Problem Of Multiple Target Tracking Research Articles

Gaussian mixture probability hypothesis density filter with dynamic probabilities: Application to road traffic surveillance

With the development of autonomous driving system, accurate tracking of the number of vehicles and their states is critical to ensure traffic safety. Since the numbers and states of the vehicles are unknown and time-varying, the road traffic surveillance can be considered as a multiple-target tracking (MTT) problem. The Gaussian mixture probability hypothesis density (GM-PHD) filter is a popular approach in solving MTT problem and has been successfully applied to a range of applications including ground vehicle tracking. However, due to the road constraint, the maneuverability of a ground vehicle is limited to the road lane. Thus, the target detection probability, survival probability, and birth rate, which are assumed to be constant in the original GM-PHD filter, vary according to the surveillance area as well as its own state. To address this, we propose an algorithm that dynamically updates the probability of detection and survival, and birth rate simultaneously in a mathematically rigorous way. This is done by integrating the semantic information inferred from the environment with the state-dependent GM-PHD filter. The performance and effectiveness of the proposed algorithm are demonstrated via illustrative road traffic scenarios.

Passive Sonar Multiple-Target Tracking with Nonlinear Doppler and Bearing Measurements Using Multiple Sensors

The major advantage of the passive multiple-target tracking is that the sonars do not emit signals and thus they can remain covert, which will reduce the risk of being attacked. However, the nonlinearity of the passive Doppler and bearing measurements, the range unobservability problem, and the measurement to target data association uncertainty make the passive multiple-target tracking problem challenging. To deal with the target to measurement data association uncertainty problem from multiple sensors, this paper proposed a batch recursive extended Rauch-Tung-Striebel smoother- (RTSS-) based probabilistic multiple hypothesis tracker (PMHT) algorithm, which can effectively handle a large number of passive measurements including clutters. The recursive extended RTSS which consists of a forward filter and a backward smoothing is used to deal with the nonlinear Doppler and bearing measurements. The target range unobservability problem is avoided due to using multiple passive sensors. The simulation results show that the proposed algorithm works well in a passive multiple-target tracking system under dense clutter environment, and its computing cost is low.

Simultaneous tracking of a maneuvering ship and its wake using Gaussian processes

Maneuvering ship tracking has applications in maritime surveillance and the performance of tracking algorithms is affected by background clutter and ship-generated wake. Previous works consider the wake as a form of clutter with some specific spatial distribution. In this paper, the Kelvin wake, which assumes that the ship-generated wake is V-shaped, is modelled as an extended target with an irregular and time-varying shape. Thus, the problem of maneuvering ship tracking in the presence of wake becomes a special multiple target tracking problem, where the ship is a point target and the wake is an extended target. A Gaussian Processes based Multiple Model Joint Probabilistic Data Association (GP-MM-JPDA) method is proposed to estimate the ship state and the wake state simultaneously. Two versions of the GP-MM-JPDA are implemented. The first one can be applied to most multiple target tracking problems and the second one is designed especially for the ship tracking problem. Both versions can improve the ship tracking performance. In addition, the proposed methods can also infer the ship length using the estimated wake angle, which facilitates ship classification. Simulation results show the effectiveness of the proposed methods.

Multiple Target Tracking Based on Multiple Hypotheses Tracking and Modified Ensemble Kalman Filter in Multi-Sensor Fusion

In multi-sensor fusion (MSF), the integration of multi-sensor observation data with different observation errors to achieve more accurate positioning of the target has always been a research focus. In this study, a modified ensemble Kalman filter (EnKF) is presented to substitute the traditional Kalman filter (KF) in the multiple hypotheses tracking (MHT) to deal with the high nonlinearity that always shows up in multiple target tracking (MTT) problems. In addition, the multi-source observation data fusion is also realized by using the modified EnKF, which enables the low-precision observation data to be corrected by high-precision observation data, and the accuracy of the corrected data can be calibrated by the statistical information provided by the EnKF. Numerical studies are given to demonstrate the effectiveness of our proposed method and the results show that the MHT-EnKF method can achieve remarkable enhancement in dealing with nonlinear movement variation and positioning accuracy for MTT problems in MSF scenario.

Variational Bayesian adaptation of noise covariances in multiple target tracking problems

Multiple Target Tracking (MTT) is the process of computing the number of targets present in a surveillance area. MTT requires estimation of state variables and data association. New measurements are associated with existing tracks, clutter or new tracks. MTT generally involves unknown number of targets. Mostly because of computational complexity faced by MTT algorithms, it is a difficult and challenging problem. Computational load, underlying assumptions of known number of targets, and high cluttered environment are the main reasons, which available methods cannot address properly. Rao-Blackwellized has been used for multiple target tracking. It uses Kalman filter for state estimation and particle filter for data association. Our objective is to extend Rao-Blackwellized Monte Carlo Data Association (RBMCDA) that estimates number of targets and maintains track continuity enabling persistent tracking of targets. RBMCDA has been tested with seven different resampling methods in an effort to obtain the best resampling method. Gating validation and Variational Bayesian have been incorporated for multi target tracking problem. The modified RBMCDAs are applied to different case studies for its performance evaluation.

