Traditional target tracking using monostatic radar systems typically rely on centralized or decentralized architectures, where all data is transmitted to a fusion center for estimating the position and velocity of mobile agents. This approach introduces a single point of failure and can significantly increase communication costs, particularly when the fusion center is far from individual radar nodes. To overcome these issues, we introduce a distributed Alternating Direction Method of Multipliers (ADMM) for target localization using a radar network, wherein each radar node shares its observed data only with its immediate neighboring nodes, and achieves consensus with the radar network on the estimated target locations and velocities. We perform simulations incorporating critical system parameters such as the number of radar nodes and Signal-to-Noise Ratio (SNR) to assess their impact of estimation accuracy and convergence speed of the proposed distributed ADMM algorithm. We highlight the additional benefits of our proposed solution, and present directions for future work.

Gaussian process regression (GPR), is a powerful non-parametric approach for data modeling, which has garnered considerable interest in the past decade, however its widespread application is impeded by the significant computational burden for larger datasets. The computational complexity for both inference and hyperparameter learning in GPs lead to O(N³) for N training points. The current state-of-the-art approximations, such as structured kernel interpolation (SKI)-based methods e.g., Kernel Interpolation for Scalable Structured Gaussian Process (KISSGP), have emerged to mitigate this challenge by providing a scalable inducing point alternatives. However, the choice of the optimal number of grid points, which influences the accuracy and efficiency of the model, typically remains fixed and is chosen arbitrarily. In this work, we introduce a novel approximation framework, Malleable KISSGP (MKISSGP), which dynamically adjusts grid points using a new hyperparameter of the model called density, which adapts to the changes in the kernel hyperparameters in each training iteration. In comparison with the state-of-the-art KISSGP and irrespective of changes in hyperparameters, our proposed MKISSGP algorithm exhibits consistent error levels in the reconstruction of the kernel matrix, and offers reduced computational complexity. We present extensive simulations to validate the improved performance of the proposed MKISSGP, and give directions for future research.

Efficient path planning is key for safe autonomous navigation over complex and unknown terrains. Lunar Zebro (LZ), a project of the Delft University of Technology, aims to deploy a compact rover, no larger than an A4 sheet of paper and weighing not more than 3 kilograms. In this work, we introduce a Robust Artificial Potential Field (RAPF) algorithm, a new path-planning algorithm for reliable local navigation solution for lunar microrovers. RAPF leverages and improves state of the art Artificial Potential Field (APF)-based methods by incorporating the position of the robot in the generation of bacteria points and considering local minima as regions to avoid. We perform both simulations and on field experiments to validate the performance of RAPF, which outperforms state-of-the-art APF-based algorithms by over 15% in reachability within a similar or shorter planning time. The improvements resulted in a 200% higher success rate and 50% lower computing time compared to the conventional APF algorithm. Near-optimal paths are computed in real-time with limited available processing power. The bacterial approach of the RAPF algorithm proves faster to execute and smaller to store than path planning algorithms used in existing planetary rovers, showcasing its potential for reliable lunar exploration with computationally constrained and energy constrained robotic systems.

A new method for joint ranging and Phase Offset (PO) estimation of multiple drones/aircrafts is proposed in this paper. The proposed method employs the superimposed uncoordinated Automatic Dependent Surveillance-Broadcast (ADS-B) packets broadcasted by drones/aircrafts for joint range and PO estimation. It jointly estimates range and PO prior to ADS-B packet decoding; thus, it can improve air safety when packet decoding is infeasible due to packet collision. Moreover, it enables coherent detection of ADS-B packets, which can result in more reliable multiple target tracking in aviation systems using cooperative sensors for detect and avoid (DAA). By minimizing the Kullback-Leibler Divergence (KLD) statistical distance measure, we show that the received complex baseband signal coming from K uncoordinated drones/aircrafts corrupted by Additive White Gaussian Noise (AWGN) at a single antenna receiver can be approximated by an independent and identically distributed (i.i.d.) Gaussian Mixture (GM) with 2^{K} mixture components in the two-dimensional (2D) plane. While direct joint Maximum Likelihood Estimation (MLE) of range and PO from the derived GM Probability Density Function (PDF) leads to an intractable maximization, our proposed method employs the Expectation-Maximization (EM) algorithm to estimate the modes of the 2D Gaussian mixture followed by a reordering estimation technique through combinatorial optimization to estimate range and PO. An extension to a multiple antenna receiver is also investigated in this article. While the proposed estimator can estimate the range of multiple drones/aircrafts with a single receive antenna, a larger number of drones/aircrafts can be supported with higher accuracy by the use of multiple antennas at the receiver. The effectiveness of the proposed estimator is supported by simulation results. We show that the proposed estimator can jointly estimate the range of multiple drones/aircrafts accurately.

