314 
NEURAL NETWORK STAR PATTERN 
RECOGNITION FOR SPACECRAFT ATTITUDE 
DETERMINATION AND CONTROL 
Phillip Alvclda, A. Miguel San Martin 
Thc Jct Propulsion Laboratory, 
California Institutc of Tcchnology, 
Pasadcna, Ca. 91109 
ABSTRACT 
Currently, the most complex spacecraft attitude determination 
and control tasks are ultimately governed by ground-based 
systems and personnel. Conventional on-board systems face 
severe computational bottlenecks introduced by serial 
microprocessors operating on inherently parallel problems. New 
computer architectures based on the anatomy of the human brain 
seem to promise high speed and fault-tolerant solutions to the 
limitations of serial processing. This paper discusses the latest 
applications of artificial neural networks to the problem of star 
pattern recognition for spacecraft attitude determination. 
INTRODUCTION 
By design, a conventional on-board microprocessor can perform only 
one comparison or calculation at a time. Image or pattern recognition 
problems involving large template sets and high resolution can require 
an astronomical number of comparisons to a given database. Typical 
mission planning and optimization tasks require calculations involving 
a multitude of parameters, where each element has an inherent degree 
of importance, reliability and noise. Even the most advanced 
supercomputers running the latest software can require seconds and 
even minutes to execute a complex pattern recognition or expert system 
task, often providing incorrect or inefficient solutions to problems that 
prove trivial to ground control specialists. 
The intent of ongoing research is to develop a neural network based 
satellite attitude determination system prototype capable of determining 
its current three-axis inertial orientation. Such a system that can 
determine in real-time, which direction the satellite is facing, is needed 
in order to aim antennas, science instruments, and navigational 
equipment. For a satellite to be autonomous (an important criterion in 
interplanetary missions, and most particularly so in the event of a 
system failure), this task must be performed in a reasonable amount of 
time with all due consideration to actual environmental, noise and 
precision constraints. 
CELESTIAL ATTITUDE DETERMINATION 
Under normal operating conditions there is a whole repertoire of 
spacecraft systems that operate in conjunction to perform the attitude 
determination task, the backbone of which is the Gyro. But a Gyro 
measures only changes in orientation. The current attitude is stored in 
Neural Network Star Pattern Recognition 315 
volatile on-board memory and is updated as the gyro system integrates 
velocity to provide change in angular position. When there is a power 
system failure for any reason such as a single-event-upset due to cosmic 
radiation, all currently stored attitude information is LOST! 
One very attractive way of recovering attitude information with no 
a priori knowledge is by using on-board imaging and computer systems 
to: 
1.) Image a portion of the sky, 
2.) 
Compare the characteristic pattern of stars in the sensor field- 
of-view to an on-board star catalog, 
3.) Thereby identify the stars in the sensor FOV [Field Of View], 
4.) Retrieve the identified star coordinates, 
5.) 
Transform and correlate FOV and real-sky coordinates to 
determine spacecraft attitude. 
But the problem of matching a limited field of view that contains a 
small number of stars (out of billions and billions of them), to an on- 
board full-sky catalog containing perhaps thousands of stars has long 
been a severe computational bottleneck. 
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GEOMETRIC 
CONSTRAINTS 
STORED PAIR ADDRESS 
Filum l.) Serial star I.D. cat1ol format and methodology. 
The latest serial algorithm to perform this task requires 
approximately 650 KBytcs of RAM to store the on-board star catalog. 
It incorporates a highly optimized algorithm which uses a motorola 
68000 to search a sorted database of more than 70,000 star-pair distance 
values for correlations with the decomposed star pattern in the sensor 
FOV. It performs the identification process on the order of I second 
316 Alvelda and San Martin 
with a success rate of 99 percent. But it does not fit in the spacecraft 
on-board memory, and therefore, no such system has flown on a 
planetary spacecraft. 
 USES SUN SENSOR AND ATTITUDE MANEUVERS 
TO SUN 
TO SUN 
Figure 2.) Current Spacecrafft &ttitude inform&tion recovery sequence. 
As a result, state-of-the-art interplanetary spacecraft use several 
independent sensor systems in c. onjunction to determine attitude with no 
a priori knowledge. First, the craft is commanded to slew until a Sun 
Sensor (aligned with the spacccraft's major axis) has locked-on to the 
sun. The craft must then rotate around that axis until an appropriate 
star pattern at approximately ninety degrees to the sun is acquired to 
provide three-axis orientation information. The entire attitude 
acquisition sequence requires an absolute minimum of thirty minutes, 
and presupposes that all spacecraft actuator and maneuvering systems 
arc operational. At the phenomenal rendezvous speeds involved in 
interplanetary navigation, a system failure near mission culmination 
could mcan an almost complete loss of the most valuable scientific data 
while the spacecraft performs its initial attitude acquisition sequence. 
