Radar networks: A review of features and challenges

Networks of multiple radars are typically used for improving the coverage and tracking accuracy. Recently, such networks have facilitated deployment of commercial radars for civilian applications such as healthcare, gesture recognition, home security, and autonomous automobiles. They exploit advanced signal processing techniques together with efficient data fusion methods in order to yield high performance of event detection and tracking. This paper reviews outstanding features of radar networks, their challenges, and their state-of-the-art solutions from the perspective of signal processing. Each discussed subject can be evolved as a hot research topic.Comment: To appear soon in Information Fusio

Channel Prediction and Target Tracking for Multi-Agent Systems

Mobile moving agents as part of a multi-agent system (MAS) utilize the wireless communication channel to disseminate information and to coordinate between each other. This channel is error-prone and the transmission quality depends on the environment as well as on the configuration of the transmitter and the receiver. For resource allocation and task planning of the agents, it is important to have accurate, yet computationally efficient, methods for learning and predicting the wireless channel. Furthermore, agents utilize on-board sensors to determine both their own state and the states of surrounding objects. To track the states over time, the objects’ dynamical models are combined with the sensors’ measurement models using a Bayesian filter. Through fusion of posterior information output by the agents’ filters, the awareness of the agents is increased. This thesis studies the uncertainties involved in the communication and the positioning of MASs and proposes methods to properly handle them.A framework to learn and predict the wireless channel is proposed, based on a Gaussian process model. It incorporates deterministic path loss and stochastic large scale fading, allowing the estimation of model parameters from measurements and an accurate prediction of the channel quality. Furthermore, the proposed framework considers the present location uncertainty of the transmitting and the receiving agent in both the learning and the prediction procedures. Simulations demonstrate the improved channel learning and prediction performance and show that by taking location uncertainty into account a better communication performance is achieved. The agents’ location uncertainties need to be considered when surrounding objects (targets) are estimated in the global frame of reference. Sensor impairments, such as an imperfect detector or unknown target identity, are incorporated in the Bayesian filtering framework. A Bayesian multitarget tracking filter to jointly estimate the agents’ and the targets’ states is proposed. It is a variant of the Poisson multi-Bernoulli filter and its performance is demonstrated in simulations and experiments. Results for MASs show that the agents’ state uncertainties are reduced by joint agent-target state trackingcompared to tracking only the agents’ states, especially with high-resolution sensors. While target tracking allows for a reduction of the agents’ state uncertainties, highresolution sensors require special care due to multiple detections per target. In this case, the tracking filter needs to explicitly model the dimensions of the target, leading to extended target tracking (ETT). An ETT filter is combined with a Gaussian process shape model, which results in accurate target state and shape estimates. Furthermore, a method to fuse posterior information from multiple ETT filters is proposed, by means of minimizing the Kullback-Leibler average. Simulation results show that the adopted ETT filter accurately tracks the targets’ kinematic states and shapes, and posterior fusion provides a holistic view of the targets provided by multiple ETT filters

Distributed Multi-Object Tracking Under Limited Field of View Heterogeneous Sensors with Density Clustering

We consider the problem of tracking multiple, unknown, and time-varying numbers of objects using a distributed network of heterogeneous sensors. In an effort to derive a formulation for practical settings, we consider limited and unknown sensor field-of-views (FoVs), sensors with limited local computational resources and communication channel capacity. The resulting distributed multi-object tracking algorithm involves solving an NP-hard multidimensional assignment problem either optimally for small-size problems or sub-optimally for general practical problems. For general problems, we propose an efficient distributed multi-object tracking algorithm that performs track-to-track fusion using a clustering-based analysis of the state space transformed into a density space to mitigate the complexity of the assignment problem. The proposed algorithm can more efficiently group local track estimates for fusion than existing approaches. To ensure we achieve globally consistent identities for tracks across a network of nodes as objects move between FoVs, we develop a graph-based algorithm to achieve label consensus and minimise track segmentation. Numerical experiments with a synthetic and a real-world trajectory dataset demonstrate that our proposed method is significantly more computationally efficient than state-of-the-art solutions, achieving similar tracking accuracy and bandwidth requirements but with improved label consistency

On Topology of Sensor Networks Deployed for Multi-Target Tracking

In this paper, we study topologies of sensor networks deployed for tracking multiple targets. Tracking multiple moving targets is a challenging problem. Most of the previously proposed tracking algorithms simplify the problem by assuming access to the signal from an individual target for tracking. Recently, tracking algorithms based on blind source separation (BSS), a statistical signal-processing technique widely used to recover individual signals from mixtures of signals, have been proposed. BSS-based tracking algorithms are proven to be effective in tracking multiple indistinguishable targets. The topology of a wireless sensor network deployed for tracking with BSS-based algorithms is critical to tracking performance because the topology affects separation performance, and the topology determines accuracy and precision of estimation on the paths taken by targets. We propose cluster topologies for BSS-based tracking algorithms. Guidelines on parameter selection for proposed topologies are given in this paper. We evaluate the proposed cluster topologies with extensive experiments. Our experiments show that the proposed topologies can significantly improve both the accuracy and the precision of BSS-based tracking algorithms

Bathtub-Shaped Failure Rate of Sensors for Distributed Detection and Fusion

We study distributed detection and fusion in sensor networks with bathtub-shaped failure (BSF) rate of the sensors which may or not send data to the Fusion Center (FC). The reliability of semiconductor devices is usually represented by the failure rate curve (called the “bathtub curve”), which can be divided into the three following regions: initial failure period, random failure period, and wear-out failure period. Considering the possibility of the failed sensors which still work but in a bad situation, it is unreasonable to trust the data from these sensors. Based on the above situation, we bring in new characteristics to failed sensors. Each sensor quantizes its local observation into one bit of information which is sent to the FC for overall fusion because of power, communication, and bandwidth constraints. Under this sensor failure model, the Extension Log-likelihood Ratio Test (ELRT) rule is derived. Finally, the ROC curve for this model is presented. The simulation results show that the ELRT rule improves the robust performance of the system, compared with the traditional fusion rule without considering sensor failures