Tracking Estimation Research Articles

Modified Gaussian Mixture Probability Hypothesis Density Filtering using Clutter Density Estimation for Multiple Target Tracking

Modified Gaussian Mixture Probability Hypothesis Density Filtering using Clutter Density Estimation for Multiple Target Tracking

Channel Parameter Estimation of mmWave MIMO System in Urban Traffic Scene: A Training Channel-Based Method

In frequency selective channel environment, channel estimation in hybrid precoding millimeter-wave (mmWave) massive multiple input multiple output (MIMO) system is a challenge issue. To solve this problem, we propose an effective channel estimation scheme for frequency selective channel, which is based on the training channel model in urban traffic environment. Considering that the practical mmWave MIMO channel is sparsity and the subcarrier multi-channels have the same sparse structure, we regard the channel estimation problem as the sparse channel recovery, and propose a multipath simultaneous matching tracking estimation method. It is assumed that the noise between the practical channels has a certain correlation, and the noise correlation has an impact on the selection of the optimal atomic support set in the process of channel recovery. Therefore, noise weighting is introduced in our proposed method. The simulation results prove the validity of this proposed method in frequency selective mmWave MIMO channel. Without increasing the complexity of the algorithm, the proposed method can achieve better local performance than the traditional classical methods.

Suboptimal Leader-to-Coordination Control for Nonlinear Systems With Switching Topologies: A Learning-Based Method.

In the cooperative control for multiagent systems (MASs), the key issues of distributed interaction, nonlinear characteristics, and optimization should be considered simultaneously, which, however, remain intractable theoretically even to this day. Considering these factors, this article investigates leader-to-formation control and optimization for nonlinear MASs using a learning-based method. Under time-varying switching topology, a fully distributed state observer based on neural networks is designed to reconstruct the dynamics and the state trajectory of the leader signal with arbitrary precision under jointly connected topology assumption. Benefitted from the observers, formation for MASs under switching topologies is transformed into tracking control for each subsystem with continuous state generated by the observers. An augmented system with discounted infinite LQR performance index is considered to optimize the control effect. Due to the complexity of solving the Hamilton-Jacobi-Bellman equation, the optimal value function is approximated by a critic network via the integral reinforcement learning method without the knowledge of drift dynamics. Meanwhile, an actor network is also presented to assure stability. The tracking errors and estimation weighted matrices are proven to be uniformly ultimately bounded. Finally, two illustrative examples are given to show the effectiveness of this method.

Object Detection from Enhanced IR Images Based on Preprocessing and Cumulative Histogram Estimation for Tracking and Gait Recognition Applications

Object Detection from Enhanced IR Images Based on Preprocessing and Cumulative Histogram Estimation for Tracking and Gait Recognition Applications

Spacecraft attitude control based on generalised dynamic inversion with adaptive neural network

AbstractThis paper proposes a robust generalised dynamic inversion (GDI) control system design with adaptive neural network (NN) estimation for spacecraft attitude tracking under the absence of knowledge of the spacecraft inertia parameters. The robust GDI control system works to enforce attitude tracking, and the adaptive NN augmentation compensates for the lack of knowledge of the spacecraft inertia parameters. The baseline GDI control law consists of a particular part and an auxiliary part. The particular part of the GDI control law works to realise a desired attitude dynamics of the spacecraft, and the auxiliary part works for finite-time stabilisation of the spacecraft angular velocity. Robustness against modeling uncertainties and external disturbances is provided by augmenting a siding mode control element within the particular part of the GDI control law. The singularity that accompanies GDI control is avoided by modifying the Moore-Penrose generalised inverse by means of a dynamic scaling factor. The NN weighting matrices are updated adaptively through a control Lyapunov function. A detailed stability analysis shows that the closed loop system is semi-global practically stable. For performance assessment, a spacecraft model is developed, and GDI-NN control is investigated for its attitude control problem through numerical simulations. Simulation results reveal the efficacy, robustness and adaptive attributes of proposed GDI-NN control for its application to spacecraft attitude control.

