The Asteroid Impact & Deflection Assessment (AIDA) is a collaboration between the NASA DART mission and ESA Hera mission. The aim scope is to study the asteroid deflection through a kinetic collision. DART spacecraft will collide with Didymos-B, while ground stations monitor the orbit change. HERA spacecraft will study the post-impact scenario. The HERA spacecraft is composed by a main spacecraft and two small CubeSats. HERA will monitor the asteroid through cameras, Radar, Satellite-to-Satellite doppler tracking, LIDAR, seismometry and gravimetry. In this paper is reported the first iteration on the LIDAR ENGINEERING MODEL ALTIMETER named HELENA. HELENA is a TOF altimeter that provides time-tagged distances and velocity measurements. The LIDAR can be used for support near asteroid navigation and provides scientific information. The HELENA design comprises a microchip laser and low noise sensor. The synergies between these two technologies enable developing a compact instrument for range measurements of up to 14 km. Thermal-mechanical and radiometric simulations of the HELENA telescope are reported in this paper. The design is subjected to vibrational, static and thermal conditions, and it was possible to conclude by the results that the telescope is compliant with the random vibration levels, the static load and the operating temperatures.

Purpose This paper aims to report the first iteration on the Light Detection and Ranging (LIDAR) Engineering Model altimeter named HELENA. HELENA is a Time of Flight (TOF) altimeter that provides time-tagged distances and velocity measurements. The LIDAR can be used for support near asteroid navigation and provides scientific information. The HELENA design comprises two types of technologies: a microchip laser and low noise sensor. The synergies between these two technologies enable developing a compact instrument for range measurements of up to 14 km. Thermal-mechanical and radiometric simulations of the HELENA telescope are reported in this paper. The design is subjected to vibrational, static and thermal conditions, and it was possible to conclude by the results that the telescope is compliant with the random vibration levels, the static load and the operating temperatures. Design/methodology/approach The Asteroid Impact & Deflection Assessment (AIDA) is a collaboration between the NASA DART mission and ESA Hera mission. The aim scope is to study the asteroid deflection through a kinetic collision. DART spacecraft will collide with Didymos-B, while ground stations monitor the orbit change. HERA spacecraft will study the post-impact scenario. The HERA spacecraft is composed by a main spacecraft and two small CubeSats. HERA will monitor the asteroid through cameras, radar, satellite-to-satellite doppler tracking, LIDAR, seismometry and gravimetry. Findings The HELENA design comprises two types of technologies: a microchip laser and low noise sensor. The synergies between these two technologies enable developing a compact instrument for range measurements of up to 14 km. Originality/value In this paper is reported the first iteration on the LIDAR Engineering Model altimeter named HELENA. HELENA is a TOF altimeter that provides time-tagged distances and velocity measurements. The LIDAR can be used for support near asteroid navigation and provides scientific information. The HELENA design comprises two types of technologies: a microchip laser and low noise sensor. The synergies between these two technologies enable developing a compact instrument for range measurements of up to 14 km.