Consensus control of multiagent systems arises in various applications such as rendezvous and formation control. The input to these algorithms, e.g., the (relative) positions of neighboring agents need to be measured using various sensors. Recent works aim to reconstruct these positions, i.e., achieve localization using Euclidean distance measurements instead of displacements, for cost efficiency and scalability. However, this approach inherently introduces ambiguities, such as a rotation or a reflection, which can cause stability issues in practice without corrections by some anchors. In this letter, we conduct a thorough analysis of the stability of consensus control in the presence of localization-induced rotational ambiguities, in several scenarios including, e.g., proper and improper rotation, and the homogeneity of rotations. We give stability criteria and stability margin on the rotations, which are numerically verified with two traditional examples of consensus control.

Unmanned Aerial Systems (UAS) have rapidly gained attraction in recent years as a promising solution to revolutionize numerous applications and meet the growing demand for efficient and timely delivery services due to their highly automated operation framework. Beyond Visual Line of Sight (BVLOS) operations, in particular, offer new means of delivering added-value services via a wide range of applications. This "plateau of productivity" holds enormous promise, but it is challenging to equip the drone with affordable technologies which support the BVLOS use case. To close this gap, this work showcases the convergence of the automotive and aviation industries to advance BVLOS aviation for UAS in a practical setting by studying a combination of Commercial Off-The-Shelf (COTS) technologies and systems. A novel risk-based approach of investigating the key technological components, architectures, algorithms, and protocols is proposed that facilitate highly reliable and autonomous BVLOS operations, aiming to enhance the alignment between market and operational needs and to better identify integration requirements between the different capabilities to be developed.

A cooperative sense and avoid (SAA) algorithm for safe navigation of small-sized unmanned aerial vehicles (UAVs) within an airspace is proposed in this article. The proposed method relies upon cooperation between the UAV and the surrounding transponder-equipped aviation obstacles. To do so, the aviation obstacles share their altitude and identification code with the UAV by using the Mode S operation of the secondary surveillance radar (SSR) after interrogation. The proposed SAA algorithm benefits from the estimate of the aviation obstacle's elevation angle for ranging. This results in more accurate ranging compared to the round-trip time-based ranging, which is currently used in existing SAA systems. We also propose a low-complexity and accurate radial velocity estimator for the Mode S operation of the SSR, which is employed in the proposed SAA system. Furthermore, by considering the pulse-position modulation of the transponder reply as a waveform of pulse radar with random pulse repetition intervals, the maximum unambiguous radial velocity is obtained. The proposed SAA is equipped with an intruder identification method that determines the risk level of the surrounding transponder-equipped aviation obstacles. Given the estimated parameters, the intruder identification method classifies the aviation obstacles into high- medium-, and low-risk intruders. The output of the classifier enables the UAV to plan its path or maneuver for safe navigation accordingly. The root mean square error of the proposed estimators are analytically derived, and the effectiveness of our SAA solution is confirmed through simulation experiments.

Accurate global localization is crucial for autonomous navigation and planning. To this end, various GPS-aided Visual-Inertial Odometry (GPS-VIO) fusion algorithms are proposed in the literature. This paper presents a novel GPS-VIO system that is able to significantly benefit from the online calibration of the rotational extrinsic parameter between the GPS reference frame and the VIO reference frame. The behind reason is this parameter is observable. This paper provides novel proof through nonlinear observability analysis. We also evaluate the proposed algorithm extensively on diverse platforms, including flying UAV and driving vehicle. The experimental results support the observability analysis and show increased localization accuracy in comparison to state-of-the-art (SOTA) tightly-coupled algorithms.

In this paper, we propose a new method for joint ranging and Phase Offset (PO) estimation of multiple transponder-equipped aviation vehicles (TEAVs), including Manned Aerial Vehicles (MAVs) and Unmanned Aerial Vehicles (UAVs). The proposed method employs the overlapping uncoordinated Automatic Dependent Surveillance-Broadcast (ADS-B) packets broadcasted by the TEAVs for joint range and PO estimation prior to ADS-B packet decoding; thus, it can improve air safety when packet decoding is infeasible due to packet collision. Moreover, it enables coherent detection of ADS-B packets, which can result in more reliable multiple target tracking in aviation systems using cooperative sensors for sense and avoid. By minimizing the Kullback-Leibler Divergence (KLD), we show that the received complex baseband signal, coming from K uncoordinated TEAVs, which is corrupted by Additive White Gaussian Noise (AWGN) at a single antenna receiver can be approximated by an independent and identically distributed (i.i.d.) Gaussian Mixture (GM) with 2^K mixture components in the two-dimensional plane. The proposed estimator employs the Expectation-Maximization (EM) algorithm to estimate the modes of the 2D Gaussian mixture followed by a reordering estimation technique to jointly estimate range and PO. Simulation results show that the proposed joint estimator outperforms excising methods, such as the time segmentation method and the blind adaptive beamforming.

