scholarly journals A Theoretical Analysis of Mobility Detection in Connectivity-Based Localization for Short-Range Networks

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1162
Author(s):  
Sangwoo Lee ◽  
Ilmu Byun ◽  
Sungjin Kim ◽  
Sunwoo Kim
Keyword(s):  
Theoretical Analysis ◽  
False Alarm ◽  
Minimum Distance ◽  
Short Range ◽  
Detection Threshold ◽  
Average Error ◽  
Mobility Models ◽  
Shadow Fading ◽  
Connectivity Information ◽  
Mobility Tracking

This paper presents a theoretical analysis of mobility detection in connectivity-based localization, which exploits connectivity information as range measurements to anchors at a known location, to investigate how well and how precise mobility can be detected with connectivity in short-range networks. We derive mobility detection, miss detection, and false alarm probabilities in terms of a mobility detection threshold, defined as the minimum distance to detect the mobility, under the shadow fading channel and arbitrary mobility models to take into account practical and general scenarios. Based on the derivations, we address the threshold determination with the criteria in the sense of the minimum average error from miss detection and false alarm. Numerical and simulation evaluations are performed to verify our theoretical derivations, to show that increasing anchor numbers can improve the mobility detection probability with a smaller detection threshold, and that the probabilities are bounded by the weights of miss detection and false alarm. This work is the first attempt at addressing the performance of mobility detection using connectivity, and it can be utilized as a baseline for connectivity-based mobility tracking.

scholarly journals A Compact UWB Indoor and Through-Wall Radar with Precise Ranging and Tracking

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Yinan Yu ◽  
Jian Yang ◽  
Tomas McKelvey ◽  
Borys Stoew
Keyword(s):  
Signal Processing ◽  
Theoretical Analysis ◽  
Short Range ◽  
Indoor Environment ◽  
Low Cost ◽  
Moving Target ◽  
Radiation Characteristics ◽  
Uwb Radar ◽  
Signal Processing Algorithms ◽  
Processing Algorithms

Ultrawideband (UWB) technology has many advantages compared to its narrowband counterpart in many applications. We present a new compact low-cost UWB radar for indoor and through-wall scenario. The focus of the paper is on the development of the signal processing algorithms for ranging and tracking, taking into account the particular properties of the UWB CMOS transceiver and the radiation characteristics of the antennas. Theoretical analysis for the algorithms and their evaluations by measurements are presented in the paper. The ranging resolution of this UWB radar has achieved 1-2 mm RMS accuracy for a moving target in indoor environment over a short range, and Kalman tracking algorithm functions well for the through-wall detection.

scholarly journals Offshore Platform Extraction Using RadarSat-2 SAR Imagery: A Two-Parameter CFAR Method Based on Maximum Entropy

Entropy ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 556 ◽  
Author(s):  
Qi Wang ◽  
Jing Zhang ◽  
Fenzhen Su
Keyword(s):  
False Alarm ◽  
Maximum Entropy ◽  
Target Detection ◽  
Detection Threshold ◽  
River Estuary ◽  
Offshore Platform ◽  
Accuracy Evaluation ◽  
Oil And Gas Development ◽  
Offshore Oil ◽  
Two Parameter

The ability to determine the number and location of offshore platforms is of great significance for offshore oil spill monitoring and offshore oil and gas development. Considering the problem that the detection threshold parameters of the two-parameter constant false alarm rate (CFAR) algorithm require manual and repeated adjustment of the during the extraction of offshore platform targets, this paper proposes a two-parameter CFAR target detection method based on maximum entropy based on information entropy theory. First, a series of threshold parameters are obtained using the two-parameter CFAR algorithm for target detection. Then, according to the maximum entropy principle, the optimal threshold is estimated to obtain the target detection results of the possible offshore platform. Finally, the neighborhood analysis method is used to eliminate false alarm targets such as ships, and the final target of the offshore platform is obtained. In this study, we conducted offshore platform extraction experiments and an accuracy evaluation using data from the Pearl River Estuary Basin of the South China Sea. The results show that the proposed method for platform extraction achieves an accuracy rate of 97.5% and obtains the ideal offshore platform distribution information. Thus, the proposed method can objectively obtain the optimal target detection threshold parameters, greatly reduce the influence of subjective parameter setting on the extraction results during the target detection process and effectively extract offshore platform targets.

