A Method Exploiting Compressive Sampling for Localization of Radio Frequency Emitters

One typical use of sensor networks is monitoring targets. The sensor networks classify, detect, locate, and track targets. The ML (Maximum likelihood) algorithm is one of the estimation algorithms of target location and has high accuracy to estimate target location. However, the calculation amount of the ML estimation algorithm is large. Energy-Ratios Source Localization Nonlinear Least Square (ER-NLS) is proposed to realize the ML algorithm. ER-NLS is the algorithm of estimating source location by using the ratio of sensors' receiving energies. However, ER-NLS has to search all the areas, so that the calculation amount of ER-NLS is large. In this paper we propose a method of reducing search area for localization. The proposed method uses the ratio of sensors' receiving energies. It can be used with the ML algorithm. We show that the proposed method with the ML algorithm can reduce the search areas to estimate the target location and thus reduce the complexity, while achieving the RMSE (root mean square error) close to that of the ML algorithm.

We present a simple and robust symbol timing recovery algorithm for OFDM systems, such as IEEE 802.11a/g. The algorithm is derived according to a maximum likelihood (ML) algorithm utilizing the cyclic extension or cyclic prefix (CP), which is appended in front of each OFDM symbol. Simulation results show that the performance of the proposed algorithm is better than the conventional ML algorithm in the additive white Gaussian noise (AWGN) channel and approaches to the ML algorithm in the Rayleigh fading channel. Furthermore, the implementation complexity is lower than that of the ML algorithm.

After decades of research in Direction of Arrival (DoA) estimation, today Maximum Likelihood (ML) algorithms still provide the best performance in terms of resolution capabilities. At the cost of a multidimensional search, ML algorithms achieve a significant reduction of the outlier production mechanism in the threshold region, where the number of snapshots per antenna and/or the signal to noise ratio (SNR) are low. The objective of this paper is to characterize the resolution capabilities of ML algorithms in the threshold region. Both conditional and unconditional versions of the ML algorithms are investigated in the asymptotic regime where both the number of antennas and the number of snapshots are large but comparable in magnitude. By using random matrix theory techniques, the finite dimensional distributions of both cost functions are shown to be Gaussian distributed in this asymptotic regime, and a closed form expression of the corresponding asymptotic covariance matrices is provided. These results allow to characterize the asymptotic behavior of the resolution probability, which is defined as the probability that the cost function evaluated at the true DoAs is smaller than the values that it takes at the positions of the other asymptotic local minima.

A novel OWDM system with blind identification based on the maximum likelihood (ML) algorithm is studied for the multipath fading channel. New OWDM structures based on the ML algorithm and blind identification and H̃ matrix based on the ZF (zero forcing) algorithm are the two novel aspects of this system. The performance of the system under the multipath fading channel is analysed.

Enhancing security requires sensing devices surveilling over critical infrastructures for early warning of radio frequency (RF) emitter geolocations. Conventional geolocation of RF emitters has adopted active methodologies by illuminating targets, such as radar sensors. However, active geolocation draws unwanted attention of adversary, thus highly increases threats and vulnerabilities of the sensing devices. As security issues become a central concern in sensing applications, we propose a small unmanned‐aerial‐system for passive geolocation of RF emitters. Multiple electronic surveillance sensors are mounted on small unmanned‐aerial‐vehicles (UAVs) to provide geolocation awareness. Both sensors‐to‐UAV and UAVs‐to‐fusion‐center communications are investigated. Different with previous studies, our approach is based on an empirical path loss and log‐normal shadowing model, from a wireless communication and signal processing vision. The communication degradation from UAV to fusion center has been taken into consideration other than assuming lossless performance. The analyzed geolocation error and the error probability of distance shows that this approach provides robust performance at high frequencies, thus offers an effective solution for the security issue in RF emitter geolocation. Copyright © 2012 John Wiley & Sons, Ltd.

