## 1 Introduction
In response to possible \(C\)/\(N_{0}\) mutations, a \(C\)/\(N_{0}\) estimation method that is based on an amplitude Kalman filter is typically used by vector receivers [4; 11]. To track rapid changes in the signal amplitude, the ratio of the signal power to the noise power density is monitored [4]. When the signal changes beyond a preset threshold, the covariance matrix of the filter is adjusted. This approach has adaptive processing capability and can track the rapid change of \(C\)/\(N_{0}\). However, the thresholds for detecting signal changes are a predetermined set of fixed values. The thresholds differ among working environments; hence, this method cannot adapt to a wide range of changes in the receiver operating environment. In addition, in a weak signal environment, the amplitude Kalman filter has difficulty accurately determining the measurement noise. Based on this estimation method, an adaptive \(C\)/\(N_{0}\) estimation method is presented in [6]. The signal \(C\)/\(N_{0}\) is monitored in real time via statistical mutation in a specified time window. The \(C\)/\(N_{0}\) estimation algorithm adaptively switches between the differential method and the strong tracking Kalman filter (STKF) estimator. By utilizing the strong tracking filter's ability to track sudden changes in state, the main drawback of adjusting the covariance matrix according to a preset threshold is avoided. However, the time window statistic for state mutation detection also has a time delay problem, and the measurement noise estimation problem at low \(C\)/\(N_{0}\) is not considered. In this study, we focus on the timely and accurate estimation of \(C\)/\(N_{0}\) and the measurement noise statistics in a weak signal environment. This paper proposes a \(C\)/\(N_{0}\) estimation method that is based on the strong tracking Kalman filter [12] and the Allan variance-based measurement noise estimation method. The proposed \(C\)/\(N_{0}\) estimator avoids the estimation delay that is caused by signal mutation detection and algorithm switching selection. Facilitated by the adaptive estimation of the measurement noise, the estimation accuracy in the weak signal environment is guaranteed. Based on the software receiver, vector-tracking loops and an adaptive \(C\)/\(N_{0}\) estimation algorithm are implemented. To evaluate the estimation accuracy and the performance of the algorithm, simulation tests and a static field test were conducted. The adaptive Kalman filter estimator and two popular algorithms (the NWPR method and the VSM method) were selected as references for comparative analysis. The experimental resultsdemonstrate that the C/N\({}_{0}\) estimation method that is based on the adaptive strong tracking Kalman filter (ASTKF) has strong advantages in terms of real-time performance and estimation accuracy. The remainder of this paper is organized in the following manner. The second section describes the basic principles of the vector receiver and the mathematical model that are used in this paper. The algorithmic details of the adaptive C/N\({}_{0}\) estimation method are presented in the third section. In the fourth and fifth sections, the proposed method is evaluated via simulation experiments and a static field test. The conclusions of this study are presented in the final section.