A  particle  filter  is  a  Montecarlo-based  method  suitable  for predicting   future   states   of   non-linear   systems   with   non-Gaussian noise.

It  is  based  on  a  set  of  samples  of  the  state 

where each individual sample is called particle. These particles are weighted according to the real measure of the state in order to  estimate  the  future  state  of  the  system

Moreno González, Félix Antonio

Pérez Daza, David

Villaverde San José, Mónica

Servicio de Coordinación de Bibliotecas de la Universidad Politécnica de Madrid

Towards an Adaptive Hardware Parallel Particle Filter 
David Pérez, Mónica Villaverde and Félix Moreno 
Centro de Electrónica Industrial (CEI), ETSII 
Universidad Politécnica de Madrid (UPM) 
Madrid, Spain 
{david.perez.daza, monica.villaverde, felix.moreno}@upm.es  
 
I. INTRODUCTION 
A particle filter is a Montecarlo-based method suitable for 
predicting future states of non-linear systems with non-
Gaussian noise. It is based on a set of samples of the state 
where each individual sample is called particle. These particles 
are weighted according to the real measure of the state in order 
to estimate the future state of the system. Particle filter is 
widely used in numerous applications ranging from prediction 
of failures [1] and prognosis [2] to object tracking [3]. 
Nevertheless, it is a very computationally expensive approach 
since it requires many resources to generate a huge number of 
particles, to evaluate them and to assign an appropriated weight 
to each one of them. However, its main bottleneck lies in the 
particle weights assignment since due to the sequential 
execution every particle has to wait for the others before the 
beginning of the resampling stage (Fig. 1). 
 
Fig. 1. Basic particle filter flowchart. 
 Furthermore, there are other decisive factors which have a 
huge influence over their implementation dependent on the 
kind of application. The most important ones are execution 
time, precision and consumption. In this work we propose a 
novel design which deals with all those issues making the 
particle filter adaptive for concrete application requirements. 
The execution time is an important factor when the 
application demands a high processing speed to avoid 
malfunctions in real time. Particle filters are inherently 
sequential; consequently their execution time depends on the 
number of particles N, latency L -which represents the time 
wasted to process the first particle- and clock frequency fCLK 
(1).  
ݐ௘௫௘௖௨௧௜௢௡ = (2 · ܰ + ܮ − 1) · ଵ௙಴ಽ಼          (1) 
We propose to distribute the total number of particles into 
K different modules to reduce the execution time maintaining 
the same precision. All modules work in parallel splitting 
among them the total number of particles. Therefore, in this 
case, the execution time will be minimized as remarks the 
equation given by (2).  
ݐ௘௫௘௖௨௧௜௢௡ = ቀ2 · ே௄ + ܮ − 1ቁ · ଵ௙಴ಽ಼          (2) 
There is a wide background concerned with how a particle 
filter can be parallelized through splitting the filter in different 
modules. In that case it is important to note that, for 
maintaining the Montecarlo philosophy, modules cannot be 
completely independent, so they must share some kind of 
information (i.e. particles). On the one hand, each module can 
evaluate a group of particles in order to generate a “big 
particle” which will be weighted. Therefore, each module has 
its own weight and the particle exchanging is carried out during 
the resampling stage [4]. Other alternative is shown in [5] 
whereas each module has a fixed number of particles and the 
same pairs of particles are always exchanged. On the other 
hand, a little more different option is presented in [6] since 
authors explain a specific resampling stage based on a 
Metropolis-Hastings algorithm. 
II. THE ADAPTIVE HARDWARE PARALLEL PARTICLE FILTER 
(AHP-PF) 
Our proposal goes further since we propose an Adaptive 
Hardware Parallel Particle Filter (AHP-PF). An AHP-PF 
includes a hardware reconfigurable mechanism to adapt the 
parallelization to different application requirements during run-
time. The need to implement the filter with real parallelism 
makes a hardware implementation better than a software one. 
Moreover, we have to assure that precision and consumption 
are suitable according to these application claims. Therefore 
the filter has to be able to adapt itself taking into account which 
is the most important factor for the application scope. This 
filter adaptation is achieved by adjusting its internal structure 
varying the number of required modules and their sizes. Thus, 
if we want to increase the execution speed and, at the same 
time maintain the accuracy of the algorithm, a module 
composed of N/2 particles should be substituted by two others 
composed of N/4 since when using more modules the 
execution time is reduced as demonstrated in (2). On the other 
hand if, at a given moment, precision is not the priority, the 
filter should remove some modules to simplify the algorithm 
execution and consequently consume less power (Fig. 2). 
Particle initialization
Weights updating
New state estimation
Particle resampling
Stop 
condition?
START
STOP
YES
NO
 
Fig. 2. Adaptive modular division of the particle filter. 
A case study has to be defined in order to test the proposed 
method. Therefore, we have decided to focus our work on the 
object tracking field in order to incorporate our future system 
to autonomous driving application. However, this method 
could be applied to other fields related with signal prediction 
such as fault diagnosis or life cycle forecasting. 
Our proposal for object tracking is to implement a HW/SW 
co-design mainly composed of a Raspberry Pi and a Xilinx 
FPGA (Fig. 3).  
 