3D position of radiation sources using an automated gamma camera and ML algorithm with energy-dependent response functions

In this paper, a novel wavelet packet division multiplexing (WPDM) system based on Fractionally Spaced Equalizers (FSE) and Maximum Likelihood (ML) algorithm is studied for multipath fading channels. New discrete wavelet packet transform structure based on FSE, which uses Least Mean Square (LMS) algorithm and ML detecting algorithm, are the two novel aspects of the presented system. It is demonstrated by simulation results that the FSE method is superior to the Zero-Forcing equalizers (ZFE) method in performance and the FSE-WPDM scheme can provide greater immunity to multipath fading channels and AWGN than the FSE-OFDM scheme.

Wavelet packet division multiplexing (WPDM) is a high-capacity, flexible, and robust multiple-signal transmission technique. In this paper, a novel WPDM system based on optimum pilot symbol assisted modulation (OPSAM) and a maximum likelihood (ML) algorithm is studied for Rayleigh fading channels. The ML detecting algorithm and the new discrete wavelet packet transform structure, which is based on pilot symbol assisted modulation (PSAM) using a least mean squares algorithm, are two novel aspects of the presented system. An expression for the bit error rate of the WPDM scheme on quadrature phase-shift keying (QPSK) is derived in the presence of flat fading and Gaussian noise. It is demonstrated by simulation results that the OPSAM WPDM scheme can provide greater immunity to flat fading channels and Gaussian noise than the OPSAM orthogonal frequency division multiplexing scheme, the differential QPSK WPDM scheme, and the normal PSAM WPDM scheme.

The need to classify sentinel-2 satellite images to extract land use /land cover (LULC) are essential to analysis the processes of environment problems and to improve living conditions. Hence, this research aims to create LULC maps from sentinel-2 satellite images through Maximum likelihood (ML) algorithm using remote sensing and GIS. The selected study area for this research is Baghdad city because of it has a unique political stability and due to rapid urbanization, that lead to rise additional request for natural resources and affected on LULC in Baghdad city. After preprocessing and processing of satellite images, thematic maps were created and classified into five main classes based on visual interpretation and visit the field of the study area that containing: urban, vegetation, soil, asphalt roads, and water bodies. The results showed that classification accuracy assessment of ML algorithm is acceptable because of overall accuracy and Kappa index equal (86%, 0.82) respectively.

In this work, we validated a task-based performance figure-of-merit (FOM) by investigating ranking inconsistencies due to lurking variable/factors. We applied a falsifiable search assessment theory to assessing digital breast tomosynthesis (DBT) image quality using a scanning channelized Hotelling observer (CHO) on a simulated DBT dataset. We compared the performance of five reconstruction algorithms: filter back projection (FBP), maximum likelihood (ML), simultaneous algebraic reconstruction technique (SART), total-variation regularized least square estimator (TVLS) with strong and mild regularization settings. The results showed that the location-known-exactly (LKE) detection performance was almost identical for the five reconstruction algorithms. However the search characteristic as described by effective set size (M*) and search AUC value, ranked them differently. To falsify/corroborate our evaluations on search characteristic and performance, we conducted an image-size test. This test demonstrated an agreement between theoretical predictions and empirically measured observer performance in absolute performance levels, except for the ML algorithm. We concluded that evidence corroborated our evaluations, except that for the ML algorithm where our evaluation was wrong. Further investigation of the wrong evaluation in the ML case revealed a lurking variable that affected system performance ranking in search when AUC value was used as the FOM. This further confirmed that our evaluation in its current form for the ML algorithm was indeed wrong. We also noted that the ranking inconsistencies exist even when the AUC value was used as the FOM, and the falsifiable nature of M* allowed such inconsistencies to be identified.

Past emission of metal-bearing particulate matter, sulphur dioxide (SO2), and sulphuric acid by base metal smelters in the Sudbury region led to widespread loss of vegetation, contamination of soils, and formation of black coatings on rock surfaces. These black coatings formed through the incorporation of smelter-borne particulate matter into the partly dissolved uppermost layers of siliceous minerals on exposed rock, and are characterized by high heavy-metal content. This study involved assessment of the reflectance properties of black coatings in the Sudbury region, and determination of the geographic distribution of coatings through supervised classification of reflectance data derived from a Landsat Enhanced Thematic Mapper Plus (ETM+) image. Classifications involved the use of the Spectral Angle Mapper (SAM), Maximum Likelihood, and Feedforward Backpropagation Neural Network algorithms. The reflectance spectra of black coatings in the Sudbury region are relatively flat and featureless, and are characterized by reflectance values less than ~13% across the visible, near-infrared, and short-wave infrared. Spectral properties are similar to those of magnetite, a spinel-group mineral known to be present in Sudbury coatings. The presence of carbon-rich soot particles may be an important influence on the reflectance properties of coatings. SAM classification results are characterized by the widespread mislabelling of uncoated urban and open-pit sites as mantled by black coatings, and neural network results problematically mislabel some uncoated wetland sites as coated. Results generated by the Maximum Likelihood algorithm most usefully depict the distribution of exposed black coatings in the Sudbury region. The mapping of black coatings using remote-sensing methods can provide useful information on the spatial character of environmental degradation in the vicinity of smelters, and should be helpful in the monitoring of environmental recovery where emissions have been reduced or eliminated.

