Toward Deep Learning-Based Human Target Analysis

In this chapter, we describe methods toward deep learning-based human target analysis. Firstly, human target analysis in 2D and 3D domains of radar signal is introduced. Furthermore, range-Doppler surface for human target analysis using ultra-wideband radar is described. The construction of range-Doppler surface involves range-Doppler imaging, adaptive threshold detection, and isosurface extraction. In comparison with micro-Doppler profiles and high-resolution range profiles, range-Doppler surface contains range, Doppler, and time information simultaneously. An ellipsoid-based human motion model is designed for validation. Range-Doppler surfaces simulated for different human activities are demonstrated and discussed. With the rapid emergence of deep learning, the development of radar target recognition has been accelerated. We describe several deep learning algorithms for human target analysis. Finally, a few future research considerations are listed to spark inspiration

Dual-band polarimetric HRRP recognition via a brain-inspired multi-channel fusion feature extraction network

Radar high-resolution range profile (HRRP) provides geometric and structural information of target, which is important for radar automatic target recognition (RATR). However, due to the limited information dimension of HRRP, achieving accurate target recognition is challenging in applications. In recent years, with the rapid development of radar components and signal processing technology, the acquisition and use of target multi-frequency and polarization scattering information has become a significant way to improve target recognition performance. Meanwhile, deep learning inspired by the human brain has shown great promise in pattern recognition applications. In this paper, a Multi-channel Fusion Feature Extraction Network (MFFE-Net) inspired by the human brain is proposed for dual-band polarimetric HRRP, aiming at addressing the challenges faced in HRRP target recognition. In the proposed network, inspired by the human brain’s multi-dimensional information interaction, the similarity and difference features of dual-frequency HRRP are first extracted to realize the interactive fusion of frequency features. Then, inspired by the human brain’s selective attention mechanism, the interactive weights are obtained for multi-polarization features and multi-scale representation, enabling feature aggregation and multi-scale fusion. Finally, inspired by the human brain’s hierarchical learning mechanism, the layer-by-layer feature extraction and fusion with residual connections are designed to enhance the separability of features. Experiments on simulated and measured datasets verify the accurate recognition capability of MFFE-Net, and ablative studies are conducted to confirm the effectiveness of components of network for recognition

An introduction to radar Automatic Target Recognition (ATR) technology in ground-based radar systems

This paper presents a brief examination of Automatic Target Recognition (ATR) technology within ground-based radar systems. It offers a lucid comprehension of the ATR concept, delves into its historical milestones, and categorizes ATR methods according to different scattering regions. By incorporating ATR solutions into radar systems, this study demonstrates the expansion of radar detection ranges and the enhancement of tracking capabilities, leading to superior situational awareness. Drawing insights from the Russo-Ukrainian War, the paper highlights three pressing radar applications that urgently necessitate ATR technology: detecting stealth aircraft, countering small drones, and implementing anti-jamming measures. Anticipating the next wave of radar ATR research, the study predicts a surge in cognitive radar and machine learning (ML)-driven algorithms. These emerging methodologies aspire to confront challenges associated with system adaptation, real-time recognition, and environmental adaptability. Ultimately, ATR stands poised to revolutionize conventional radar systems, ushering in an era of 4D sensing capabilities

Application of the fruit fly optimization algorithm to an optimized neural network model in radar target recognition

With the development of computer technology, there are more and more algorithms and models for data processing and analysis, which brings a new direction to radar target recognition. This study mainly analyzed the recognition of high resolution range profile (HRRP) in radar target recognition and applied the generalized regression neural network (GRNN) model for HRRP recognition. In order to improve the performance of HRRP, the fruit fly optimization algorithm (FOA) algorithm was improved to optimize the parameters of the GRNN model. Simulation experiments were carried out on three types of aircraft. The improved FOA-GRNN (IFOA-GRNN) model was compared with the radial basis function (RBF) and GRNN models. The results showed that the IFOA-GRNN model had a better convergence accuracy, the highest average recognition rate (96.4 %), the shortest average calculation time (275 s), and a good recognition rate under noise in-terference. The experimental results show that the IFOA-GRNN model has a good performance in radar target recognition and can be further promoted and applied in practice

