Multi-static Parameter Estimation in the Near/Far Field Beam Space for Integrated Sensing and Communication Applications

This work proposes a maximum likelihood (ML)-based parameter estimation framework for a millimeter wave (mmWave) integrated sensing and communication (ISAC) system in a multi-static configuration using energy-efficient hybrid digital-analog arrays. Due to the typically large arrays deployed in the higher frequency bands to mitigate isotropic path loss, such arrays may operate in the near-field regime. The proposed parameter estimation in this work consists of a two-stage estimation process, where the first stage is based on far-field assumptions, and is used to obtain a first estimate of the target parameters. In cases where the target is determined to be in the near-field of the arrays, a second estimation based on near-field assumptions is carried out to obtain more accurate estimates. In particular, we select beamfocusing array weights designed to achieve a constant gain over an extended spatial region and re-estimate the target parameters at the receivers. We evaluate the effectiveness of the proposed framework in numerous scenarios through numerical simulations and demonstrate the impact of the custom-designed flat-gain beamfocusing codewords in increasing the communication performance of the system.Comment: 16 page

Aerial moving target detection based on motion vector field analysis

An efficient automatic detection strategy for aerial moving targets in airborne forward-looking infrared (FLIR) imagery is presented in this paper. Airborne cameras induce a global motion over all objects in the image, that invalidates motion-based segmentation techniques for static cameras. To overcome this drawback, previous works compensate the camera ego-motion. However, this approach is too much dependent on the quality of the ego-motion compensation, tending towards an over-detection. In this work, the proposed strategy estimates a robust motion vector field, free of erroneous vectors. Motion vectors are classified into different independent moving objects, corresponding to background objects and aerial targets. The aerial targets are directly segmented using their associated motion vectors. This detection strategy has a low computational cost, since no compensation process or motion-based technique needs to be applied. Excellent results have been obtained over real FLIR sequences

Ecological non-linear state space model selection via adaptive particle Markov chain Monte Carlo (AdPMCMC)

We develop a novel advanced Particle Markov chain Monte Carlo algorithm that is capable of sampling from the posterior distribution of non-linear state space models for both the unobserved latent states and the unknown model parameters. We apply this novel methodology to five population growth models, including models with strong and weak Allee effects, and test if it can efficiently sample from the complex likelihood surface that is often associated with these models. Utilising real and also synthetically generated data sets we examine the extent to which observation noise and process error may frustrate efforts to choose between these models. Our novel algorithm involves an Adaptive Metropolis proposal combined with an SIR Particle MCMC algorithm (AdPMCMC). We show that the AdPMCMC algorithm samples complex, high-dimensional spaces efficiently, and is therefore superior to standard Gibbs or Metropolis Hastings algorithms that are known to converge very slowly when applied to the non-linear state space ecological models considered in this paper. Additionally, we show how the AdPMCMC algorithm can be used to recursively estimate the Bayesian Cram\'er-Rao Lower Bound of Tichavsk\'y (1998). We derive expressions for these Cram\'er-Rao Bounds and estimate them for the models considered. Our results demonstrate a number of important features of common population growth models, most notably their multi-modal posterior surfaces and dependence between the static and dynamic parameters. We conclude by sampling from the posterior distribution of each of the models, and use Bayes factors to highlight how observation noise significantly diminishes our ability to select among some of the models, particularly those that are designed to reproduce an Allee effect

The potential of energy substitution in the industrial sector

The extent of substitutability between energy and the other factors (i.e. labour and capital) and between individual fuels (coal, electricity, natural gas and petroleum) is an extremely important question and quite central to energy policy, planning and analysis. This study considers the possibilities of energy substitution in the industrial sector of 5 major energy producers of the developing world (Brazil, China, India, Indonesia and Venezuela). The theoretical model utilized in the study is the two-stage trans-log cost function. The model is estimated using time series data over the period 1978 to 2003. The results indicate substantial inter-factor and inter-fuel substitutions are possible in the industrial sector. Substitution possibilities were found (1) between capital and labour, between capital and energy and between energy and labour in the inter-factor model and (2) for most combination's of fuel types in the inter-fuel model. This implies that there is some flexibility in energy policy options and energy utilization

Voltage security evaluation based on perturbation method

This is the post-print version of the final paper published in International Journal of Electrical Power & Energy Systems. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2009 Elsevier B.V.This paper proposes a new algorithm for estimating voltage security margin. The algorithm is based on the perturbation method and has significant computational efficiency. The proposed algorithm can be used for on-line voltage security evaluation. It has been validated using IEEE-14, IEEE-30 and IEEE-57 bus systems. Results from the tests show higher efficiency and smaller error margins compared to continuation power flow (CPF) method. Voltage collapse is a serious threat to the security of stressed power systems; therefore, voltage security (VS) has become a major challenge for management of power systems. The motivation for this research is a direct consequence of the deregulation of electricity industries and markets worldwide

A Dynamic Approach to Linear Statistical Calibration with an Application in Microwave Radiometry

The problem of statistical calibration of a measuring instrument can be framed both in a statistical context as well as in an engineering context. In the first, the problem is dealt with by distinguishing between the 'classical' approach and the 'inverse' regression approach. Both of these models are static models and are used to estimate exact measurements from measurements that are affected by error. In the engineering context, the variables of interest are considered to be taken at the time at which you observe it. The Bayesian time series analysis method of Dynamic Linear Models (DLM) can be used to monitor the evolution of the measures, thus introducing an dynamic approach to statistical calibration. The research presented employs the use of Bayesian methodology to perform statistical calibration. The DLM's framework is used to capture the time-varying parameters that maybe changing or drifting over time. Two separate DLM based models are presented in this paper. A simulation study is conducted where the two models are compared to some well known 'static' calibration approaches in the literature from both the frequentist and Bayesian perspectives. The focus of the study is to understand how well the dynamic statistical calibration methods performs under various signal-to-noise ratios, r. The posterior distributions of the estimated calibration points as well as the 95% coverage intervals are compared by statistical summaries. These dynamic methods are applied to a microwave radiometry data set.Comment: 26 pages, 10 figure