Rapid and accurate timing synchronization is the first and foremost task in ultra-wideband (UWB) systems. The timing with dirty templates (TDT) algorithm introduced in [10] is a promising method with low complexity and relaxed operation conditions in the presence of unknown time hopping and multipath channel. Its optimality, however, remains unexplored. In this paper, we develop the maximum-likelihood (ML) timing algorithm and obtain its optimum training sequence. We show that the optimum training sequence of the ML timing estimator coincides with that of the TDT algorithm. In addition, we prove that, using this training sequence, the ML algorithm can be simplified, and that the simplified ML (SML) is equivalent to TDT.

ABSTRACTThis paper presents a newly developed compressive sensing (CS), maximum likelihood (ML), and rule-based method for classification of power quality disturbances (PQDs). The first PQD signals are sampled using compressive sampling method and reduced PQD signal dimensionally, and then the data are directly used as features for the classification. PQD signals are divided into two groups: at first, impulsive, oscillatory, harmonics notch, flicker and DC offset, and the second, sag, swell, and interruption. Compressive sensing maximum likelihood (CSML) classifier is used for the classification of the first group and the rule-based method is used for the classification of the second group. CSML classifier and the rule-based method are tested utilizing 1000 PQD events under noiseless and noisy environment. About 100% correct classification rate is achieved without noise and 99.8% correct classification result is obtained with noise. In both methods, noise was considered from 20 to 40 dB. Results show that the proposed CS and ML algorithms and rule-based method can be efficiently used for PQD classification.

This paper presents an ultra-low-power non-uniform sampling scheme using a derivative-based algorithm that can maintain a comparable accuracy to other non-uniform sampling schemes but with less complexity and lower power consumption. In this method, the change in the derivative of the signal above certain threshold values is used to identify high signal activity for retention of the significant points of the signal. The scheme is implemented using simple building blocks that calculate and compare the change in approximate real-time derivative to a tunable threshed value that can be adjusted to obtain the desired Compression Factor (CF) and Post-Reconstruction Signal-to-Noise plus Distortion Ratio (PR-SNDR) for different signal types. Fabricated in TSMC's <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.13 \mu \text{m}$ </tex-math></inline-formula> CMOS technology and tested with real-world biomedical signals, the proposed Derivative Dependent Sampling (DDS) system consumes a maximum power of 155 nW while achieving a CF of more than 6 for an Electrocardiography (ECG) signal. By adding the proposed DDS block to a data acquisition and processing system, the non-uniform sampling can reduce the power dissipation of the entire system.

A comparison is made of two fast Newton-Gauss algorithms for implementation of the exact maximum-likelihood (ML) and an approximate ML (AML) previously introduced by the authors (1989). The AML is impeded by the need of an eigenvalue decomposition of the covariance matrix, but it exhibits better robustness and a lower signal-to-noise ratio (SNR) threshold. The availability of efficient ML algorithms opens the way to interesting recursive or adaptive tracking methods. If an a priori Gaussian probability is assumed for bearing parameters, the algorithm can be easily modified, and it provides the updated estimates and their posteriori covariance. A dynamic model may be also introduced, and then all the requirements for building a Kalman-like recursive estimation scheme for fast moving sources tracking are presented. The update may be made with a small number of snapshots, even a single one, when the a priori covariance has converged to a small enough value. This one snapshot case is very interesting, since the ML algorithm then takes a much simplified form. >