Multi-Sensor Task Assignment Using Linear Matrix Inequalities in the Multiple Target Tracking Problem

Due to significant advancements in embedded systems, sensor devices and wireless communication technology, sensor networks have been attracting widespread attention in areas such as target tracking, monitoring, and surveillance. Technological advancements made it possible to deploy a large number of inexpensive but technically advanced sensors to cover wide areas. However, when a tracking system has to track a large number of targets, the computation and communication loads arise. In this paper we propose a task assignment algorithm based on linear matrix inequalities (LMI) to reduce the computational complexity and communication load. Simulation results and a comparison with the Kalman filtering strategy confirm the suitability of the approach.

A Bootstrapped PMHT with Feature Measurements

The probabilistic multi-hypothesis tracker (PMHT), a tracking algorithm of considerable theoretical elegance based on the expectation-maximization algorithm, will be considered for the problem of multiple target tracking with multiple sensors in clutter. In addition to position observations, continuous measurements associated with the unique, constant—and statistically unknown —feature of each target are incorporated to jointly estimate the states and features of the targets for the sake of tracking and classification, leading to a bootstrapped implementation of the PMHT. In addition, the information matrix for the stacked vector of states for all the targets at all the time steps during the observation time is derived.

Moving Path Following for Unmanned Aerial Vehicles With Applications to Single and Multiple Target Tracking Problems

This paper introduces the moving path following (MPF) problem, in which a vehicle is required to converge to and follow a desired geometric moving path, without a specific temporal specification, thus generalizing the classical path following that only applies to stationary paths. Possible tasks that can be formulated as an MPF problem include tracking terrain/air vehicles and gas clouds monitoring, where the velocity of the target vehicle or cloud specifies the motion of the desired path. We derive an error space for MPF for the general case of time-varying paths in a two-dimensional space and subsequently an application is described for the problem of tracking single and multiple targets on the ground using an unmanned aerial vehicle (UAV) flying at constant altitude. To this end, a Lyapunov-based MPF control law and a path-generation algorithm are proposed together with convergence and performance metric results. Real-world flight tests results that took place in Ota Air Base, Portugal, with the ANTEX-X02 UAV demonstrate the effectiveness of the proposed method.

New Multiple-Target Tracking Strategy Using Domain Knowledge and Optimization

This paper proposes an environment-dependent vehicle dynamic modeling approach considering interactions between the noisy control input of a dynamic model and the environment in order to make best use of domain knowledge. Based on this modeling, a new domain knowledge-aided moving horizon estimation (DMHE) method is proposed for ground moving target tracking. The proposed method incorporates different types of domain knowledge in the estimation process considering both environmental physical constraints and interaction behaviors between targets and the environment. Furthermore, in order to deal with a data association ambiguity problem of multiple-target tracking in a cluttered environment, the DMHE is combined with a multiple-hypothesis tracking structure. Numerical simulation results show that the proposed DMHE-based method and its extension could achieve better performance than traditional tracking methods which utilize no domain knowledge or simple physical constraint information only.

Associating optical measurements of MEO and GEO objects using Population-Based Meta-Heuristic methods

Currently several thousands of objects are being tracked in the MEO and GEO regions through optical means. The problem faced in this framework is that of Multiple Target Tracking (MTT). The MTT problem quickly becomes an NP-hard combinatorial optimization problem. This means that the effort required to solve the MTT problem increases exponentially with the number of tracked objects. In an attempt to find an approximate solution of sufficient quality, several Population-Based Meta-Heuristic (PBMH) algorithms are implemented and tested on simulated optical measurements. These first results show that one of the tested algorithms, namely the Elitist Genetic Algorithm (EGA), consistently displays the desired behavior of finding good approximate solutions before reaching the optimum. The results further suggest that the algorithm possesses a polynomial time complexity, as the computation times are consistent with a polynomial model. With the advent of improved sensors and a heightened interest in the problem of space debris, it is expected that the number of tracked objects will grow by an order of magnitude in the near future. This research aims to provide a method that can treat the association and orbit determination problems simultaneously, and is able to efficiently process large data sets with minimal manual intervention.

