## 1 Introduction
Inertial Navigation System (INS) and GNSS integrations are most widely applied because of their complementary properties. The Inertial Navigation System (INS) [9], vision system [10,11], and Wi-Fi [12] all have been take into consideration. Among the above, INS measurements are immune to external interference. They can precisely provide high-rate velocity and attitude, and give an ephemeral positioning by dead reckoning when satellite observation is absent. In turn, the accurate positioning results obtained by RTK can correct inertial errors accumulated over time. In an RTK/INS integrated navigation system, the overall accuracy still depends on the positioning result of RTK; thus, the purpose of the effective integration is to exploit inertial information to increase the search speed and success rate of the AR technique. According to the measurement model, RTK/INS integrated systems can be classified as loosely and tightly coupled structures. In the case of loosely coupled techniques, RTK and INS calculate the position and velocity results independently [13]; thus, they fail to enhance the accuracy and availability by providing additional information for the AR procedure. By contrast, in tightly coupled RTK/INS integrations, using inertial information as an accurate position prediction in an AR procedure can effectively improve the performance of the navigation system when the satellite observation condition is poor. Conventional tightly coupled RTK/INS algorithms generally include two stages in order to ensure that their own precision is identical to the RTK results. The first stage is to use the INS measurements to assist the AR process, and the second stage is determination of whether the AR results are valid. If AR validation failed, the pseudorange measurements are utilized to correct the state variables, including position, velocity, attitude, and the biases of inertial sensors. Otherwise, the carrier-phase measurements with fixed solutions are used as more accurate ranging information to complete the correction instead. There are two different implementations of the conventional tightly coupled RTK/INS algorithms. Both of them use an extended Kalman filter (EKF) to correct state variables and solve integer ambiguity estimation in the ambiguity domain. The first kind of tightly coupled structure is realized by fusing raw pseudorange and carrier-phase measurements as well as INS navigation solutions through an EKF; then, the float ambiguities can be estimated as filter states [14]. Subsequently, based on the information of the filter covariance matrix, a search process such as LAMBDA is performed in the ambiguity domain. The resulting fixed solution is then used to correct the remaining state variables. This implementation exploits the ambiguity constraints between adjacent epochs, making it practicable for consumer-grade receivers to achieve the desired accuracy in open sky conditions. However, adding float ambiguities to filter states also has one obvious drawback. Since the ambiguities at the previous moment affect both current state estimation and the accuracy of the carrier-phase measurement as ranging information, this algorithm is very sensitive to cycle slips in the receiver's phase-locked loop (PLL). If the ambiguity estimation for a certain satellite is biased, then this biased estimation will affect the entire filtering process until the corresponding satellite becomes unavailable. Furthermore, in the case of frequent changes in satellite visibility, ambiguities at each GNSS epoch must be researched and fixed; thus, the benefits brought by the invariance constraint of the ambiguities will no longer exist. In order to free the ambiguities from filter states, the second kind of tightly coupled implementation uses the Least-Squares (LS) estimation to obtain the float solution of ambiguities instead of the EKF, and its next integer search and correction steps are the same as the implementation mentioned above [15].