODerate-resolution Imaging Spectroradiometer (MODIS) surface reflectance data that were extracted with a temporal sliding window. The method uses a feature extraction process that creates meaningful sequential time series that can be analyzed and processed for change detection. The method was evaluated on real and simulated land cover change examples and obtained a change detection accuracy higher than 76% on real land cover conversion and more than 70% on simulated land cover conversion.

It is proposed that the normalized difference vegetation index time series derived from Moderate Resolution Imaging Spectroradiometer satellite data can be modeled as a triply (mean, phase, and amplitude) modulated cosine function. Second, a nonlinear extended Kalman filter is developed to estimate the parameters of the modulated cosine function as a function of time. It is shown that the maximum separability of the parameters for natural vegetation and settlement land cover types is better than that of methods based on the fast Fourier transform using data from two study areas in South Africa.

It is proposed that the NDVI time series derived from MODIS multitemporal remote sensing data can be modelled as a triply (mean, phase and amplitude) modulated cosine function. A non-linear Extended Kalman Filter was developed to estimate the parameters of the modulated cosine function as a function of time. It was shown that the maximum separability of the parameters for different vegetation land cover was better than that of a spectral method based on the Fast Fourier Transform (FFT). Thus it is theorized that the cosine function parameters estimated using the EKF is superior for both classifying land cover and detecting change over time when compared to methods based on the FFT. Results from two study areas in Southern Africa are provided to show the improved separability using MODIS data.

This paper shows that a feedforward Multilayer Perceptron (MLP) operating over a temporal sliding window of multi-spectral time series MODerate-resolution Imaging Spectroradiometer (MODIS) satellite data is able to detect land cover change that was artificially introduced by concatenating time series belonging to different types of land cover. The method employs an iteratively retrained MLP that is a supervised method, and thus captures all local environmental patterns. Depending on the length of the temporal sliding window used in the short-term Fourier transform, an overall change detection accuracy of between 87.62% and 97.02% was achieved. It is shown that for this type of simulated land cover change, where land cover change was abrupt, a short-term FFT window of 18 months or less, using only the two NDVI spectral bands of MODIS data was sufficient to detect change reliably.

The paper introduces an Artificial Neural Network (ANN) operating on a sliding window over time for detecting land cover class changes. If a class change is detected, then change detection has been accomplished. The ANN is presented as well as experimental results usingMODIS time-series data derived from 8-day 500m MODIS [MOD09] composite images in two study areas in Southern Africa. The method is supervised, i.e. it assumes training data is available, but after training it is able to detect change on testing data not seen before.

An iterative maximum likelihood sequence estimation (MLSE) equalizer (detector) with hard outputs, that has a computational complexity quadratic in the data block and the channel length, is proposed. Its performance is compared to the Viterbi MLSE algorithm that has a computational complexity that is linear in the block length and exponential in the channel memory length. It is shown via computer simulation that the proposed iterative MLSE detector is able to detect binary phase-shift keying (BPSK) signals in systems with significantly larger channel length than what is possible with the Viterbi algorithm, for frequency selective Rayleigh fading channels.

Turbo equalization improves communication system performance by iteratively exchanging information between soft-input soft-output (SISO) equalizer and SISO channel decoder. The trellis-based maximum a posteriori probability (MAP) algorithm serves as the optimum SISO equalizer for turbo equalization. However, MAP algorithm is unsuitable for systems with large modulation constellation size and severe inter-symbol interference (ISI) due to its prohibitively high computational complexity. In this paper, an improved SISO block decision feedback equalizer (BDFE) is proposed for low complexity turbo equalization. Unlike other sub-optimum equalizers which perform symbol by symbol detection, the proposed equalizer generates the soft output for each data bit by collecting information from a sequence of samples as in MAP algorithm. The sequence-based equalization is enabled by using not only soft a priori input from channel decoder, but also hard a priori information obtained from BDFE in previous iteration. The combination of soft a priori information and hard a priori information renders better performance with less iterations compared to other sub-optimum algorithms. In addition, the computational complexity of the proposed algorithm is on the same order as conventional SISO BDFE algorithm, and is much lower compared to the trellis-based MAP algorithm.

We compare eleven sum-of-sinusoids simulation models for generating a Rayleigh-fading envelope in this paper. We consider the correlation properties and level-crossing rates of the simulation models, as these properties influence the error rates of many communication systems. The paper concludes with the selection of the most suitable simulation models for use in flat fading and frequency selective fading channel simulators.