Information-Theoretic Active Perception for Multi-Robot Teams

Multi-robot teams that intelligently gather information have the potential to transform industries as diverse as agriculture, space exploration, mining, environmental monitoring, search and rescue, and construction. Despite large amounts of research effort on active perception problems, there still remain significant challenges. In this thesis, we present a variety of information-theoretic control policies that enable teams of robots to efficiently estimate different quantities of interest. Although these policies are intractable in general, we develop a series of approximations that make them suitable for real time use. We begin by presenting a unified estimation and control scheme based on Shannon\u27s mutual information that lets small teams of robots equipped with range-only sensors track a single static target. By creating approximate representations, we substantially reduce the complexity of this approach, letting the team track a mobile target. We then scale this approach to larger teams that need to localize a large and unknown number of targets. We also examine information-theoretic control policies to autonomously construct 3D maps with ground and aerial robots. By using Cauchy-Schwarz quadratic mutual information, we show substantial computational improvements over similar information-theoretic measures. To map environments faster, we adopt a hierarchical planning approach which incorporates trajectory optimization so that robots can quickly determine feasible and locally optimal trajectories. Finally, we present a high-level planning algorithm that enables heterogeneous robots to cooperatively construct maps

UNDERSTANDING THE IMPACT THE HOSPITAL READMISSION RATE PROGRAM AND VALUE BASED PURCHASING HAS HAD ON THE FINANCIAL VIABILITY OF ACADEMIC HEALTH CENTERS, 2011 TO 2015.

Academic Health Centers (AHCs) hold a unique place in today’s health care environment. They service their communities through a tripartite mission of education, research, and provision of complex care to disadvantaged populations. To achieve this mission, AHCs face challenges in funding and cost containment compared to non-AHCs. Additionally, the implementation of government programs like the Hospital Readmission Rate Program (HRRP) and Value Based Purchasing (VBP) have the potential to affect AHCs differently from non-AHCs. While AHC’s unique features are known and there has been research to date on HRRP and VBP, literature has yet to statistically explore the financial differences between AHCs and non-AHCs and how HRRP and VBP may have differentially affected AHCs compared to non-AHCs. The objectives of this study are to explore financial differences between AHCs and non-AHCs and the impact that HRRP and VBP has had on these two types of organizations through the use of a contingency theory framework. Contingency theory is an organizational theory that seeks to explain variations in organizational performance over time by studying internal and external environmental influences. Guided by Contingency Theory, the study used a non-randomized, quasi-experimental, retrospective study design to evaluate two hypotheses. The study sample consisted of a total of 10,157 (991 AHCs) US non-rural hospital years from 2011 through 2015. The study used operating margin and total margin as the key measures of hospital financial performance for the dependent variables. HRRP and VBP were combined into a single independent variable along with hospital type differentiating AHCs from non-AHCs. Covariates of Herfindahl-Hirschman Index, Medicaid expansion, health system affiliation, and ownership structure were used to control for other environmental influences. A repeated measure analysis of variance was employed to test the difference between the two hospital groups in isolation of HRRP, VBP, and covariates and a repeated measure analysis of variance with covariance was used to test the full model, which incorporated HRRP, VBP, and covariates. The results of the analysis support the significance of HRRP and VBP on hospital operating margin, but the results did not support a differential effect of these programs on AHCs as compared to non-AHCs. While the results did not support the two main hypotheses, it did provide valuable insight into the financial differences between AHCs and non-AHCs and the importance of VBP and HRRP on hospital financial performance. The results also provide important policy implications and thoughts on potential managerial actions given the HRRP and VBP programs