Target State Estimation Research Articles

Joint Beam Selection and Power Allocation for Multiple Target Tracking in Netted Colocated MIMO Radar System

In this paper, a joint beam selection and power allocation (JBSPA) strategy is developed for multiple target tracking in netted colocated multiple-input multiple-output radar system. Each radar in this network adopts a multibeam working mode, in which multiple simultaneous transmit beams are synthesized. The basis of the JBSPA strategy is to use the optimization technique to control the limited beam and power resource of each radar in order to achieve accurate target state estimation. The Bayesian Cramer–Rao lower bound is derived, normalized, and subsequently utilized, as the optimization criterion for the JBSPA strategy. The resulting optimization problem consists of two adaptable parameters, one for beam selection and the other for power allocation. By introducing an auxiliary vector, a fast two-step solution technique is presented to jointly decide the number of beams generated by each radar, as well as the assignment and transmit power of each beam, subject to some resource constraints. Simulation results verify the superiority of the proposed JBSPA algorithm, in terms of the worst-case tracking accuracy of the multiple targets.

Fuzzy Neural Network-Based Interacting Multiple Model for Multi-Node Target Tracking Algorithm.

An interacting multiple model for multi-node target tracking algorithm was proposed based on a fuzzy neural network (FNN) to solve the multi-node target tracking problem of wireless sensor networks (WSNs). Measured error variance was adaptively adjusted during the multiple model interacting output stage using the difference between the theoretical and estimated values of the measured error covariance matrix. The FNN fusion system was established during multi-node fusion to integrate with the target state estimated data from different nodes and consequently obtain network target state estimation. The feasibility of the algorithm was verified based on a network of nine detection nodes. Experimental results indicated that the proposed algorithm could trace the maneuvering target effectively under sensor failure and unknown system measurement errors. The proposed algorithm exhibited great practicability in the multi-node target tracking of WSNs.

Multi-Target Video Tracking Based on Improved Data Association and Mixed Kalman/<inline-formula> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math> </inline-formula> Filtering

This paper proposes a novel multi-target video tracking (MTVT) method based on improved data association and mixed Kalman/ $H_{\infty }$ filtering. First, multiple features of video targets, such as local texture, spatial color distribution, and edge-oriented gradients, are extracted to form the fused-feature matching matrix. Second, an efficient and accurate video targets association method integrating an improved probabilistic data association (IPDA) and a simplified joint probabilistic data association (SJPDA) is developed. The IPDA combines the augmented posterior probability matrix with the fused-feature matching matrix for a multitarget association. On the other hand, the SJPDA ensures the efficiency of data association, and better accuracy in the presence of low peak signal-to-noise ratio and sparse target environment by sifting out big probability events. Finally, a mixed Kalman/ $H_{\infty }$ filtering fusing the covariances of state estimation is proposed for the fast and robust state estimation of video targets. Experimental results demonstrate that the proposed multifeature fusion MTVT is able to describe video targets accurately, guarantee both the efficiency and accuracy in the video targets association, and also it is more reliable and precise under noisy environments.

Generalized estimation and predictive guidance for evasive targets

A generalized and unified target state estimation formulation is presented for accurately estimating the states of an airborne target, which can either be nonmaneuvering or undergoing various maneuvers. A nonlinear predictive zero effort miss-based guidance algorithm is also presented here. Extensive high-fidelity six degrees-of-freedom simulation experiments show that near-zero miss distance (i.e., hit-to-kill level performance) can be obtained when these two new techniques are applied together. Moreover, there is a substantial saving in control energy.

Iterative RANSAC based adaptive birth intensity estimation in GM-PHD filter for multi-target tracking

This paper investigates a novel multi-target tracking algorithm for jointly estimating the number of multiple targets and their states from noisy measurements in the presence of data association uncertainty, target birth, clutter and missed detections. Probability hypothesis density (PHD) filter is a popular multi-target Bayes filter. But the standard PHD filter assumes that the target birth intensity is known or homogeneous, which usually results in inefficiency or false tracks in a cluttered scene. To solve this weakness, an iterative random sample consensus (I-RANSAC) algorithm with a sliding window is proposed to incrementally estimate the target birth intensity from uncertain measurements at each scan in time. More importantly, I-RANSAC is combined with PHD filter, which involves applying the PHD filter to eliminate clutter and noise, as well as to discriminate between survival and birth target originated measurements. Then birth targets originated measurements are employed to update the birth intensity by the I-RANSAC as the input of PHD filter. Experimental results prove that the proposed algorithm can improve number and state estimation of targets even in scenarios with intersections, occlusions, and birth targets born at arbitrary positions.

