Future autonomous ships will need several redundant positioning systems to navigate reliably. Global Navigation Satellite Systems are highly accurate but they are susceptible to disruptions and intentional jamming. Maritime radars have long range and are robust against bad weather and darkness, but the use for ownship motion estimation has received relatively little attention in the research field. In this work, we present a radar odometry estimation method inspired by advances in visual odometry and simultaneous localization and mapping. The method works on raw radar data in a coastal environment and combines the Kanade-Lucas-Tomashi tracker with a factor graph back-end. We test it on data from a large ship with a maritime radar with a range of 19 km. We find that it is robust with only a small drift and no erroneous jumps in the estimate.

This paper presents a low-cost lightweight UAV payload for remote sensing purposes using a small imaging spectrometer made from COTS components. The novel design with an additional upward-facing spectrometer allows for reflectance computation by correcting the push-broom imaging spectrometer data of the ocean surface for different lighting- and atmospheric conditions.

A system that needs to interact with the physical world in a timely manner is called a real-time system. When such a system is composed of multiple subsystems, or nodes, each of which is a geographically separate system, such a system of systems is called a distributed real-time system. The computation at each node must adhere to the timing requirements, and the connecting communication channels must never cause delays that trigger further timing violations. In this paper, we introduce Deterministic Network Channels, a network construct using Time Sensitive Networking QoS mechanisms that add reliable and deterministic communication for distributed tasks. Introducing such network channels as a construct allows designers to focus on higher-level primitives when building distributed systems. We describe our reference implementation and evaluate it by extending Timed C with network channels. Building on this, we also perform a thorough performance evaluation to determine practical bounds for both Linux and TSN under heavy workloads and adverse network conditions to show how the proposed reference implementation performs in real-world scenarios. In our tests, we can synchronize two separate machines running commercial off-the-shelf hardware to within 15μs of each other under severe internal and external interference.

The assignment problem is a fundamental optimization problem and a crucial part of many systems. For example, in multiple object tracking, the assignment problem is used to associate object detections with hypothetical target tracks and solving the assignment problem is one of the most compute-intensive tasks. To enable low-latency real-time implementations, efficient solutions to the assignment problem is required. In this work, we present Sparse and Speculative (SaS) Auction, a novel implementation of the popular Auction algorithm for FPGAs. Two novel optimizations are proposed. First, the pipeline width and depth are reduced by exploiting sparsity in the input problems. Second, dependency speculation is employed to enable a fully pipelined design and increase the throughput. Speedups as high as 50 × are achieved relative to the state-of-the-art implementation for some input distributions. We evaluate the implementation both on randomly generated datasets and realistic datasets from multiple object tracking.