Finding sets of binary sequences with low auto- and cross-correlation properties is a hard combinatorial optimization problem with numerous applications, including multiple-input-multiple-output (MIMO) radar and global navigation satellite systems (GNSS). The sum of squared correlations, sometimes referred to as the integrated sidelobe level (ISL), is a quartic function in the variables and is a commonly-used metric of sequence set quality. In this paper, we show that the ISL minimization problem may be formulated as a mixed-integer quadratic program (MIQP). We then present a block coordinate descent (BCD) algorithm that iteratively optimizes over subsets of variables. The subset optimization subproblems are also MIQPs which may be handled more efficiently using specialized solvers than using exhaustive search; this allows us to perform BCD over larger variable subsets than previously possible. Our approach was used to find sets of four binary sequences of lengths up to 1023 with better ISL performance than Gold codes and sequence sets found using existing BCD methods.

Learning-based trajectory planners in robotics have attracted growing interest given their ability to plan for complex tasks. These planners are typically trained in simulation under nominal conditions before being implemented on real robots. However, in real settings, the presence of motion and sensing uncertainties causes the robot to deviate from planned reference trajectories potentially leading to unsafe outcomes such as collisions. In this paper we present a reachability analysis to predict such deviations and to evaluate robot safety along reference trajectories. We then use the reachability analysis to safeguard a learning-based planner. Finally, we demonstrate the applicability of our safeguarding algorithm for learning-based planners via multiple simulations and real robot experiments.

Unmanned aircraft systems (UAS) generally use Global Navigation Satellite System (GNSS) measurements to estimate their state (position and orientation) for outdoor navigation. However, in urban environments, GNSS pseudorange measurements contain biases due to multipath effects and signal blockages by nearby buildings. For safe navigation in such environments, it is beneficial to predict the state uncertainty while accounting for the effect of measurement biases. Reachability analysis is a commonly used tool to predict the state uncertainty of a system. However, existing works do not account for the effect of measurement biases on state estimation, which consequently affects the predicted state uncertainty. Additionally, majority of the existing literature focuses on linear systems, whereas the dynamics of widely used practical systems are better captured by non-linear models. Thus, in this paper we present a non-linear stochastic reachability analysis to predict bounds on the state uncertainty while accounting for measurement biases. We derive the analysis for a fixed-wing UAS navigating using ranging measurements. In order to evaluate our predicted bounds for GNSS-based navigation, we simulate a 3D urban environment and the pseudorange biases due to multipath effects. We validate the predicted bounds for multiple trajectories in the simulated environment. Finally, we demonstrate the applicability of our predicted bounds towards ensuring safe UAS navigation in a shared airspace.

We propose an image-based cross-view geolocalization method that estimates the global pose of a UAV with the aid of georeferenced satellite imagery. Our method consists of two Siamese neural networks that extract relevant features despite large differences in viewpoints. The input to our method is an aerial UAV image and nearby satellite images, and the output is the weighted global pose estimate of the UAV camera. We also present a framework to integrate our crossview geolocalization output with visual odometry through a Kalman filter. We build a dataset of simulated UAV images and satellite imagery to train and test our networks. We show that our method performs better than previous camera pose estimation methods, and we demonstrate our networks ability to generalize well to test datasets with unseen images. Finally, we show that integrating our method with visual odometry significantly reduces trajectory estimation errors.

Recent technologies, such as, Internet of Things and cloud services, increases the usage of small and low-cost networked unmanned aerial vehicles (UAVs), which needs to be robust against malicious global positioning system (GPS) attacks. Due to the availability of low-cost GPS jammers in the commercial market, there has been a rising risk of multiple jammers and not just one. However, it is challenging to locate multiple jammers because the traditional jammer localization via multilateration is applicable for only a single jammer case. Also, during a jamming attack, the positioning capability of an on-board GPS receiver is compromised given its inability to track GPS signals. We propose a simultaneous localization of multiple jammers and receivers (SLMR) algorithm by analyzing the variation in the front-end signal power recorded by the GPS receivers on-board a network of UAVs. Our algorithm not only locates multiple jammers but also utilizes these malicious sources as additional navigation signals for positioning the UAVs. We design a Gaussian mixture probability hypothesis density filter over a graph framework, which is optimized using a Levenberg-Marquardt minimizer. Using a simulated experimental setup, we validate the convergence and localization accuracy of our SLMR algorithm for various cases, including attacks with a single jammer, multiple jammers, and a varying number of jammers. We also demonstrate that our SLMR algorithm is able to simultaneously locate multiple jammers and UAVs, even for a larger transmitted power of the jammers.