A Multi‐Autonomous Underwater Vehicle System for Autonomous Tracking of Marine Life

This paper presents a multi-autonomous underwater vehicle system capable of cooperatively and autonomously tracking and following marine targets (i.e., fish) tagged with an acoustic transmitter. The AUVs have been equipped with stereo-hydrophones that receive signals broadcasted by the acoustic transmitter tags to enable real-time calculation of bearing-to-tag and distance-to-tag measurements. These measurements are shared between AUVs via acoustic modem and fused within each AUV's particle filter for estimating the target's position. The AUVs use a leader/follower multi-AUV control system to enable the AUVs to drive toward the estimated target state by following collision-free paths. Once within the local area of the target, the AUVs circumnavigate the target state until it moves to another area. The system builds on previous work by incorporating a new SmartTag package that can be attached to an individual's dorsal fin. The SmartTag houses a full inertial measurement unit (INU), video logger, acoustic transmitter, and timed release mechanism. After real-time AUV tracking experiments, the SmartTag is recovered. Logged IMU data are fused with logged AUV-obtained acoustic tag measurements within a particle filter to improve state estimation accuracy. This improvement is validated through a series of multi-AUV shark and boat tracking experiments conducted at Santa Catalina Island, California. When compared with previous work that did not use the SmartTag package, results demonstrated a decrease in mean position estimation error of 25–75%, tag orientation estimation errors dropped from 80° to 30° , the sensitivity of mean position error with respect to distance to the tag was less by a factor of 50, and the sensitivity of mean position error with respect to acoustic signal reception frequency to the tag was 25 times less. These statistics demonstrate a large improvement in the system's robustness when the SmartTag package is used.

Passive tracking in heavy clutter with sensor location uncertainty

In order to address the problem of passive tracking from multiple asynchronous angle-only sensors with location uncertainty in heavy clutter, a new iterative maximum-likelihood probabilistic data-association algorithm is proposed in this paper. An iterative prediction–update framework is adopted in the algorithm to simultaneously estimate the target state as well as the sensor state. At the prediction stage, a deterministic sampling approach is used to adjust the measurement covariance with sensor location uncertainty. Then a two-step grid-search technique is proposed to optimize the log-likelihood ratio, combined with a gradient-based search method. At the update stage, the operational sensor states are updated with target state estimates and measurements in corresponding validation gates. The updated sensor states are used to establish a more accurate log-likelihood ratio in the next iteration, which leads to better parameter estimation. In addition, the effects of the sensor location uncertainty on the track acceptance test and the posterior Cram´er–Rao lower bound are also analyzed. Simulation results show that the proposed method provides a computationally efficient way to improve track initialization performance in heavy clutter with sensor location uncertainty. The proposed work has applications in sonar tracking, geolocation, electronic support measures, and infrared search and tracking systems.

Joint estimation of target state and ionospheric height bias in over‐the‐horizon radar target tracking

Up to now, all over-the-horizon radar-based tracking methods have the prerequisite that the virtual ionospheric height, as the key ionosphere state, should be known, or should be estimated from the provided ionospheric measurements, or at least the prior statistical information of the ionosphere should be given. However, the ionospheric height is intermittently evolving and hence deviates from its nominal value. Therefore, no matter the virtual ionospheric height is directly given by ionosondes or estimated from the ionospheric measurements or prior statistical information, it may have ionospheric height bias. Here, the authors propose the problem of joint estimation of target state and ionospheric height bias in clutters. The problem is reformulated as the multi-rate state estimation with random coefficient matrices. Then, the linear minimum mean square error estimator with causality constraints is designed and extended to the non-linear measurement model via iterative optimisation. The proposed method is shown effective in the simulation about tracking one target in the case of four resolvable propagation modes.

