Multisensor Multitarget Tracking Algorithm Research Articles

Multiradar Joint Tracking of Cluster Targets Based on Graph-LSTMs

The cluster target brings a serious challenge to the traditional multisensor multitarget tracking algorithm because of its large number of members and the cooperative interaction between members. Using multiradar joint tracking cluster target is an alternative method to solve the problem of cluster target tracking, but it inevitably brings the problem of radar-target assignment and tracking information fusion. Aiming at the problem of radar-target assignment and tracking information fusion, a joint tracking method based on graph-long short-term memory neural nets (Graph-LSTMs) is proposed. Firstly, we use multivariable stochastic differential equations (SDE) to model the cooperative interaction of cluster members and transform the derived state space model of cluster members into the same form as the constant velocity (CV) motion model, and the target state equation of cluster which can be used for Bayesian filtering iteration is established. Secondly, based on the detection relationship between radars and cluster members, we introduce the detection confirmation matrix and propose a radar-target assignment method to achieve multiple measurements of single member and detection coverage of all cluster members. Then, each radar uses δ-GLMB filter to estimate the motion state of the assigned targets. Finally, on the basis of spatial discretization, the labels of multiple estimates of cluster member states are obtained. We use the designed Graph-LSTMs to learn the cooperative relationship between target states to fuse the labels and obtain better tracking effect. The experimental results show that the proposed method effectively simulates the cluster motion and realizes the joint estimation of cluster target motion state by multiradar. Our method makes up for the defect that a single radar cannot stably track adjacent multiple targets and achieves better estimation fusion effect than the expectation-maximization (EM) algorithm and mean method.

Multisensor-multitarget tracking based on belief propagation against false data injection attacks and denial of service attacks

This paper is concerned with the multisensor multitarget tracking where the sensor network can potentially be compromised by adversarial attacks, including false data injection (FDI) attacks and denial of service (DoS) attacks. We propose a multisensor multitarget tracking algorithm against FDI and DoS attacks based on belief propagation (BP) message passing method. With the factorization of joint posterior density, the statistical structure of the tracking problem is described by a factor graph. A BP-based algorithm is derived based on the factor graph for an efficient evaluation of the marginal posterior densities of the target states. The marginal posterior densities are then utilized for the detection and estimation of the multitarget states. Then, we develop an efficient Gaussian mixture implementation of the proposed BP-based algorithm for the linear Gaussian measurement and state evolution model. Simulation results illustrate that the proposed multisensor multitarget tracking algorithm can provide reliable tracking performance against FDI and DoS attacks.

Optimizing Distributed Multi-Sensor Multi-Target Tracking Algorithm Based on Labeled Multi-Bernoulli Filter

Optimizing Distributed Multi-Sensor Multi-Target Tracking Algorithm Based on Labeled Multi-Bernoulli Filter

Multi-Sensor Fusion Tracking Algorithm Based on Augmented Reality System

With the rapid development of modern science and technology, multi-sensor information fusion target tracking technology has gradually become an increasingly important topic in the field of computer vision. It is not only of great significance in traffic monitoring, unmanned vehicle systems, human-computer interaction, etc., but also has a wide range of applications in target tracking. Aiming at the problem of multi-sensor multi-target tracking, this article proposes a multi-sensor multi-target tracking algorithm based on random finite set. The basic theory of random sets, random finite set statistics and the probability hypothesis density of random sets are introduced. The distribution function of random sets and the derivation process of set derivatives and set integrals are discussed, and the theoretical framework of information fusion based on random sets is given. The single-sensor single-target specification Bayes modeling method is extended to the multi-sensor multi-target situation, and the significance of the probability hypothesis density is explained, and the multi-sensor multi-target tracking is realized by the probability hypothesis density filtering. The simulation experiment results show that the algorithm has a better tracking effect for the maneuvering target in the nonlinear motion model stage, and it is verified that the unscented Kalman filter has smaller filtering error and higher estimation accuracy than the extended Kalman filter.

Bayesian information fusion and multitarget tracking for maritime situational awareness

The goal of maritime situational awareness (MSA) is to provide a seamless wide-area operational picture of ship traffic in coastal areas and the oceans in real time. Radar is a central sensing modality for MSA. In particular, oceanographic high-frequency surface-wave (HFSW) radars are attractive for surveying large sea areas at over-the-horizon distances, due to their low environmental footprint and low power requirements. However, their design is not optimal for the challenging conditions prevalent in MSA applications, thus calling for the development of dedicated information fusion and multisensor-multitarget tracking algorithms. In this study, the authors show how the multisensor-multitarget tracking problem can be formulated in a Bayesian framework and efficiently solved by running the loopy sum-product algorithm on a suitably devised factor graph. Compared to previously proposed methods, this approach is advantageous in terms of estimation accuracy, computational complexity, implementation flexibility, and scalability. Moreover, its performance can be further enhanced by estimating unknown model parameters in an online fashion and by fusing automatic identification system (AIS) data and context-based information. The effectiveness of the proposed Bayesian multisensor-multitarget tracking and information fusion algorithms is demonstrated through experimental results based on simulated data as well as real HFSW radar data and real AIS data.

