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

Arithmetic Average Density Fusion -- Part III: Heterogeneous Unlabeled and Labeled RFS Filter Fusion

This paper proposes a heterogenous density fusion approach to scalable multisensor multitarget tracking where the inter-connected sensors run different types of random finite set (RFS) filters according to their respective capacity and need. These diverse RFS filters result in heterogenous multitarget densities that are to be fused with each other in a proper means for more robust and accurate detection and localization of the targets. Our approach is based on Gaussian mixture implementations where the local Gaussian components (L-GCs) are revised for PHD consensus, i.e., the corresponding unlabeled probability hypothesis densities (PHDs) of each filter best fit their average regardless of the specific type of the local densities. To this end, a computationally efficient, coordinate descent approach is proposed which only revises the weights of the L-GCs, keeping the other parameters unchanged. In particular, the PHD filter, the unlabeled and labeled multi-Bernoulli (MB/LMB) filters are considered. Simulations have demonstrated the effectiveness of the proposed approach for both homogeneous and heterogenous fusion of the PHD-MB-LMB filters in different configurations.Comment: 11 pages, 14 figures. IEEE Transactions on Aerospace and Electronics Systems, 202

Moving target detection in multi-static GNSS-based passive radar based on multi-Bernoulli filter

Over the past few years, the global navigation satellite system (GNSS)-based passive radar (GBPR) has attracted more and more attention and has developed very quickly. However, the low power level of GNSS signal limits its application. To enhance the ability of moving target detection, a multi-static GBPR (MsGBPR) system is considered in this paper, and a modified iterated-corrector multi-Bernoulli (ICMB) filter is also proposed. The likelihood ratio model of the MsGBPR with range-Doppler map is first presented. Then, a signal-to-noise ratio (SNR) online estimation method is proposed, which can estimate the fluctuating and unknown map SNR effectively. After that, a modified ICMB filter and its sequential Monte Carlo (SMC) implementation are proposed, which can update all measurements from multi-transmitters in the optimum order (ascending order). Moreover, based on the proposed method, a moving target detecting framework using MsGBPR data is also presented. Finally, performance of the proposed method is demonstrated by numerical simulations and preliminary experimental results, and it is shown that the position and velocity of the moving target can be estimated accuratel

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

Robust Multi-target Tracking with Bootstrapped-GLMB Filter

This dissertation presents novel multi-target tracking algorithms that obviate the need for prior knowledge of system parameters such as clutter rate, detection probabilities, and birth models. Information on these parameters is unknown but important to tracking performance. The proposed algorithms exploit the advantages of existing RFS trackers and filters by bootstrapping them. This configuration inherits the efficiency of tracking target trajectories from the RFS trackers and low complexity in parameter estimation from the RFS filters

Some New Results in Distributed Tracking and Optimization

The current age of Big Data is built on the foundation of distributed systems, and efficient distributed algorithms to run on these systems.With the rapid increase in the volume of the data being fed into these systems, storing and processing all this data at a central location becomes infeasible. Such a central \textit{server} requires a gigantic amount of computational and storage resources. Even when it is possible to have central servers, it is not always desirable, due to privacy concerns. Also, sending huge amounts of data to such servers incur often infeasible bandwidth requirements. In this dissertation, we consider two kinds of distributed architectures: 1) star-shaped topology, where multiple worker nodes are connected to, and communicate with a server, but the workers do not communicate with each other; and 2) mesh topology or network of interconnected workers, where each worker can communicate with a small number of neighboring workers. In the first half of this dissertation (Chapters 2 and 3), we consider distributed systems with mesh topology.We study two different problems in this context. First, we study the problem of simultaneous localization and multi-target tracking. Multiple mobile agents localize themselves cooperatively, while also tracking multiple, unknown number of mobile targets, in the presence of measurement-origin uncertainty. In situations with limited GPS signal availability, agents (like self-driving cars in urban canyons, or autonomous vehicles in hazardous environments) need to rely on inter-agent measurements for localization. The agents perform the additional task of tracking multiple targets (pedestrians and road-signs for self-driving cars). We propose a decentralized algorithm for this problem. To be effective in real-time applications, we propose efficient Gaussian and Gaussian-mixture based filters, rather than the computationally expensive particle-based methods in the existing literature. Our novel factor-graph based approach gives better performance, in terms of both agent localization errors, and target-location and cardinality errors. Next, we study an online convex optimization problem, where a network of agents cooperate to minimize a global time-varying objective function. Only the local functions are revealed to individual agents. The agents also need to satisfy their individual constraints. We propose a primal-dual update based decentralized algorithm for this problem. Under standard assumptions, we prove that the proposed algorithm achieves sublinear regret and constraint violation across the network. In other words, over a long enough time horizon, the decisions taken by the agents are, on average, as good as if all the information was revealed ahead of time. In addition, the individual constraint violations of the agents, averaged over time, are zero. In the next part of the dissertation (Chapters 4), we study distributed systems with a star-shaped topology. The problem we study is distributed nonconvex optimization. With the recent success of deep learning, coupled with the use of distributed systems to solve large-scale problems, this problem has gained prominence over the past decade. The recently proposed paradigm of Federated Learning (which has already been deployed by Google/Apple in Android/iOS phones) has further catalyzed research in this direction. The problem we consider is minimizing the average of local smooth, nonconvex functions. Each node has access only to its own loss function, but can communicate with the server, which aggregates updates from all the nodes, before distributing them to all the nodes. With the advent of more and more complex neural network architectures, these updates can be high dimensional. To save resources, the problem needs to be solved via communication-efficient approaches. We propose a novel algorithm, which combines the idea of variance-reduction, with the paradigm of carrying out multiple local updates at each node before averaging. We prove the convergence of the approach to a first-order stationary point. Our algorithm is optimal in terms of computation, and state-of-the-art in terms of the communication requirements. Lastly in Chapter 5, we consider the situation when the nodes do not have access to function gradients, and need to minimize the loss function using only function values. This problem lies in the domain of zeroth-order optimization. For simplicity of analysis, we study this problem only in the single-node case. This problem finds application in simulation-based optimization, and adversarial example generation for attacking deep neural networks. We propose a novel function value based gradient estimator, which has better variance, and better query-efficiency compared to existing estimators. The proposed estimator covers the most commonly used existing estimators as special cases. We conduct a comprehensive convergence analysis under different conditions. We also demonstrate its effectiveness through a real-world application to generating adversarial examples from a black-box deep neural network