scholarly journals Receiver selection for multi-target tracking in multi-static Doppler radar systems

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yun Zhu ◽  
Li Zhao ◽  
Yumei Zhang ◽  
Xiaojun Wu
Keyword(s):  
Target Tracking ◽  
Doppler Radar ◽  
Selection Criterion ◽  
Tracking Accuracy ◽  
Doppler Measurement ◽  
Target State ◽  
Radar Systems ◽  
Multi Target Tracking ◽  
Markov Decision ◽  
Finite Set

AbstractThis paper presents a novel receiver selection method for multi-target tracking in multi-static Doppler radar systems. The assumption is that in the surveillance volume of interest, a single transmitter with a known frequency is active and several spatially distributed radar receivers collect and report Doppler-only measurements. The Doppler measurements are not only affected by the additive noise but also contaminated by false and missed detections. In this paper, multi-target tracking is obtained by modeling the multi-target state as a labeled multi-Bernoulli random finite set and receiver selection is implemented during tracking. Receiver selection is solved under the partially observed Markov decision framework, and the variance of the cardinality estimate is used as the selection criterion. To increase the diversity of the selected sensors and overcome the low observability of the Doppler measurement, the receivers selected at previous time steps are taken into account by adding a window. Simulation studies demonstrate the tracking performance of the proposed method with different window lengths. The results show that the observability of the target state is a crucial factor in determining the performance of receiver selection. The proposed method with a suitable window length can effectively improve the tracking accuracy.

An Improved Joint Target Tracking and Classification Algorithm Based on Data Fusion

2014 ◽  
Vol 904 ◽  
pp. 325-329
Author(s):  
Hong Wei Quan ◽  
Lin Chen ◽  
Dong Liang Peng
Keyword(s):  
Data Fusion ◽  
Target Tracking ◽  
Classification Algorithm ◽  
Target Classification ◽  
Tracking Accuracy ◽  
Traditional Methods ◽  
System Models ◽  
Target State ◽  
Classification Probability

This paper addresses the problem of the joint target tracking and classification based on data fusion. In traditional methods, a separate suite of sensors and system models are used, target tracking and target classification are usually treated as separate problems. In our JTC framework, the link between target state and class is considered and the feasibility of JTC techniques is discussed. The tracking accuracy and classification probability are improved to some extent with the more accurate classification results from classifier based on data fusion feedback to state filter.

Sensor cooperative scheduling method for low-altitude maneuvering target tracking in complex environment

Sensor Review ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yunpu Zhang ◽  
Gongguo Xu ◽  
Ganlin Shan
Keyword(s):  
Target Tracking ◽  
Sensor Scheduling ◽  
Multiple Model ◽  
Complex Environment ◽  
Tracking Accuracy ◽  
Content Type ◽  
Target State ◽  
Maneuvering Targets ◽  
Scheduling Method ◽  
Low Altitude

Purpose Continuous and stable tracking of the low-altitude maneuvering targets is usually difficult due to terrain occlusion and Doppler blind zone (DBZ). This paper aims to present a non-myopic scheduling method of multiple radar sensors for tracking the low-altitude maneuvering targets. In this scheduling problem, the best sensors are systematically selected to observe targets for getting the best tracking accuracy under maintaining the low intercepted probability of a multi-sensor system. Design/methodology/approach First, the sensor scheduling process is formulated within the partially observable Markov decision process framework. Second, the interacting multiple model algorithm and the cubature Kalman filter algorithm are combined to estimate the target state, and the DBZ information is applied to estimate the target state when the measurement information is missing. Then, an approximate method based on a cubature sampling strategy is put forward to calculate the future expected objective of the multi-step scheduling process. Furthermore, an improved quantum particle swarm optimization (QPSO) algorithm is presented to solve the sensor scheduling action quickly. Optimization problem, an improved QPSO algorithm is presented to solve the sensor scheduling action quickly. Findings Compared with the traditional scheduling methods, the proposed method can maintain higher target tracking accuracy with a low intercepted probability. And the proposed target state estimation method in DBZ has better tracking performance. Originality/value In this paper, DBZ, sensor intercepted probability and complex terrain environment are considered in sensor scheduling, which has good practical application in a complex environment.

scholarly journals Sensor Selection for Decentralized Large-Scale Multi-Target Tracking Network

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4115 ◽  
Author(s):  
Feng Lian ◽  
Liming Hou ◽  
Bo Wei ◽  
Chongzhao Han
Keyword(s):  
Target Tracking ◽  
Large Scale ◽  
Sequential Monte Carlo ◽  
Signal To Noise Ratio ◽  
Computational Cost ◽  
Gaussian Mixture ◽  
Descent Method ◽  
Sensor Selection ◽  
Multi Target Tracking ◽  
Finite Set

A new optimization algorithm of sensor selection is proposed in this paper for decentralized large-scale multi-target tracking (MTT) network within a labeled random finite set (RFS) framework. The method is performed based on a marginalized δ-generalized labeled multi-Bernoulli RFS. The rule of weighted Kullback-Leibler average (KLA) is used to fuse local multi-target densities. A new metric, named as the label assignment (LA) metric, is proposed to measure the distance for two labeled sets. The lower bound of LA metric based mean square error between the labeled multi-target state set and its estimate is taken as the optimized objective function of sensor selection. The proposed bound is obtained by the information inequality to RFS measurement. Then, we present the sequential Monte Carlo and Gaussian mixture implementations for the bound. Another advantage of the bound is that it provides a basis for setting the weights of KLA. The coordinate descent method is proposed to compromise the computational cost of sensor selection and the accuracy of MTT. Simulations verify the effectiveness of our method under different signal-to- noise ratio scenarios.

