Time-varying Number Of Targets Research Articles

Multiple targets tracking by using improved labeled multi-target multi-Bernoulli filter with FDA-MIMO radar

In the traditional multi-target multi-Bernoulli (MeMBer) filter, only the radar signal after constant false alarm rate (CFAR) is considered. In addition, it is also necessary to assume that the detection probability of the target and the covariance matrix of the measurement noise are known. In fact, the detection probability of the target and the covariance matrix of the noise are difficult to determine. Therefore, this paper proposes an improved labeled multi-target multi-Bernoulli (IL-MeMBer) filter that can avoid considering the detection probability of the target and the covariance matrix of the measurement noise. Firstly, the multi-target superposition measurement model of FDA-MIMO is given in the presence of false targets. Secondly, the target likelihood function under the superposition measurement model is derived by analyzing the characteristics of the traditional point measurement likelihood function and the Music spatial spectrum. Thirdly, considering the unknown and time-varying number of targets, the original echo signals without detection probability before (CFAR) processing, and no target tracks output by the traditional MeMBer filter, we put forward an analytical solution of the IL-MeMBer filter. Finally, a fusion strategy based on Euclidean distance is proposed to solve the problem that the same target can be estimated to have multiple tracks in the IL-MeMBer filter. In addition, the sequential Monte Carlo (SMC) implementation of the IL-MeMBer filter is provided. Numerical experiences verify the effectiveness and superiority of the proposed methods.

Online clustering-based multi-camera vehicle tracking in scenarios with overlapping FOVs

Multi-Target Multi-Camera (MTMC) vehicle tracking is an essential task of visual traffic monitoring, one of the main research fields of Intelligent Transportation Systems. Several offline approaches have been proposed to address this task; however, they are not compatible with real-world applications due to their high latency and post-processing requirements. This lack of suitable approaches motivates our proposal: A new low-latency online approach for MTMC tracking in scenarios with partially overlapping fields of view (FOVs), such as road intersections. Firstly, the proposed approach detects vehicles at each camera. Then, the detections are merged between cameras by applying cross-camera clustering based on appearance and location. Lastly, the clusters containing different detections of the same vehicle are temporally associated to compute the tracks on a frame-by-frame basis. The experiments show promising low-latency results while addressing real-world challenges such as the a priori unknown and time-varying number of targets and the continuous state estimation of them without performing any post-processing of the trajectories. Our code is available at http://www-vpu.eps.uam.es/publications/Online-MTMC-Tracking.

Adaptive Kernel Kalman Filter Based Belief Propagation Algorithm for Maneuvering Multi-Target Tracking

This letter incorporates the adaptive kernel Kalman filter (AKKF) into the belief propagation (BP) algorithm for multi-target tracking (MTT) in single-sensor systems. The algorithm is capable of tracking an unknown and time-varying number of targets, in the presence of false alarms, clutter and measurement-to-target association uncertainty. Experiment results reveal that the proposed method has a favourable tracking performance using the generalized optimal sub-patten assignment (GOSAP) metrics at substantially less computation cost than the particle filter (PF) based multi-target tracking (MTT) BP algorithm.

Multi-target identity management for unknown and time-varying number of targets in clutter

A novel multi-target identity management algorithm, called IM-GMPHD (Identity management with Gaussian mixture probability hypothesis density) algorithm, is proposed to simultaneously track an unknown and time-varying number of targets in clutter and manage the targets’ identities effectively and efficiently over time. The targets’ identities are managed by recursively computing the target identity beliefs using the target-measurement correlation. An augmented GMPHD filter is also developed in this paper to calculate the target-measurement correlation. In addition, the target identity beliefs can be updated to a lower uncertainty level whenever there is available target identity information. The proposed multi-target identity management algorithm is demonstrated through various illustrative simulation scenarios.

