In this paper, we propose a distributed multiobject tracking algorithm through the use of multi-Bernoulli (MB) filter based on generalized covariance intersection (G-CI). Our analyses show that the G-CI fusion with two MB posterior distributions does not admit an accurate closed-form expression. To solve this problem, we first approximate the fused posterior as the unlabeled version of $\delta$ -generalized labeled MB distribution, referred to as generalized MB (GMB) distribution. Then, to allow the subsequent fusion with another MB posterior distribution, e.g., fusion with a third sensor node in the sensor network, or fusion in the feedback working mode, we further approximate the fused GMB posterior distribution as an MB distribution which matches its first-order statistical moment. The proposed fusion algorithm is implemented using sequential Monte Carlo technique and its performance is highlighted by numerical results.

Distributed Fusion With Multi-Bernoulli Filter Based on Generalized Covariance Intersection

DIGITAL COMMUNICATION ADVANTAGES AND DISADVANTAGES TOTALECER. INTRODUCTION TO DIGITAL COMMUNICATIONS DIGITAL. INTRODUCTION TO EECS II DIGITAL COMMUNICATION SYSTEMS. PRINCIPLES OF DIGITAL COMMUNICATION SHARIF UNIVERSITY OF. SIMON S HAYKIN DIGITAL COMMUNICATION SYSTEMS WILEY 2013. DIGITAL COMMUNICATION SYSTEMS. DIGITAL COMMUNICATION SYSTEMS NPTEL. DIGITAL COMMUNICATION SYSTEM MODULATION BANDWIDTH. DIGITAL COMMUNICATION SYSTEMS FREE EBOOKS DOWNLOAD. COMMUNICATION SYSTEMS ANALOG VS DIGITAL WIKIBOOKS OPEN. DIGITAL COMMUNICATION SYSTEMS WILEY COM. COMMUNICATION SYSTEMS II. DIGITAL COMMUNICATION DEFINITION OF DIGITAL. MODERN DIGITAL AND ANALOG COMMUNICATION THE AMAZON COM. DIGITAL COMMUNICATION SYSTEMS MCQ SERIES. DIGITAL COMMUNICATIONS FUNDAMENTALS AND APPLICATIONS. DIGITAL COMMUNICATION SYSTEMS 1ST EDITION AMAZON COM. DIGITAL COMMUNICATIONS SYSTEMS MASTER’S DEGREE. DIGITAL COMMUNICATION SYSTEM SLIDESHARE. DIGITAL COMMUNICATION SYSTEMS GOOGLE BOOKS. ANALOG AND DIGITAL COMMUNICATION SYSTEMS BY MARTIN S RODEN. WILEY DIGITAL COMMUNICATION SYSTEMS 1ST EDITION SIMON. DIGITAL COMMUNICATION SYSTEM MANAGEMENT STUDY GUIDE. DIGITAL MODULATION IN COMMUNICATIONS SYSTEMS – AN INTRODUCTION. DATA TRANSMISSION WIKIPEDIA. DIGITAL COMMUNICATION SYSTEMS EBOOK BY SIMON HAYKIN. DESIGN OF A DIGITAL COMMUNICATION SYSTEM SIGNAL PROCESSING. DIGITAL COMMUNICATIONS J S CHITODE GOOGLE BOOKS. INTRODUCTIONTOCOMMUNICATIONSYSTEMS UC SANTA BARBARA. INTRODUCTION TO DIGITAL COMMUNICATION MIT OPENCOURSEWARE. INTRODUCTION TO DIGITAL COMMUNICATIONS SYSTEM. DCSL DIGITAL COMMUNICATION SYSTEMS LIMITED. TYPES OF DIGITAL COMMUNICATION TECHWALLA COM. TELECOMMUNICATION WIKIPEDIA. DIGITAL COMMUNICATION SYSTEMS UNIVERSITY OF OTTAWA. DIGITAL COMMUNICATION BY SIMON HAYKIN PDF 4ED TOTALECER. FUNDAMENTALS OF DIGITAL COMMUNICATIONS SYSTEMS 1 1. COMMUNICATION SYSTEMS ADVANTAGES OF DIGITAL TRANSMISSION. 3 INTRODUCTION TO DIGITAL COMMUNICATION SYSTEMS YOUTUBE. DIGITAL COMMUNICATION SYSTEMS SLIDESHARE. THEORY AND DESIGN OF DIGITAL COMMUNICATION SYSTEMS. ELEMENTS OF A DIGITAL COMMUNICATION SYSTEM. DIGITAL COMMUNICATION SYSTEMS UOTECHNOLOGY EDU IQ. ELEMENTS OF DIGITAL COMMUNICATION SYSTEMS. DIGITAL AND ANALOG COMMUNICATION SYSTEMS. DIGITAL AMP ANALOG COMMUNICATION SYSTEMS 8TH EDITION PDF. DIGITAL COMMUNICATION SYSTEM QUESTIONS AND ANSWERS. DIGITAL COMMUNICATIONS TRAINING SYSTEMS LAB VOLT. DIGITAL COMMUNICATION TUTORIAL. EE2EE2 4 COMMUNICATION SYSTEMS4 COMMUNICATION SYSTEMS