AN AUTOMATIC EVENT DETECTOR AT THE TONTO FOREST SEISMIC OBSERVATORY

Geophysics ◽  
1974 ◽  
Vol 39 (5) ◽  
pp. 633-643 ◽  
Author(s):  
R. R. Blandford
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Kuril Island ◽  
Detection Threshold ◽  
Maximum Effect ◽  
False Alarms ◽  
Constant False Alarm Rate ◽  
Statistical Bias ◽  
Short Period ◽  
On Line

The on‐line operation of an automatic event detector has been evaluated at the Tonto Forest Observatory short‐period seismic array. For 31 seismometers and one fixed threshold, the 90 percent incremental detection threshold on the Kuril Island beam, centered at Δ=70 degrees, is [Formula: see text] with an experimentally determined false alarm rate of 0.17 per day. This compares favorably with the capabilities of a human operator. Storms in the Kurils significantly affect the distribution of amplitudes of the F-statistic detection trace, and we estimate that most of the false alarms observed at the operating threshold can be traced to the statistical bias introduced by this storm‐generated energy. If the threshold were adjusted to maintain a constant false alarm rate, the maximum effect on the threshold magnitude would be [Formula: see text].

Predominance of short range Coulomb forces in phosphate-water interactions—a theoretical analysis

2016 ◽  
Vol 145 (11) ◽  
pp. 115101 ◽  
Author(s):  
Benjamin P. Fingerhut ◽  
Rene Costard ◽  
Thomas Elsaesser
Keyword(s):  
Theoretical Analysis ◽  
Short Range

KINETIC ROUGHENING AND INTERACTING REACTING SPECIES: NONLINEAR ASPECTS

Fractals ◽  
1993 ◽  
Vol 01 (03) ◽  
pp. 470-474 ◽  
Author(s):  
I.M. SOKOLOV ◽  
P. ARGYRAKIS ◽  
A. BLUMEN
Keyword(s):  
Monte Carlo ◽  
Theoretical Analysis ◽  
Diffusion Process ◽  
Monte Carlo Simulations ◽  
Short Range ◽  
Reaction Diffusion ◽  
Kinetic Roughening ◽  
Constant Rate ◽  
Good Agreement

We consider the A+B→0 reaction, in which particles interact through short-range forces. The analysis leads to expressions akin in form to those which describe kinetic roughening. In a situation in which particles are generated with a constant rate j0, their concentration n(t) grows as [Formula: see text] in d=1. Here the theoretical analysis predicts γ=1/5 and β=2/5, in very good agreement with direct Monte-Carlo simulations of the reaction-diffusion process.

Biologically inspired collective comparisons by robotic swarms

2011 ◽  
Vol 30 (5) ◽  
pp. 524-535 ◽  
Author(s):  
Chris A C Parker ◽  
Hong Zhang
Keyword(s):  
Theoretical Analysis ◽  
Short Range ◽  
Social Insect ◽  
Peer To Peer ◽  
Biologically Inspired ◽  
Peer Communication ◽  
Robotic Swarms ◽  
Robotic Swarm ◽  
Series Of Experiments ◽  
Comparison Strategy

Intelligent entities must often make decisions by comparing several candidate alternatives and selecting the best one. This is just as true for autonomous swarms as it is for solitary robots, but to date there has been little work to propose efficient comparison behaviors for autonomous robotic swarms that are not tied to specific environments. In this work, we examine an elegant collective comparison strategy that is used by at least three different species of social insect and adapt it for artificial systems. The behavior is particularly attractive for robotic implementations because it relies only on short range explicit peer-to-peer communication, eliminating the need for chemical trails or other forms of stigmergy. The proposed comparison strategy is proven to converge, and a series of experiments using real robots with noisy sensors is presented that validates our theoretical analysis. Using the proposed behavior, a robotic swarm is able to compare alternatives collectively more accurately than its member robots would be able to individually.

scholarly journals Robust Control for the Detection Threshold of CFAR Process in Cluttered Environments

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3904
Author(s):  
Jeong Hoon Shin ◽  
Youngjin Choi
Keyword(s):  
Robust Control ◽  
False Alarm ◽  
Detection Threshold ◽  
Detection Performance ◽  
Time Interval ◽  
Constant False Alarm Rate ◽  
Cluttered Environments ◽  
Probability Of False Alarm ◽  
Feedback Structure ◽  
Improved Performance

The constant false alarm rate (CFAR) process is essential for target detection in radar systems. Although the detection performance of the CFAR process is normally guaranteed in noise-limited environments, it may be dramatically degraded in clutter-limited environments since the probabilistic characteristics for clutter are unknown. Therefore, sophisticated CFAR processes that suppress the effect of clutter can be used in actual applications. However, these methods have the fundamental limitation of detection performance because there is no feedback structure in terms of the probability of false alarm for determining the detection threshold. This paper presents a robust control scheme for adjusting the detection threshold of the CFAR process while estimating the clutter measurement density (CMD) that uses only the measurement sets over a finite time interval in order to adapt to time-varying cluttered environments, and the probability of target existence with finite measurement sets required for estimating CMD is derived. The improved performance of the proposed method was verified by simulation experiments for heterogeneous situations.