<p>The NASA DART and the ESA HERA missions aim to provide an experiment in asteroid defection, though a kinetic collision. DART spacecraft will be sent to collide at high speed, approximately at 6.6 km/s, with the smaller asteroid, usually called Didymoon of the binary asteroid system Didymos. HERA spacecraft will be sent to study the effects of the impact, so that our knowledge of the energy transmission due to the collision is improved. HERA spacecraft will evaluate Didymoon orbit change, structure of the asteroid, crater size [1].</p> <p>HERA spacecraft carries several payload instruments to provide these studies, namely: Cameras, Radar, Satellite-to-Satellite Doppler tracking, LIDAR, Seismometer and Gravimeter.</p> <p>In this work we report the LIDAR, also known as PALT for HERA, conception, design, and manufacturing process that is currently ongoing, as well its scientific aims and contribution to spacecraft navigation.</p> <p>PALT is a ToF altimeter that provides time tagged distances measurements. The instrument can be used to support near asteroid navigation and provides scientific information (e.g. asteroid 3D topography and fall velocity) and also reports the power of the received pulse being possible to calculate the target reflectivity.</p> <p>PALT first version EM is based on a Laser Landing Altimeter Engineering Model developed by EFACEC and Faculdade de Ciências, Universidade de Lisboa (FCUL), in the frame of an ESA NEO-MAPP project. THE PALT comprises a compact low power consumption microchip laser that emits 1.5 µm light pulses and a low noise sensor. This laser technology enables rangefinder compact designs. The synergies between these two technologies enable the development of a compact instrument for range measurements of from 500 m to 14 km with a low power consumption and envelope of 12 cm×15 cm×10 cm. The PALT electronics was designed to endure a TID of 100 krads.</p> <p>PALT has four main blocks, power supply, processing unit, electronics frontend, ToF optical front end. Optical front end is composed by emitter and receiver.</p> <p>Power supply uses a traditional flyback solution, optimised for the altimeter secondary powers consumption and outputs filtering.</p> <p>Processing unit is based on a FPGA since it simplifies the process of keeping precise timings, required to operate the ToF unit. FPGA is also responsible to perform all the housekeeping acquisitions, to monitor the health of the altimeter and for the interface with the spacecraft, via Universal Serial Link.</p> <p>ToF is the key block of the LIDAR altimeter with respect to its accuracy and precision. This unit is responsible to time tag all the laser emitter pulses as well as all the APD receptions, with a precise timed tag that will be then managed by the processing unit FPGA to compute the distance.</p> <p>Frontend Electronics is responsible for the Laser power supply and triggering, also for the Laser pulses digitalization (emitted and received).</p> <p>The preferred LASER source for PALT is currently being developed at FCUL. The laser used as source is a diode pumped, passively Q-switched Yb-Er Microchip Laser targeting a 100 μJ Gaussian pulse with a FWHM of 2 ns. The backscattered radiation is a gaussian pulse shape.</p> <p>The main optical specifications of the optical front end follow the receiver, emitter, and filter parameters. The receiver optical aperture diameter and obscuration are 100 mm and 30 mm, respectively. The FOV receiver has 1.5 mrad value, a transmittance of 0.91; a sensor with a 230 kV/W responsivity. Relatively to the emitter properties, it has a FOV of 1 mrad and optics transmittance of 0.94. The energy budget was calculated using (1), which allowed an estimation of the magnitude of the returned power [2]:</p> <p>E<sub>r</sub>≈ E<sub>TR</sub> r<sub>s</sub>⁄π A<sub>r</sub>⁄D<sup>2 </sup>τ<sub>R </sub>OV          (1)</p> <p>where E<sub>TR</sub> is the emitter transmittance, r<sub>s</sub> is the asteroid reflectance, A<sub>r</sub> is the telescope area, D is the distance, τ<sub>R</sub> is the receiver transmittance and OV is the overlap.</p> <p>Considering the emitted laser pulse has FWHM of 2 ns and Gaussian shape, the receiver power can be calculated.</p> <p>The returned peak power Figure 1 along with saturation limits of the sensor and minimum detectable power considering solar background, sensor NEP and M=20.</p> <p><img src="data:image/png;base64, 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

Near-Earth Objects (NEO) are the topic of several research studies, with objects smaller than 1km in size posing the most threats and being the less understood of this scientific domain. The Asteroid Impact and Deflection Assessment (AIDA) mission involves NASA and ESA with the main mission goal to perform and analyze the asteroid deflection using the Kinetic Impactor technique. The mission target is Didymos-B, a moon of a binary asteroid called Didymos. NASA oversees the Double Asteroid Redirection Test (DART probe), and ESA is responsible for HERA probe, that will measure the Dydimos-B deflection caused by the impact. The Light Detection and Ranging (LIDAR), the Radar, the Satellite-to-Satellite Doppler tracking, the Seismometer, and the Gravimeter are instruments integrated into HERA spacecraft. Information synergy between the instruments allows the detailed characterization of the asteroid including internal structure. This experiment allows further understanding and will provide important information to improve the current NEO understanding and modelling. In this paper, scientific advances related to the LIDAR instrument are reported, including the innovative optomechanical design resulting from thermal and mechanical optimizations. The LIDAR has a compact design and needs to withstand extreme conditions, such as radiative and thermal conditions, without compromise its high accuracy measurements. The LIDAR is a time-of-flight altimeter instrument that will measure the distances from the HERA spacecraft to the target. It provides information for a 3D topographic mapping and calculates the asteroid reflectivity. The measurements are to be performed at a distance from 500 m to 14 km while operations such as fly byes or landings remain a possibility.