scholarly journals A Novel Structure for a Multi-Bernoulli Filter without a Cardinality Bias

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1484
Author(s):  
Weijian Si ◽  
Hongfan Zhu ◽  
Zhiyu Qu
Keyword(s):  
Detection Probability ◽  
Posterior Probability ◽  
Density Approximation ◽  
Signal To Noise ◽  
Generating Functional ◽  
Significant Bias ◽  
Novel Structure ◽  
High Detection ◽  
Multi Target Tracking ◽  
Bernoulli Filter

The original multi-target multi-Bernoulli (MeMBer) filter for multi-target tracking (MTT) is shown analytically to have a significant bias in its cardinality estimation. A novel cardinality balance multi-Bernoulli (CBMeMBer) filter reduces the cardinality bias by calculating the exact cardinality of the posterior probability generating functional (PGFl) without the second assumption of the original MeMBer filter. However, the CBMeMBer filter can only have a good performance under a high detection probability, and retains the first assumption of the MeMBer filter, which requires measurements that are well separated in the surveillance region. An improved MeMBer filter proposed by Baser et al. alleviates the cardinality bias by modifying the legacy tracks. Although the cardinality is balanced, the improved algorithm employs a low clutter density approximation. In this paper, we propose a novel structure for a multi-Bernoulli filter without a cardinality bias, termed as a novel multi-Bernoulli (N-MB) filter. We remove the approximations employed in the original MeMBer filter, and consequently, the N-MB filter performs well in a high clutter intensity and low signal-to-noise environment. Numerical simulations highlight the improved tracking performance of the proposed filter.

scholarly journals Measurement-Driven Multi-Target Multi-Bernoulli Filter

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Shijie Li ◽  
Humin Lei
Keyword(s):  
Detection Probability ◽  
Traditional Methods ◽  
Target Distribution ◽  
Detection Probabilities ◽  
High Detection ◽  
Bernoulli Filter ◽  
Simulation Results ◽  
The Stability ◽  
Stability And Accuracy

A measurement-driven multi-target multi-Bernoulli (MeMBer) filter which modifies the MeMBer filter by the measurements information is proposed in this paper. The proposed filter refines both the legacy estimates and the data-induced estimates of the MeMBer filter. For the targets under the legacy track set, the detection probabilities derived from the measurements are employed to refine the multi-target distribution. And for the targets under the data-induced track set, the multi-target distribution is further improved by the modified existence probabilities of the legacy tracks. Unlike the cardinality balanced MeMBer (CBMeMBer) filter, the proposed filter removes the cardinality bias in the MeMBer filter by utilizing the measurements information. Simulation results show that, compared with the traditional methods, the proposed filter can improve the stability and accuracy of the estimates and does not need the high detection probability hypothesis.

scholarly journals Time-Matching Random Finite Set-Based Filter for Radar Multi-Target Tracking

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4416 ◽  
Author(s):  
Defu Jiang ◽  
Ming Liu ◽  
Yiyue Gao ◽  
Yang Gao ◽  
Wei Fu ◽  
...  
Keyword(s):  
Target Tracking ◽  
Gaussian Mixture ◽  
Bayesian Filtering ◽  
Probability Hypothesis Density ◽  
Random Finite Set ◽  
Bayesian Formulation ◽  
Multi Target Tracking ◽  
Finite Set ◽  
Density Filter ◽  
Bernoulli Filter

The random finite set (RFS) approach provides an elegant Bayesian formulation of the multi-target tracking (MTT) problem without the requirement of explicit data association. In order to improve the performance of the RFS-based filter in radar MTT applications, this paper proposes a time-matching Bayesian filtering framework to deal with the problem caused by the diversity of target sampling times. Based on this framework, we develop a time-matching joint generalized labeled multi-Bernoulli filter and a time-matching probability hypothesis density filter. Simulations are performed by their Gaussian mixture implementations. The results show that the proposed approach can improve the accuracy of target state estimation, as well as the robustness.

scholarly journals Hierarchy Graph Based Barrier Coverage Strategy with a Minimum Number of Sensors for Underwater Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2546 ◽  
Author(s):  
Juan Chang ◽  
Xiaohong Shen ◽  
Weigang Bai ◽  
Ruiqin Zhao ◽  
Bin Zhang
Keyword(s):  
Sensor Networks ◽  
Detection Probability ◽  
Barrier Coverage ◽  
Underwater Sensor Networks ◽  
Simulation Studies ◽  
Concept Hierarchy ◽  
High Detection ◽  
Minimum Number ◽  
Long Lifetime ◽  
Novel Concept

Underwater sensor networks ( UWSNs ) based barrier coverage is increasingly important for intrusion detection due to the scarcity of underwater sensor resource. To improve UWSNs’ detection performance and prolong their lifetime, an efficient barrier coverage strategy is very important. In this paper, a novel concept: hierarchy graph is proposed. Hierarchy graph can make the network’s topology more clarity. In accordance with the hierarchy graph, 1-barrier coverage algorithm and k-barrier coverage algorithm are presented to construct the barrier with less sensors for higher energy efficiency. Both analytical and simulation studies demonstrate that the proposed algorithms can provide high detection probability and long lifetime for UWSNs.