scholarly journals An analysis of energy detector based on improved approximations of the chi-square distributions

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Felipe G. M. Elias ◽  
Evelio M. G. Fernández
Keyword(s):  
False Alarm ◽  
Detection Probability ◽  
Total Error ◽  
Detection Threshold ◽  
False Alarm Probability ◽  
Optimal Number ◽  
Energy Detector ◽  
Chi Square ◽  
Total Error Rate ◽  
Wide Range

AbstractClosed-form expressions for the detection probability, the false alarm probability and the energy detector constant threshold are derived using approximations of the central chi-square and non-central chi-square distributions. The approximations used show closer proximity to the original functions when compared to the expressions used in the literature. The novel expressions allow gains up to 6% and 16% in terms of measured false alarm and miss-detection probability, respectively, if compared to the Central Limit Theorem approach. The throughput of cognitive network is also enhanced when these novel expressions are implemented, providing gains up to 9%. New equations are also presented that minimize the total error rate to obtain the detection threshold and the optimal number of samples. The analytical results match the results of the simulation for a wide range of SNR values.

scholarly journals Target Detection in High-Resolution SAR Image via Iterating Outliers and Recursing Saliency Depth

2021 ◽  
Vol 13 (21) ◽  
pp. 4315
Author(s):  
Zongyong Cui ◽  
Yi Qin ◽  
Yating Zhong ◽  
Zongjie Cao ◽  
Haiyi Yang
Keyword(s):  
High Resolution ◽  
False Alarm ◽  
Target Detection ◽  
Detection Threshold ◽  
Graph Model ◽  
Conditional Entropy ◽  
Image Features ◽  
Feature Model ◽  
Sar Image ◽  
Constant False Alarm Rate

In dealing with the problem of target detection in high-resolution Synthetic Aperture Radar (SAR) images, segmenting before detecting is the most commonly used approach. After the image is segmented by the superpixel method, the segmented area is usually a mixture of target and background, but the existing regional feature model does not take this into account, and cannot accurately reflect the features of the SAR image. Therefore, we propose a target detection method based on iterative outliers and recursive saliency depth. At first, we use the conditional entropy to model the features of the superpixel region, which is more in line with the actual SAR image features. Then, through iterative anomaly detection, we achieve effective background selection and detection threshold design. After that, recursing saliency depth is used to enhance the effective outliers and suppress the background false alarm to realize the correction of superpixel saliency value. Finally, the local graph model is used to optimize the detection results. Compared with Constant False Alarm Rate (CFAR) and Weighted Information Entropy (WIE) methods, the results show that our method has better performance and is more in line with the actual situation.

scholarly journals Inhomogeneous Analysis of Novel Model of CFAR Approaches to Detect Two-Degrees of Freedom Partially-Correlated χ2-Targets

2021 ◽  
Vol 20 ◽  
pp. 28-43
Author(s):  
Mohamed Bakry El-Mashade
Keyword(s):  
False Alarm ◽  
Degrees Of Freedom ◽  
Detection Threshold ◽  
Detection Performance ◽  
Closed Form Expression ◽  
False Alarms ◽  
Artificial Noise ◽  
Constant False Alarm Rate ◽  
Two Degrees Of Freedom ◽  
Novel Strategy

Reliable and high performance radar systems have ubiquitous demand. The operation of such systems is affected by the presence of natural and artificial noise sources. One of the basic radar concepts is to decide whether the target is present or not. Meanwhile, the general objective of all radar detection schemes is to ensure that false alarms don't fluctuate randomly. Thus, to cope with an inhomogeneous changing clutter environment, it is beneficial to be able to detect both high- and low-fidelity targets while maintaining the rate of false alarm fixed. This calls for an adaptive thresholding strategy that vary the detection threshold as a function of the sensed environment, and most modern radars implement this approach automatically. The feature of constant false alarm rate (CFAR) activates the threshold in such a way that it becomes adaptive to the local clutter environment. Many alternatives have been proposed to achieve such demanded property. Owing to the diversity of the radar search environment (target multiplicity & clutter edges), there exists no universal CFAR procedure. This prompts the necessity to investigate the composite architecture as a novel strategy. The goal of this paper is to analyze the fusion of CA, OS, and TM processors in post-detection integration of M-pulses. The primary and outlying targets are assumed to obey χ 2 -distribution with two-degrees of freedom in their fluctuation. Closed-form expression is derived for the detection performance. Our simulation results show robust behavior of the new model in the absence as well as in the presence of outlying targets. In addition, a significant improvement of the detection performance of novel strategy over the individual CFAR detectors is noticed. Moreover, the outweighing, over Neyman-Pearson (N-P) detector, of the fusion model, in ideal background, is evidently demonstrated. This ability to obtain improved performance compared to existing models is the major contribution of this work.

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