Target State Estimation Research Articles

Research on Ocean Multitarget Tracking Control System Based on the Echo State Network

Wang, T. and Wang, X., 2019. Research on ocean multitarget tracking control system based on the echo state network. In: Li, L.; Wan, X., and Huang, X. (eds.), Recent Developments in Practices and Research on Coastal Regions: Transportation, Environment and Economy. Journal of Coastal Research, Special Issue No. 98, pp. 235–238. Coconut Creek (Florida), ISSN 0749-0208.In recent years, with the rapid development of artificial intelligence technology, the echo state network (ESN) has shown some advantages in nonlinear time series processing and dynamic prediction system, which has attracted extensive attention of domestic and foreign researchers. As an improved recurrent neural network, the ESN uses a large-scale sparse connection structure as the hidden layer. By adjusting the weights of output connections only, the whole training stage is made simply and efficiently, which overcomes the shortcomings of the classical recurrent neural network learning rules, such as being difficult to implement and having a long running time. The ESN has gradually become an important method for the prediction of nonlinear time series. Aiming at the slow tracking speed of traditional ocean multitarget tracking control system, this paper designs an ocean multitarget tracking control system based on the echo state network. Through the target detection algorithm, we select the ocean multiobjective control area and calculate the target center position. In this way, the result of the target state estimation is obtained. By confirming the target trajectory, the multiobjective ocean control based on the ESN is achieved. The simulation results show that the speed of the system is 22.78% higher than the traditional speed when it is designed for target control, which meets the requirements of ideal ocean multiobjective control.

Multi-Target Localization and Tracking Using TDOA and AOA Measurements Based on Gibbs-GLMB Filtering.

This paper deals with mobile multi-target detection and tracking. In the traditional method, there are uncertainties such as misdetection and false alarm in the measurement data, and it will be inevitable having to deal with the data association. To solve the target trajectory and state estimation problem under a cluttered environment, this paper proposes a non-concurrent multi-target acoustic localization tracking method based on the Gibbs-generalized labelled multi-Bernoulli (Gibbs-GLMB) filter and considers an acoustic array of a fixed arrangement for the tracking of targets by joint time difference of arrival (TDOA) and angle of arrival (AOA) measurements. Firstly, the TDOAs are calculated by using the generalized cross-correlation algorithm (GCC) and the AOAs are derived from the received signal directions. Secondly, we assume the independence of the targets and fuse the measurements which are used to track the multiple targets via the Gibbs-GLMB filter. Finally, the effectiveness of the method is verified by Monte Carlo simulation experiments.

Trajectory Estimation of Hypersonic Glide Vehicle Based on Analysis of Aerodynamic Performance

Due to high speed and high maneuverability of hypersonic glide vehicles (HGVs), the state estimation of such targets has always been a research hotspot. In order to improve accuracy of the trajectory estimation, a nonlinear aerodynamic parameter model for target estimation based on aerodynamic performance analysis is proposed. Firstly, the dynamic characteristics of the hypersonic glide vehicle during the hypersonic gliding stage was analyzed. Then, aiming at HTV-2-liked vehicle, the engineering calculation method was used to form the reference aerodynamic model for the target estimation. Secondly, a deviation model described by first-order Markov process was introduced to compensate the uncertainties of the unknown maneuver information from the target. Finally, extended Kalman filter was utilized to estimate the state of the target. The simulation results show that the proposed model is able to improve the accuracy of acceleration estimation for the HTV-2-liked hypersonic gliding vehicles.

Estimation of a Random Feedback Control System With Unknown Input

A method to estimate/track the state of a high-speed target that can be modeled as a feedback control system in three-dimensional (3-D) space is presented in this paper. The target's lateral acceleration input is not known, but it is known that the target will probably reach a destination position at a future time. The trajectory may involve random maneuvers. If the parameters of the modeled control loop are observable, the proposed model reduces the prediction and estimation errors. If the target's destination is known, then the accuracy can be increased further. The model, based on proportional navigation (PN), is nonlinear and can be used in a multiple-model estimator if the target can also perform random maneuvers along its route to the final destination. It is applied to a 3-D maneuvering aerial target state estimation problem with a target capable of high-magnitude, random lateral accelerations under a PN control policy. It is shown that due to the observability of the feedback control parameters, the filter significantly reduces the error of estimated position/velocity and provides a flexible estimation model for laterally maneuvering point targets in a 3-D fluid.

