Hardware Implementation of the Spot Payload for Orbiting Objects Detection Using Star Sensors

Space debris issue has become an attractive challenge for many applications in the framework of Space Situational Awareness (SSA) and Space Surveillance and Tracking (SST). The Star sensor image on-board Processing for orbiting Objects deTection (SPOT) fits in this field as an innovative space based autonomous and versatile system for Resident Space Objects’ optical detection via star sensors and for different Earth orbits scenarios. This system is planned to be a payload for an In-Orbit Validation (IOV) activity in the next future. The purpose of this paper is to show the architecture of the SPOT system together with its implementation on a System on Chip (SoC)/Field Programmable Gate Array (FPGA) space representative board. The SPOT algorithms involve several layers of filters which are relatively expensive in terms of computational latency, limiting their applicability to real-time image processing applications. This work presents the design and implementation of SPOT algorithm on the Zynq-7000 SoC using Xilinx FPGA and ARM CPU. Algorithms have been modelled with Simulink and implemented on FPGA using Xilinx system generator with aiming to optimize both processing time and area usage. A Hardware-In-the-Loop (HIL) setup was developed as well, to verify the performances and robustness of the SPOT algorithms and simulating critical scenario by using real night sky images from acquisition campaig

Real-time multi-camera video acquisition and processing platform for ADAS

The paper presents the design of a real-time and low-cost embedded system for image acquisition and processing in Advanced Driver Assisted Systems (ADAS). The system adopts a multi-camera architecture to provide a panoramic view of the objects surrounding the vehicle. Fish-eye lenses are used to achieve a large Field of View (FOV). Since they introduce radial distortion of the images projected on the sensors, a real-time algorithm for their correction is also implemented in a pre-processor. An FPGA-based hardware implementation, re-using IP macrocells for several ADAS algorithms, allows for real-time processing of input streams from VGA automotive CMOS cameras

Design and construction of a configurable full-field range imaging system for mobile robotic applications

Mobile robotic devices rely critically on extrospection sensors to determine the range to objects in the robot’s operating environment. This provides the robot with the ability both to navigate safely around obstacles and to map its environment and hence facilitate path planning and navigation. There is a requirement for a full-field range imaging system that can determine the range to any obstacle in a camera lens’ field of view accurately and in real-time. This paper details the development of a portable full-field ranging system whose bench-top version has demonstrated sub-millimetre precision. However, this precision required non-real-time acquisition rates and expensive hardware. By iterative replacement of components, a portable, modular and inexpensive version of this full-field ranger has been constructed, capable of real-time operation with some (user-defined) trade-off with precision

Evaluation of Single-Chip, Real-Time Tomographic Data Processing on FPGA - SoC Devices

A novel approach to tomographic data processing has been developed and evaluated using the Jagiellonian PET (J-PET) scanner as an example. We propose a system in which there is no need for powerful, local to the scanner processing facility, capable to reconstruct images on the fly. Instead we introduce a Field Programmable Gate Array (FPGA) System-on-Chip (SoC) platform connected directly to data streams coming from the scanner, which can perform event building, filtering, coincidence search and Region-Of-Response (ROR) reconstruction by the programmable logic and visualization by the integrated processors. The platform significantly reduces data volume converting raw data to a list-mode representation, while generating visualization on the fly.Comment: IEEE Transactions on Medical Imaging, 17 May 201

STR: a student developed star tracker for the ESA-LED ESMO moon mission

In the frame of their engineering degree, ISAE’s students are developing a Star Tracker, with the aim of being the core attitude estimation equipment of the European Moon Student Orbiter. This development goes on since several years and is currently in phase B. We intend to start building an integrated breadboard for the end of the academic year. The STR is composed of several sub-systems: the optical and detection sub-system, the electronics, the mechanics and the software. The optical detection part is based on an in-house developed new generation of APS detectors. The optical train is made of several lenses enclosed in a titanium tube. The electronics includes a FPGA for the pre-processing of the image and a microcontroller in order to manage the high level functions of the instrument. The mechanical part includes the electronics box, as well as the sensor baffle. The design is optimized to minimize the thermo-elastic noise of the assembly. Embedded on ESMO platform, this Star Tracker will be able to compute the satellite‘s attitude, taking into account the specific requirements linked to a Moon mission (illumination, radiation requirements and baffle adaptation to lunar orbit). In order to validate the design, software end-to-end simulation will include a complete simulation of the STR in its lunar dynamic environment. Therefore, we are developing a simple orbital model for the mission (including potential dazzling by celestial bodies)

A committee machine gas identification system based on dynamically reconfigurable FPGA

This paper proposes a gas identification system based on the committee machine (CM) classifier, which combines various gas identification algorithms, to obtain a unified decision with improved accuracy. The CM combines five different classifiers: K nearest neighbors (KNNs), multilayer perceptron (MLP), radial basis function (RBF), Gaussian mixture model (GMM), and probabilistic principal component analysis (PPCA). Experiments on real sensors' data proved the effectiveness of our system with an improved accuracy over individual classifiers. Due to the computationally intensive nature of CM, its implementation requires significant hardware resources. In order to overcome this problem, we propose a novel time multiplexing hardware implementation using a dynamically reconfigurable field programmable gate array (FPGA) platform. The processing is divided into three stages: sampling and preprocessing, pattern recognition, and decision stage. Dynamically reconfigurable FPGA technique is used to implement the system in a sequential manner, thus using limited hardware resources of the FPGA chip. The system is successfully tested for combustible gas identification application using our in-house tin-oxide gas sensors

EChO Payload electronics architecture and SW design

EChO is a three-modules (VNIR, SWIR, MWIR), highly integrated spectrometer, covering the wavelength range from 0.55 $\mu$m, to 11.0 $\mu$m. The baseline design includes the goal wavelength extension to 0.4 $\mu$m while an optional LWIR module extends the range to the goal wavelength of 16.0 $\mu$m. An Instrument Control Unit (ICU) is foreseen as the main electronic subsystem interfacing the spacecraft and collecting data from all the payload spectrometers modules. ICU is in charge of two main tasks: the overall payload control (Instrument Control Function) and the housekeepings and scientific data digital processing (Data Processing Function), including the lossless compression prior to store the science data to the Solid State Mass Memory of the Spacecraft. These two main tasks are accomplished thanks to the Payload On Board Software (P-OBSW) running on the ICU CPUs.Comment: Experimental Astronomy - EChO Special Issue 201