scholarly journals Multisensor RFS Filters for Unknown and Changing Detection Probability

Electronics ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 741
Author(s):  
Zhang ◽  
Li ◽  
Sun
Keyword(s):  
Detection Probability ◽  
A Priori ◽  
Gaussian Mixture ◽  
Multitarget Tracking ◽  
Probability Hypothesis Density ◽  
Inverse Gamma ◽  
Motion State ◽  
Performance Deterioration ◽  
Cphd Filter ◽  
Monitoring Area

The detection probability is an important parameter in multisensor multitarget tracking. The existing multisensor multi-Bernoulli (MS-MeMBer) filter and multisensor cardinalized probability hypothesis density (MS-CPHD) filter require that detection probability is a priori. However, in reality, the value of the detection probability is constantly changing due to the influence of sensors, targets, and other environmental characteristics. Therefore, to alleviate the performance deterioration caused by the mismatch of the detection probability, this paper applies the inverse gamma Gaussian mixture (IGGM) distribution to both the MS-MeMBer filter and the MS-CPHD filter. Specifically, the feature used for detection is assumed to obey the inverse gamma distribution and is statistically independent of the target’s spatial position. The feature is then integrated into the target state to iteratively estimate the target detection probability as well as the motion state. The experimental results demonstrate that the proposed methods can achieve a better filtering performance in scenarios with unknown and changing detection probability. It is also shown that the distribution of the sensors has a vital influence on the filtering accuracy, and the filters perform better when sensors are dispersed in the monitoring area.

scholarly journals Active Sonar Target Tracking Based on the GM-CPHD Filter Algorithm

2018 ◽  
Vol 36 (4) ◽  
pp. 656-663
Author(s):  
Xiao Chen ◽  
Yaan Li ◽  
Yuxing Li ◽  
Jing Yu
Keyword(s):  
Target Tracking ◽  
Detection Probability ◽  
Gaussian Mixture ◽  
Time Varying ◽  
Adaptive Detection ◽  
Probability Hypothesis Density ◽  
Active Sonar ◽  
Multi Target Tracking ◽  
Cphd Filter ◽  
The Relationship

The estimation of underwater multi-target state has always been the difficult problem of active sonar target tracking.In order to get the variable number of target and their state, the random finite set theory is applied to multi-target tracking system.This theory not only effectively avoids the problem of multi-target tracking data association, and also realizes the estimation of time-varying number of targets and their states.Due to Probability Hypothesis Density(PHD) recursion propagates cardnality distribution with only a single parameter, a new generalization of the PHD recursion called Cardinalized Probability Hypothesis Density(CPHD) recursion, which jointly propagates the intensity function and the cardnality distribution, while have a big computation than PHD.Also there did not have closed-form solution for PHD recursion and CPHD recursion, so for linear Gaussian multi-target tracking system, the Gaussian Mixture Probability Hypothesis Density and Gaussian Mixture Cardinalized Probability Hypothesis Density(GM-CPHD) filter algorithm is put forward.GM-CPHD is more accurate than GM-PHD in estimation of the time-varying number of targets.In this paper, we use the ellipse gate tracking strategy to reduce computation in GM-CPHD filtering algorithm.At the same time, according to the characteristics of underwater target tracking, using active sonar equation, we get the relationship between detection probability, distance and false alarm, when fixed false alarm, analytic formula of the relationship between adaptive detection probability and distance is obtained, we puts forward the adaptive detection probability GM-CPHD filtering algorithm.Simulation shows that the combination of ellipse tracking gate strategy and adaptive detection probability GM-CPHD filtering algorithm can realize the estimation of the time-varying number of targets and their state more accuracy in dense clutter environment.

scholarly journals Multitarget Tracking Algorithm Using Multiple GMPHD Filter Data Fusion for Sonar Networks

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3193 ◽  
Author(s):  
Xueli Sheng ◽  
Yang Chen ◽  
Longxiang Guo ◽  
Jingwei Yin ◽  
Xiao Han
Keyword(s):  
Data Fusion ◽  
Detection Probability ◽  
Gaussian Mixture ◽  
Tracking Algorithm ◽  
Statistical Characteristics ◽  
Multitarget Tracking ◽  
Fusion Algorithm ◽  
Acoustic Shadow ◽  
Multisensor Data Fusion ◽  
Lower Detection