Hierarchical search strategy in particle filter framework to track infrared target

A target of interest may exhibit significant appearance variations because of its complex maneuvers, ego-motion of the camera platform, etc. Currently, target tracking in forward-looking infrared (FLIR) sequences is still a challenging problem in the field of computer vision. Although many efforts have been devoted, there are still some issues to be addressed. First, state particles generated by prior information cannot approximate the probability density function well when the target state changes obviously. Second, plenty of particles have to be employed to obtain satisfying estimation of target state which will cause heavy computational burden in turn. In this paper, a hierarchical search strategy (HS tracker) is proposed to track infrared target in the particle filter framework, and there are two observation models employed to locate the target robustly. In the first stage, a saliency map leads the redistributed state particles to cover the salient areas that can provide a rough prediction of the target areas. In the second stage, sparse representation is employed to search for a subset of true ones from all the target candidates; thus, only efficient state particles are used to estimate the target state. The proposed method is tested on numerous FLIR sequences from the US army aviation and missile command database, and experimental results demonstrate the excellent performance.

Augmented consensus filter for simultaneous localization and tracking with limited sense range

In this paper, we investigate how to exploit distributed average consensus fusion for conducting simultaneous localization and tracking (SLAT) by using wireless sensor networks. To this end, we commence by establishing a limited sense range (LSR) nonlinear system that characterizes the coupling of target state and sensor localization with respect to each sensor. We then employ an augmented extended Kalman filter to estimate the sensor and target states of our system. Furthermore, we adopt a consensus filtering scheme which fuses the information from neighboring sensors. We thus obtain a two-stage distributed filtering framework that not only obtains updated sensor locations trough augment filtering but also provides an accurate target state estimate in consensus filtering. Additionally, our framework is computationally efficient because it only requires neighboring sensor communications. The simulation results reveal that the proposed filtering framework is much more robust than traditional information fusion methods in limited ranging conditions.

Group Sparsity Based Multi-Target Tracking in Passive Multi-Static Radar Systems Using Doppler-Only Measurements

In this paper, we consider the problem of tracking multiple targets in a passive multi-static radar system using Doppler-only measurements. The number of targets is assumed unknown and time-varying. The Doppler measurements are subject to additive noise, clutter, and missed detections. Doppler-only measurements from a single sensor provide incomplete information about the target state, commonly referred to as single-sensor unobservability. In a passive multi-static radar system, the availability of multiple bistatic links naturally lends itself to the fusion of measurements from spatially distributed sensors. However, data fusion emerges as a computationally intensive problem in multi-sensor multi-target tracking algorithms. We propose a two-step sequential approach to solve the underlying problem. We first cast the underlying problem as a group sparse problem in a discretized position-velocity space. A group sparsity based algorithm is applied to simultaneously exploit the multi-static Doppler frequency measurements to directly obtain the instantaneous target state estimates in the Cartesian coordinate system. These estimates are then fed as inputs to the linear Gaussian mixture probability hypothesis density (GMPHD) filter, which removes the false measurements, compensates for missed detections and reduces the localization error. The optimal sub-pattern assignment metric, which jointly comprises a weighted contribution of cardinality error and localization error, is used to evaluate the performance of the proposed method. Simulation results show that the proposed method successfully handles the multi-target tracking problem and outperforms the existing random receiver selection based multi-sensor implementation of the GMPHD filter.

Single- and Multi-UAV Trajectory Control in RF Source Localization

In this paper, two trajectory control approaches are presented for any number of unmanned aerial vehicles (UAVs) in radio frequency (RF) source localization. The UAVs observe the received signal strength (RSS) in distinctive time intervals to localize a stationary RF source. The location of the source is estimated recursively applying the extended Kalman filter. The objective of the optimal trajectory control is to steer the UAVs to the locations which minimize the uncertainty about the target state. The Fisher information matrix (FIM) is inversely proportional to the estimation variance. Since the true target state is unknown, the FIM is approximated by the estimated target state. Two criteria based on the approximated FIM are applied to measure the information content of the observations to optimize the UAV waypoints: The D-optimality and the A-optimality. The objective of the present paper is to propose two trajectory control approaches for any number of UAVs in RSS-based localization to increase the target localization accuracy. The superiority of the trajectory optimization approach based on the D-optimality in terms of mean squared error is illustrated through simulation examples.