Digital Communication Systems

Multichannel adaptive signal detection jointly uses the test and training data to form an adaptive detector, and then make a decision on whether a target exists or not Remarkably, the resulting adaptive detectors usually possess the constant false alarm rate (CFAR) properties, and hence no additional CFAR processing is needed Filtering is not needed as a processing procedure either, since the function of filtering is embedded in the adaptive detector Moreover, adaptive detection usually exhibits better detection performance than the filtering-then-CFAR detection technique Multichannel adaptive signal detection has been more than 30 years since the first multichannel adaptive detector was proposed by Kelly in 1986 However, there are fewer overview articles on this topic In this paper we give a tutorial overview of multichannel adaptive signal detection, with emphasis on Gaussian background We present the main deign criteria for adaptive detectors, investigate the relationship between adaptive detection and filtering-then-CFAR detection, relationship between adaptive detectors and adaptive filters, summarize typical adaptive detectors, show numerical examples, give comprehensive literature review, and discuss some possible further research tracks

Multichannel adaptive signal detection: Basic theory and literature review

For colocated multiple-input multiple-output (MIMO) radar target detection in colored noise, we propose two adaptive detectors according to the Rao andWald test criteria. These detectors do not need training data and possess constant false alarm rate properties. We investigate the manner in which they work. From a detection viewpoint, we show that there is no need for matched filtering for colocated MIMO radar.We derive the statistical distributions of the proposed detectors and then obtain the analytical expressions for the probabilities of false alarm and detection for both deterministic and random signals. Numerical examples are provided to compare the detection performance of the Rao andWald tests with an existing detector.

Adaptive detection without training data in colocated MIMO radar

This study considers the detection problem of multiple-input multiple-output (MIMO) radar in the presence of a compound-Gaussian clutter. Two new detectors based on the Rao and Wald criteria are devised under the known covariance matrix. The theoretical expression for the probability of false alarm is developed and constant false alarm (CFA) rate behaviour is remarked on. Furthermore, the fully adaptive approximated Rao and Wald tests are investigated in place of the exact covariance matrix with a suitable estimator using secondary data. Finally, several numerical simulations with typical parameters are provided and discussed.

Rao and Wald tests design of multiple-input multiple-output radar in compound-Gaussian clutter

This study mainly deals with the problem of multiple-input multiple-output (MIMO) radar in compound-Gaussian clutter-dominated scenario with unknown covariance matrix. The general MIMO model, with widely separated sub-arrays and co-located antennas at each sub-array, is adopted. First, the signal model is developed to compound-Gaussian clutter, and then, the generalised likelihood rate test (GLRT) and Rao detectors are derived with known covariance matrix. To make the detectors fully adaptive, the secondary with signal-free data is collected to estimate the covariance. A thorough performance assessment is also given, which considers the sense with co-located antennas configuration of transmitters and receivers array. Via several numerical examples, it is shown that the derived GLRT and Rao detectors can provide excellent detection performance in spikier clutter compared with the detector, which is derived by Jian Li in Gaussian clutter. To this end, the Rao detector outperforms best.