Design and Implementation of Marine Ship Tracking System Based on Multi-Target Tracking Algorithm

Zhang, P.C., 2020. Design and implementation of marine ship tracking system based on multi-target tracking algorithm. In: Al-Tarawneh, O. and Megahed, A. (eds.), Recent Developments of Port, Marine, and Ocean Engineering. Journal of Coastal Research, Special Issue No. 110, pp. 47–49. Coconut Creek (Florida), ISSN 0749-0208.In order to realize the multiple target tracking in a complicated Marine environment, we design a Marine ship tracking system based on the multiple target tracking algorithm. Based on an overview of the principles of distributed adaptive multi-sensor multi-target tracking algorithm, we use MO components to create ocean ship tracking system platform, achieve the real-time massive amounts of data processing, and has a good human-computer interaction interface, as well as realize the C ++ language efficiency and visualization functions, it provides a system platform for the tracking, positioning, command and dispatch of marine ships.

Distributed Multisensor Multitarget Tracking Algorithm with Time-Offset Registration

In multisensor systems, the signal processing delay, measurement acquisition delay, and other factors will lead to imprecisely time-stamped measurements, namely, the problem of time-offset. To deal with the measurement time offsets in distributed multisensor systems, a distributed multisensor multitarget tracking algorithm with time-offset registration is proposed. The local processors track multiple targets in the presence of false alarms and missed detections based on the joint probabilistic data association (JPDA) algorithm and the extended Kalman filter (EKF), providing the time-biased local tracks. In the global processor, in allusion to the global track accuracy degradation introduced by the time offsets of local tracks, the equivalent measurements are firstly constructed based on local tracks by using the inverse Kalman filter. The pseudo-measurement equation of time offset for constant velocity targets is derived and the pseudo-measurement calculation method is presented. Then, the pseudo-measurement based relative time-offset estimation algorithm is presented, by using the recursive least squares estimation (RLSE) and the Kalman filter (KF) to jointly estimate the state in space and time domains, respectively. Finally, a framework of distributed multisensor multitarget tracking with time-offset registration is presented, where the time-varying relative time-offset estimation and compensation, 'equivalent measurement to global track' association, and global track update are included. Simulations for multisensor multitarget tracking in the presence of false alarms and missed detections are conducted, demonstrating that the present algorithm effectively improves the accuracy of fused global tracks.

Self-Tuning Algorithms for Multisensor-Multitarget Tracking Using Belief Propagation

Situation-aware technologies enabled by multitarget tracking algorithms will create new services and applications in emerging fields such as autonomous navigation and maritime surveillance. The system models underlying multitarget tracking algorithms often involve unknown parameters that are potentially time-varying. A manual tuning of unknown model parameters by the user is prone to errors and can, thus, dramatically reduce target detection and tracking performance. We address this challenge by proposing a framework of “self-tuning” multisensor-multitarget tracking algorithms. These algorithms adapt in an online manner to time-varying system models by continuously inferring unknown model parameters along with the target states. We describe the evolution of the parameters by a Markov chain and incorporate them in a factor graph that represents the statistical structure of the tracking problem. We then use a belief propagation scheme to efficiently calculate the marginal posterior distributions of the targets and model parameters. As a concrete example, we develop a self-tuning tracking algorithm for maneuvering targets with multiple dynamic models and sensors with time-varying detection probabilities. The performance of the algorithm is validated for simulated scenarios and for a real scenario using measurements from two high-frequency surfacewave radars.

Distributed Multitarget Tracking Based on Diffusion Strategies Over Sensor Networks

We consider the distributed multitarget tracking over sensor networks, where each node only communicates with its neighbors. We develop a diffusion-based distributed multisensor multitarget tracking algorithm. The state update of the diffusion-based distributed algorithm is mainly composed of two phases: an adaptation phase and a combination phase. During the adaptation phase, each node updates its local estimate by using all its neighbors' measurements. It is achieved based on a multi-sensor cardinalized probability hypothesis density filter. During the combination phase, each node fuses all its neighbors' local estimates. It is achieved based on a generalized version of covariance intersection technique. Compared to the consensus-based distributed algorithm, the proposed algorithm has two advantages. First, it can provide more accurate and robust tracking results, especially when the detection probability that the sensors detect the targets is low. Second, it has lower communication load because the consensus iterations are not required. Numerical results are provided to illustrate the performance of the proposed algorithm.

Multi-Sensor Multi-Target Tracking Using Domain Knowledge and Clustering

This paper proposes a novel joint multi-target tracking and track maintenance algorithm over a sensor network. Each sensor runs a local joint probabilistic data association (JPDA) filter using only its own measurements. Unlike the original JPDA approach, the proposed local filter utilizes the detection amplitude as domain knowledge to improve the estimation accuracy. In the fusion stage, the DBSCAN clustering in conjunction with statistical test is proposed to group all local tracks into several clusters. Each generated cluster represents the local tracks that are from the same target source and the global estimation of each cluster is obtained by the generalised covariance intersection algorithm. Extensive simulation results clearly confirm the effectiveness of the proposed multi-sensor multi-target tracking algorithm.

