Mechanization and Error Analysis of Aiding Systems in Civilian and Military Vehicle Navigation Using Matlab Software

In present scenario GPS is widely used to provide extremely accurate position information for navigation. From, where the GPS does not give continuous localization in environments where signal blockages are present, Inertial Navigation System comes into action. Because of sensors present in INS and time integration process, errors get accumulated over time. Henceforth, an aiding system is integrated with INS. The aim of this paper is to model VMS and RADAR and aid it with INS in order to overcome its errors. VMS is aided to INS to achieve acceptable accuracy and ease of implementation, much needed in civilian navigation. Different trajectories are generated to offer solutions in a practical scenario. Whereas, for highly accurate positioning in military navigation a reliable aiding system, Radar has been opted. The Kalman filter is designed and modeled as the integrating element in INS/RADAR, to provide an optimal estimate of navigation solutions. An error analysis has been done for both INS aided VMS and INS aided Radar systems. The navigation performance of VMS and Radar aiding system is compared and their merits have been brought out. We besides give the readers a more honest insight of the demand for an aiding system in different environments based on various simulation results. 1.INTRODUCTION Inertial Navigation System (INS) is a self sustained dead-reckoning navigation system, which is used to determine attitude, velocity, and position of a moving vehicle from the prior knowledge of the states and measurements of the land vehicle’s motion. It is an navigating aid that uses motion sensors (accelerometers) and rotation sensors (gyroscopes) with gravity computer to compute the location and orientation values respectively. Pure inertial navigation system errors grow over time. To control the error growth some form of external aiding is necessary. Altimeter, Vehicle Motion Sensor (VMS),Radar and Global Navigation Satellite Systems (GNSS) can be used as aiding sources. Civilian vehicle navigation has very rigid need in context of size, cost, reliability and ease of implementation of the integrated system. Vehicle Motion Sensor (VMS) aids the Inertial navigation system, which performs satisfactorily for these attributes with acceptable accuracy. The primary use of VMS is to Convert vehicle's odometer cable shaft revolutions into pulse trains representing forward and reverse vehicle motion. Kinematic model which describes the motion of the vehicle with constant velocity input is used to generate the trajectory. This paper showcases different trajectories (such as 'linear', 'S' and '8') which can be generated by varying acceleration components in x, y, z direction. International Journal of Control Theory and Computer Modeling (IJCTCM) Vol.5, No.1, January 2015 16 For terrestrial defense applications, particularly for combat vehicles, navigation information is mandatory for completing the mission and operational effectiveness. Attributes such as accurate positioning, high data rates, reliability are accomplished by an aiding system called Radar. Radar measures range, direction and speed of objects by transmitting and receiving radio signals. Kalman filter is a recursive data processing algorithm, which is used as the binding element for INS/Radar. Hence, Radar aiding outperforms other aids in military navigation. Error analysis provides us the clear view of position and velocity errors in both the aiding systems. It likewise helps us understand the divergence of the estimated values of the true values. The remainder of this paper is organized as follows. Initially this paper provides an overview of application oriented stand-alone INS, INS aided with VMS and Radar system. While in second, a brief description about the integration of INS/VMS for different trajectories in civilian navigation has been talked about. In third Radar aiding for defense application with KalmanEstimator has been elaborated. Finally, error analysis is done and its results are used to explain various characteristics of the two aiding systems depending upon various attributes. 2.RELATED WORK This section provides an up-to-date survey of most major contributions to the pool of INS and its integrated systems. It also includes a thorough overview of the limitations of standalone INS to that of its integrated system, and ways to overcome it. In an urban environment, automotive applications cannot rely on GPS based geolocalization only, since satellite signal outages are frequent. Hence, inertial navigation system is adopted. But, still INS also have internal accumulated errors (sensor errors) which makes it less efficient. Thus, to overcome rapid navigation error drift, a self-retained dynamic-assisted error rectification method can be used in the absence of aiding sensors. Fuzzy logic is one of the vehicle dynamic identification system which is highly recommendable. Through an experiment it has been found out that this method is extremely suited for ground vehicle navigation, where severe GPS outages, frequent vehicle halt and turning dynamics subsist. An efficient method to improve the positioning accuracy and reliability is the multi-sensor integrated positioning system. Vehicle Motion Sensor is used as an integrating aid to standalone INS, which improves the error performance and independent ability of the system in civilian applications. Alignment of the vehicle body frame (VBF) with respect to INS body frame plays a crucial part in INS mechanization. Misalignment between the frames incurs serious errors in the integration of INS/VMS. The operation of VMS requires to the point scale factor to ensure navigation accuracy. Scale factor of VMS is affected by skidding, temperature, inflation and abrasion of vehicle tires. Strong tracking Kalman filter is employed to overcome the in-motion alignment errors of SINS/VMS integration system. The ability of RADAR to estimate the vehicle’s location with a high accuracy which, makes it a very good aiding part to INS in military application. Radar can be abbreviated as Radio Detection and Ranging, which can also be used for civilian purposes. It records and processes signal International Journal of Control Theory and Computer Modeling (IJCTCM) Vol.5, No.1, January 2015 17 information, which uses radio-frequencies to locate and track vehicles. And the integration of INS/RADAR is achieved by using a non-linear Kalman filter. In the open loop system, INS is the primary source of data, and RADAR provides discrete aiding data to support the ideas. In case of abrupt faults, the robust Kalman filter algorithm is suggested to overcome the malfunctioning of INS/RADAR open loop system. From the above discussion, some important conclusions have been made.They are summarized as follows. • Stand-alone INS provides satisfactorily navigation results during GPS outages. • VMS aiding is highly beneficial for civilian vehicles because of ease of implementation. • However, VMS as an aiding system cannot be implemented in military applications, because of its high sensitivity to varying environments. • Radar aiding is aptly suited for a robust environment without compromising on accuracy. • In the context of civilian users, implementation complexity of Radar prohibits them from its usage. Moreover, acceptable accuracy is required in contrast of defense applications. 3.AID FOR CIVILIAN NAVIGATION A. Stand-Alone INS The inertial navigation system is a selfcontained within the vehicle, does not depend on the transmission of signals from the vehicle or reception from the external source. Sensors (Accelerometers and Gyroscopes) present in the INS are used to determine the acceleration and orientation of the vehicle in which they are installed. Accelerometer basically senses specific force (total acceleration acceleration due to gravity) along three mutually perpendicular sensitive axes. The direction cosine matrix (DCM) for this transformation is given as, Here, c and s represents cosine and sine respectively. Generally gyroscope senses earth rate, transport rate, and body rate of the vehicle. The turn rate of the earth is assumed to be constant (15.041067 deg/hr). International Journal of Control Theory and Computer Modeling (IJCTCM) Vol.5, No.1, January 2015