A Fuzzy-based Adaptive Unscented Kalman Filter for State Estimation of Three-dimensional Target Tracking

A Fuzzy-based Adaptive Unscented Kalman Filter for State Estimation of Three-dimensional Target Tracking

Diffusion Nonlinear Estimation and Distributed UAV Path Optimization for Target Tracking with Intermittent Measurements and Unknown Cross-Correlations

This paper focuses on distributed state estimation (DSE) and unmanned aerial vehicle (UAV) path optimization for target tracking. First, a diffusion cubature Kalman filter with intermittent measurements based on covariance intersection (DCKFI-CI) is proposed, to address state estimation with the existence of detection failure and unknown cross-correlations in the network. Furthermore, an alternative transformation of DCKFI-CI based on the information form is developed utilizing a pseudo measurement matrix. The performance of the proposed DSE algorithm is analyzed using the consistency and the bounded error covariance of the estimate. Additionally, the condition of the bounded error covariance is derived. In order to further improve the tracking performance, a UAV path optimization algorithm is developed by minimizing the sum of the trace of fused error covariance, based on the distributed optimization method. Finally, simulations were conducted to verify the effectiveness of the proposed algorithm.

Cooperative learning formation control of multiple autonomous underwater vehicles with prescribed performance based on position estimation

This paper proposes a cooperative learning formation control method with finite-time prescribed performance based on position estimation for parametric path tracking of multiple autonomous underwater vehicles (AUVs) with uncertainties and external disturbances. The parametric path used in the control law permits the velocity to be specified independently while tracking the path accurately. The localized radial basis function neural networks learn the uncertainties cooperatively while tracking the period path, and the knowledge gained from learning is utilized to construct an empirically based formation control law using experience to cope with similar uncertainties rather than repeatedly using adaptive methods, which reduces the computing burden. The position of the leader is assumed to be available only for the leader's neighboring AUV, and a novel finite-time distributed observer is presented for the followers to estimate the leader's position. Based on this, the control law is derived from the prescribed performance control method using a finite-time performance function rather than exponential decaying function to enable the tracking error converges in finite time, which accelerates the learning process. The simulation results confirm the validity of the presented control protocol.

SMO Based Position Sensorless BLDC Motor Drive Employing Canonical Switching Cell Converter for Light Electric Vehicle

In this paper, an adaptive sliding mode observer (ASMO) based rotor position sensorless brushless DC (BLDC) motor drive with energy regeneration for light electric vehicle (LEV) is designed. A canonical switching cell (CSC) converter is introduced for optimized MPPT performance and soft starting of the motor is assured during sensorless start-up. The CSC converter provides ripple free current in the output to the voltage source inverter (VSI) and minimizes the losses. It also acts as a fundamental building block for a DC-DC converter with the least element counts. For position sensorless commutation of BLDC motor drive, ASMO technique with self-correction ability of commutation error is used. Estimation of rotor angular position and continuous speed tracking for closed loop control are achieved using this method. This control is robust and derived from linear model of BLDC motor. Proper stability analysis of the observer is derived and ensured using pole placement method. Optimal gain scheduling technique is used in ASMO, which reduces the order of the error function making the computation easier. This model is truly adaptive to any parameter variation and load perturbation. To ensure green mobility, a pulse-width modulated regenerative braking algorithm is also integrated using the same VSI, which avoids any additional converter. Experimental results validate this method as effective for EV applications

Game Theoretic Approach for Robust Trajectory Tracking by a Multirotor

In this article, the problem of precisely tracking a nominal trajectory by an under-actuated multirotor platform is formulated as a game against bounded external disturbances, measurement noise, and initial condition uncertainty. The nominal trajectory is composed of desired position and yaw angle, which is known to be a differentially flat output for the multirotor state dynamics. Using this property, a linearized error dynamics is obtained in the vicinity of the nominal trajectory. Using the linearized error dynamics, a linear-quadratic differential game approach is used to propose an integrated estimation and control method for trajectory tracking. A salient feature of this approach is that the drag coefficients are estimated online from the information of the tracking error. Moreover, there are only six parameters to be tuned for both estimation and control loops. The intuitive rationale behind the tuning of these parameters is also discussed. Simulations as well as experimental validations are presented to demonstrate the performance and applicability of the controllers.