In this paper, we study path planning algorithms of resource constrained mobile agents in unknown cluttered environments, which include but are not limited to various terrestrial missions e.g., search and rescue missions by drones in jungles, and space missions e.g., navigation of rovers on the Moon. In particular, we focus our attention on artificial potential field (APF) based methods, in which the target is attractive while the obstacles are repulsive to the mobile agent. In this paper, we propose two major updates to the classical APF algorithm which significantly improve the performance of path planning using APF. First, we propose to improve an existing classical method that replaces the gradient descent optimization of the potential field cost function on a continuous domain with a combinatorial optimization on a set of predefined points (called bacteria points) around the agent's current location. Our proposition includes an adaptive hyperparameter that changes the value of the potential function associated to each bacteria point based on the current environmental measurements. Our proposed solution improves the navigation performance in terms of convergence to the target at the expense of minimal increase in computational complexity. Second, we propose an improved potential field cost function of the bacteria points by introducing a new branching cost function which further improves the navigation performance. The algorithms were tested on a set of Monte Carlo simulation trials where the environment changes for each trial. Our simulation results show 25% lower navigation time and around 300% higher success rate compared to the conventional potential field method, and we present future directions for research.

Sensor calibration is an indispensable task in any networked cyberphysical system. In this paper, we consider a sensor network plagued with offset errors, measuring a rank-1 signal subspace, where each sensor collects measurements under a linear model with additive zero-mean Gaussian noise. Under varying assumptions on the underlying noise covariance, we investigate the effect of using an arbitrary reference for estimating the sensor offsets, in contrast to the 'average of all the unknown offsets' as a reference. We first show that the average reference yields an efficient minimum variance unbiased estimator. If the underlying noise is homoscedastic in nature, then we prove the average reference yields a factor 2 improvement on the variance, as compared to any arbitrarily chosen reference within the network. Furthermore, when the underlying noise is independent but not identical, we derive an expression for the improvement offered by the average reference. We demonstrate our results using the problem of clock synchronization in sensor networks, and discuss directions for future work.

Gaussian Process (GP) is a flexible non-parametric method which has a wide variety of applications e.g., field estimation using multi-agent systems. However, the training of the hyperparameters suffers from high computational complexity. Recently, distributed hyperparameter optimization with proximal gradients has been proposed to reduce complexity, however only for a network with a central station. In this work, exploiting edge-based constraints, we propose two fully-distributed algorithms pxADMMfd and pxADMMfd,fast for a network of multi-agent systems, which do not rely on a central station. In addition, asynchronous versions of the algorithms are also proposed to reduce the synchronization overhead in heterogeneous networks. Simulations are conducted for a field estimation problem, using both artificial, and real-world datasets, which show that the proposed fully-distributed algorithms successfully converge, at the cost of an increased number of iterations.

Affine formation control of multiagent systems has recently received increasing attention in various applications. The distributed control of these agents, under single integrator dynamics, relies on the observations of relative positions of the neighboring agents, which when unavailable is detrimental to the mission. In this paper, we propose an adaptive fusion estimator of the relative positions under intermittent and consecutive observation loss settings. A relative affine localization (RAL) solution is developed by exploiting the geometry of affine formation, which is then embedded into a distributed relative Kalman filtering (RKF) framework, leading to the geometry-aware relative Kalman filter (GA-RKF). We show through simulations that the GA-RKF exhibits enhanced robustness to both intermittent and consecutive observation losses, as compared to RAL and existing state-of-art methods.

Multi-agent networks are known for their scalability, robustness, flexibility, and are typically tasked with a variety of tasks such as target tracking, surveillance, traffic control, and environmental monitoring. Distributed Particle Filters (DPF) are often employed when the for non-linear parameter estimation with non-Gaussian noise. In this paper, we propose a novel distributed particle filter whose transmitted quantities are particles. The fusion process of particles is implemented in a distributed and iterative fashion. To reduce the communication overhead, we adopt the Gaussian process-enhanced resampling algorithm, which reduces the size of local particle set, while still ensures acceptable filtering performance. To determine the local particle set after the communication, we propose two solutions. Our first algorithm (GP-DPF) adopts a “scoring mechanism”, allowing local agents score the received particles and using the scores as the selection criterion. Our second proposed solution (FA-DPF) is a meta-heuristic approach, which uses the well known firefly algorithm as a selection method for particle-based distributed particle filtering. Our simulations demonstrate the superiority of our proposed algorithms under the condition of limited communication and computational resources against other state-of-the-art distributed particle filters.