The Correlation between Vehicle Vertical Dynamics and Deep Learning-Based Visual Target State Estimation: A Sensitivity Study.

Automated vehicles will provide greater transport convenience and interconnectivity, increase mobility options to young and elderly people, and reduce traffic congestion and emissions. However, the largest obstacle towards the deployment of automated vehicles on public roads is their safety evaluation and validation. Undeniably, the role of cameras and Artificial Intelligence-based (AI) vision is vital in the perception of the driving environment and road safety. Although a significant number of studies on the detection and tracking of vehicles have been conducted, none of them focused on the role of vertical vehicle dynamics. For the first time, this paper analyzes and discusses the influence of road anomalies and vehicle suspension on the performance of detecting and tracking driving objects. To this end, we conducted an extensive road field study and validated a computational tool for performing the assessment using simulations. A parametric study revealed the cases where AI-based vision underperforms and may significantly degrade the safety performance of AVs.

PCRB and IMM for Target Tracking in the Presence of Specular Multipath

This article considers the general problem of tracking a noncooperative target in the presence of specular multipath. We revisit a novel posterior Cramér-Rao bound (PCRB) methodology for this problem [1] and develop a robust interacting multiple model (IMM) extended Kalman filter (EKF) implementation. Both the PCRB and the IMM-EKF augment the target state to include the multipath parameters. The approaches are demonstrated via a simulated scenario, in which an airborne radar tracks a low-altitude airborne target. It is shown that the PCRB increases rapidly subsequent to the multipath effect occurring, indicating that tracking performance is significantly degraded at such times. Furthermore, it is shown that the IMM-EKF offers near-optimal performance, with target state estimation errors consistently within 15% of the PCRB. The IMM-EKF also consistently provides credible estimates. It is concluded that the PCRB methodology and the IMM-EKF tracker provide powerful tools for performance assessment and target state estimation in challenging multipath scenarios.

Exactly Decoupled Kalman Filtering for Multitarget State Estimation With Sensor Bias

The problem of multisensor multitarget state estimation in the presence of constant but unknown sensor biases is investigated. The classical approach to this problem is to augment the state vector to include the states of all the targets and the sensor biases, and then implement an augmented state Kalman filter (ASKF). In this paper, we propose a novel decoupled Kalman filtering algorithm. The decoupled Kalman filtering first processes each target in a separate branch, namely the single-target Kalman filtering branch, where the single-target states and the sensor biases are estimated. Then the bias estimate is refined by fusing the former bias estimates across all the single-target Kalman filtering branches. Finally, the refined bias estimate is fed back to each single-target Kalman filtering branch to improve the target state estimation. We prove that the proposed decoupled Kalman filtering is exactly equivalent to the ASKF in terms of the estimation results under a usual initial condition. The equivalence is also confirmed via the numerical example. Moreover, we further validate the proposed algorithm using the field experimental data of a multistatic passive radar.

Distributed Motion State Estimation of Mobile Target with Switching Topologies

For the problem of motion state estimation of mobile target tracked by sensor nodes, the information-weighted Kalman consensus filter (IKCF) is introduced for sensor networks with switching communication topologies. In order to improve the dynamic performance of the IKCF, the low-pass consensus filtering algorithm is used to estimate the motion acceleration of the target. Moreover, an improved low-pass consensus filter is proposed to make the estimated motion accelerations converge to a smaller range. With low-pass consensus filtering for measured motion accelerations, the switched linear system of collective estimation errors of the IKCF is derived. Furthermore, it is proved that the switched linear system of collective estimation errors is globally uniformly asymptotically stable with a weighted $${l_2}$$-gain. Consequently, the conclusion is deduced that estimations of the target motion state can achieve consensus and converge to the target motion state within a bounded region as $$t \rightarrow \infty $$ under switching topologies. Finally, the effectiveness of the proposed approaches is illustrated by several illustrative examples.