3D Multi-Object Tracking Based on Dual-Tracker and D-S Evidence Theory

Most of the current self-driving cars are equipped with a variety of sensors (such as lidar, camera), and it has become a trend to integrate multi-sensor information to achieve 3D multi-object tracking (MOT). In addition, unlike the perfect experimental conditions on public datasets, cars on real roads may experience a 3D sensor failure due to weather, mutual interference, etc. In this paper, we propose a dual-tracker-based 3D MOT system, which fuses 2D and 3D information to track objects and can still ensure good tracking accuracy when the 3D sensor fails in a single frame or multiple consecutive frames. Among them, two internal trackers complete the data association of 3D and 2D information, respectively. However, the outputs of the two internal trackers may conflict. First, we calculate the degree of association of potential matched pairs from the features extracted by each sensor, and then normalize it into the mass function of D-S evidence theory. Finally, we combine the evidence to obtain the final matched pairs of detection instances and track estimates. We do extensive experiments on the KITTI MOT dataset and simulate sensor failure scenarios by ignoring the output of the 3D detector. Using the latest evaluation measure for comparison, the results show that our method outperforms other advanced open-source 3D MOTs in a variety of scenarios.

Research on a Variable-Leakage-Flux Permanent Magnet Motor Control System Based on an Adaptive Tracking Estimator

Due to the characteristics of inductance parameter mismatch and back electromotive force harmonics caused by novel leakage flux branches and other non-ideal factors for the variable-leakage-flux permanent magnet (VLF-PM) motor, its control system suffers from a deteriorated performance of the rotor position estimation. To overcome the problems mentioned above, an adaptive tracking estimator of the rotor position is proposed in this paper for the VLF-PM motor control system. First, the proposed method simplifies the VLF-PM motor mathematical model and reduces the effect of inductance parameter variations according to the active flux concept. Then, robust and gradient descent algorithms are utilized to maintain the robustness of inductance parameter variations and eliminate the specific order harmonics owing to the novel leakage flux branches. Meanwhile, the accuracy and stability are enhanced. Furthermore, the position compensation based on the current adaptive tracking strategy is proposed to compensate the rotor position error caused by other non-ideal factors. Finally, the feasibility of the proposed estimated system is verified.

Spike-based Motion Estimation for Object Tracking through Bio-inspired Unsupervised Learning.

Neuromorphic vision sensors, whose pixels output events/spikes asynchronously with a high temporal resolution according to the scene radiance change, are naturally appropriate for capturing high-speed motion in the scenes. However, how to utilize the events/spikes to smoothly track high-speed moving objects is still a challenging problem. Existing approaches either employ time-consuming iterative optimization, or require large amounts of labeled data to train the object detector. To this end, we propose a bio-inspired unsupervised learning framework, which takes advantage of the spatiotemporal information of events/spikes generated by neuromorphic vision sensors to capture the intrinsic motion patterns. Without off-line training, our models can filter the redundant signals with dynamic adaption module based on short-term plasticity, and extract the motion patterns with motion estimation module based on the spike-timing-dependent plasticity. Combined with the spatiotemporal and motion information of the filtered spike stream, the traditional DBSCAN clustering algorithm and Kalman filter can effectively track multiple targets in extreme scenes. We evaluate the proposed unsupervised framework for object detection and tracking tasks on synthetic data, publicly available event-based datasets, and spiking camera datasets. The experiment results show that the proposed model can robustly detect and smoothly track the moving targets on various challenging scenarios and outperforms state-of-the-art approaches.

Training-Based Multiple Source Tracking Using Manifold-Learning and Recursive Expectation-Maximization