Combining particle MCMC with Rao-Blackwellized Monte Carlo data association for parameter estimation in multiple target tracking

We consider state and parameter estimation in multiple target tracking problems with data association uncertainties and unknown number of targets. We show how the problem can be recast into a conditionally linear Gaussian state-space model with unknown parameters and present an algorithm for computationally efficient inference on the resulting model. The proposed algorithm is based on combining the Rao-Blackwellized Monte Carlo data association algorithm with particle Markov chain Monte Carlo algorithms to jointly estimate both parameters and data associations. Both particle marginal Metropolis–Hastings and particle Gibbs variants of particle MCMC are considered. We demonstrate the performance of the method both using simulated data and in a real-data case study of using multiple target tracking to estimate the brown bear population in Finland.

Bayesian Multiple-Hypothesis Tracking of Merging and Splitting Targets

This paper presents a Bayesian model for the multiple-target tracking problem that handles a varying number of splitting and merging targets applied to convective cloud tracking. The model decomposes the tracking solution into events and target states. The events include target births, deaths, splits, and merges. The target states contain both the target positions and attributes. By updating the target attributes and conditioning the events on their updated values, we can include high-level domain knowledge into the system. This strategy improves the tracking accuracy and the computational efficiency since we focus only on likely events for each situation. A two-step multiple-hypothesis tracking algorithm has been developed to estimate the model state. The proposed approach is tested by both simulation and real data for mesoscale convective system tracking.

Multiple-target tracking and track management for an FMCW radar network

A multiple-target tracking problem for a frequency-modulated continuous-wave (FMCW) radar network is formulated and an integrated track management system is presented to solve the tracking problem in the presence of clutter. The FMCW radar network obtains beat frequency measurements with multiple collocated radars, each transmitting a sequence of chirps. The beat frequency measurements are associated to tracks directly in the beat frequency measurement space. The direct association eliminates range/range-rate calculations and multilateration processing, and it allows to process beat frequency measurements sequentially on a chirp by chirp basis. The sequential processing effectively decomposes the measurement-to-track association problem into a series of two-dimensional assignment problems that can be solved with much less computational effort. The solution to the measurement-to-track association problem is utilized to initiate and form new tracks and to update or delete existing tracks. Monte Carlo simulations were performed to evaluate the performance of the track management system.

Managing Multi-Modal Sensor Networks Using Price Theory

We propose a unified framework for sensor management in multi-modal sensor networks, which is inspired by the trading behavior of economic agents in commercial markets. Each sensor node (SN) acts as a seller who wants to sell the data it collects, to the sensor network manager (SM) who acts as a buyer. The resources and the data are priced by looking to balance global supply and demand, with the SN required to purchase resources for producing the data, and the SM required to purchase data to accomplish his tasks. We model this interaction as a double sided market, with both consumers and producers, and propose an iterative double auction mechanism for computing the equilibrium of such a market. We relate the equilibrium point to the solutions of sensor selection (SS), resource allocation (RA), and data fusion (DF) problems, which constitute the sensor management. The proposed framework will enable the system to determine the kind and the amount of data that should be produced, and to combine the data that is produced at each SN. To illustrate this framework, we consider the problem of multiple-target tracking as an example. Numerical examples demonstrate the effectiveness of the proposed method, and show that appropriate sensor management will result in an accurate estimate of the number of targets in the scene, higher correct identifications of the targets, and a lower mean-squared error in the estimates of their positions and velocities.

Improving Accuracy by Iterated Multiple Particle Filtering

This paper analyzes and validates an enhanced implementation of the multiple particle filter that improves its accuracy when applied to high dimensional problems. The algorithm combines the divide et impera philosophy of the multiple particle filter, which avoids the collapse of traditional particle filters, with game theory strategies that provide with a powerful tool to improve the performance. The problem of multiple target tracking with received signal strength measurements is addressed and the results show remarkable improvement over both standard particle filtering and multiple particle filtering.

A low-complexity multi-target tracking algorithm in urban environments using sparse modeling

We propose a novel sparsity-based algorithm for multiple-target tracking in a time-varying multipath environment. We develop a sparse measurement model for the received signal, by considering a finite dimensional representation of the time-varying system function which characterizes the transmission channel. The measurement model allows us to exploit the joint delay–Doppler diversity offered by the environment. We reformulate the problem of multiple-target tracking as a block support recovery problem and we derive an upper bound on the overall error probability of wrongly identifying the support of the sparse signal. Using this bound, we prove that spread-spectrum waveforms are ideal candidates for signaling. We also prove that under spread-spectrum signaling, the dictionary of the sparse measurement model exhibits a special structure. We exploit this structure to develop a computationally inexpensive support recovery algorithm by projecting the received signal on to the row space of the dictionary. Numerical simulations show that tracking using proposed algorithm for support recovery performs better when compared to tracking using other sparse reconstruction algorithms and tracking using a particle filter. The proposed algorithm takes significantly less time when compared to the time taken by other methods.