NEURAL MOTIVATION 
The parallel architecture and collective computation properties of a 
neural network based system address several problems associated with 
the implementation and performance of the scrim star ID algorithm. 
Instead of searching a lengthy database one clement at a time, each 
stored star pattern is correlated with the field of view concurrently. 
And whereas standard memory storage technology requires one address 
in RAM per star-pair distance, the neural star pattern representations are 
stored in characteristic matrices of interconnections between neurons. 
This distributed data set representation has several desirable properties. 
First of all, the 2N redundancy of the scrim star-pair scheme (i.e. which 
star is at which end of a pair) is discarded and a new more compressed 
representation emerges from the ncuromorphic architecture. Secondly, 
noise, both statistical (i.c thermal noise) and systematic (i.e. sensor 
precision limitations), and pattern invariancc characteristics are 
Neural Network Star Pattern Recognition 317 
incorporated directly into the preprocessing and neural architecture 
without extra circuitry. 
The first neural approach 
The primary motivation from the NASA perspective is to improve 
satellite attitude determination performance and enable on-board system 
implementations. The problem methodology for the neural architecture 
is then slightly different than that of the serial model. 
Instead of identifying every detected tfir in the field of view, the 
neural system identifies a single 'Guide Star' with respect to the pattern 
of dimmer stars around it, and correlates that star's known position with 
the sensor FOV to determine the pointing axis. If needed, only one other 
star is then required to fix the roll angle about that axis. So the core 
of the celestial attitude determination problem changes from multiple 
star identification and correlation, single star pattern identification. 
The entire system consists of several modules in a marriage of 
different technologies. The first neural system architecture uses already 
mature(i.e. sensor/preprocessor) technologies where they perform well, 
and neural technology only where conventional systems prove 
intractable. With an eye towards rapid prototyping and implementation, 
the system was designed with technologies (such as neural VLSI) that 
will be available in less than one year. 
SYSTEM ARCHITECTURE 
The Star Tracker sensor system 
The system input is based on the ASTROS II star tracker under 
development in the Guidance and Control section at the Jet Propulsion 
Laboratory. The Star tracker optical system images a defocussed portion 
of the sky (a star sub-field) onto a charged coupled device (C.C.D.). The 
tracker electronics then generate star centtold position and intensity 
information and passes this list to the preprocessing system. 
The Preprocessing system 
This centrol[ nd intensity information is passed to the preprocessing 
subsystem where the star pattern is treated to extract noise and pattern 
invariance. A 'pattern field-of-view' is defined as centered around the 
brightest (i.e. 'Guide Star') in the central portion of the sensor field-f- 
view. Since the pattern FOV radius is one half that of the sensor FOV 
the pattern for that 'Guide Star' is then based on a portion of the image 
that is complete, or invariant, under translational perturbation. The 
preprocessor then introduces rotational invariance to the 'guide-star' 
pattern by using only the distances of all other dimmer stars inside the 
pattern FOV to the central guide star. 
These distances are then mapped by the preprocessor onto a two 
dimensional coordinate system of distance versus relative magnitude 
(normalized to the guide star, the brightest star in the Pattern FOV) to 
be sampled by the neural associative star catalog. The motivation for 
this distance map format become clear when issues involving noise 
invariance and memory capacity are considered. 
318 Alvelda and San Martin 
Because the ASTROS Star Tracker is a limited precision instrument, 
most particularly in the absolute and relative intensity measures, two 
major problems arise. First, dimmer stars with intensities near the 
bottom of the dynamic range of the C.C.D. may or may not be included 
in the star pattern. So, the entire distance map is scaled to the brightest 
star such that the bright, high-confidence measurements are weighted 
more heavily, while the dimmer and possibly transient stars are of less 
importance to a given pattern. Secondly, since there are a very large 
number of stars in the sky, the uniqueness of a given star pattern is 
governed mostly by the relative star distance measures (which, by the 
way, are the highest precision measurements provided by the star 
tracker). 
In addition, because of the limitations in expected neural hardware, 
a discrete number of neurons must sample a continuous function. To 
retain the maximum sample precision with a minimum number of 
neurons, the neural system uses the biological mechanism of a receptive 
field for hyperaeuity. In other words, a number of neurons respond to 
a single distance stimulus. The process is analogous to that used on the 
defocussed image of a point source on the C.C.D. which was integrated 
over several pixels to generate a eentroid at sub-pixel accuracies. To 
relax the demands on hardware development for the neural module, this 
point smoothing was performed in the preprocessor instead of being 
introduced into the neural network architecture and dynamics. The 
equivalent neural response function then becomes: 
xi 
N 
where: 
is the sampling activity of neuron i 
is the number of stars in the Pattern Field Of View 
is the position of neuron i on the sample axis 
is the position of the stimulus from star k on the 
sample axis 
is the magnitude scale factor of star k, normalized 
to the brightest star in the PFOV, the 'Guide star' 
is the width of the gaussian point spread function 
The Neural system 
The neural system, a 106 neuron, three-layer, feed-forward network, 
samples the scaled and smoothed distance map, to provide an output 
vector with the highest neural output activity representing the best 
match to one of the pre-trained guide star patterns. The network 
training algorithm uses the standard backwards error propagation 
Neural Network Star Pattern Recogrdtion 319 
algorithm to set network interconnect weights from a training set of 
'Guide Star' patterns derived from the software simulated sky and 
sensor models. 