Joint Target Detection and Tracking in Multipath Environment: A Variational Bayesian Approach

We consider multitarget detection and tracking problem for a class of multipath detection system where one target may generate multiple measurements via multiple propagation paths, and the association relationship among targets, measurements, and propagation paths is unknown. In order to effectively utilize multipath measurements from one target to improve detection and tracking performance, a tracker has to handle high-dimensional estimation of latent variables including target existence state, target kinematic state, and multipath data association. Based on variational Bayesian inference, we propose a novel joint detection and tracking algorithm that incorporates multipath data association, target detection, and target state estimation in a unified Bayesian framework. The posterior probabilities of these latent variables are derived in a closed-form iterative manner, which is effective for reducing the performance deterioration caused by the coupling between estimation errors and identification errors. Loopy belief propagation is exploited to approximately calculate the probability of multipath data association, saving the computational cost significantly. Simulation results of over-the-horizon radar multitarget tracking show that the proposed algorithm outperforms multihypothesis multipath track fusion and multidetection (hypothesis-oriented) multiple hypothesis tracker, especially under low signal-to-noise ratio circumstance.

Seamless Tracking of Apparent Point and Extended Targets Using Gaussian Process PMHT

In practical target tracking scenarios, targets of different sizes (or extents) may be near or far away from the sensor, which may result in targets appearing as point sources or as extended targets spanning one or more resolution cells, respectively, depending on distance and sensor resolution. In this paper, a new Gaussian Process (GP) measurement model is proposed to explicitly describe the observation about each basis point of GP by an individual dynamic Poisson measurement rate. By employing this model, a novel algorithm to track multiple point targets and extended targets, simultaneously and seamlessly, in the presence of clutter and missed detections is proposed within the Probabilistic Multi-Hypothesis Tracker (PMHT) framework. The proposed algorithm can adapt to spatio-temporally varying target sizes or extents of extended targets and temporally varying target cardinality. In addition, the posterior Cramer-Rao lower bound (PCRLB) for extended targets, which quantifies the accuracies of estimates of multiple extended target states in scenarios with clutter, is derived. Simulations with a scenario consisting of multiple extended targets and point targets are used to verify the effectiveness of the proposed algorithm and to compare its performance with the extended target PCRLB and with those of existing extended target tracking algorithms.

Target State Estimation Based on Kalman Filter in Terminal Guidance

In order to settle the requirement of target location and line-of-sight (LOS) rate estimation with multi-angle measurement in terminal guidance process, the state estimation algorithm based on Kalman filter is proposed in this paper. The linearized system model is established, and the extended Kalman filter is used to estimate the position and LOS rate of the target. The simulation results show that this method exhibits high accuracy for position estimation, and it can precisely track the change of LOS angular rate in real time. The algorithm is simple in principle, easy to implement and of high value in practice.

Collaborative detection and power allocation framework for target tracking in multiple radar system

In reality, multiple radar system (MRS) may have some limited resource, such as the data computation capacity of the fusion center and the transmit energy of each radar. To better exploit its limited system resource, a collaborative detection and power allocation (CDPA) scheme is developed for the application of target tracking in clutter. The basis of the CDPA scheme is to use optimization technique to control the false alarm rate (FAR) and transmit power of each radar in view of the aforementioned resource constraints, while achieving better target state estimation accuracy. The Bayesian Cramér–Rao lower bound is derived, relaxed, and subsequently utilized, as the optimization criterion for the CDPA strategy. The resulting nonlinear and nonconvex optimization problem consists of two adaptable vectors, one for FAR selection and the other for power allocation. By introducing an auxiliary vector, a fast two-step solution technique is presented to jointly select the FAR and distribute the transmit power. Simulation results demonstrate that, with given data computation capability and system total power budget, the CDPA scheme can evidently expand the detection range, increase the resource utilization efficiency of the MRS, and improve the target tracking accuracy.