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.

Radar Target Tracking Error Research Using Kalman Filter-Based Algorithms

2012 ◽  
Vol 239-240 ◽  
pp. 1184-1187
Author(s):  
Qian Long Chai ◽  
Yu Long Bai ◽  
Cun Hui Dong
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Unscented Kalman Filter ◽  
Tracking Error ◽  
Substantial Effect ◽  
Specific Model ◽  
Radar Target ◽  
Tracking Accuracy ◽  
Tracking Errors ◽  
Radar Systems

The methods of radar target tracking have a substantial effect on the accuracy of the whole radar systems. The basic principles and implementing steps of the Extended Kalman filter (the EKF) and the Unscented Kalman filter (the UKF) are briefly introduced. The main sources of radar observation errors and the limitation of the current methods are analyzed. According to the requirements of tracking a CV target, the EKF and the UKF are used to simulate the experiments by establishing the specific model of radar target tracking. The results show that the tracking errors can be constrained within a certain range and the whole systems also have the high tracking accuracy.

scholarly journals Improved multi-target tracking algorithm based on SMC-CBMeMBer for the airborne Doppler radar

2019 ◽  
Vol 2019 (20) ◽  
pp. 6377-6381
Author(s):  
Muyang Luo ◽  
Hemin Sun ◽  
Weihua Wu ◽  
Xin Xie ◽  
Surong Jiang
Keyword(s):  
Target Tracking ◽  
Doppler Radar ◽  
Tracking Algorithm ◽  
Multi Target Tracking ◽  
Airborne Doppler Radar

scholarly journals Multi-Target Tracking Algorithm Based on 2-D Velocity Measurements Using Dual-Frequency Interferometric Radar

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 1969
Author(s):  
Saima Ishtiaq ◽  
Xiangrong Wang ◽  
Shahid Hassan
Keyword(s):  
Target Tracking ◽  
Antenna Arrays ◽  
Doppler Radar ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Dual Frequency ◽  
Velocity Measurements ◽  
Cubature Kalman Filter ◽  
Multi Target Tracking ◽  
Mtt Algorithm

Multi-target tracking (MTT) generally requires either a network of Doppler radar receivers distributed at different locations or a phased array radar. The targets moving with small/no radial velocity or angular velocity only cannot be detected and localized completely by deploying Doppler radar without antenna arrays or multiple receivers. To resolve this issue, we present a new MTT algorithm based on 2-D velocity measurements, namely, radial and angular velocities, using dual-frequency interferometric radar. The contributions of the proposed research are twofold: First, we introduce the mathematical model and implementation of the proposed algorithm by explicitly establishing the relationship between 2-D velocity measurements and kinematic state of the target in terms of Cartesian coordinates. Based on 2-D velocity measurement function, the proposed MTT algorithm comprises the following steps: (i) data association using global nearest neighbor (GNN) method (ii) target state estimation using interacting multiple model (IMM) estimator combined with square-root cubature Kalman filter (SCKF) (iii) track management using rule-based M/N logic. Second, performance of the proposed algorithm is evaluated in terms of tracking accuracy, computational complexity and IMM mean model probabilities. Simulation results for different scenarios with multiple targets moving in different tracks have been presented to verify the effectiveness of the proposed algorithm.

scholarly journals A Multi-Target Tracking and Detection Algorithm for Wireless Sensor Networks

2021 ◽  
Vol 15 ◽  
pp. 661-665
Author(s):  
Gang Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Target Tracking ◽  
Detection Algorithm ◽  
Wireless Sensor ◽  
Main Function ◽  
Multiple Target ◽  
Target State ◽  
Multi Target Tracking ◽  
Simulation Results

There are a large number of sensor nodes in wireless sensor network, whose main function is to process data scientifically, so that it can better sense and cooperate. In the network coverage, it can comprehensively collect the main information of the monitoring object, and send the monitoring data through short-range wireless communication to the gateway. Although there are many applications in WSNs, a multi-Target tracking and detection algorithm and the optimization problem of the wireless sensor networks are discussed in this paper. It can be obviously seen from the simulation results that this node cooperative program using particle CBMeMBer filtering algorithm can perfectly handle multi-target tracking, even if the sensor model is seriously nonlinear. Simulation results show that the tracking - forecasting data association scheme applying GM-CBMeMBer, which is proposed in this paper, runs well in identifying multiple target state, and can improve the estimation accuracy of multiple target state.

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