Siwei Zhang

Application-driven design of aerial communication networks

Networks of micro aerial vehicles (MAVs) equipped with various sensors are increasingly used for civil applications, such as monitoring, surveillance, and disaster management. In this article, we discuss the communication requirements raised by applications in MAV networks. We propose a novel system representation that can be used to specify different application demands. To this end, we extract key functionalities expected in an MAV network. We map these functionalities into building blocks to characterize the expected communication needs. Based on insights from our own and related real-world experiments, we discuss the capabilities of existing communications technologies and their limitations to implement the proposed building blocks. Our findings indicate that while certain requirements of MAV applications are met with available technologies, further research and development is needed to address the scalability, heterogeneity, safety, quality of service, and security aspects of multi- MAV systems.

Swarm exploration and navigation on mars

We propose autonomous robotic swarm exploration to search for extra-terrestrial life in the Valles Marineris canyon system on Mars. The swarm consists of unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs). Key technologies are robust flight and swarm control algorithms as well as infrastructure-less swarm navigation. The swarm navigation uses inertial navigation, laser scanners, cameras, and relative radio positioning systems. The later one employs hybrid time-division multiple access (TDMA) - frequency division multiple access (FDMA) and interleaved round-trip delay ranging measurements. For TDMA, an autonomous distributed slot synchronization algorithm is presented.We present a swarm scenario with initially ten elements, adding one element after 6.25 s, until 25 elements are active. The synchronization algorithm is stable transmitting only five out of eight possible symbols per TDMA slot for 25 swarm elements, but is only stable for 17 swarm elements transmitting all eight symbols per slot. Nevertheless, a distributed swarm navigation particle filter achieves an accuracy of 1m or better for 21 swarm elements in the later case.

On prospects of positioning in 5G

Technologies envisaged for a 5G communications system provide interesting prospects which are beneficial for co-operative positioning. Since 5G development is in the early stages, there is a unique opportunity to develop and integrate mobile radio based positioning technology in 5G from the beginning. In this paper we discuss 5G concepts to ensure seamless positioning by cooperative positioning. In cooperative positioning, mobile terminals (MTs) collaborate to help each other to determine their own position. We address key 5G prospects like smaller cells, higher MT densities and the capability of device-to-device (D2D) communication to enable cooperative positioning. By using the Cramér-Rao lower bound we investigate cooperative positioning performance for signal propagation delay based pseudo ranging in an exemplary typical urban environment. Numerical results for an exemplary environment have shown that with MT densities D > 1100 MTs per square kilometer sub-meter positioning accuracy with outage probabilities converging to zero can be achieved.

Multipath Assisted Positioning with Simultaneous Localization and Mapping

This paper describes an algorithm that exploits multipath propagation for position estimation of mobile receivers. We apply a novel algorithm based on recursive Bayesian filtering, named Channel-SLAM. This approach treats multipath components as signals emitted from virtual transmitters, which are time synchronized to the physical transmitter and static in their positions. Contrary to other approaches, Channel-SLAM considers also paths occurring due to multiple numbers of reflections or scattering as well as the combination. Hence, each received multipath component increases the number of transmitters resulting in a more accurate position estimate or enabling positioning when the number of physical transmitters is insufficient. Channel-SLAM estimates the receiver position and the positions of the virtual transmitters simultaneously; hence, the approach does not require any prior information, such as a room-layout or a database for fingerprinting. The only prior knowledge needed is the physical transmitter position as well as the initial receiver position and moving direction. Based on simulations, the position precision of Channel-SLAM is evaluated by a comparison to simplified algorithms and to the posterior Cramér-Rao lower bound. Furthermore, this paper shows the performance of Channel-SLAM based on measurements in an indoor scenario with only a single physical transmitter.

System-level performance analysis for Bayesian cooperative positioning: From global to local

Cooperative positioning (CP) can be used either to calibrate the accumulated error from inertial navigation or as a stand-alone navigation system. Though intensive research has been conducted on CP, there is a need to further investigate the joint impact from the system level on the accuracy. We derive a posterior Cramér-Rao bound (PCRB) considering both the physical layer (PHY) signal structure and the asynchronous latency from the multiple access control layer (MAC). The PCRB shows an immediate relationship between the theoretical accuracy limit and the effective factors, e.g. geometry, node dynamic, latency, signal structure, power, etc. which is useful to assess a cooperative system. However, for a large-scale decentralized cooperation network, calculating the PCRB becomes difficult due to the high state dimension and the absence of global information. We propose an equivalent ranging variance (ERV) scheme which projects the neighbors positioning uncertainty to the distance measurement inaccuracy. With this scheme, the interaction among the mobile terminals (MTs), e.g. measurement and communication can be decoupled. We use the ERV to derive a local PCRB (L-PCRB) which approximates the PCRB locally at each MT with low complexity. We further propose combining the ERV and L-PCRB together to improve the precision of the Bayesian localization algorithms. Simulation with an L-PCRB-aided distributed particle filter (DPF) in two typical cooperative positioning scenarios show a significant improvement comparing with the non-cooperative or standard DPF.