Simulation testbed 
The computer simulation testbed includes a realistic celestial field 
model, as well as a detector model that properly represents achievable 
position and intensity resolution, sensor scan rates, dynamic range, and 
signal to noise properties. Rapid identification of star patterns was 
observed in limited training sets as the simulated tracker was oriented 
randomly within the celestial sphere. 
PERFORMANCE RESULTS AND PROJECTIONS 
In terms of improved performance the neural system was quite a 
success, but not however in the areas which were initially expected. 
While a VLSI implementation might yield considerable system speed-up, 
the digital simulation testbed neural processing time was of the same 
order as the serial algorithm, perhaps slightly better. The success rate of 
the serial system was already better than 99%. The neural net system 
achieved an accuracy of 100% when the systematic noise (i.e. dropped 
stars) of the sensor was neglected. 
When the dropped star effect was introduced, the performance 
figure dropped to 94%. It was later discovered that the reason for this 
'low' rate was due mostly to the limited size of the Yale Bright Star 
catalog at higher magnitudes (lower star brightness). In sparse regions 
of the sky, the pattern in the sensor FOV presented by the limited sky 
model occasionally consisted of only two or three dim stars. When one 
or two of them drop out because of the Star sensor magnitude precision 
limitations, at times, there was no pattern left to identify. Further 
experiments and parametric studies are under way using a more 
complete Harvard Smithsonian catalog. 
The big gain was in terms of required memory. The serial algorithm 
stored over 70,000 star pairs at high precision in addition to code for a 
rather complex heuristic, artificial intelligence type of algorithm for a 
total size of 650 KBytes. The Neural algorithm used a eonneetionist 
data representation that was able to abstract from the star catalog, 
pattern class similarities, orthagonalities, and invarianees in a highly 
compressed fashion. Network performance remained essentially 
constant until interconnect precision was decreased to less than four bits 
per synapse. 3000 synapses at four bits per synapse requires very little 
computer memory. 
These simulation results were all derived from a monte carlo run of 
approximately 200,000 iterations using the simulator testbed. 
320 Alwlda and San Martin 
CONCLUSIONS 
By means of a clever combination of several technologies and an 
appropriate data set representation, a star ID system using one of the 
most simple neural algorithms outperforms those using the classical 
serial ones in several aspects, even while running a software simulated 
neural network. The neural simulator is approximately ten times faster 
than the equivalent serial algorithm and requires less than one seventh 
the computer memory. With the transfer to neural VLSI technology, 
memory requirements will virtually disappear and processing speed will 
increase by at least an order of magnitude. 
Where power and weight requirements scale with the hardware chip 
count, and every pound that must be launched into space costs millions 
of dollars, neural technology has enabled real-time on-board absolute 
attitude determination with no a priori information, that may 
eventually make several accessory satellite systems like horizon and sun 
sensors obsolete, while increasing the overall reliability of spacecraft 
systems. 
Acknowledgments 
We would like to acknowledge many fruitfull conversations with C. E. 
Bell, J. Barhen and S. Gulati. 
References 
R. W. H. van Bezooijen. Automated Star Pattern Recognition for Use 
With the Space Infrared Telescope Facility (SIRTIF). Paper for 
internal use at The Jet Propulsion Laboratory. 
P. Gorman, T. J. Sejnowski. Workshop on Neural Network Devices and 
Applications (Jet Propulsion Laboratory, Pasaden, Ca.) Document D- 
4406, pp.224-237. 
J. L. Lunkins. Star pattern Recognition for Real Time Attitude 
Determination. The Journal of Astronautical Science(1979). 
D. E. Rummelhart, G. E. Hinton. Parallel Distributed Processing, eds. 
(MIT Press, Cambridge, Ma.) Vol. I pp. 318-364. 
P.M. Salomon, T. A. Glavieh. image Signal Processing and Sub-Pixel 
Accuracy Star Trackers. SPE vol. 252 Smart Sensors II (1980). 
Neural Network Star Pattern Recognition 321 
C.C.D. 
Image 
Preprocessor 
Distance 
Map 
Radius from Guide Star 
Sampler anCl1-1 a oC] [--] 12) a 
oDo 
Neu I 
Output 
Star Attitude 
Look-up Table 
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28 
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St  R.A. Dec. 
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32: Alvelda and San Ms.rtn 
PROTOTYPE HARDWARE IMPLEMENTATION 
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TRACKER 
SERIAL PROCE. SSOR I TO ACS 
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