A 3D Model Watermarking Algorithm

2013 ◽  
Vol 798-799 ◽  
pp. 708-711
Author(s):  
Xiu Hu Tan
Keyword(s):  
Detection Probability ◽  
3D Model ◽  
False Alarm Probability ◽  
3D Models ◽  
Model Potential ◽  
Least Mean Square ◽  
New Approach ◽  
Geometry Model ◽  
3D Geometry ◽  
High Detection

The popularity of 3D content is on the rise since it provides an immersive experience to viewers. In this paper, we present a new approach to watermarking 3D models based on optimization statistics. Through choosing the vertexes, we are able to obtain to the embedded watermark that has the least modified to topology transform of the 3D geometry model, and then project the watermark to the space that has the least mean square error value. So, we obtain that the robustness of the approach lies in hiding a watermark in the space that is least susceptible to the 3D model potential modification. Through analysis and constraint the conditions, we can obtain a high detection probability, a low false alarm probability. The robustness of our method is demonstrated by various attacks through computer simulation.

An Array Nonlinear Kalman Tracker for Indoor GPS Signal

2010 ◽  
Vol 44-47 ◽  
pp. 3864-3868
Author(s):  
Ji Cheng Ding ◽  
Lin Zhao ◽  
Jia Liu ◽  
Shuai He Gao
Keyword(s):  
Dynamic Programming ◽  
Kalman Filter ◽  
Detection Probability ◽  
Viterbi Algorithm ◽  
Signal Quality ◽  
Tracking Loop ◽  
Central Difference ◽  
Signal Tracking ◽  
High Detection ◽  
Central Difference Kalman Filter

To implement indoor GPS signal tracking in standalone mode when the tracking loop is unlocked and data bit edge is unknown, the paper develops a modified Viterbi Algorithm (MVA) based on dynamic programming, and it was applied for GPS bit synchronization. Besides, two combination carrier tracking schemes based on Central Difference Kalman Filter (CDKF) and MVA module were designed for indoor GPS signal. The testing results indicate that the methods can successful detect bit edge position with high detection probability whether or not the tracking loop is locked. The co-operational tracking scheme is still able to perform when the signal quality deteriorate.

A high detection probability method for Gm-APD photon counting laser radar

2013 ◽  
Author(s):  
Zi-jing Zhang ◽  
Yuan Zhao ◽  
Yong Zhang ◽  
Long Wu ◽  
Jian-zhong Su
Keyword(s):  
Detection Probability ◽  
Photon Counting ◽  
Laser Radar ◽  
Probability Method ◽  
High Detection

Factors influencing detection probabilities of frogs in the monsoonal tropics of northern Australia: implications for the design of monitoring studies

2013 ◽  
Vol 40 (5) ◽  
pp. 393 ◽  
Author(s):  
P. L. Dostine ◽  
S. J. Reynolds ◽  
A. D. Griffiths ◽  
G. R. Gillespie
Keyword(s):  
Detection Probability ◽  
Individual Species ◽  
Sampling Effort ◽  
Northern Australia ◽  
Wet Season ◽  
Factors Influencing ◽  
Detection Probabilities ◽  
High Detection ◽  
Frog Species ◽  
A Site

Context Failure to acknowledge potential bias from imperfect detection of cryptic organisms such as frogs may compromise survey and monitoring programmes targeting these species. Aims The aims of the present study were to identify proximate factors influencing detection probabilities of a range of frog species in monsoonal northern Australia, and to estimate the number of repeat censuses required at a site to have confidence that non-detected species are absent. Methods Data on detection or non-detection of frog species based on calling individuals were recorded during 10 wet-season censuses of 29 survey sites in the Darwin region. Factors influencing detection probabilities were identified using occupancy models; model selection was based on the Akaike information criterion. Sampling effort for individual species was calculated using model predictions at different stages of the wet season. Key results The covariate water temperature featured in the best-supported models for 7 of the 14 frog species. Six of these species were more likely to be detected when water temperatures were below 30°C. Detection probabilities were also correlated with the number of days since the commencement of the wet season, time since last significant rainfall, air temperature and time after sunset. Required sampling effort for individual species varied throughout the wet season. For example, a minimum of two repeat censuses was required for detection of Litoria caerulea in the early wet season, but this number increased to 13 in the middle stage of the wet season. Conclusions Variability in environmental conditions throughout the wet season leads to variability in detection probabilities of frog species in northern Australia. Lower water temperatures, mediated by rainfall immediately before or during surveys, enhances detectability of a range of species. For most species, three repeat surveys under conditions resulting in a high detection probability are sufficient to determine presence at a site. Implications Survey and monitoring programmes for frogs in tropical northern Australia will benefit from the results of the present study by allowing targeting of conditions of high detection probability for individual species, and by incorporating sufficient repeat censuses to provide accurate assessment of the status of individual species at a site.

scholarly journals On the application of frequency tagging

2020 ◽  
Author(s):  
Zoltan Derzsi
Keyword(s):  
Phase Noise ◽  
Detection Probability ◽  
Signal To Noise Ratio ◽  
Lower Probability ◽  
Weak Signal ◽  
Signal To Noise ◽  
Noise Component ◽  
Spectral Evaluation ◽  
Human Electrophysiology ◽  
Additional Phase

To detect a weak signal in human electrophysiology that is a response of a periodic external stimulus, spectral evaluation is mostly used. The recorded signal’s amplitude and phase noise components of the signal are statistically independent from each other, but both of them are decreasing the signal-to-noise ratio, which results in a lower probability of successful signal detection. Provided that the phase information of the stimuli is preserved, we found that a way to reject an additional phase noise component, which improves the detection probability considerably, by analysing the signal’s phase coherency instead of its spectrum.

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