Multisensor Poisson Multi-Bernoulli Filter for Joint Target-Sensor State Tracking

In a typical multitarget tracking (MTT) scenario, the sensor state is either assumed known, or tracking is performed in the sensor's (relative) coordinate frame. This assumption does not hold when the sensor, e.g., an automotive radar, is mounted on a vehicle, and the target state should be represented in a global (absolute) coordinate frame. Then it is important to consider the uncertain location of the vehicle on which the sensor is mounted for MTT. In this paper, we present a multisensor low complexity Poisson multi-Bernoulli MTT filter, which jointly tracks the uncertain vehicle state and target states. Measurements collected by different sensors mounted on multiple vehicles with varying location uncertainty are incorporated sequentially based on the arrival of new sensor measurements. In doing so, targets observed from a sensor mounted on a well-localized vehicle reduce the state uncertainty of other poorly localized vehicles, provided that a common non-empty subset of targets is observed. A low complexity filter is obtained by approximations of the joint sensor-feature state density minimizing the Kullback-Leibler divergence (KLD). Results from synthetic as well as experimental measurement data, collected in a vehicle driving scenario, demonstrate the performance benefits of joint vehicle-target state tracking.Comment: 13 pages, 7 figure

TOA-based indoor localization and tracking with inaccurate floor plan map via MRMSC-PHD filter

This paper proposes a novel indoor localization scheme to jointly track a mobile device (MD) and update an inaccurate floor plan map using the time-of-arrival measured at multiple reference devices (RDs). By modeling the floor plan map as a collection of map features, the map and MD position can be jointly estimated via a multi-RD single-cluster probability hypothesis density (MSC-PHD) filter. Conventional MSC-PHD filters assume that each map feature generates at most one measurement for each RD. If single reflections of the detected signal are considered as measurements generated by map features, then higher-order reflections, which also carry information on the MD and map features, must be treated as clutter. The proposed scheme incorporates multiple reflections by treating them as virtual single reflections reflected from inaccurate map features and traces them to the corresponding virtual RDs (VRDs), referred to as a multi-reflection-incorporating MSC-PHD (MRMSC-PHD) filter. The complexity of using multiple reflection paths arises from the inaccuracy of the VRD location due to inaccuracy in the map features. Numerical results show that these multiple reflection paths can be modeled statistically as a Gaussian distribution. A computationally tractable implementation combining a new greedy partitioning scheme and a particle-Gaussian mixture filter is presented. A novel mapping error metric is then proposed to evaluate the estimated map's accuracy for plane surfaces. Simulation and experimental results show that our proposed MRMSC-PHD filter outperforms the existing MSC-PHD filters by up to 95% in terms of average localization and by up to 90% in terms of mapping accuracy

New multiple target tracking strategy using domain knowledge and optimisation

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

Multiple Space Object Tracking Using A Randomized Hypothesis Generation Technique

In order to protect assets and operations in space, it is critical to collect and maintain accurate information regarding Resident Space Objects (RSOs). This collection of information is typically known as Space Situational Awareness (SSA). Ground-based and space-based sensors provide information regarding the RSOs in the form of observations or measurement returns. However, the distance between RSO and sensor can, at times, be tens of thousands of kilometers. This and other factors lead to noisy measurements that, in turn, cause one to be uncertain about which RSO a measurement belongs to. These ambiguities are known as data association ambiguities. Coupled with uncertainty in RSO state and the vast number of objects in space, data association ambiguities can cause the multiple space object-tracking problem to become computationally intractable. Tracking the RSO can be framed as a recursive Bayesian multiple object tracking problem with state space containing both continuous and discrete random variables. Using a Finite Set Statistics (FISST) approach one can derive the Random Finite Set (RFS) based Bayesian multiple object tracking recursions. These equations, known as the FISST multiple object tracking equations, are computationally intractable when solved in full. This computational intractability provokes the idea of the newly developed alternative hypothesis dependent derivation of the FISST equations. This alternative derivation allows for a Markov Chain Monte Carlo (MCMC) based randomized sampling technique, termed Randomized FISST (R-FISST). R-FISST is found to provide an accurate approximation of the full FISST recursions while keeping the problem tractable. There are many other benefits to this new derivation. For example, it can be used to connect and compare the classical tracking methods to the modern FISST based approaches. This connection clearly defines the relationships between different approaches and shows that they result in the same formulation for scenarios with a fixed number of objects and are very similar in cases with a varying number of objects. Findings also show that the R-FISST technique is compatible with many powerful optimization tools and can be scaled to solve problems such as collisional cascading

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 utilises 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 generalized covariance intersection (GCI) algorithm. Extensive simulation results clearly confirms the effectiveness of the proposed multisensor multi-target tracking algorithm

Multi-Objective Multi-Agent Planning for Jointly Discovering and Tracking Mobile Object

We consider the challenging problem of online planning for a team of agents to autonomously search and track a time-varying number of mobile objects under the practical constraint of detection range limited onboard sensors. A standard POMDP with a value function that either encourages discovery or accurate tracking of mobile objects is inadequate to simultaneously meet the conflicting goals of searching for undiscovered mobile objects whilst keeping track of discovered objects. The planning problem is further complicated by misdetections or false detections of objects caused by range limited sensors and noise inherent to sensor measurements. We formulate a novel multi-objective POMDP based on information theoretic criteria, and an online multi-object tracking filter for the problem. Since controlling multi-agent is a well known combinatorial optimization problem, assigning control actions to agents necessitates a greedy algorithm. We prove that our proposed multi-objective value function is a monotone submodular set function; consequently, the greedy algorithm can achieve a (1-1/e) approximation for maximizing the submodular multi-objective function.Comment: Accepted for publication to the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20). Added algorithm 1, background on MPOMDP and OSP

Efficient approximations of the multi-sensor labelled multi-Bernoulli filter

In this paper, we propose two efficient, approximate formulations of the multi-sensor labelled multi-Bernoulli (LMB) filter, which both allow the sensors' measurement updates to be computed in parallel. Our first filter is based on the direct mathematical manipulation of the multi-sensor, multi-object Bayes filter's posterior distribution. Unfortunately, it requires the division of probability distributions and its extension beyond linear Gaussian applications is not obvious. Our second filter is based on covariance intersection and it approximates the multi-sensor, multi-object Bayes filter's posterior distribution using the geometric mean of each sensor's measurement-updated distribution. This filter can be used for distributed fusion under non-linear conditions; however, it is not as accurate as our first filter. In both cases, we approximate the LMB filter's measurement update using an existing loopy belief propagation algorithm, which we adapt to account for object existence. Both filters have a constant complexity in the number of sensors, and linear complexity in both number of measurements and objects. This is an improvement on an iterated-corrector LMB filter, which has linear complexity in the number of sensors. We evaluate both filters' performances on simulated data and the results indicate that the filters are accurate