Robust visual tracking based on global-and-local search with confidence reliability estimation

Visual object tracking is an open and challenging problem, an online tracker must be able to keep track of the target object for a long time period even in complex scenarios, such as target drift and background occlusion. Discriminative correlation filters (DCF) have shown excellent performance in short-term target tracking problems thanks to their circular dense sampling mechanism and fast computation with a discrete Fourier transform. However, they tend to drift from the target when the target encounters drastic deformation, fast motion, or background occlusion. This can result in a bad model update since the tracker searches the target in a local region centered at the position where target was located in the previous frame. There is no recovery mechanism for target re-identification and re-location. To handle this issue, this paper proposes a global-and-local-search technique that applies a DCF-based tracking model with a novel target-aware detector in a collaborative way. Our tracking model performs the local search process with high tracking confidence, and the target-aware detector is executed to re-identify and locate the target via global search from the entire frame when the model instability and confidence fluctuation are detected by proposed tracking system. Additionally, we designed an enhanced peak-to-sidelobe ratio (EPSR) for confidence estimation, which indicates system instability and fluctuation degree. Thus, the local tracking model and target-aware detector are collaboratively applied for both final target state estimation and online model updates. This not only avoids model corruption from bad updates, but also prevents our tracker from drifting problems for long-term tracking. Experiments on OTB-100 and VOT2016 benchmarks demonstrate that the proposed tracking method achieves state-of-the-art tracking performance in terms of accuracy and robustness, with 22 fps tracking speed (close to realtime) run on a single GPU.

Virtual intersecting location based UAV circumnavigation and bearings-only target-tracking techniques

The use of agents to circle a moving target and estimate its states with bearing measurements only is a kind of passive tracking technique, and has received wide attention in recent years. However, the performance of the existing estimation and control laws for moving agent are still not so satisfactory when the target position is not known beforehand. In this paper, an unbiased estimator to locate the position of the target and a controller to circumnavigate around the target at a prescribed circular distance are investigated for an unmanned aircraft vehicle (UAV). First, a rough target-state estimator and a rough two-stage saturation UAV circumnavigation strategy are introduced, and their convergence performance is analyzed. Second, a virtual intersecting localization algorithm (VILA) is proposed based on a rough estimation strategy, and then the accurate target-state estimation is obtained with a Kalman filter. Third, the UAV rough circumnavigating strategy is modified with the accurate target-state estimation. By numerical simulation, it is verified that the steady estimation error with VILA is asymptotically unbiased, and the estimation accuracy is much higher than that of the existing estimation methods. It is also proven that the control error by the proposed UAV circumnavigating strategy can asymptotically converge to zero by the modified controller, which is also superior to that for the existing circumnavigating controller.

Constrained state estimation using noisy destination information

Tracking performance can be improved by the incorporation of a destination constraint. However, directly using available noise-corrupted destination information in the existing destination constrained filtering methods may lead to performance degradation. To address this limitation, in this paper, the true destination is also treated as a state to be estimated along with the target state. Then, the destination constraint information is leveraged by using the relationship between the state components and a newly constructed pseudo-measurement. An uncertain destination constrained augmented state filter (UDC-ASF) with its state being augmented by destination, in which the unscented Kalman filter (UKF) is used to deal with the strong nonlinearity of the measurements, is proposed to produce both constrained target state and destination estimates. Moreover, the unknown slope and the intercept of the straightline representing the destination constraint can also be augmented into the state vector and two pseudo-measurements are constructed in the process. The corresponding UDC-ASF with slope and intercept (UDC-ASF-SI) is derived. The a priori noisy destination is used to initialize the two proposed filters. An extension of the UDC-ASF to track multiple targets heading to the same destination is also discussed. Simulation results demonstrate the effectiveness of the proposed methods.

Target location estimation for vehicle dual radar based on unscented Kalman filter