Parallel search strategy in kernel feature space to track FLIR target

Robust tracking in the forward looking infrared (FLIR) sequences is still a challenging problem in the field of computer vision. Because images acquired by the infrared sensors are characterized by low signal-to-clutter ratios (SCR) and targets of interest may exhibit profound appearance variations due to ego-motion of the sensor platform and complex maneuvers. Though many efforts have been delivered, there are still some issues to be addressed. First, intensity features are not enough to deal with complex appearance variations for the challenging sequence. Second, to obtain satisfying estimation of target state, a plenty of particles have to be employed to approximate its probability density function (pdf). To deal with the two problems, a parallel search strategy based on kernel sparse representation (KL1PS tracker) is proposed to perform the tracking task in the FLIR sequences. With the ability of capturing the nonlinear features, kernel method is introduced to deal with complex appearance variations. After the kernel function is constructed based on the histogram features, both the target templates and candidates are mapped into the kernel feature space. Then efficient state particles are selected based on sparse representation which can be used to estimate the target state. The proposed method is tested on the AMCOM database, and the experimental results demonstrate its excellent performance in tracking accuracy compared with some state-of-the-art trackers.

A Newborn Track Detection and State Estimation Algorithm Using Bernoulli Random Finite Sets

In multi-target tracking (MTT) problems, there are many important issues that affect performance, including statistical filtering, measurement-target association, and estimating the number of targets. While newborn target detection and state estimation should also be considered as important factors in MTT, only a few studies have addressed these topics. In this paper, a novel newborn track detection and state estimation method is proposed using the concept of Bernoulli random finite sets. The posterior finite set statistical probability density function (FISST PDF) of a newborn target is analytically derived, and a tractable implementation scheme is proposed using importance sampling. Finally, the validity of the proposed method is demonstrated via integration with a Gaussian mixture probability hypothesis density (GM-PHD) filter and subsequent application to MTT problems.

Adaptive genetic MM-CPHD filter for multitarget tracking

Multitarget tracking is an important topic in visual surveillance system. Considering imperfections of the cardinalized probability hypothesis density (CPHD) filter and the target maneuvers, we propose an adaptive genetic multiple-model CPHD filter in this paper. First, we discuss the filtering process and combined the standard CPHD filter with the multiple-model-based framework. Afterward, the sequential Monte Carlo implementation of the proposed filter for the nonlinear and non-Gaussian state estimates is presented in detail. To enhance the tracking performance as target start to maneuver, the adaptive genetic algorithm is used to improve the target state estimation accuracy at the time of state switching with the excellent particles. On the other hand, the undetected component of the measurement-updated weight of survival particle is compensated by the excess weight of newborn particle to correct the number estimates of targets. The simulation results are provided to illustrate the reliability and efficiency of the proposed filter.

Smoothing data association for target trajectory estimation in cluttered environments

For heavily cluttered environments with low target detection probabilities, tracking filters may fail to estimate the true number of targets and their trajectories. Smoothing may be needed to refine the estimates based on collected measurements. However, due to uncertainties in target motions, heavy clutter, and low target detection probabilities, the forward prediction and the backward prediction may not be properly matched in the smoothing algorithms, so that the smoothing algorithms may fail to detect the true target trajectories. In this paper, we propose a new smoothing algorithm to overcome such difficulties. This algorithm employs two independent integrated probabilistic data association (IPDA) tracking filters: one running forward in time (fIPDA) and the other running backward in time (bIPDA). The proposed algorithm utilizes bIPDA multi-tracks in each fIPDA path track for fusing through data association to obtain the smoothing innovation in a fixed-lag interval. The smoothing innovation is used to obtain the smoothing data association probabilities which update the target trajectory state and the probability of target existence. The fIPDA tracks are updated after smoothing using the smoothing data association probabilities, which makes the fIPDA path tracks robust for maneuvering target tracking in clutter. This significantly improves the target state estimation accuracy compared to the IPDA. The proposed algorithm is called fixed-lag smoothing data association based on IPDA (FLIPDA-S). A simulation study shows that the proposed algorithm improves false track discrimination performance for maneuvering target tracking in clutter.