In this paper, the authors designed an 802.16e-2005 (WiMAX) wireless environment to analyze the performance of low density parity-check codes (LDPC). The authors used a numerical technique to optimize the given LDPC code and observe improvements over the communication channel. In this paper only the worst given case of the serving base station was evaluated. The paper concludes on the use of these numerical techniques to the given code proposed in the standard.

A novel cochannel single antenna interference cancellation (SAIC) receiver is proposed for synchronised Group Special Mobile (GSM) systems. The receiver uses a two-stage strategy, where in the first stage cochannel interference is cancelled by a widely linear filter, while inter-symbol interference due to the GSM frequency-selective Rayleigh-fading environment is removed by a second-stage equaliser. Analytical results for the optimal widely linear filter coefficients are derived. Simulation results show excellent performance with large gains over the conventional receiver under interference limited channel conditions. It is shown that the conventional maximum likelihood sequence estimator or maximum a posteriori probability receiver is optimal when cochannel interference it not the dominant impairment, and it is proposed that the SAIC algorithm is disabled when the estimated carrier-to-interference (C/I) ratio is above a certain threshold.

An adaptive space time transmit diversity scheme with simple feedback is proposed for the next generation wireless communication systems. By combining orthogonal space time block codes with adaptive sub-group antenna encoding, this new diversity scheme can effectively exploit the diversity potential provided by multiple antenna arrays without introducing interference among the signals transmitted at different antennas. In order to reduce the amount of feedback information as well as the computational complexity, a new quadrant phase constraining method is introduced for the computation of the feedback information. With simple operations at both the transmitter and the receiver, the new adaptive diversity scheme outperforms not only open loop space time block encoding techniques, but also some closed loop transmit diversity techniques with the same amount of feedback.

The channel estimation and equalization for EDGE system with time-varying and frequency-selective fading channels are discussed. It is shown that the fast fading channel during a selected slot in the EDGE system can be modeled as a linear function of time, and a linear least-squares algorithm is proposed to estimate the fading channel. For typical channel profiles of the EDGE system, the channel impulse response is not in its minimum phase form, thus cannot be directly used in computationally efficient equalizers, such as delayed decision feedback sequence estimation or reduced state sequence estimation. To overcome this problem, a Cholesky decomposition-based method is introduced to transform the estimated channel impulse response into its minimum phase form. The simulation results show that the proposed algorithms can effectively combat the time-varying and frequency-selective channel fading with Doppler frequency being in a wide range up to 300 Hz.

In this paper, an accurate and computationally efficient algorithm is proposed for estimating a time-varying and frequency-selective fading channel with unequally spaced pilot symbols. By employing the time-varying coefficient polynomial interpolation method, it is proved that the time-varying channel impulse response can be estimated by the product of a constant interpolation matrix and the fading information at pilot symbol positions. Furthermore, a least square off-line training algorithm is presented to optimally calculate the constant matrix, taking into consideration the statistics of channel fading and noise. Simulation results indicate that the bit error rate performance of our new estimation algorithm is close to that of the perfect channel estimation.

We provide simple analytical results for the coefficients of a finite-impulse response (FIR) prefilter and the effective channel impulse response (IR) for use in cellular communication systems. We show that using a FIR filter with both causal and anticausal filter taps, it is possible to find the jointly optimized impulse response, such that the signal-to-noise ratio is maximized in the least-squares sense. We show via computer simulation for 8-ary phase-shift keying in Enhanced Data rates for Global Evolution (EDGE) that the joint optimization of the prefilter and IR produces results similar to the minimum mean-square error decision-feedback equalizer prefilter in thermal noise, but yields gain in colored noise.

A new method for designing IIR digital filters with linear phase in the passband is proposed. This method is based on frequency-weighted least-square error optimization using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method. The gradient of the cost function with respect to the design parameters, required for the implementation of the BFGS method, is derived. The proposed method is started by obtaining an initial IIR filter design using model reduction of a linear phase FIR filter. Based on this initial design the cost function is minimized using the BFGS method. An example shows that the proposed method leads to very good filter designs.

Turbo equalization (TE) is an iterative receiver algorithm repeating equalization and channel decoding to improve receiver performance. As equalization and decoding are performed several times, the complexity of the TE receiver increases respectively. Furthermore, the decoder needs to provide probability information as output, which increases the computational burden even more. In this paper we introduce a new method to reduce the decoding complexity of the TE receiver. This soft trellis decoding (STD) technique utilises information obtained from the previous decoding step to select those trellis metrics that need to be recalculated. Hence, the most unlikely trellis branches are eliminated. We evaluate STD performance in the Enhanced General Packet Radio System (EGPRS) platform by simulations. We show that STD reaches performance close to full trellis decoding, but requires only a fraction of the computational power.