Critical government and industry sectors (such as law enforcement, transportation, communication, and finance) are growing increasingly dependent on Global Navigation Satellite Systems (GNSS) for positioning, navigation, and timing. At the same time, the availability of low-cost GNSS jamming devices are presenting a serious threat to GNSS and increasing the likelihood of outages to infrastructures relying on GNSS. The attacks range from malicious parties intentionally jamming GNSS signals within a targeted geographical region to uninformed users causing accidental interference. This paper is an overview of different approaches adopted to date to mitigate GNSS disruption caused by intentional and unintentional jamming. The first approach outlined in this paper is the use of inertial systems to aid GNSS. The second and third approaches are the filtering of jamming/interference in the spatial and time-frequency domains, respectively. The fourth approach is vector tracking of GNSS signals in the receiver.

Multipath and jamming interference affects the accuracy, availability, and continuity of global navigation satellite systems. The U.S. Global Positioning System and the Russian Global'naya Navigatsionnaya Sputnikovaya Sistema (GLONASS) are being joined by the European Galileo and the Chinese BeiDou. An increasing number of satellites in multiple constellations enable users to use high-mask-angle antennas (HMAAs) to mitigate interference signals coming from a low-elevation angle. This paper studies the optimal antenna mask angle that maximizes the suppression of interference but still maintains the performance of a single constellation with a low-mask-angle antenna. This paper first proves a novel lower bound on the expectation of dilution of precision (DOP) and derives closed-form formulas that relate the lower bound to the antenna mask angle and the number of satellites. Then, through extensive simulations, a variety of optimal mask angles are obtained with respect to different constellation settings, different DOP metrics, and different assumptions of range accuracy. The numerical results highly agree with our theory. Both of them show that two constellations can match the performance of one constellation with a 5°-14° higher mask, and three constellations can match the performance of one constellation with an 11°-23° higher mask, depending on the DOP metric and the range error model used. The numerical results also show that using HMAAs is more beneficial to users interested in positioning accuracy than to users interested in time transfer accuracy.

Secure reliable position information is indispensable for many transportation systems and services, such as traffic monitoring, fleet management, electronic toll collection, route guidance, vehicle telematics, and emergency response. Unfortunately, civil Global Positioning System (GPS) signals are vulnerable to spoofing attacks. This paper introduces a signal authentication architecture based on a network of cooperative GPS receivers. A receiver in the network correlates its received military P(Y) signal with those received by other receivers (hereinafter referred to as cross-check receivers) to detect spoofing attacks. This paper describes three candidate structures to implement this architecture and evaluates spoofing detection performance through theoretical analyses and field experiments. We show that the spoofing detection performance improves exponentially with increasing number of cross-check receivers. Even if the cross-check receivers are low cost, unreliable, and in challenging environment, cooperative authentication can match, if not outperform, a single high-quality reliable reference receiver in terms of spoofing detection performance.

Localizability and accuracy are two fundamental problems in many range-based localization schemes for sensor networks. This poster addresses the two problems theoretically by introducing two new concepts: the effective degree and the lower bound of geometric dilution of precision (GDOP). We prove that the network is not localizable unless the average effective degree is greater than or equal to the dimension of the location space. We further show that the average localization accuracy is approximately inversely proportional to the average degree.

Accuracy is a fundamental performance requirement in network localization. This paper studies the accuracy of range-based localization schemes for random sensor networks with respect to network connectivity and scale. We show that the variance of localization errors is proportional to the average geometric dilution of precision (AGDOP). The paper proves a novel lower bound of expectation of AGDOP (LB-E-AGDOP). Our analysis based on LB-E-AGDOP shows that localization accuracy is approximately inversely proportional to the average degree of network. A further analysis shows that when network connectivity merely guarantees localizability, increasing sensor nodes leads to bounded monotonic increase in AGDOP; when a network is densely connected, increasing sensor nodes leads to bounded monotonic decrease in AGDOP. Finally, these conclusions are validated by numerical simulations.