Mitigation of GPS L2 signal in the HI observation based on NLMS algorithm

In the radio observation based on the large single antenna, in order to reduce the interference of navigation signal L2 from observation of the red-shifted HI spectral line at L-band, we used the an auxiliary channel and normalized LMS algorithm, and proposed new evaluating indicators deduced by theoretical method. Finally, we verified the suppression performance and the indicators’ utility by multiple simulations and statistical method. Introduction With the rapid development of the radio astronomy, the technology of the large radio antennas is improving. However, the radio frequency interference (RFI) significantly limit the research of the radio astronomy in China. Usually, radio astronomy signal is very weak and almost the receipt power spectral flux density by radio signal source is less than 1Jy (1Jansky=10W/m/Hz). So that the radio signals is susceptible to RFI. In addition, as radio telescopes are high-sensitivity, high-resolution and wide range of operating frequency, they are sensitive to weak interference, such as navigation signal. Even though the minimum level of navigation signals just approximately -160dB [1], it still has a huge impact on radio astronomy observations. The observation of HI is an important issue in radio astronomy observation. As HI is one of main components of the interstellar mediums and distribute throughout the galaxy, the study for HI will contribute to galaxy kinematic [2]. The HI without red-shift is usually called 21cm spectral line and its center frequency is 1420.406MHz. However, the frequency range of in actual observation is from 400MHz to 1400MHz or even wider due to the red-shift which caused by cosmic expansion [3]. Moreover, navigation signals almost distribute on L-band as well, such as L2 (1227.06 MHz), L3 (1381.05MHz) and so on, which observation frequency overlaps red-shift HI. Therefore, we need an effective anti-interference algorithm to eliminate the interference and obtain the useful HI. Usually, the methods to eliminate the interference by navigation signals are classified into two pieces. One is only using the statistical data of radio observation, including threshold processing in time or frequency domain and the model reconstruction method. The core idea of threshold processing is that we can drop a part of data which is obvious over the variance or other eigenvalues. It is easy and convenience for auto-processing, but it has an influence on the integrity of the radio signal [4, 5]. The model reconstruction method is using priori information of navigation signals to model the interference signal, then we estimate the interference by navigation signals to eliminate it [6]. This simulation method can often get obvious effect on anti-interference, even reaching to 20dB, but it need a large number prior information to model, which can hardly apply on reality. The second one is adaptive noise cancelling (ANC) [7, 8] method, which is using the correlation of main antenna and auxiliary antenna to eliminate the interference. The method performs very well on kinds of interferences, however, we cannot research the performance of ANC on radio observation as the limitation of evaluation criteria. To solve those problems proposed before, this paper put forward some evaluation criteria for performance of anti-interference in simulating HI and L2 signal, and this simulation is based on the 25-meter cassegrain antenna in Xinjiang Observatory, and in consideration of the free space propagation loss, atmospheric loss, different observing angles at the same time [9]. These criteria are radio signal attenuation, Gs and interference suppression, GI. This paper apply 2nd International Conference on Electrical, Computer Engineering and Electronics (ICECEE 2015) © 2015. The authors Published by Atlantis Press 1025 single-auxiliary-antenna ANC to eliminate the interference from signals of main antenna. On the one hand, we verify and study anti-interference effectiveness of the method that applying the LMS algorithm in radio observation by those criteria we proposed before. On the other hand, we verify the effectiveness of the criteria itself. Basic principle Table 1. The power of each part of received signals Signal (dBW) Antenna HI L2 Noise Main antenna ( ) r n 91.89 53.32 44.39 Auxiliary antenna ( ) aux r n 159.86 78.49 88.38 From table 1, there are the power comparison of the main and auxiliary antenna through the processes which amplify, mixing and so on. The navigation signal L2 will interfere the radio signal HI observation in the 25-meter antenna, according to the table, the interference power is more than the HI’s, but less than the noise’s in the main antenna, as result of the low interference-to-noise ratio (INR), the LMS algorithm show the poor performance. Therefore when navigation satellite pass the main-lobe, we can obtain a strong L2. The auxiliary antenna can improve the received power of L2 by rising the antenna aperture, and the data in the table is the result of 5-meter auxiliary antenna aperture. The power of L2 is more than the noise’s in auxiliary antenna, and the power ratio of L2 to HI reaches 10, therefore the HI in auxiliary antenna can be ignored. Algorithm Signal model. From the above analysis, it can be seen that the received signal r(n) of the main antenna is the sum of HI from main lobes, L2 from the third side lobes (the observing angle is 2 ) and the Gaussian noise: ( ) ( ) ( ) ( ) r n s n i n N n    (1) ( ) s n , ( ) i n are respectively HI signal and L2 signal, ( ) N n is Gaussian noise, all of them are obtained from the main antenna receiver IF amplifier. Similarly, the ( ) aux r n from auxiliary antenna is as follows: ( ) ( ) ( ) aux aux aux r n i n N n   (2) where, ( ) aux i n and ( ) aux N n express L2 and the Gaussian noise from the auxiliary antenna. Defines the HI in the computer simulation environment as Gaussian white noise [10]. Algorithm. For the correlation between ( ) i n and ( ) aux i n in time domain, we can use the adaptive algorithms to eliminate the L2 in main antenna. There are least squares /recursive least squares (LS/RLS) and least mean square (LMS) algorithm. Compare to the former algorithms, the latter has a lower convergence rate, but a simpler structure, a smaller amount of computation, a more stable performance and easily implemented. Due to the extremely weak power of signal in main and auxiliary antenna, a very large step factor (about 10 orders of magnitude) in LMS algorithm is required in order to make the weight vector convergence. The normalized LMS (NLMS) is used to figure it out, and increased the convergence speed and stability, the procedure is divided into two steps, and details are as follows: