<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&#160;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 Ci&#234;ncias, 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&#160;&#181;m 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&#160;m to 14&#160;km with a low power consumption and envelope of 12&#160;cm&#215;15&#160;cm&#215;10&#160;cm. The PALT electronics was designed to endure a TID of 100&#160;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&#160;&#956;J Gaussian pulse with a FWHM of 2&#160;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&#160;mm and 30&#160;mm, respectively. The FOV receiver has 1.5&#160;mrad value, a transmittance of 0.91; a sensor with a 230&#160;kV/W responsivity. Relatively to the emitter properties, it has a FOV of 1&#160;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>&#8776; E<sub>TR</sub> r<sub>s</sub>&#8260;&#960; A<sub>r</sub>&#8260;D<sup>2&#160;</sup>&#964;<sub>R&#160;</sub>OV&#160; &#160; &#160; &#160; &#160; (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, &#964;<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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
Incoming solar energy is firstly collected 137 small parabolic mirrors, 180mm in diameter, 210mm in focal length and then coupled by 137 optical fibers with 2mm in diameter each, to a diffusion bounded thin-disk Nd:YAG laser material. The flexibility of optical fiber allows the placement of laser cavity in a convenient place away from the solar collection mirrors. The conical polishing of the fiber output sections permits further enhancement of pump light absorption by the Nd:YAG thin-disk, diffusion bounded to an undoped YAG cap with 80mm maximum diameter. For optimal pumping condition with 1.8mm diameter polished tip, 20W laser power was numerically calculated, corresponding to a collection efficiency of 5.9 W/m 2 . M 2 factors of M x2 =88.5 and M y2 =89.4 were also attained, indicating an almost symmetrical absorption profile. The proposed scheme can provide a solution to the thermal problems that has plagued the solarpumped lasers for many years.
This paper reports the implementation of two critical technologies used in LiDARs: 1) A microchip Q-switched laser breadboard and 2) breadboard of an Indium gallium arsenide avalanche photodiode working at 300 K with high reverse polarization voltages. Microchip Q-switched lasers are small solid state back pumped lasers, that can generate high energy short pulses. The implemented breadboard used an Erbium and Ytterbium co doped phosphate glass, a COMALO crystal with 98% (initial transparency) and an output coupler of 98% reflectivity. For the sensor test, a system for the simultaneous operation in vacuum and wide range of temperatures was developed. Avalanche photodiodes are reverse polarized photodiodes with high internal gain, due to their multiple layer composition, capable of building up high values of photocurrent from small optical signals by exploiting the avalanche breakdown effects. The test avalanche photodetector was assembled to be operated in two modes: Linear and Geiger mode, to achieve this behavior, a transimpedance amplifier circuit was implemented. These two technologies are critical for mobile LiDAR applications, due to its low mass and high efficiency. The paper describes the breadboard implementation method and sensor characterization at low temperature and high voltage (beyond breakdown voltage).
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