In this paper we propose a data-driven approach for multiple speaker tracking in reverberant enclosures. The speakers are uttering, possibly overlapping, speech signals while moving in the environment. The method comprises two stages. The first stage executes a single source localization using semi-supervised learning on multiple manifolds. The second stage, which is unsupervised, uses time-varying maximum likelihood estimation for tracking. The feature vectors, used by both stages, are the relative transfer functions (RTFs), which are known to be related to source positions. The number of sources is assumed to be known while the microphone positions are unknown. In the training stage, a large database of RTFs is given. A small percentage of the data is attributed with exact positions (namely, labelled data) and the rest is assumed to be unlabelled, i.e. the respective position is unknown. Then, a nonlinear, manifold-based, mapping function between the RTFs and the source positions is inferred. Applying this mapping function to all unlabelled RTFs constructs a dense grid of localized sources. In the test phase, this RTFs grid serves as the centroids for a (MoG) model. The MoG parameters are estimated by applying a recursive variant of the (EM) procedure that relies on the sparsity and intermittency of the speech signals. We present a comprehensive simulation study in various reverberation levels, including static and dynamic scenarios, for both two or three (partially) overlapping speakers. For the dynamic case we provide simulations with several speakers trajectories, including intersecting sources. The proposed scheme outperforms baseline methods that use a simpler propagation model in terms of localization accuracy and tracking capabilities.

Design of data-driven mode-free iterative learning controller based higher order parameter estimation for multi-agent systems consistency tracking

In this study, an adaptive data-driven control protocol design scheme based on higher order parameter (HOP) estimation and iterative learning is proposed for a class of nonaffine multi-agent systems (MAS) with unknown nonlinearity to solve the consistency tracking problem of MAS with fixed and switching topology communications. The control protocol mainly comprises HOP estimation and an iterative learning controller. The iterative learning control (ILC) algorithm is mainly used for system synergy, and the adaptive control algorithm based on HOP estimation mainly completes the system stabilization. The main advantage of the control protocol is that it uses only the input/output (I/O) data of the agents to complete the consensus tracking task and does not require specifying the dynamical system of each agent. The control protocol has a modular design, in which the iterative learning and HOP estimation algorithms complement each other without affecting the structure of the controller. The effectiveness of the control protocol is demonstrated using two simulation experiments.

Detection, Mode Selection, and Parameter Estimation in Distributed Radar Networks: Algorithms and Implementation Challenges

Target detection, parameter estimation, and tracking capabilities of a radar system can be significantly enhanced by deploying a distributed network. In this article, we consider a network without a central coordinator, wherein only neighboring nodes share limited target related information. We demonstrate that target detection and estimation of range, Doppler, and angle parameters can be significantly improved with the distributed network compared to the localized single-node system. The nodes that observe low signal-to-noise ratio (SNR) values can improve their detection and estimation capabilities considerably by cooperating with others that observe high SNR values. However, when nodes observing low SNR begin to have adverse effects on the global detection performance, these nodes should then be confined to passive sensing, yielding resources to the better performing nodes. We propose a deep learning approach with long-short term memory (LSTM) networks so that each node can compensate neighboring nodes’ observations with respect to its own. Several methodologies for addressing challenges, such as joint data association and parameter estimation for efficient multitarget tracking, distributed detection, and estimation in cluttered radar environments, optimizing network resource allocations, increasing algorithm robustness against channel impairments, and implementing synchronization approaches, are discussed for the distributed radar networks.

Perceptual analysis of distance sampling and transmittance estimation techniques in biomedical volume visualization

In volumetric path tracer, distance sampling and transmittance estimation techniques play a vital role in producing high-quality final rendered images. Previously, these techniques were implemented for production volume rendering, and were analyzed for faster convergence. In this article, we have augmented additional transmittance estimators including ratio tracking, residual ratio tracking and unbiased ray marcher in a GPU-based volumetric path tracer (Exposure Render) for biomedical datasets. We have also analyzed distance sampling methods and transmittance estimators perceptually using CIEDE2000 and Structural Similarity Index (SSIM). It was found that ratio and residual ratio tracking estimators performed close to each other and were better than unbiased ray marching perceptually. In addition, ray marching was observed to be better than delta tracking for distance sampling. We also validated these results by conducting a user study where different users were shown rendered images using varied distance samplers and transmittance estimators. Although, as expected, datasets had an impact on the rendering result for each technique, the perceptual differences did exist between distance samplers and transmittance estimators. As a major contribution of this work, we have found that distance sampling and transmittance estimation techniques have a crucial role for biomedical visualization due to having a direct impact on the final rendered image which is used in the diagnosis and prognosis of disease.

