Report on Autonomous Underwater Vehicles(AUV)

INSTRUMENTATION

Table of Contents
1. Autonomous underwater vehicles(AUV)..................................................................3
2. Partial filters for sensor fusion...............................................................................3
3. Method of Adding Noise to maintain the diversity of population...................................7
4. Code for Gaussian noise....................................................................................... 7
5. Simulated histogram of white noise.........................................................................8
6. Different types of sampling using the particle filter....................................................9
7. Systematic coding for resampling process..............................................................10
8. Effectiveness of a particle filters...........................................................................11
9. Efficiency of the particle filters............................................................................11
10. Kalman filter.................................................................................................... 11
11. Conclusion....................................................................................................... 12

Abstract
In this section we have to estimate the particle filter using matlab.it used to estimate
the function of the autonomous underground vehicles.in this autonomous underground
vehicle is most effectiveness to compare other underground systems.in this autonomous
system used to improve the efficiency of the vehicles. A mat lab function used to estimate the
plot of discrete filters by using this partial filters in mat lab programmes. A particle filters are
most featured in underground systems. The acoustic technologies can be used but, it have a
low bandwidth and limited precisions. The advance system of underground water vehicles is
more features of interest. The launch vessel to improve the reception an autonomous surface
vessel (ASV).The partial filtering is a general method for performing the discrete state-space
models.it used to Evolves in time and information about the state.

1. Autonomous underwater vehicles (AUV)
To allow the close inspection of underwater features of interest, the use of
Autonomous Underwater Vehicles (AUVs) is attractive. Such vehicles can per-form tasks
such as visual inspection (Roelfs em aetal 2015) or water sampling that are much more
difficult to perform from a surface vessel. However, the ability to intervene when a system
failure occurs is limited. In this practical, a simulated AUV has undergone a systems failure
that
prevents it from returning to its launch vessel.
This AUV is neutrally beyond and drifting under the effects of water currents. Enough
power remains that the vehicle can transmit an acoustic signal, a ping, at regular intervals, but
cannot communicate an accurate position estimate. The launch vessel has a receiver that can
estimate the bearing of the ping relative to the vessels heading, however reception is
intermittent and inaccurate. To improve reception, the launch vessel will launch an
Autonomous Surface Vessel (ASV) with similar localisation capabilities on a separate course
2. Partial filters for sensor fusion
The most the communication process and localisation technologies are based on the
uses of the radio spectrum .in this radio spectrum tasks are very complicated to surveying of
the oceans.it having low bandwidth and limited precision. Most communication and
localisation technologies are based on the use of radio.
In a general the discrete time space model,it states evolves according to:
xk = fk(xk-1,vk-1)
where xk is a vector representing the state of a system at the time k,vk-1 is the state the
noise vector value,the fk is a function of time-dependent. The measurement noisy filter zk,
which is governed by the equation:
zk = hk(xk,nk)
To import the data from excel to mat lab, the following waveforms are appear in the
mat lab window. The prac_1.csv file import to the matlab.it generate a function in mat lab.
The prac_1.csv file contain the variables are VarName1, VarName2, VarName3,
VarName4, VarName5 and VarName6. The following waveforms are shown in below.