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In this paper we consider the general problem of managing an array of sensors in order to track multiple targets in the presence of clutter. There are three complicating factors. The first is that because of physical limitations (e.g., communication bandwidth) only a small subset of the available sensors can be utilized at any one time. The second complication is that the associations of measurements to targets/clutter are unknown. The third complication is that the total number of targets in the surveillance region is unknown and possibly time varying. It are these second and third factors that extend previous work [14]. Hence sensors must be utilized in an efficient manner to alleviate association ambiguities and to allow accurate estimation of the states of a varying number of targets. We pose the problem as a bi-criterion optimization with the two objectives of 1) controlling the posterior Cramer-Rao lower bound ((PCRLB) which provides a measure of the optimal achievable accuracy of target state estimation), and 2) maximizing the probability of detecting new targets. Only recently have expressions for multitarget PCRLBs been determined [7], and the necessary simulation techniques are computationally expensive. However, in this paper we show the existence of a muititarget information reduction matrix (IRM) which can be calculated off-line in most cases. Additionally, we propose some approximations that further reduce the computational load. We present solution methodologies that, in simulations, are shown to determine efficient utilization strategies for the available sensor resources, with some sensors selected to track existing targets and others given the primary task of surveillance in order to identify new threats.

Initializing and maintaining a track for a low observable (LO) (low SNR, low target detection probability and high false alarm rate) target can be very challenging because of the low information content of measurements. In addition, in some scenarios, target-originated measurements might not be present in many consecutive scans because of mispointing, target maneuvers, or erroneous preprocessing. That is, one might have a set of noninformative scans that could result in poor track initialization and maintenance. In this paper an algorithm based on the expectation-maximization (EM) algorithm combined with maximum likelihood (ML) estimation is presented for tracking slowly maneuvering targets in heavy clutter and possibly noninformative scans. The adaptive sliding-window EM-ML approach, which operates in batch mode, tries to reject or weight down noninformative scans using the Q-function in the M-step of the EM algorithm. It is shown that target features in the form of, for example, amplitude information (AI), can also be used to improve the estimates. In addition, performance bounds based on the supplemented EM (SEM) technique are also presented. The effectiveness of new algorithm is first demonstrated on a 78-frame long wave infrared (LWIR) data sequence consisting of an F1 Mirage fighter jet in heavy clutter. Previously, this scenario has been used as a benchmark for evaluating the performance of other track initialization algorithms. The new EM-ML estimator confirms the track by frame 20 while the ML-PDA (maximum likelihood estimator combined with probabilistic data association) algorithm, the IMM-MHT (interacting multiple model estimator combined with multiple hypothesis tracking) and the IMM-PDA estimator previously required 28, 38, and 39 frames, respectively. The benefits of the new algorithm in terms of accuracy, early detection, and computational load are illustrated using simulated scenarios as well.

Most present-day radar systems use monopulse techniques to extract angular measurements of subbeam accuracy. The familiar 'monopulse ratio' is a very effective means to derive the angle of a single target within a radar beam. For the simultaneous estimation of the angles of two closely-spaced targets, a modification on the monopulse ratio was derived in [7], while [21] presented a maximum likelihood (ML) technique via numerical search. In this paper it is shown that the ML solution can in fact be found explicitly, and the numerical search of [21] is unnecessary. However, the ML solution requires the signal to noise ratio (SNR) for each target to be known, and hence we generalize it so it requires only the relative SNR. Several versions of expectation maximization (EM) joint angle estimators are also derived, these differing in the degree to which prior information on SNR and on beam pattern are assumed. The performances of the different direction-of-arrival (DOA) estimators for unresolved targets are studied via Monte Carlo, and it is found that most hove similar performance: this is remarkable since the use of prior information (SNR, relative SNR, beam pattern) varies widely between them. There is, however, considerable performance variability as a function of the two targets' off-boresight angles. A simple combined technique that fuses the results from different approaches is thus proposed, and it performs well uniformly.

This paper examines the impact of capital structure on corporate performance. The study is based on secondary data of sixteen listed large-sized power sector companies forming part of the BSEPOWER index as identified from the Capitaline database for the period 2006-2017. The corporate performance is proxied using two accounting measures viz. return on asset (ROA) and return on equity (ROE). Leverage is taken as the independent variable which is measured not only by the overall debt-equity ratio but also long-term and short-term debt ratios. The square of these leverage measures is taken to test the linearity of the relationship. The control variables include size, tangibility, liquidity and age of the firm. The study reveals a mixed result. While the impact of leverage on ROE is positive, the impact on ROA is negative. 'Size' shows a significant negative effect in all cases in contrast to positive effect in few cases only. Liquidity and operating expense ratio do not have a significant influence on corporate performance. [ABSTRACT FROM AUTHOR]

This correspondence corrects a minor error in the description of maximum likelihood (ML) estimators that extract angle measurements from two unresolved targets using measurements from a monopulse radar. [ABSTRACT FROM AUTHOR]

Prolonged diabetes ultimately leads to Diabetic Retinopathy (DR) which is one of the leading causes of preventable blindness in the world. Through advanced image analysis techniques are used for abnormalities detection in retina that define and correlate the severity of DR. A thorough study is done in this area in recent past years and on the basis of these studies we have developed a computer based prediction model that is used to determine the severity of DR. To identify severity DR, we have analyzed the human eye image. We have extracted some important features from human eye image i.e. Blood Artery, Optical disc, Exudates. Based on these image and data we have designed an automated system for the determination of DR severity. This automated DR severity assessment methods can be used to predict the clinical case and conditions when young clinicians would agree or disagree with their more experienced fellow members. The algorithms described in this study may be used in clinical practice to validate or invalidate the diagnoses. Algorithms or method developed here may also be used for pooling diagnostic knowledge for serving mankind. Here we have described a computational based low cost retinal diagnostic approach which can aid an ophthalmologist to quickly diagnose the various stages of DR. This system can accept retinal images and can successfully detect any pathological condition associated with DR.