In the research of unmanned vehicle, the state estimation of target detected by sensors is one of the key issues in environmental sensing technology. In this paper, an algorithm based on unscented Kalman filter is proposed to predict and update the position of the target based on the obtained radar data, which is used to estimate the target position of the unmanned vehicle dual radar system. The vehicle radar system in this paper is composed of four lines laser and millimeter wave radar. The calibrated vehicle coordinate system is a two-dimensional coordinate system parallel to the ground. On the basis of the system and coordinate system, the real radar data are collected and simulated in the experimental site. Experiments show that compared with single sensor, the measurement error of radar combination model is effectively reduced, and the accuracy of fusion data is improved. Compared with the most commonly used extended Kalman filtering algorithm, the mean square error of the moving direction of vehicle descends from 6.15 per thousand to 4.83 per thousand. The mean square error value of the average position decreases from 4.24 per thousand to 2.99 per thousand in the direction parallel to the front axle, which indicates that the estimation of the target position of this algorithm is more accurate and closer to the real value. In addition, in the same operating environment, the average time of processing 500 groups of radar data is reduced from 5.9 ms to 2.1 ms, proving that the algorithm has a higher algorithm efficiency.

Improving bearings-only target state estimation tracking problem by using adaptive and nonlinear kalman algorithms

<p><span style="font-family: 'Times New Roman'; font-size: 9pt; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">Finding the best estimate of the process state from noisy data is the main problem in tracking systems, many efforts and researches have been done to remove this noise. More useful information about the target’s state can be extracted from observations by using a more appropriate model for the target’s motion or using additional sensors. In this paper, we will introduce two methods to improve the estimation of bearing-only target tracking problem in two dimensions (2D). The first method is by adding a third sensor and making a good alignment of those sensors, and at the same time an extended Kalman filter (EKF), unscented Kalman filter (UKF) and cubature Kalman filter (CKF) are implemented. The second method is by applying an adaptive nonlinear Kalman filter (ANKF) for two sensors to solve the problem of measurement variance uncertainty.</span></p>

CRLB for Estimating Time-Varying Rotational Biases in Passive Sensors

In target tracking systems involving data fusion it is common to encounter sensor measurement biases that contribute to the tracking errors. There is extensive research into estimating sensor biases, but very little research into bias estimation in the dynamic case, meaning that biases that change over time are addressed. This paper investigates the means for and necessity of estimating bias rates of change in addition to constant sensor biases to reduce the errors in the state estimates. This is explored by comparing the Cramer–Rao lower bound and root-mean-square error of simultaneous target state and bias estimates for rotational biases with three-dimensional passive sensors with roll, pitch, and yaw biases. The present work models the dynamic biases as linearly varying over time. The iterated least squares method is used for the search of the maximum likelihood estimate, and is shown to be statistically efficient via hypothesis testing.

Space-time allocation for transmit beams in collocated MIMO radar

In this paper, we propose a space-time allocation strategy for collocated multiple-input multiple-output (MIMO) radar to determine the beam directions to multiple targets, to be tracked. The transmit beams are allocated to different targets with distinct time units in one illumination. The basis of our strategy is to achieve the optimal probing through the feedback information in the tracking recursion cycle, with the objective of improving the overall tracking accuracy of multiple targets. We derive the posterior Cramer-Rao lower bound (PCRLB), used as the optimization criterion, to quantify the target state estimate accuracy. By introducing the illumination probability in each time unit, the space-time allocation problem is converted to a convex one. Therefore, a sufficient solution is provided, which aims at the real-time demand of radar system. Simulation results demonstrate the superiority of the proposed strategy to the uniform allocation, space-only allocation and time-only allocation methods.

A Method of Target Tracking Based on Monocular Vision for Mobile Robot in Unknown Environment

In order to solve the problem of target tracking based on monocular vision for mobile robot in unknown environment, a robot simultaneous localization, map building and target tracking method is proposed. This method can realize the simultaneous online estimation of robot, environment features and target states by using the bearings only observations from monocular sensors. In the process of estimation, the monocular vision based Simultaneous localization and mapping (MVSLAM) runs independent of the Object Tracking(OT), as the basis of the OT, MVSLAM provides the state information of the robot platform for the OT, and OT uses the bearing measurements of target and robot state to estimates the state of target. MVSLAM is based on inverse depth parameterization and running under framework of full probability Extended Kalman Filter (EKF), and another EKF is also used for target tracking independently. The simulation experiments show the performance of the design method, and analyze the problem of observability of Bearing-only tracking. To solve the observability problem, a robot motion control method is presented, under the control of this method, the robot will make satellite surround motion around the target. Finally, the feasibility and accuracy of the method are verified by simulation experiments.