Bound-based spectrum allocation for cooperative positioning

This paper discusses the performance of distributed and centralised cooperative positioning system for multiple mobile terminals. A cooperative positioning system uses ranging between base stations and mobile terminals, and in addition exploits the links between the mobile terminals as well. However, the number of links in an all-to-all connected setup increases quadratically with the number of nodes. Our goal is to schedule the access for the available resources (used bandwidth) to track the nodes of a dynamic system. Therefore, it is essential to reason if the different links between the mobile terminals are useful. The reasoning assists in allocating resources properly. We investigate distributed algorithms that act based on the Cramer–Rao Lower Bound. We use as benchmark a centralised and much more complex algorithm that requires all link information and we ignore any latency aspects. The results show that our distributed algorithm using limited information performs well.

Self-organized hybrid channel access method for an interleaved RTD-based swarm navigation system

Swarm navigation systems are emerging technique to investigate unknown environment. Organizing the access of shared radio channel is one of the main challenges due to the massive number of anarchical agents and the crucial mission requirements, e.g. update-rate and position accuracy. In this paper, we design a radio-based swarm navigation system and propose a pulse-coupled oscillators (PCO)-based self-organized method to access the shared channel. The channel is accessed with the time division multiple access (TDMA) scheme. The frequency division duplexing (FDD) method is built on top to solve the temporally hidden node problem. Distances are measured with the interleaved round trip delay (I-RTD) so that multiple measurements can be taken simultaneously and without the need of clock synchronization. It takes the realistic mission requirements and the physical radio effects into consideration. The simulation results show that a swarm bootstrapping task can be accomplished in a few seconds with our proposed system.

Multi-agent flocking with noisy anchor-free localization

In this paper we focus on radio-based navigation of an autonomous dynamic swarm system of agents. We assess the anchor-free localization and investigate the impact of the performance of localization to a swarm flocking algorithm. Low latency is crucial in a dynamic autonomous swarm system to control the formation of the swarm. Therefore, an agent applies the randomized orthogonal frequency-division multiple access (OFDMA) scheme to range with multiple neighbours simultaneously by round-trip delay (RTD). The connectivity level and the ranging accuracy are the main factors affecting the localization accuracy. These two factors are coupled through the limited available spectrum. On the one hand high connectivity between agents is needed to guarantee a unique localization solution. On the other hand with a fixed amount of total spectrum the possible spectrum for each connected link decreases with the increasing number of connected agents. Therefore, the ranging accuracy between agents decreases. We investigate the relationship between the rigidity matrix and the location Cramér-Rao bound (CRB). We modify the location CRB (MCRB) with the ranging Ziv-Zakai bound (ZZB) and apply the MCRB as a noisy localization model in the flocking algorithm. Our preliminary study shows that the proposed swarm navigation system performs well under the condition that the localization error is considered in the flocking algorithm.

Anchor-Free Localization using Round-Trip Delay Measurement for Martian Swarm Exploration

Robotic swarms are promising technique to explore infrastructure-less environments. Many researches in robotic swarm focus on the swarm control and assume an external source for the swarm location information. In this paper, we investigate the radio-based anchor-free localization problem for a robotic swarm. Formation is estimated collectively with only inter-agent distance measurements using the round-trip delay (RTD) technique. Fundamental limits, such as the lower bound of anchor-free localization, tracking and ranging are derived. We further investigate the connectivity and ranging accuracy trade-off with realistic radio resource characteristics. The local Cramér-Rao Bound (CRB) and posterior Cramér-Rao Bound (PCRB) approximations are used to calculate the equivalent ranging variance (ERV). The ERV is used for distributed assessing the reliability of neighbors location information and low complexity distributed anchor-free localization algorithms design. ERV-aided distributed Gauss-Newton algorithm (ERV-DGN) and ERV-aided distributed particle filter algorithm (ERV-DPF) are proposed to achieve robust anchor-free localization. Overlooking the ambiguity is a limitation of localization CRB. This problem can be avoided by controlling the number of simultaneous RTD links from the hearability range. The performance of the ERV-DPF with real measurement data shows a sub-meter accuracy level for anchor-free localization. Hence, accurate anchor-free localization for robotic swarm using radio RTD measurements is applicable and promising.

Location-Aware Formation Control in Swarm Navigation

Goal-seeking and information-seeking are canonical problems in mobile agent swarms. We study the problem of collaborative goal-approaching under uncertain agent position information from a signal processing perspective. We propose a framework that establishes location-aware formations, resulting in a controller that accounts for agent position uncertainty with a realistic ranging model. Simulation results confirm that, as the outcome of the controller, the swarm moves towards its goal, while emerging formations conducive to high-quality localization.