Multitarget tracking algorithms based on sonar usually run into detection uncertainty, complex channel and more clutters, which cause lower detection probability, single sonar sensors failing to measure when the target is in an acoustic shadow zone, and computational bottlenecks. This paper proposes a novel tracking algorithm based on multisensor data fusion to solve the above problems. Firstly, under more clutters and lower detection probability condition, a Gaussian Mixture Probability Hypothesis Density (GMPHD) filter with computational advantages was used to get local estimations. Secondly, this paper provided a maximum-detection capability multitarget track fusion algorithm to deal with the problems caused by low detection probability and the target being in acoustic shadow zones. Lastly, a novel feedback algorithm was proposed to improve the GMPHD filter tracking performance, which fed the global estimations as a random finite set (RFS). In the end, the statistical characteristics of OSPA were used as evaluation criteria in Monte Carlo simulations, which showed this algorithm’s performance against those sonar tracking problems. When the detection probability is 0.7, compared with the GMPHD filter, the OSPA mean of two sensor and three sensor fusion was decrease almost by 40% and 55%, respectively. Moreover, this algorithm successfully tracks targets in acoustic shadow zones.

Convolution PHD Filtering for Nonlinear Non-Gaussian Models

2011 ◽  
Vol 213 ◽  
pp. 344-348
Author(s):  
Jian Jun Yin ◽  
Jian Qiu Zhang
Keyword(s):  
Parallel Implementation ◽  
Gaussian Mixture ◽  
Probability Hypothesis Density ◽  
Gaussian Sum ◽  
Multi Target Tracking ◽  
Lower Complexity ◽  
Observation Noise ◽  
Non Gaussian ◽  
Convolution Filters ◽  
Cphd Filter

A novel probability hypothesis density (PHD) filter, called the Gaussian mixture convolution PHD (GMCPHD) filter was proposed. The PHD within the filter is approximated by a Gaussian sum, as in the Gaussian mixture PHD (GMPHD) filter, but the model may be non-Gaussian and nonlinear. This is implemented by a bank of convolution filters with Gaussian approximations to the predicted and posterior densities. The analysis results show the lower complexity, more amenable for parallel implementation of the GMCPHD filter than the convolution PHD (CPHD) filter and the ability to deal with complex observation model, small observation noise and non-Gaussian noise of the proposed filter over the existing Gaussian mixture particle PHD (GMPPHD) filter. The multi-target tracking simulation results verify the effectiveness of the proposed method.

scholarly journals Tracking Ground Targets with a Road Constraint Using a GMPHD Filter

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2723 ◽  
Author(s):  
Jihong Zheng ◽  
Meiguo Gao
Keyword(s):  
Coordinate Transformation ◽  
Gaussian Mixture ◽  
State Constraint ◽  
Transformation Process ◽  
Multitarget Tracking ◽  
Probability Hypothesis Density ◽  
Target State ◽  
Cluttered Environment ◽  
Predicted Probability ◽  
Track Association

The Gaussian mixture probability hypothesis density (GMPHD) filter is applied to the problem of tracking ground moving targets in clutter due to its excellent multitarget tracking performance, such as avoiding measurement-to-track association, and its easy implementation. For the existing GMPHD-based ground target tracking algorithm (the GMPHD filter incorporating map information using a coordinate transforming method, CT-GMPHD), the predicted probability density of its target state is given in road coordinates, while its target state update needs to be performed in Cartesian ground coordinates. Although the algorithm can improve the filtering performance to a certain extent, the coordinate transformation process increases the complexity of the algorithm and reduces its computational efficiency. To address this issue, this paper proposes two non-coordinate transformation roadmap fusion algorithms: directional process noise fusion (DNP-GMPHD) and state constraint fusion (SC-GMPHD). The simulation results show that, compared with the existing algorithms, the two proposed roadmap fusion algorithms are more accurate and efficient for target estimation performance on straight and curved roads in a cluttered environment. The proposed methods are additionally applied using a cardinalized PHD (CPHD) filter and a labeled multi-Bernoulli (LMB) filter. It is found that the PHD filter performs less well than the CPHD and LMB filters, but that it is also computationally cheaper.