A distributed particle-PHD filter using arithmetic-average fusion of Gaussian mixture parameters

We propose a particle-based distributed PHD filter for tracking the states of an unknown, time-varying number of targets. To reduce communication, the local PHD filters at neighboring sensors communicate Gaussian mixture (GM) parameters. In contrast to most existing distributed PHD filters, our filter employs an “arithmetic average” fusion. For particles–GM conversion, we use a method that avoids particle clustering and enables a significance-based pruning of the GM components. For GM–particles conversion, we develop an importance sampling based method that enables a parallelization of filtering and dissemination/fusion operations. The resulting distributed PHD filtering framework is able to integrate both particle-based and GM-based local PHD filters. Simulations demonstrate the excellent performance and small communication and computation requirements of our filter.

Multi-Bernoulli Filter for Tracking Multiple Targets Using Sensor Array

This paper presents a multi-Bernoulli filter for tracking the direction of arrival (DOAs) of time-varying number of targets using sensor array. Our method operates directly on the measurements of sensor array and does not require any detection. Firstly, more information is reserved and compared with the after-detection measurements using a finite set of detected points. It can significantly improve the tracking performance, especially in low signal-to-noise ratio. Secondly, it inherits the advantages of the multi-Bernoulli approximation which models each of the targets individually. This allows more accurate multi-target state estimation, especially when targets cross. The proposed filter does not need clustering step and simulation results showcase the improved performance of the proposed filter.

A Multi-Target Track-Before-Detect Particle Filter Using Superpositional Data in Non-Gaussian Noise

This paper proposes a novel particle filter for tracking time-varying states of multiple targets jointly from superpositional data, which depend on the sum of contributions of all targets. Many conventional tracking methods rely on preprocessing for detection (e.g., thresholding), which severely limits tracking performance at a low signal-to-noise ratio (SNR). In contrast, the proposed method operates directly on raw sensor signals without requiring such preprocessing. Though there also exist methods applicable to raw sensor signals called track-before-detect, the proposed method has significant advantages over them. First, it is general without any restrictions on observation/process noise statistics (e.g., Gaussian) or the functional form of each target's contribution to the sensors (e.g., linear, separable, binary). Especially, it includes Salmond et al.'s track-before-detect particle filter for a single target as a particular example up to some implementation details. Second, it can track an unknown, time-varying number of targets without knowing their initial states owing to a target birth/death model. We present a simulation example of radio-frequency tomography, where it significantly outperformed Nannuru et al.'s state-of-the-art method based on random finite sets in terms of the optimal subpattern assignment (OSPA) metric.

Decentralized Gaussian Filters for Cooperative Self-Localization and Multi-Target Tracking

Scalable and decentralized algorithms for Cooperative Self-localization (CS) of agents, and Multi-Target Tracking (MTT) are important in many applications. In this work, we address the problem of Simultaneous Cooperative Self-localization and Multi-Target Tracking (SCS-MTT) under target data association uncertainty, i.e., the associations between measurements and target tracks are unknown. Existing CS and tracking algorithms either make the assumption of no data association uncertainty or employ a hard-decision rule for measurement-to-target associations. We propose a novel decentralized SCS-MTT method for an unknown and time-varying number of targets under association uncertainty. Marginal posterior densities for agents and targets are obtained by an efficient belief propagation (BP) based scheme while data association is handled by marginalizing over all target-to-measurement association probabilities. Decentralized single Gaussian and Gaussian mixture implementations are provided based on average consensus schemes, which require communication only with one-hop neighbors. An additional novelty is a decentralized Gibbs mechanism for efficient evaluation of the product of Gaussian mixtures. Numerical experiments show the improved CS and MTT performance compared to the conventional approach of separate localization and target tracking.