Fig. 3. System architecture. 
The Raspberry Pi will be in charge of the image processing 
taking advance of its Graphics Processor Unit (GPU) included 
in its Broadcom BCM2835 System-on-Chip (SoC). A 
Raspicam camera is also employed to capture the images 
which will be analyzed -using OpenCV- in the GPU. Using the 
information provided by the camera the GPU has to be able to 
detect a specific object in real time. In this previous stage, the 
object will be defined by a given color and the tracking 
algorithm has to find that color on the received image in order 
to calculate the object centroid (xc, yc).  
In contrast, the particle filter implementation will be carried 
out in a Virtex 5 FPGA to take advantage of its hardware 
resources. The Raspberry Pi Central Processor Unit (CPU) 
will send the (xc, yc) coordinates to the FPGA using the I2C 
protocol. That position will be processed in the FPGA applying 
the particle filter in order to estimate the object trajectory. The 
predicted trajectory (x*, y*) will be sent back from the FPGA 
to the Raspberry Pi since the object centroid and the predicted 
position will be displayed -overlapped with the image- on the 
monitor.  
The filter adaptation is a complex task since it requires a 
thorough analysis in order to determine the additional 
information necessary to reconfigure the particle filter in 
consonance with the current situation. That reconfiguration 
implies the modification of the internal structure of the particle 
filter in order to adjust its number of modules and their sizes 
according to the demanded requirements. Those needs depend 
on the kind of the application. Perhaps, in our case it is not 
enough to focus only on the target object but also we aim to 
analyze the image in depth in order to extract additional 
information. For instance, if we can realize that there are many 
obstacles in the whole image, a more accurate prediction could 
be necessary; therefore we need to increase the number of 
particles and to add more modules. In contrast, if we are able to 
detect that the target is moving quickly, the real time 
requirements will be more critical so we have to split the 
particle filter into more modules to accelerate its execution. 
 
REFERENCES 
[1] Orchard, M.E.; Vachtsevanos, G.J., "A Particle Filtering-based 
Framework for Real-Time Fault Diagnosis and Failure Prognosis in a 
Turbine Engine" Mediterranean Conference on Control & Automation, 
2007. MED '07, pp.1,6, 27-29 June 2007. 
[2] Saha, B.; Celaya, J.R.; Wysocki, P.F.; Goebel, K.F., "Towards 
Prognostics for Electronics Components" Aerospace conference, 2009 
IEEE, pp.1,7, 7-14 March 2009. 
[3] Yaowen Guan; Xiaoou Chen; Deshun Yang; Yuqian Wu, "Multi-person 
Tracking-by-Detection with Local Particle Filtering and Global 
Occlusion Handling," 2014 IEEE International Conference on 
Multimedia and Expo (ICME), pp.1,6, 14-18 July 2014. 
[4] Bolic, Miodrag; Djuric, P.M.; Sangjin Hong, "Resampling Algorithms 
and Architectures for Distributed Particle Filters," IEEE Transactions on 
Signal Processing, vol.53, no.7, pp.2442,2450, July 2005. 
[5] Yi Qiao Zhang; Sathyan, T.; Hedley, M.; Leong, P.H.W.; Pasha, A., 
"Hardware Efficient Parallel Particle Filter for Tracking in Wireless 
Networks" 2012 IEEE 23rd International Symposium on Personal 
Indoor and Mobile Radio Communications (PIMRC), pp.1734,1739, 9-
12 Sept. 2012. 
[6] Lifeng Miao; Zhang, J.J.; Chakrabarti, C.; Papandreou-Suppappola, A., 
"A new parallel implementation for particle filters and its application to 
adaptive waveform design," 2010 IEEE Workshop on Signal Processing 
Systems (SIPS), pp.19,24, 6-8 Oct. 2010. 
 
 
 
 


Towards an adaptive hardware parallel particle filter

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Towards an adaptive hardware parallel particle filter

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