scholarly journals Detection of Unresolved Targets for Wideband Monopulse Radar

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1084
Author(s):  
Tianyi Tsai ◽  
Zhiqiang Liao ◽  
Zhiquan Ding ◽  
Yuan Zhao ◽  
Bin Tang
Keyword(s):  
Target Detection ◽  
Detection Probability ◽  
High Performance ◽  
A Priori ◽  
Computation Time ◽  
Gaussian Mixture ◽  
Detection Algorithm ◽  
Ratio Test ◽  
Range Profile ◽  
Wideband Radar

Detecting unresolved targets is very important for radars in their target tracking phase. For wideband radars, the unresolved target detection algorithm should be fast and adaptive to different bandwidths. To meet the requirements, a detection algorithm for wideband monopulse radars is proposed, which can detect unresolved targets for each range profile sampling points. The algorithm introduces the Gaussian mixture model and uses a priori information to achieve high performance while keeping a low computational load, adaptive to different bandwidths. A comparison between the proposed algorithm and the latest unresolved target detection algorithm Joint Multiple Bin Processing Generalized Likelihood Ratio Test (JMBP GLRT) is carried out by simulation. On Rayleigh distributed echoes, the detection probability of the proposed algorithm is at most 0.5456 higher than the JMBP GLRT for different signal-to-noise ratios (SNRs), while the computation time of the proposed algorithm is no more than two 10,000ths of the JMBP GLRT computation time. On bimodal distributed echoes, the detection probability of the proposed algorithm is at most 0.7933 higher than the JMBP GLRT for different angular separations of two unresolved targets, while the computation time of the proposed algorithm is no more than one 10,000th of the JMBP GLRT computation time. To evaluate the performance of the proposed algorithm in a real wideband radar, an experiment on field test measured data was carried out, in which the proposed algorithm was compared with Blair GLRT. The results show that the proposed algorithm produces a higher detection probability and lower false alarm rate, and completes detections on a range profile within 0.22 ms.

scholarly journals A Labeled GM-PHD Filter for Explicitly Tracking Multiple Targets

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3932
Author(s):  
Yiyue Gao ◽  
Defu Jiang ◽  
Chao Zhang ◽  
Su Guo
Keyword(s):  
Numerical Experiments ◽  
Gaussian Mixture ◽  
Multitarget Tracking ◽  
Prior Density ◽  
Negative Effects ◽  
Density Region ◽  
Probability Hypothesis Density ◽  
Time Performance ◽  
Single State ◽  
The Individual

In this study, an explicit track continuity algorithm is proposed for multitarget tracking (MTT) based on the Gaussian mixture (GM) implementation of the probability hypothesis density (PHD) filter. Trajectory maintenance and multitarget state extraction in the GM-PHD filter have not been effectively integrated to date. To address this problem, we propose an improved GM-PHD filter. In this approach, the Gaussian components are classified and labeled, and multitarget state extraction is converted into multiple single-state extractions. This provides the identity label of the individual target and can shield against the negative effects of clutter in the prior density region on the estimates, thus realizing the integration of trajectory maintenance with state extraction in the GM-PHD filter. As no additional associated procedures are required, the overall real-time performance of the proposed filter is similar to or slightly lower than that of the basic GM-PHD filter. The results of numerical experiments demonstrate that the proposed approach can achieve explicit track continuity.

scholarly journals GM-PHD Filter Combined with Track-Estimate Association and Numerical Interpolation

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Jinguang Chen ◽  
Bugao Xu ◽  
Lili Ma ◽  
Rui Sun
Keyword(s):  
False Alarm ◽  
Detection Rate ◽  
Gaussian Mixture ◽  
Multitarget Tracking ◽  
Probability Hypothesis Density ◽  
Interpolation Technique ◽  
Numerical Interpolation ◽  
Simulation Results

For the standard Gaussian mixture probability hypothesis density (GM-PHD) filter, the number of targets can be overestimated if the clutter rate is too high or underestimated if the detection rate is too low. These problems seriously affect the accuracy of multitarget tracking for the number and the value of measurements and clutters cannot be distinguished and recognized. Therefore, we proposed an improved GM-PHD filter to tackle these problems. Firstly, a track-estimate association was implemented in the filtering process to detect and remove false-alarm targets. Secondly, a numerical interpolation technique was used to compensate the missing targets caused by low detection rate. At the end of this paper, simulation results were presented to demonstrate the proposed GM-PHD algorithm is more effective in estimating the number and state of targets than the previous ones.

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