Online Multi-Target Tracking Using Recurrent Neural Networks

We present a novel approach to online multi-target tracking based on recurrent neural networks (RNNs). Tracking multiple objects in real-world scenes involves many challenges, including a) an a-priori unknown and time-varying number of targets, b) a continuous state estimation of all present targets, and c) a discrete combinatorial problem of data association. Most previous methods involve complex models that require tedious tuning of parameters. Here, we propose for the first time, an end-to-end learning approach for online multi-target tracking. Existing deep learning methods are not designed for the above challenges and cannot be trivially applied to the task. Our solution addresses all of the above points in a principled way. Experiments on both synthetic and real data show promising results obtained at ~300 Hz on a standard CPU, and pave the way towards future research in this direction.

Efficient Approximation of the Labeled Multi-Bernoulli Filter for Online Multitarget Tracking

Online tracking time-varying number of targets is a challenging issue due to measurement noise, target birth or death, and association uncertainty, especially when target number is large. In this paper, we propose an efficient approximation of the Labeled Multi-Bernoulli (LMB) filter to perform online multitarget state estimation and track maintenance efficiently. On the basis of the original LMB filer, we propose a target posterior approximation technique to use a weighted single Gaussian component representing each individual target. Moreover, we present the Gaussian mixture implementation of the proposed efficient approximation of the LMB filter under linear, Gaussian assumptions on the target dynamic model and measurement model. Numerical results verify that our proposed efficient approximation of the LMB filer achieves accurate tracking performance and runs several times faster than the original LMB filer.

Computation-distributed probability hypothesis density filter

Particle probability hypothesis density filtering has become a promising approach for multi-target tracking due to its capability of handling an unknown and time-varying number of targets in a nonlinear, non-Gaussian system. However, its computational complexity linearly increases with the number of obtained observations and the number of particles, which can be very time consuming, particularly when numerous targets and clutter exist in the surveillance region. To address this issue, we present a distributed computation particle probability hypothesis density(PHD) filter for target tracking. It runs several local decomposed particle PHD filters in parallel while processing elements. Each processing element takes responsibility for a portion of particles but all measurements and provides local estimates. A central unit controls particle exchange among the processing elements and specifies a fusion rule to match and fuse the estimates from different local filters. The proposed framework is suitable for parallel implementation. Simulations verify that the proposed method can significantly accelerate and maintain a comparative accuracy compared to the standard particle PHD filter.

Two implementations of marginal distribution Bayes filter for nonlinear Gaussian models

The marginal distribution Bayes (MDB) filter is an efficient approach for tracking an unknown and time-varying number of targets in the presence of clutter, noise, data association uncertainty, and detection uncertainty. This filter propagates the marginal distributions and existence probabilities of each target in the filter recursion, and it admits a closed-form solution for a linear Gaussian multi-target model. However, this closed-form solution is not general enough to accommodate nonlinear multi-target models. In this paper, we propose two implementations of the MDB filter to accommodate nonlinear multi-target models. The first is the first-order Taylor approximation MDB (FTA-MDB) filter which is based on the linearization technique of nonlinear function, and the second is the unscented transform MDB (UT-MDB) filter which is based on the unscented transform technique. Simulation results demonstrate that the proposed implementations are better on multiple targets tracking than the UK-PHD filter.

A New Method for Multiple Target Tracking Based on Fuzzy Possibility

In this paper we propose a new method for estimating the time varying number of targets and their states through a sequence of uncertain observations in the presence of clutter and noise, based on fuzzy possibility. The uncertainties of target states and observations at each time step are modeled with fuzzy possibility; furthermore, the birth process of targets and each target survival and detection are modeled with possibility function too. This algorithm estimates the possibility of each target state and new target birth via some fuzzy rules by using fuzzy simulation algorithm. This approach contains a new data association algorithm based on fuzzy possibility for track valued estimate of individual targets. Simulation results show that the proposed algorithm can successfully estimate the state of survival targets, the birth process of targets and the number of targets at each time step in presence of clutter; moreover, it can associate each target with corresponding measurements independently, so this method does not need any additional data association algorithms.