SIMPLIFIED PARTICLE PHD FILTER FOR MULTIPLE-TARGET TRACKING: ALGORITHM AND ARCHITECTURE

In this paper, we propose a simplifled particle probability hypothesis density (PHD) fllter and its hardware implementation for multiple-target tracking (MTT). In the proposed algorithm, the update step of particle PHD fllter is simplifled and the time-varying number of measurements is arranged in combination series/parallel mode. This may result in flxed hardware architecture and therefore present a convenient hardware implementation of particle PHD fllter. Simulation results indicate that for the MTT problems, this proposed simplifled algorithm shows similar performance with the standard particle PHD fllter but has faster processing rate. Experiment study shows that the proposed simplifled algorithm can be e-ciently implemented in hardware and can efiectively solve the MTT problems.

Multihypothesis Viterbi Data Association: Algorithm Development and Assessment

Two algorithms for tracking in clutter, based on the Viterbi algorithm are presented: single-target Viterbi data association (ST-VDA) and multihypothesis VDA (MH-VDA). MH-VDA is designed specifically for multiple-target tracking (MTT), although ST-VDA still achieves good performance on MTT problems. The basic philosophy of both methods is to set up an optimisation problem for the sequence of measurement-to-target associations rather than directly seeking the target state estimates. The joint optimisation problem for the data association sequence is decomposed into a sequence of scalar optimisation problems by means of an approximate forward dynamic programming recursion to which the Viterbi algorithm is applicable. Once the data association problem is solved, the target state estimates can be retrieved by backtracking. The operation of the algorithms is easily visualised as a search on a trellis for the optimal path. For ST-VDA, nodes in the trellis correspond to measurements. For MH-VDA, nodes correspond to multitarget data association hypotheses. Conventional measurement gating is extended to work within this context. Results from simulations that compare the performance of ST-VDA and MH-VDA with four, standard, zero-scan-back tracking approaches are given. The performance assessment includes metrics for track loss and track swaps in a multiple crossing target context. The Viterbi data association (VDA) algorithms are shown to outperform the alternative algorithms. In particular the ST-VDA is found to have the best track swap performance, while MH-VDA has the lowest track loss figure. Average state estimation errors for both VDA algorithms are only about 10% larger than a Kalman filter with known data associations. While both variants of VDA are essentially batch processing approaches, the simulation results indicate that the algorithms can be implemented with a fixed processing lag of only a few scans without significant loss in performance.

A Short-Range Ship Navigation System Based on Ladar Imaging and Target Tracking for Improved Safety and Efficiency

A new maritime navigation system based on a laser range-finder scanner for obstacle avoidance and precise maneuvering operations is described in this paper. The main novelty of this work is the adaptation and implementation of known technology for laser range finding and algorithms for target tracking into a system that operates in real time and has been tested in different natural sea and inland navigation scenarios. The principal components of this system, namely, 1) the laser range finder, 2) the scanning unit, and 3) the data processing and displaying unit, are described in detail. Ladar images are dense horizontally and sparse vertically as a compromise between capturing relevant features and quick frame formation. Images are processed for range outlier removal, and significant observable patterns are extracted. This multiple-target tracking problem is tackled using robust Kalman filtering techniques for continuous tracking of each detected observation. We minimize unreliable track initializations and preserve tracks from deletion during temporal misobservations. The evaluation in open-sea and inland waterways gave good results, making the system valid for precise maneuvering, fluent navigation, and accident mitigation. Objects of interest, from boats to ships, are detected and robustly tracked; pier and lock chamber sketches are reliable; bridge height estimation is precise; and narrow waterways (river banks and bridge columns) are correctly detected. The prototype developed can be considered to be a very valuable complementary device to traditional radar-based techniques that are not totally valid for accurate short-range exploration, improving efficiency and safety in ship operations.

Information Theoretic Enumeration and Tracking of Multiple Sources

The problem of multiple target tracking using a passive direction-finding system is addressed when the number of targets is not known a priori. A new method is proposed that is suitable for systems operating in low signal-to-noise ratio and high clutter. Such conditions cause unpredictable variations of stochastic characteristics of noise and signals (especially for wideband frequency ones) and create ambiguity in the output of direction-finding algorithms. In this paper, we use the predictive description length (PDL) technique, which is an information theoretic approach, and by suitable modeling, we minimize the predictive codelength for statistical data description of position measurements. The PDL-dynamic programming (DP) method is also presented, which employs the DP algorithm to reduce the computational load of the PDL technique. The concept of tracking time-varying number of targets makes PDL-DP a suitable technique for target tracking in practical systems