Advancement In Reduction Of Error In Radar System In Submarines

The continuous evolution of radar technology has significantly contributed to reducing errors in radar systems employed in submarines. Through ongoing research and development efforts, engineers have implemented sophisticated algorithms and advanced signal processing techniques to enhance the accuracy and reliability of submarine radar systems. These advancements include the integration of adaptive beamforming algorithms, such as minimum variance distortionless response (MVDR) and space-time adaptive processing (STAP), which enable the suppression of interference and clutter while improving target detection in complex underwater environments. Additionally, the adoption of advanced error correction codes and improved calibration methods has played a crucial role in mitigating errors caused by environmental factors and system imperfections. Moreover, the integration of artificial intelligence (AI) and machine learning (ML) algorithms has enabled real-time data analysis and predictive maintenance, leading to proactive error detection and mitigation strategies. These collective advancements in radar technology have significantly enhanced the accuracy, sensitivity, and operational efficiency of radar systems in submarines, ensuring heightened situational awareness and mission success in challenging maritime scenarios.

ABSTRACT

Radar signal error performance was discussed in the presence of atmospheric refraction and clutter attenuation. The work exploited prior information on atmospheric refraction properties and conditions such as partial pressure, water vapour, atmospheric temperature and the associated clutter. The atmospheric properties and characteristics were used to model random and bias errors experienced in radar systems. Errors which were associated with azimuth, elevation and target velocity were considered in the performance analysis. Range resolution and Doppler resolution were key mechanisms which were implemented in the analysis of the radar signal error performance. The radar error performance was analysed using residual error, signal-to-clutter + noise ratio and thermal noise error. Errors from azimuth, elevation and target velocity were combined in investigating the total effect of errors in determining the desired signal-to-clutter + noise ratio. The study enhances target detection and tracking towards optimizing the navigation system of autonomous and semi-autonomous robotic systems using radars.

TABLE OF CONTENTS

COVER PAGE

TITLE PAGE

APPROVAL PAGE

DEDICATION

ACKNOWLEDGEMENT

ABSTRACT

CHAPTER ONE

1.0    INTRODUCTION

  • BACKGROUND OF THE PROJECT
  • PROBLEM STATEMENT
  • AIM OF THE STUDY
  • PURPOSE OF THE STUDY
  • DEFINITION OF SUBMARINE
  • RADARS ACCURACY
  • HOW IS A MEASUREMENT PERFORMED?
  • TARGET DETECTION, UNCERTAINTY AND PERFORMANCE
  • RADAR ERROR CLASSES
  • CONCLUSION

REFERENCES

 CHAPTER ONE

1.0                                          INTRODUCTION

1.1                            BACKGROUND OF THE STUDY

Radar signals are used in high-gain command-able and agile systems such as autonomous system for target detection and tracking (Chen et al., 2014). Radar systems use scanned arrays and multiple-input multiple-outputs models to increase the flexibility in the modes of operation and application (Frankford et al., 2014). Illumination of the environment with radar signals provides critical information on the energy scattered by detectable targets (Dilum Bandara et al., 2012). Scattered energy from targets and radial velocity of targets provide differentiable modes of target position and motion. Modulation of radar signals ensures accurate range determination (Hayvaci et al., 2013). Radar range and antenna characteristics provide critical information in the determination of azimuth and elevation angles of the radar system.

Resolving ambiguities associated with Doppler frequency determination ensures that targets are detected across various frequencies. The influence of detection densities allows for diversification in the detection strategies used in radar systems (Sharma et al., 2014) (Radmard et al., 2014). Timings in radar signal are influenced by the coherent and incoherent characteristics of oscillators in the radar systems. The phase reference of radar signals are hence dependent on the characteristics of the oscillators (Eustice et al., 2015) (Fellows et al., 2013).

The performance of radar system is subject to external factors such as atmospheric refraction (Panchenko et al., 2012) (Renkwitz et al., 2014) and clutter attenuation (Agarwal, et al., 2014) (Marquis, 2010). Scattering models are used to comprehend the nature and behaviour of the radar signal frequency energy distribution. Radar signals experience refraction in the elevation to and from the radar.

Splaying of radar signals in the elevation plane is also another factor that occurs when radar signals are refracted. Energy absorbed by the atmosphere from the signals also affect the performance of the radar system.

The navigational systems for autonomous and semi-autonomous systems use radars for target detection and tracking. Mobile robot obstacle detection and avoidance are critical in fulfilling their navigational objectives.

 

1.2                                   PROBLEM STATEMENT

Submarine navigation underwater requires special skills and technologies not needed by surface ships. The challenges of underwater navigation have become more important as submarines spend more time underwater, travelling greater distances and at higher speed. Military submarines travel underwater in an environment of total darkness with neither windows nor lights. Operating in stealth mode, they cannot use their active sonar systems to ping ahead for underwater hazards such as undersea mountains, drilling rigs or other submarines. Surfacing to obtain navigational fixes is precluded by pervasive anti-submarine warfare detection systems such as radar and satellite surveillance. Antenna masts and antenna-equipped periscopes can be raised to obtain navigational signals but in areas of heavy surveillance, only for a few seconds or minutes: current radar technology can detect even a slender periscope while submarine shadows may be plainly visible from the air. Error reduction techniques are required to save sailors from misfortunes and hazards.

1.3                                       AIM OF THE STUDY

The work discusses the performance error of radar systems under atmospheric refraction and clutter attenuation. Error detection mechanisms were used in the error analysis of radar signals. The results discussed in the paper are applicable in optimising the navigation systems of autonomous and semi-autonomous robotic systems.

1.4                                  PURPOSE OF THE STUDY

The purpose of advancement in reduction of error in radar system in submarines is to ensure accurate range determination.

1.5                                            DEFINITION OF SUBMARINE

A submarine is a watercraft capable of independent operation underwater. It differs from a submersible, which has more limited underwater capability. It is also sometimes used historically or colloquially to refer to remotely operated vehicles and robots, as well as medium-sized or smaller vessels, such as the midget submarine and the wet sub. Submarines are referred to as “boats” rather than “ships” irrespective of their size.

1.6                                                  RADARS ACCURACY

Accuracy is the degree of conformance between the estimated or measured position and/or the velocity of a platform at a given time and its true position or velocity. Radio navigation performance accuracy is usually presented as a statistical measure of system error and is specified as:

  1. Predictable: The accuracy of a position in relation to the geographic or geodetic coordinates of the earth.
  2. Repeatable: The accuracy in which a user can return to a position whose coordinates has been measured at a previous time with the same navigation system.
  3. Relative: The accuracy which a user can determine one position relative to another (by neglecting all possible errors).

The stated value of required accuracy represents the uncertainty of the reported value with respect to the true value and indicates the interval in which the true value lies with a stated probability. The recommended probability level is 95 percent, which corresponds to 2 standard deviations of the mean for a normal (Gaussian) distribution of the variable. The assumption that all known correction is taken into account implies that the errors in the reported values will have a mean value (or bias) close to zero.

Any residual bias should be small compared with the stated accuracy requirement. The true value is that value which, under operational conditions, characterizes perfectly the variable to be measured/observed over the representative time, area and/or volume interval required, taking into account siting and exposure.

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