Radar target tracking via robust linear filtering

In this letter, we provide a robust version of a linear Kalman filter for target tracking based on a measurement conversion technique on the nonlinear radar measurements. We prove that the state estimation error is bounded in a probabilistic sense. We compare our approach with the current state of the art in converted radar measurement-based linear filtering.

Optimality analysis of sensor-target geometries in Passive localization: Part 1 - bearing-only localization

In this paper we characterize the relative sensor-target geometry for bearing-only localization in R2. We analyze the geometry in terms of the Cramer-Rao inequality and the corresponding Fisher information matrix, aiming to characterize and state explicit results in terms of the potential localization performance. In particular, a number of interesting results are rigorously derived which highlight erroneous assumptions often made in the existing literature.

Optimality analysis of sensor-target geometries in passive localization: Part 2 - time-of-arrival based localization

In this paper we characterize the relative sensor-target geometry in R2 in terms of potential localization performance for time-of-arrival based localization. Our aim is to characterize those relative sensor-target geometries which minimize the relative Cramer-Rao lower bound.

Target tracking with range and bearing measurements via robust linear filtering

In this paper we provide a robust version of a linear Kalman filter for target tracking with nonlinear range and bearing measurements. Moreover, we prove that the state estimation error is bounded in a probabilistic sense. We compare our approach with the current state of the art in converted radar measurement based linear filtering.

Vision based target tracking using robust linear filtering

The use of perspective projection in tracking a target from a video stream involves nonlinear observations. The target dynamics, however, are modeled in Cartesian coordinates and result in a linear system. In this paper we provide a robust version of a linear Kalman filter and perform a measurement conversion technique on the nonlinear optical measurements. We show that our linear robust filter significantly outperforms the Extended Kalman Filter. Moreover, we prove that the state estimation error is bounded in a probabilistic sense.

A novel application of a microaccelerometer for target classification

This paper presents a novel method of target classification by means of a microaccelerometer. Its principle is that the seismic signals from moving vehicle targets are detected by a microaccelerometer, and targets are automatically recognized by the advanced signal processing method. The detection system based on the microaccelerometer is small in size, light in weight, has low power consumption and low cost, and can work under severe circumstances for many different applications, such as battlefield surveillance, traffic monitoring, etc. In order to extract features of seismic signals stimulated by different vehicle targets and to recognize targets, seismic properties of typical vehicle targets are researched in this paper. A technique of artificial neural networks (ANNs) is applied to the recognition of seismic signals for vehicle targets. An improved back propagation (BP) algorithm and ANN architecture have been presented to improve learning speed and avoid local minimum points in error curve. The improved BP algorithm has been used for classification and recognition of seismic signals of vehicle targets in the outdoor environment. Through experiments, it can be proven that target seismic properties acquired are correct, ANN is effective to solve the problem of classification and recognition of moving vehicle targets, and the microaccelerometer can be used in vehicle target recognition.

Drug bioactivation, covalent binding to target proteins and toxicity relevance

A number of therapeutic drugs with different structures and mechanisms of action have been reported to undergo metabolic activation by Phase I or Phase II drug-metabolizing enzymes. The bioactivation gives rise to reactive metabolites/intermediates, which readily confer covalent binding to various target proteins by nucleophilic substitution and/or Schiff's base mechanism. These drugs include analgesics (e.g., acetaminophen), antibacterial agents (e.g., sulfonamides and macrolide antibiotics), anticancer drugs (e.g., irinotecan), antiepileptic drugs (e.g., carbamazepine), anti-HIV agents (e.g., ritonavir), antipsychotics (e.g., clozapine), cardiovascular drugs (e.g., procainamide and hydralazine), immunosupressants (e.g., cyclosporine A), inhalational anesthetics (e.g., halothane), nonsteroidal anti-inflammatory drugs (NSAIDSs) (e.g., diclofenac), and steroids and their receptor modulators (e.g., estrogens and tamoxifen). Some herbal and dietary constituents are also bioactivated to reactive metabolites capable of binding covalently and inactivating cytochrome P450s (CYPs). A number of important target proteins of drugs have been identified by mass spectrometric techniques and proteomic approaches. The covalent binding and formation of drug-protein adducts are generally considered to be related to drug toxicity, and selective protein covalent binding by drug metabolites may lead to selective organ toxicity. However, the mechanisms involved in the protein adduct-induced toxicity are largely undefined, although it has been suggested that drug-protein adducts may cause toxicity either through impairing physiological functions of the modified proteins or through immune-mediated mechanisms. In addition, mechanism-based inhibition of CYPs may result in toxic drug-drug interactions. The clinical consequences of drug bioactivation and covalent binding to proteins are unpredictable, depending on many factors that are associated with the administered drugs and patients. Further studies using proteomic and genomic approaches with high throughput capacity are needed to identify the protein targetsof reactive drug metabolites, and to elucidate the structure-activity relationships of drug's covalent binding to proteins and their clinical outcomes.

Minimising risks in IT outsourcing: choosing target services

This paper provides a risk-based framework for deciding on which IT services to outsource and which to keep in-house. This framework considers the probabilities both of negative outcomes, and of failing to achieve positive outcomes. The authors examine the major components of outsourcing risk and their drivers, and from this derive a series of questions decision-makers can ask when deciding what sourcing options to adopt for different services. The framework was developed on the basis of five years of qualitative and quantitative research into the experiences of organizations involved in outsourcing IT.

A strategic approach to target marketing-choosing the preferred generic customer : a model of shareholder value performance

In recent years the contribution of the marketing function has changed and interest now centres on its contribution to a firm’s financial performance. The Marketing Science Institute in the USA has stated that it is the number one marketing issue facing corporate America. The Australian Marketing Institute is promoting a set of marketing metrics that will help Australian firms measure the function’s contribution to shareholder value creation. Much of the literature relates notions such as customer satisfaction and other marketing activities with a firm’s profit. A missing link appears to be the choice firms make in terms of which customer groups to target and the resultant impact on shareholder value performance. The generic customer groups comprise: existing customers, former customers and prospects. A review of the literature reveals that marketing costs and benefits vary across these groups. The challenge for management is to determine which group represents the best target and to allocate scarce marketing resources accordingly. The task is made even more challenging because the economic value of members within each group also varies and some product lines may be unprofitable and therefore, may not be worth pursuing. To generate superior shareholder value it may not simply be the case of acquiring the maximum number of new customers from any source but to find the appropriate mix of the generic customer groups and manage the individual customer relationships accordingly. This paper seeks to firstly summarise and review the recent literature on marketing and its relationship to shareholder value and secondly to propose a model for allocating marketing resources across generic customer groups in order to generate improved shareholder value performance. Importantly, the model not only covers increasing business with customers but also shedding customers or shedding the extent of business conducted with customers as means of generating shareholder value.