# An Efficient Ultra-Tight GPS/RISS Integrated System for Challenging Navigation Environments
## 1 Introduction  
GPS provides navigational solutions with good accuracy in open sky environments. One vital application that uses this service is land vehicles, in which navigation systems are embedded in passenger cars, farming vehicles, police cars, fire trucks, and others. These applications require reliable navigations systems to know their locations within a few meters. The problem with GPS is that under certain conditions, such as signal interference and jamming, GPS signals cannot be easily--or even be available to be--tracked, since they could be consistently interrupted or totally blocked [1; 2; 3; 4; 5]. This often leads to loss of signal lock, and hence, requires re-acquiring the signal for the corresponding satellite, a process that intensively consumes the receiver resources and might affect the navigation performance. To avoid such intensive computations, a GPS receiver needs to employ a signal tracking mechanism that could hold and/or re-lock on the signals and keep track of their dynamic changes and errors. Moreover, the signal tracking process is the source of the receiver measurements; thus, it has a direct effect on the accuracy of the obtained navigation solution. These measurements could experience inherent errors due to the common error sources affecting the GPS signals such as multi-path and signaljamming. This makes the choosing of a successful tracking method a crucial and more challenging task in GPS receiver design [6]. Conventional receiver designs utilize scalar tracking algorithms, which do not allow the receiver to benefit from the navigation state information to facilitate and enhance the signal tracking process. Figure 1 shows a block diagram for a scalar system tracking N satellites. Pseudoranges and pseudorange rates are separately generated and only processed together in the navigation filter. Thus, no information exchange happens between the different tracking channels in this mechanism. Vector-based tracking, on the other hand, feeds back [7; 8; 9] any available information about receiver position, velocity, and acceleration to the tracking stage. In this way, information exchange between the different channels is possible, allowing improved performance in conditions where there is low signal power or poor satellite availability. Figure 2 gives an overview of the general architecture of vector tracking systems. Vector tracking systems combine all the channels to produce pseudoranges and pseudorange rates and the navigation solution in one filter. The early designs also use Costas tracking loops as their core blocks. State estimation algorithms have been effectively introduced into vector-based tracking systems. At the forefront of these algorithms is the Kalman filter, in a range of versions and configurations. Several implementations employing this filter in vector tracking showed improved performance over scalar tracking systems. Nonetheless, most of the related research recommends further investigation into more efficient algorithms with faster response and less complexity. Supporting vector-based tracking systems with information from external sources can further improve their performance and reliability. INS systems are a popular choice of such a supplementary source. However, augmenting a GPS with an INS that uses a full inertial measurement unit (IMU) comes with the penalty of higher complexity and increased cost. Thus, recent research has been directed towards the use of Micro-Electro-Mechanical Systems (MEMS) grade inertial sensors for low cost navigation applications [6]. Nonetheless, these inexpensive sensors have complex error characteristics; therefore, the latest research is conducted to utilize a smaller number of inertial sensors inside the IMU to obtain the navigation solution [10]. Advantage of this trend is twofold. The first is to avoid the effect of the inertial sensor errors. The second is to reduce the cost of the IMU in general [11]. One such approach known as RISS (Reduced Inertial Sensor System) is discussed in [10; 11; 12; 13].