Marker-Less Stage Drift Correction in Super-Resolution Microscopy Using the Single-Cluster PHD Filter

Fluorescence microscopy is a technique which allows the imaging of cellular and intracellular dynamics through the activation of fluorescent molecules attached to them. It is a very important technique because it can be used to analyze the behavior of intracellular processes in vivo in contrast to methods like electron microscopy. There are several challenges related to the extraction of meaningful information from images acquired from optical microscopes due to the low contrast between objects and background and the fact that point-like objects are observed as blurred spots due to the diffraction limit of the optical system. Another consideration is that for the study of intracellular dynamics, multiple particles must be tracked at the same time, which is a challenging task due to problems such as the presence of false positives and missed detections in the acquired data. Additionally, the objective of the microscope is not completely static with respect to the cover slip due to mechanical vibrations or thermal expansions which introduces bias in the measurements. In this paper, a Bayesian approach is used to simultaneously track the locations of objects with different motion behaviors and the stage drift using image data obtained from fluorescence microscopy experiments. Namely, detections are extracted from the acquired frames using image processing techniques, and then these detections are used to accurately estimate the particle positions and simultaneously correct the drift introduced by the motion of the sample stage. A single cluster Probability Hypothesis Density (PHD) filter with object classification is used for the estimation of the multiple target state assuming different motion behaviors. The detection and tracking methods are tested and their performance is evaluated on both simulated and real data.

Target Tracking in 3-D Using Estimation Based Nonlinear Control Laws for UAVs

This paper presents an estimation based backstepping like control law design for an Unmanned Aerial Vehicle (UAV) to track a moving target in 3-D space. A ground-based sensor or an onboard seeker antenna provides range, azimuth angle, and elevation angle measurements to a chaser UAV that implements an extended Kalman filter (EKF) to estimate the full state of the target. A nonlinear controller then utilizes this estimated target state and the chaser’s state to provide speed, flight path, and course/heading angle commands to the chaser UAV. Tracking performance with respect to measurement uncertainty is evaluated for three cases: (1) stationary white noise; (2) stationary colored noise and (3) non-stationary (range correlated) white noise. Furthermore, in an effort to improve tracking performance, the measurement model is made more realistic by taking into consideration range-dependent uncertainties in the measurements, i.e., as the chaser closes in on the target, measurement uncertainties are reduced in the EKF, thus providing the UAV with more accurate control commands. Simulation results for these cases are shown to illustrate target state estimation and trajectory tracking performance.

ENERGY MANAGEMENT MODEL - AN INNOVATIVE TOOL FOR BENCHMARKING THE ENERGY MANAGEMENT SYSTEM IN INDUSTRIAL COMPANIES

Energy management is becoming an important topic for companies around the world. It influences organizational and technical processes and behaviors in the company in order to reduce the operational total energy consumption, improve resource efficiency, implement legislative requirements, and improve their corporate image. ISO 50001 International Standard was introduced as a global standard for managing energy use through that provides the unified framework for energy management. This paper introduces and describes model methodology and innovative software tool for benchmarking the energy management system in industrial companies. The software is designed to illustrate the current energy management situation in the company, the estimation of target state and to identify the energy efficiency potentials. Using the benchmark methodology, the energy management is summarized in an assessment profile. The results can be used as a reference guide for Energy Managers or extern consultants to understand the requirements and to implement ISO Standard 50001 in their company. A case study of an Austrian foundry is empirically tested and presented in order to evaluate the efficiency of the developed model.

A New Method for Ground Moving Targets Tracking Using Radar Based on Compressed Sensing

In this paper, we propose a Compressed Sensing (CS) based method under the unknown sparse degree to track ground moving targets using Pulse-Doppler (PD) radar. We use the sparsity of delay-Doppler plane in the process of disposing PD radar echo to set up a sparse signal model in each pulse interval. At the state prediction stage, we can get the predicted values of target states by dynamic equations, with which we can build a delay-Doppler grid that is used to form orthogonal dictionary. At the state update stage, we can get the target state estimation through reconstruction algorithm, so as to realize precise tracking of targets. The problem of target tracking by PD radar will be transformed into the reconstruction of the sparse signal, which is accomplished by getting the location of targets in the grid, as a result of achieving ground target tracking based on Orthogonal Matching Pursuit (OMP) [1]. Then, aiming at the sparsity problem in the method of target tracking based on Orthogonal Matching Pursuit, we propose a new target tracking method based on Sparsity Adaptive Matching Pursuit (SAMP) algorithm [2]. Numerical simulations show that our tracking method can not only provide the equivalent computational time, but also get better tracking performance than the KF-based tracking.