A beam-tracking framework for THz networks

Millimeter wave (mmWave) and terahertz (THz) frequencies are attractive for increased bandwidth applications, however are vulnerable to blockage and suffer from high pathloss. While the use of directional antennas can potentially mitigate these effects, the need for careful alignment becomes crucial, especially when the user moves. In this context, to ensure a reliable link, several parameters must be taken into account, such as the type of user’s motion, the location of the access point (AP), the shape of the area, the beamwidth, etc. In this work, the link reliability is divided into two main categories, the trajectory tracking resolution and the angular resolution. To address the challenges of both categories, a beam-tracking algorithm that promises high tracking reliability and low pilot overhead is proposed. The algorithm employs multiple cooperating APs and a hierarchical codebook and the performance of the proposed tracking method is evaluated through Monte-Carlo simulations with the probability of success, the average number of pilots per timeslot and the mean square error (MSE) as metrics, for different tracking estimation frequencies and different number of blocked links.

Improved Active Disturbance Rejection Control (ADRC) with Extended State Filters

To address time delay and noise problems in control systems, in this study, we integrated an extended state filter for signal filtering into an active disturbance rejection control (ADRC) system and derived an improved ADRC approach. In addition to the active anti-disturbance and active tracking estimation functions of the existing ADRC, the proposed approach also includes active filtering and active advance prediction functions, which can filter out the effect of measurement noise on system state observation while reducing the delay between the system control output and the detection of the sensor input. We verified through an evaluation in a simulation environment that the proposed approach may be expected to achieve improved control accuracy and increase the stability of closed-loop control systems.

Managing spatial irrigation using remote-sensing-based evapotranspiration and soil water adaptive control model

Irrigation has traditionally been managed as uniform applications where an entire field receives the same depth of water. Motivation to improve current irrigation practices has led to different approaches utilizing remotely-sensed images to inform variable rate irrigation management. This study conducted in 2019 and 2020 implemented the Spatial EvapoTranspiration Modeling Interface (SETMI), a remote-sensing-based evapotranspiration (ET) and water balance model, for managing variable rate irrigation of a maize and soybean field. This model tracked soil water content through the estimation of daily ET and tracking of various water fluxes entering and leaving a field. SETMI was used in two different irrigation treatments informed using Planet satellite (SETMI-SAT) and unmanned aerial system (UAS, SETMI-UAS) remotely-sensed images. A uniform irrigation approach managed by a professional crop consultant and a non-irrigated approach were used as the baseline in comparing irrigation management approaches. The irrigation treatments were evaluated on dry grain yield, gross irrigation, actual ET, deep percolation, change in soil water content, and water productivity. The uniform irrigation approach managed by the crop consultant applied the highest irrigation in 2019 and 2020 for maize (2019: 155 mm, 2020: 213 mm) and soybean (2019: 124 mm; 2020: 183 mm) while the SETMI irrigation treatments applied less irrigation for maize (2019: 131, 132 mm; 2020: 154, 140 mm) and soybean (2019: 116, 94 mm; 2020: 154, 175 mm). Maize yield was highest for the uniform irrigation approach in 2019 (14.9 Mg ha−1) and 2020 (13.3 Mg ha−1). The highest soybean yield was produced by the SETMI-SAT irrigation management approach in 2019 (5.0 Mg ha−1) and 2020 (4.8 Mg ha−1). Significant differences (p-value < 0.05) in applied irrigation between the uniform and SETMI irrigation management approaches were observed while there were no significant differences in dry grain yield for both maize and soybean in 2019 and 2020. At least one of the SETMI irrigation treatments produced higher crop, irrigation, and ET water productivity values in comparison to those produced by the uniform irrigation treatment for all crop-years. A post-season analysis was completed using the SETMI-UAS and SETMI-SAT treatments to evaluate the accuracy of estimated rootzone soil water depletion provided by SETMI. Rootzone depletion calculated from neutron probe volumetric soil water content measurements were compared to the modeled depletion from the SETMI-UAS and SETMI-SAT treatments. The 2020 modeled and measured depletion comparison produced better agreement resulting in a root mean squared error (RMSE) < 17 mm compared to 2019 (RMSE < 27 mm). The VRI center pivot malfunctioned during the 2019 season which caused unresolved discrepancies between actually applied irrigation and what the system was programmed to apply. The VRI system was fixed before the 2020 season.