scholarly journals 3D Underwater Uncooperative Target Tracking for a Time-Varying Non-Gaussian Environment by Distributed Passive Underwater Buoys

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 902
Author(s):  
Xianghao Hou ◽  
Jianbo Zhou ◽  
Yixin Yang ◽  
Long Yang ◽  
Gang Qiao
Keyword(s):  
Target Tracking ◽  
Simulated Data ◽  
Optimal Estimation ◽  
Optimal Topology ◽  
Bayesian Posterior Probability ◽  
Time Varying ◽  
Tracking Problem ◽  
Monte Carlo Techniques ◽  
Time Optimal ◽  
Non Gaussian

Accurate 3D passive tracking of an underwater uncooperative target is of great significance to make use of the sea resources as well as to ensure the safety of our maritime areas. In this paper, a 3D passive underwater uncooperative target tracking problem for a time-varying non-Gaussian environment is studied. Aiming to overcome the low observability drawback inherent in the passive target tracking problem, a distributed passive underwater buoys observing system is considered and the optimal topology of the distributed measurement system is designed based on the nonlinear system observability analysis theory and the Cramer–Rao lower bound (CRLB) analysis method. Then, considering the unknown underwater environment will lead to time-varying non-Gaussian disturbances for both the target’s dynamics and the measurements, the robust optimal nonlinear estimator, namely the adaptive particle filter (APF), is proposed. Based on the Bayesian posterior probability and Monte Carlo techniques, the proposed algorithm utilizes the real-time optimal estimation technique to calculate the complex noise online and tackle the underwater uncooperative target tracking problem. Finally, the proposed algorithm is tested by simulated data and comprehensive comparisons along with detailed discussions that are made to demonstrate the effectiveness of the proposed APF.

scholarly journals Robust Weighted l1,2 Norm Filtering in Passive Radar Systems

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3270 ◽  
Author(s):  
Baris Satar ◽  
Gokhan Soysal ◽  
Xue Jiang ◽  
Murat Efe ◽  
Thiagalingam Kirubarajan
Keyword(s):  
Target Detection ◽  
Impulsive Noise ◽  
Simulated Data ◽  
Real Data ◽  
Detection Performance ◽  
Passive Radar ◽  
Mobile Telecommunication ◽  
Telecommunication System ◽  
Conventional Methods ◽  
Non Gaussian

Conventional methods such as matched filtering, fractional lower order statistics cross ambiguity function, and recent methods such as compressed sensing and track-before-detect are used for target detection by passive radars. Target detection using these algorithms usually assumes that the background noise is Gaussian. However, non-Gaussian impulsive noise is inherent in real world radar problems. In this paper, a new optimization based algorithm that uses weighted l 1 and l 2 norms is proposed as an alternative to the existing algorithms whose performance degrades in the presence of impulsive noise. To determine the weights of these norms, the parameter that quantifies the impulsiveness level of the noise is estimated. In the proposed algorithm, the aim is to increase the target detection performance of a universal mobile telecommunication system (UMTS) based passive radars by facilitating higher resolution with better suppression of the sidelobes in both range and Doppler. The results obtained from both simulated data with α stable distribution, and real data recorded by a UMTS based passive radar platform are presented to demonstrate the superiority of the proposed algorithm. The results show that the proposed algorithm provides more robust and accurate detection performance for noise models with different impulsiveness levels compared to the conventional methods.

Dynamic Target Tracking Based on Particle Filter in Actual Environment

2013 ◽  
Vol 683 ◽  
pp. 824-827
Author(s):  
Tian Ding Chen ◽  
Chao Lu ◽  
Jian Hu
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Target Tracking ◽  
Monitoring System ◽  
Filter Method ◽  
Complex Scene ◽  
Actual Environment ◽  
Dynamic Target ◽  
Gaussian System ◽  
Non Gaussian

With the development of science and technology, target tracking was applied to many aspects of people's life, such as missile navigation, tanks localization, the plot monitoring system, robot field operation. Particle filter method dealing with the nonlinear and non-Gaussian system was widely used due to the complexity of the actual environment. This paper uses the resampling technology to reduce the particle degradation appeared in our test. Meanwhile, it compared particle filter with Kalman filter to observe their accuracy .The experiment results show that particle filter is more suitable for complex scene, so particle filter is more practical and feasible on target tracking.

Proportional Navigation (PN) Based Tracking of Ground Targets by Quadrotor UAVs

2013 ◽  
Author(s):  
Ruoyu Tan ◽  
Manish Kumar
Keyword(s):  
Target Tracking ◽  
Proportional Navigation ◽  
Tracking Method ◽  
Maneuvering Targets ◽  
Time Optimal ◽  
Aerial Vehicle ◽  
Quadrotor Uavs ◽  
The One ◽  
Rotary Wing ◽  
Guidance Laws

This paper addresses the problem of controlling a rotary wing Unmanned Aerial Vehicle (UAV) tracking a target moving on ground. The target tracking problem by UAVs has received much attention recently and several techniques have been developed in literature most of which have been applied to fixed wing aircrafts. The use of quadrotor UAVs, the subject of this paper, for target tracking presents several challenges especially for highly maneuvering targets since the development of time-optimal controller (required if target is maneuvering fast) for quadrotor UAVs is extremely difficult due to highly non-linear dynamics. The primary contribution of this paper is the development of a proportional navigation (PN) based method and its implementation on quad-rotor UAVs to track moving ground target. The PN techniques are known to be time-optimal in nature and have been used in literature for developing guidance systems for missiles. There are several types of guidance laws that come within the broad umbrella of the PN method. The paper compares the performance of these guidance laws for their application on quadrotors and chooses the one that performs the best. Furthermore, to apply this method for target tracking instead of the traditional objective of target interception, a switching strategy has also been designed. The method has been compared with respect to the commonly used Proportional Derivative (PD) method for target tracking. The experiments and numerical simulations performed using maneuvering targets show that the proposed tracking method not only carries out effective tracking but also results into smaller oscillations and errors when compared to the widely used PD tracking method.

Decoding Neural Spike Trains: Calculating the Probability That a Spike Train and an External Signal Are Related

2002 ◽  
Vol 87 (3) ◽  
pp. 1659-1663 ◽  
Author(s):  
Terence D. Sanger
Keyword(s):  
Spike Train ◽  
Cell Activity ◽  
Simulated Data ◽  
External Signal ◽  
Time Varying ◽  
Neural Firing ◽  
Sensory Stimuli ◽  
Correlation Methods ◽  
A Cell ◽  
Recursive Calculation

Experimental and clinical applications of extracellular recordings of spiking cell activity frequently are used to relate the activity of a cell to externally measurable signals such as surface potentials, sensory stimuli, or movement measurements. When the external signal is time-varying, correlation methods have traditionally been used to quantify the degree of relation with the neural firing. However, in some circumstances correlation methods can give misleading results. A new algorithm is described that estimates the extent to which a spike train is related to a continuous time-varying signal. The technique calculates the probability of generating a spike train with Poisson statistics if the time-varying signal determines the Poisson rate. This is accomplished by successive division of the signal and the spike train into halves and recursive calculation of the probability of each half-signal. The performance of the new algorithm is compared with the performance of correlation methods on simulated data.

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