Anish C. Turlapaty

A Hybrid Approach for Building Extraction From Spaceborne Multi-Angular Optical Imagery

The advent of high resolution spaceborne images leads to the development of efficient detection of complex urban details with high precision. This urban land use study is focused on building extraction and height estimation from spaceborne optical imagery. The advantages of such methods include 3D visualization of urban areas, digital urban mapping, and GIS databases for decision makers. In particular, a hybrid approach is proposed for efficient building extraction from optical multi-angular imagery, where a template matching algorithm is formulated for automatic estimation of relative building height, and the relative height estimates are utilized in conjunction with a support vector machine (SVM)-based classifier for extraction of buildings from non-buildings. This approach is tested on ortho-rectified Level-2a multi-angular images of Rio de Janeiro from WorldView-2 sensor. Its performance is validated using a 3-fold cross validation strategy. The final results are presented as a building map and an approximate 3D model of buildings. The building detection accuracy of the proposed method is improved to 88%, compared to 83% without using multi-angular information.

A joint design of transmit waveforms for radar and communications systems in coexistence

In this paper, we propose a dynamic spectrum allocation approach for the coexistence of a radar system with a communications system whose operating frequency ranges overlap. We develop a combined mutual information criterion for the joint waveform and power spectrum design to optimize the performance of the radar and communications systems. The performance is evaluated in terms of the minimum separation distance between the coexisting systems under the constraint that a predefined operational SINR must be achieved for both systems. Significant performance gains close to 3.5 dB for the communication system and 1 dB for the radar system are observed in terms of the minimum separation distance compared to the scenario when the waveform with equal power allocation across the frequency bandwidth is used.

Range and velocity estimation of radar targets by weighted OFDM modulation

In this paper, we address the problem of spectrum sharing and co-existence between radar and communications systems by using a reconfigurable dual-use radar/comm system on a single RF platform. We develop a nonlinear least-squares method for range and velocity estimation of moving targets using weighted orthogonal division multiplexing (WOFDM) waveform modulation scheme. The corresponding Cramer-Rao low bounds for the target parameter estimator are derived. Weighted OFDM waveform is derived subject to a low peak to average power ratio constraint. The proposed WOFDM modulation scheme shows improved accuracy for delay estimation compared with the classic constant envelope OFDM modulation method while achieving a lower peak to average power ratio.

Parameter estimation and waveform design for cognitive radar by minimal free-energy principle

In this paper we develop a new framework for Bayesian parameter estimation using adaptive waveforms by the minimal free energy (FE) principle in the context of cognitive radar. Unlike conventional approaches, the new method utilizes the minimal FE principle as a unifying criterion for optimal estimator design and waveform design. The FE principle seeks to approximate the true density of the unknown parameters in response to sequential measurement data. In the case of a single unknown parameter we show that the estimators based on the FE principle and the conventional Bayesian estimator are identical. Moreover, the waveform design based on the FE principle results in similar water-filling solution as the traditional mutual information method.

Fusion of Radiation and Electromagnetic Induction Data for Buried Radioactive Target Detection and Characterization

In general, buried penetrators made of Depleted Uranium (DU) become hazardous waste. In addition to the detection of DU waste, it is also of interest to know their state of oxidation. However, radioactive target detection techniques usually do not differentiate between metal and oxide. In this study, data fusion techniques are applied to combine results from both the radiation detection and the electromagnetic induction (EMI) detection, so that further differentiation among DU metal, DU oxide, and non-DU metal debris may be achieved. A two-step approach is developed to accomplish decision level fusion. The approach is based on techniques such as majority voting (MV) and weighted majority voting (WMV), in combination with a set of decision rules. The fusion approach has been tested successfully with survey data collected on simulation targets.

Precipitation data fusion using vector space transformation and artificial neural networks

We have developed a new methodology to fuse several precipitation datasets, available from different estimation techniques. The method is based on artificial neural networks and vector space transformation function. The final merged product is statistically superior to any of the individual datasets over a seasonal period. The results have been tested against ground-based measurements of rainfall over a study area. This method is shown to have average success rates of 85% in the summer, 68% in the fall, 77% in the spring, and 55% in the winter.

Bayesian Sequential Parameter Estimation by Cognitive Radar With Multiantenna Arrays

In this paper we consider the problem of Bayesian sequential parameter estimation of extended targets for cognitive radar with multi-antenna arrays using adaptive waveforms. The target is modeled as a complex Gaussian random process. Using iterative waveform transmission, the cognitive radar estimates the targets characteristic parameters and updates its probabilistic model based on new measurements. The adaptive waveform is designed by minimizing the conditional entropy from the posterior density of the model parameters. We analyze the performance of the developed Bayesian sequential estimation algorithm and derive expressions for the signal-to-noise ratio gain, the asymptotical posterior Cramer Rao bound, and the mutual information gain. The analysis and numerical simulations demonstrate that the adaptive sequential Bayesian estimator yields accelerated convergence of the estimate towards its true value and a smaller estimation error compared with the conventional Bayesian estimator that uses fixed waveform transmission under Gaussian or non-Gaussian noise.

A pattern recognition based approach to consistency analysis of geophysical datasets

Remotely sensed data from satellites are often validated by comparing them against ground-based measurements which usually are relatively sparse. Conventional consistency analysis methods provide information on each data point individually and in relation to its neighbors. In this study, a consistency analysis method based on wavelet-based feature extraction and one-class support vector machines is proposed. This method performs a consistency assessment of the entire time series in relation to others and provides a spatial distribution of consistency levels. The presented method is tested on soil moisture product from Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) on board Aqua satellite for the years 2005-2006. Time series of in-situ soil moisture measurements from the USDA Soil Climate Analysis Network (SCAN) are used as training data. Spatial distribution of consistency levels are presented as consistency maps for a region, including the states of Mississippi, Arkansas, and Louisiana in the USA. These results are correlated with the spatial distributions of averaged quality control information, mean soil moisture, and the cumulative counts of dense vegetation. Moreover, the methodology is tested for its robustness by examining its sensitivity to the spatial distribution of the network of training data sites. Finally, seasonal consistency maps for soil moisture data are developed. The degree to which the satellite estimates agree with the in-situ measurements has been represented seasonally as consistency maps which are helpful in interpreting the overall quality of the soil moisture product retrieved from satellite observations.

Detection and classification of buried radioactive-metal objects using wideband EMI data

Gamma-ray spectroscopy is frequently used for the detection of radioactive materials. As an alternative, we explore the use of electromagnetic induction (EMI) data for detection and classification of radioactive-metal objects, i.e., depleted uranium (DU), in this study. To reduce false alarms, a pattern recognition approach based on a decision tree structure is proposed. In an initial experiment, the DU rounds were placed in rows at three different depths in a rectangular field and EMI measurements are taken. The DU objects placed up to depth 30 cm below surface were successfully detected and identified along with the depth information. The algorithm also outperformed traditional threshold detection based method in terms of discriminating objects at 30cm depth.

Multi-parameter estimation for cognitive radar in compound Gaussian clutter

In this paper, we consider the problem of multi-parameter estimation in the presence of compound Gaussian clutter for cognitive radar using the variational Bayesian approach. The main advantage of variational Bayesian is that the estimation of multi-variate parameters is decomposed to multiple estimation of univariate parameters, thus enabling analytically tractable approximations. Numerical tests demonstrate that the proposed approach leads to improved estimation accuracy than the expectation maximization (EM) method, particularly in the case of non-Gaussian nonlinear models and a small sample size.