Cramer–Rao type bounds for sparsity-aware multi-sensor multi-target tracking

Conventionally, sparsity-aware multi-sensor multi-target tracking (MTT) algorithms comprise a two-step architecture that cascades group sparse reconstruction and MTT algorithms. The group sparse reconstruction algorithm exploits the apriori information that the measurements across multiple sensors share a common sparse support in a discretized target state space and provides a computationally efficient technique for centralized multi-sensor information fusion. In the succeeding step, the MTT filter performs the data association, compensates for the missed detections, removes the clutter components, and improves the accuracy of multi-target state estimates according to the pre-defined target dynamic model. In a recent work, a novel technique was proposed for sparsity-aware multi-sensor MTT that deploys a recursive feedback mechanism such that the group sparse reconstruction algorithm also benefits from the apriori knowledge about the target dynamics. As such, it is of significant interest to compare the tracking performance of these methods to the optimal multi-sensor MTT solution, with and without considering the missing samples. In this paper, we analytically evaluate the Cramer-Rao type performance bounds for these two schemes for sparsity-aware MTT algorithms and show that the recursive learning structure outperforms the conventional approach, when the measurement vectors are corrupted by missing samples and additive noise.

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.

Adaptive Tracking Algorithm of Multi-Target Based on Fuzzy Clustering

For multi-target tracking system, aiming at solving the problem of low precision of state estimation caused by the data correlation ambiguity, the paper presents a novel multi-sensor multi-target adaptive tracking algorithm based on fuzzy clustering theory. Based on the joint probability data association algorithm, the new approach takes account of the case that whether the measure is validated and its possibility of belong to false alarm, and improves the correlation criterion of effective measurement with existing track on the basis of fuzzy clustering theory, which all perfect the update equation of target state estimation and the covariance. Meanwhile, with the adaptive distributed fusion processing structure, it enhance the robustness of the system and without prejudice to the real-time tracking. With the simulation case studies of radar/infrared sensor fusion multi-target tracking system, it verifies the effectiveness of the proposed approach.

Research on Multi-Sensor Multi-Target Tracking Algorithm

The tracking performance of JPDA-based algorithms will be decreased with the increase of sensors amount and clutter density. Thus it is necessary to find out a multi-sensor data fusion tracking algorithm which has lower computation and higher tracking accuracy. Since the motion direction of the targer is viewed as a key factor in data correlation, the pseudodirection tracking gate can be improved. The feature of location and speed are combined to reduce the amount of the related affairs, which can reduce the computation of JPDA algorithm. This idea is adopted to improve the FDA algorithm and a sequenced fast algorithm for data association based on double-gate tracking. The simulations proved that this method has legitimately limited the effective number of echoes, which ensures the accuracy of correlation and tracking. Its computation is less than original FDA algorithm with keeping and it has good performance for maneuvering target tracking

Precision large scale air traffic surveillance using IMM/assignment estimators

We present the development and implementation of a multisensor-multitarget tracking algorithm for large scale air traffic surveillance based on interacting multiple model (IMM) state estimation combined with a 2-dimensional assignment for data association. The algorithm can be used to track a large number of targets from measurements obtained with a large number of radars. The use of the algorithm is illustrated on measurements obtained from 5 FAA radars, which are asynchronous, heterogeneous, and geographically distributed over a large area. Both secondary radar data (beacon returns from cooperative targets) as well as primary radar data (skin returns from noncooperative targets) are used. The target IDs from the beacon returns are not used in the data association. The surveillance region includes about 800 targets that exhibit different types of motion. The performance of an IMM estimator with linear motion models is compared with that of the Kalman filter (KF). A number of performance measures that can be used on real data without knowledge of the ground truth are presented for this purpose. It is shown that the IMM estimator performs better than the KF. The advantage of fusing multisensor data is quantified. It is also shown that the computational requirements in the multisensor case are lower than in single sensor case, Finally, an IMM estimator with a nonlinear motion model (coordinated turn) is shown to further improve the performance during the maneuvering periods over the IMM with linear models.

A Comparison of Parallel and Sequential Implementations of a Multisensor Multitarget Tracking Algorithm

Parallel and sequential implementations of the Multisensor Joint Probabilistic Data Association (MSJPDA) tracking algorithm are analyzed and compared. Two non-simulation techniques for comparison of multisensor probabilistic data association filters are developed and are used to compare tracking performance of the sequential and the parallel implementation of the algorithm. The non-simulation techniques are shown to accurately predict performance trends observed in simulations, that is the sequential implementation gives better tracking performance in terms of RMS position error and track lifetime. For the sequential implementation the question of processing order for different sensors is also briefly addressed. We also show that the sequential implementation is exponentially less computationally complex as the number of sensors increases. Thus, while sequential and parallel implementations are equivalent in multisensor filtering when no data association routine is needed, the sequential implementation gives superior tracking performance when data association is required.