Multiple-input multiple-output radar detectors design in non-Gaussian clutter

This paper deals with the multiple targets localization problem via multi-channel ultra-wideband (UWB) imaging radar non-line-of-sight (NLOS) signal processing. A novel matching-based radar imaging algorithm is proposed to obtain the positions of multiple targets in the L-shaped corner scenario with complex multipath ghost signals. Firstly, a multipath propagation model for the multiple targets scenario is established. Then the positions of the actual multipath ghosts are extracted from the radar image, and the candidate targets corresponding to these multipath ghosts are derived. Secondly, the ellipse-cross-localization method is proposed to obtain the positions of the candidate multipath ghosts, followed by two defined matching factors to measure the similarity between actual and candidate multipath ghosts. According to the similarity, decision rules are designed to determine the actual targets. Compared with the localization algorithm based on one-dimensional range profile, the proposed algorithm can effectively cope with the cases of multiple targets, even in the cases of rough walls and noise. Finally, simulations and experimental data are used to validate the effectiveness of the proposed algorithm.

Multiple Targets Localization Behind L-Shaped Corner via UWB Radar

Motivated by the intrinsic dynamics of physical motion as well as establishment of target motion model, this article addresses the problem of human motion recognition with ultra wide band (UWB) through-the-wall radar (TWR) in a novel view of range profile serialization. Specifically, we first convert the original radar echoes into range profiles. Then, an auto-encoder network (AEN) with three dense layers is adopted to reduce the dimension and extract the features of each range profile. After that, a gated recurrent unit (GRU) network with two hidden layers is employed to deal with the features of each time-range slice and output the recognition results at each slice in real time. Finally, experimental data with respect to four different behind-wall human motions is collected by self-developed UWB TWR to validate the effectiveness of the proposed model. The results show that the proposed model can validly recognize the human motion serialization and achieve 93% recognition accuracy within the initial 20% duration of the activities (the average durations are 4s, 5.5s, 3s and 4.5s), which is of great significance for real-time human motion recognition.

/pdf/human-motion-serialization-recognition-with-through-the-wall-1le5w3hgez.pdf

Human Motion Serialization Recognition With Through-the-Wall Radar

This paper deals with the real-time recognition from multiple spatial angles of concealed human motions with ultra wide band (UWB) through-the-wall radar (TWR). To conquer the performance loss incurred by diverse human motion in a single view, a multi-view real-time human motion recognition model based on ensemble learning is proposed. Specifically, we first proposes a multi-view human motion recognition model based on Stacking parallel ensemble learning algorithm, which is used to realize the real-time human motion recognition based on UWB TWR. Secondly, in order to address the irrationality of the existing accuracy evaluation criteria to evaluate the real-time motion recognition algorithm, a real-time model evaluation criterion based on normalized harmonic weighted intersection over union (NHW-IOU) is proposed. Finally, the collected multi-view human motion data are used to verify the effectiveness of the proposed algorithm. The actual measurement results show that the average recognition performance of the proposed Stacking model has improved by 14.76% compared with the single-view model, which is of great significance for using multi-view data to improve network performance. Moreover, compared with the bi-directional long short-term memory (Bi-LSTM) and Gated Recurrent Unit (GRU) models, the proposed model has better performance in accuracy and time delay.

Xiaqing Yang

Papers

Rao and Wald tests design of multiple-input multiple-output radar in compound-Gaussian clutter

Multiple-input multiple-output radar detectors design in non-Gaussian clutter

Multiple Targets Localization Behind L-Shaped Corner via UWB Radar

Human Motion Serialization Recognition With Through-the-Wall Radar

Multi-View Real-Time Human Motion Recognition Based on Ensemble Learning