This decade has seen growing interest in Mars exploration. Advances in distributed systems, miniaturization and commoditization of space electronics and innovations in communications permit us to rethink the current paradigm of relying on a few heavy, slow and expensive high-tech rovers for Mars surface exploration. In this work, we address the demanding communication needs for a mission that deploys a swarm of uncontrolled wind-driven exploration rovers onto the martian surface. The concept for these lightweight, autonomous, ellipsoid ”Tumbleweed” rovers — named after the desert plant — is not new, and was studied and validated by NASA researchers decades ago. Recently, a new plan to turn the Tumbleweed mission into reality has been proposed to the ESA open space innovation platform (OSIP). The idea is to launch approximately 90 Tumbleweeds in one transfer vehicle and release them on the martian surface to survey the northern hemisphere of Mars over a mission duration of three months. Reliable communication is one of the key challenges for this mission. The rovers’ instruments, e.g. cameras, generate large volumes of data, and many rovers need to be served simultaneously. Furthermore, the tumbling motion on the martian surface constitutes unprecedented challenges in terms of antenna pointing for planetary exploration rovers. We present a trade-off analysis between direct to Earth communication and relayed communication using satellites orbiting Mars culminating in a baseline communication architecture for the Tumbleweed mission. For this purpose we model the kinematics of the Tumbleweed rovers and relay satellites w.r.t the Earth. A numerical simulation of all potential communication links over the full mission duration is conducted. The analysis shows that direct communication to Earth is infeasible due to the rolling motion of the rover. Hence, the relayed communication scenario is proposed, as it does not require a directional antenna on the Tumbleweed rovers. Therefore, we propose a constellation of three relay satellites in a circular, Earth-facing orbital plane around Mars, which communicate with the Tumbleweed rovers using the UHF frequency band. Commercial ground stations on Earth in Ka-band are used for the relay-ground link. The proposed communications architecture is estimated to achieve a raw data throughput of ě84Mbit per Tumbleweed rover per Sol.

Mega-constellations in Low Earth Orbit have the potential to revolutionise worldwide internet access. The concomitant potential of these mega-constellations to impact space sustainability, however, has prompted concern from space actors as well as provoking concern in the ground-based astronomy community. Increasing the knowledge of the orbital state of satellites in mega-constellations improves space situations awareness, reducing the need for collision avoidance manoeuvres and allowing astronomers to prepare better observational mitigation strategies. In this paper, we create a model of Phase 1 of Starlink, one of the more well-studied megaconstellations, and investigate the potential of cooperative localisation using time-ofarrival measurements from the optical inter-satellite links in the constellation. To this end, we study the performance of any unbiased estimator for localisation, by calculating the instantaneous Crame´r-Rao bound for two situations; one in which inter-satellite measurements and measurements from ground stations were considered, and one in which only relative navigation from inter-satellite measurements were considered. Our results show that localisation determined from a combination of inter-satellite measurements and ground stations can have at best an an average RMSE of approximately 10.15 metres over the majority of a satellite's orbit. Relative localisation using only inter-satellite measurements has a slightly poorer performance with an average RMSE of 10.68 metres. The results show that both anchored and anchorless inter-satellite cooperative localisation are dependent on the constellation's geometry and the characteristics of the inter-satellite links, both of which could inform the use of relative navigation in large satellite constellations in future.

Coordination for truck platooning refers to the active formation of a group of heavy-duty vehicles traveling at close spacing to reduce the overall truck operations costs. Conventionally, this coordination is achieved by optimizing various truck-related parameters, such as schedules, velocities, and routes, based on an objective function that minimizes a certain cost, for example, fuel usage. However, prevalent algorithms for the coordination problem are typically integer-constrained, which are not only hard to solve but are not readily scalable to increasing fleet sizes and networks. In this paper, to overcome these limitations, we propose a centralized formulation to optimize the truck parameters and solve a multidimensional objective cost function including fuel, operation time costs and preferential penalty. Furthermore, to improve the scalability of our proposed approach, we propose a decentralized algorithm for the platoon coordination problem involving multiple fleets and objectives. We perform both theoretical and numerical studies to evaluate the performance of our decentralized algorithm against the centralized solution. Our analysis indicates that the computation time of the proposed decentralized algorithms is invariant to the increasing fleet size, at the cost of a small relative gap to the optimum cost given by the centralized method. We discuss these results and present future directions for research.