The Gaussian Mixture Probability Hypothesis Density Filter and Its Application to Multi-Target Tracking

A algorithm based on random sets and point process theory is proposed for jointly estimate the time-varying number of targets and their states.The main contributions include:(1) Analyze multi-target tracking model based on random finite sets;(2) The Probability Hypothesis Density recursive formulas are deduced based on random sets and point process theory;(3) A analytic implementation of the Probability Hypothesis Density Filter is proposed under the linear Gaussian assumptions;(4) Two simulation results validate GMPHD performance and then compare GMPHD and JPDA performance under clutter and detection probability change;(5) Point out some the algorithm's lack and research direction.

Clustered Mixture Particle Filter for Underwater Multitarget Tracking in Multistatic Active Sonobuoy Systems

The problem of multitarget tracking in underwater multistatic active sonobuoy systems is challenging because of the large number of false contacts and multiple reflections that reach the receivers. Targeting a robust solution that can track an unknown time-varying number of multiple targets, while keeping continuous tracks even in scenarios with large number of false contacts per ping, a particle filter (PF)-based technique is proposed in this paper. The PF is a nonlinear filtering technique that can accommodate arbitrary sensor characteristics, motion dynamics, and noise distributions. An enhanced version of the PF called the mixture PF is utilized in this paper. While the sampling/importance resampling PF samples from the prior importance density and weights the particles according to the observation likelihood, the mixture PF samples from both importance densities and weights the different groups of particles respectively. The usage of this mixture of importance densities provides better performance and faster convergence to the true targets locations. In order to track an unknown time-varying number of targets, two mixture PFs are used (one for target detection and the other for tracking multiple targets), and a density-based clustering technique. The first filter starts with random uniformly distributed samples over the surveillance area and resets every five pings. Just before the reset, the clustering technique runs to detect the clusters that corresponds to different targets and passes them to the second filter. The performance of the proposed technique is examined and demonstrated by different simulated scenarios and some real datasets from the SEABAR07 trial by the NATO Underwater Research Center.

基于MMPHDF 的多机动目标联合检测、跟踪与分类算法

To account for joint detection, tracking and classification (JDTC) of multiple maneuvering targets in dense clutter environment, this paper introduces an algorithm based on the multiple-model probability hypothesis density filter (MMPHDF). The MMPHDF can be applied to jointly detect and track multiple maneuvering targets, but its filter performance deteriorates rapidly in dense clutter environment. The proposed JDTC algorithm (MMPHDF-JDTC) utilizes target classification information and target kinematic information to simultaneously estimate the time-varying number of targets, their kinematic states and types. The main idea is to augment the kinematic state vector with the target class vector, and then use their combined measurement likelihood to integrate the target classification information into the update process of MMPHDF. The combined target kinematic state and class measurement likelihood improves the discrimination of different class targets and clutter, so better detection and tracking performance can be expected compared with the original MMPHDF. Alternately, accurate detection and tracking results is the foundation for correct target classification. A particle implementation of the MMPHDF-JDTC has been given. Simulation results validate the above conclusions.

A Gaussian Mixture PHD Filter for Jump Markov System Models

The probability hypothesis density (PHD) filter is an attractive approach to tracking an unknown and time-varying number of targets in the presence of data association uncertainty, clutter, noise, and detection uncertainty. The PHD filter admits a closed-form solution for a linear Gaussian multi-target model. However, this model is not general enough to accommodate maneuvering targets that switch between several models. In this paper, we generalize the notion of linear jump Markov systems to the multiple target case to accommodate births, deaths, and switching dynamics. We then derive a closed-form solution to the PHD recursion for the proposed linear Gaussian jump Markov multi-target model. Based on this an efficient method for tracking multiple maneuvering targets that switch between a set of linear Gaussian models is developed. An analytic implementation of the PHD filter using statistical linear regression technique is also proposed for targets that switch between a set of nonlinear models. We demonstrate through simulations that the proposed PHD filters are effective in tracking multiple maneuvering targets.

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