Journal of Electrical and Computer Engineering

Volume 2018 (2018), Article ID 2436472, 10 pages

https://doi.org/10.1155/2018/2436472

## A Detection Algorithm for the BOC Signal Based on Quadrature Channel Correlation

School of Information Science and Engineering, Shenyang Ligong University, Shenyang 110159, China

Correspondence should be addressed to Yongxin Feng; ten.362@nixgnoygnef

Received 31 October 2017; Revised 11 February 2018; Accepted 22 February 2018; Published 1 April 2018

Academic Editor: Iickho Song

Copyright © 2018 Bo Qian et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

In order to solve the problem of detecting a BOC signal, which uses a long-period pseudo random sequence, an algorithm is presented based on quadrature channel correlation. The quadrature channel correlation method eliminates the autocorrelation component of the carrier wave, allowing for the extraction of the absolute autocorrelation peaks of the BOC sequence. If the same lag difference and height difference exist for the adjacent peaks, the BOC signal can be detected effectively using a statistical analysis of the multiple autocorrelation peaks. The simulation results show that the interference of the carrier wave component is eliminated and the autocorrelation peaks of the BOC sequence are obtained effectively without demodulation. The BOC signal can be detected effectively when the SNR is greater than −12 dB. The detection ability can be improved further by increasing the number of sampling points. The higher the ratio of the square wave subcarrier speed to the pseudo random sequence speed is, the greater the detection ability is with a lower SNR. The algorithm presented in this paper is superior to the algorithm based on the spectral correlation.

#### 1. Introduction

The Binary Offset Carrier (BOC) signal is used in the global navigation satellite system (GNSS) and is characterized by multiple peaks in its autocorrelation function and spectrum splitting [1–3]. By using a square wave to modulate again, the synchronization precision of the BOC signal is improved and the interference of the same-frequency signals is decreased [4]. On the other hand, there are multiple side-peaks around the main peak of autocorrelation function of the BOC sequence, thus causing the ambiguity problem. To deal with the problem, several unambiguous techniques have been proposed [5, 6]. A novel cancellation technique of correlation side-peaks is proposed, by employing a combination of the subcorrelations making up the BOC autocorrelation [6].

The pseudo random sequence of the BOC signal has the characteristics of pseudo randomness and infinite periods in a short time, which is used in secret communications. Therefore, it is difficult to detect a BOC signal under noncooperative conditions. In addition, by utilizing the direct sequence spread spectrum (DSSS), the BOC signal can be transmitted under a negative signal to noise ratio (SNR) and because the anti-interception ability is strong, it is more difficult to detect the signal.

To date, new methods of BOC signal recognition and parameter estimation have been proposed [7–11]. The detection methods are based on spectral correlation [7–9] and the methods for parameter estimation are based on autocorrelation [10, 11]. The basis of the spectral correlation methods is based on the cyclostationary characteristic of the BOC signal, so that the parameters of the carrier, square wave, and pseudo random sequence can be estimated. However, when the pseudo random sequence has an infinite period in a short time, the methods based on spectral correlation cannot work effectively.

The autocorrelation methods are based on the characteristics of the multiple autocorrelation peaks of the BOC signal. Based on demodulating the BOC signal, the parameters can be estimated effectively based on how the BOC signal correlates with the multiple autocorrelation peaks. Considering that the BOC signal is transmitted under a negative SNR in secret communications, demodulation is not easily achieved; therefore, it is difficult to estimate the parameters in a real-life environment.

In this paper, an algorithm for detecting the BOC signal is presented, using a long-period pseudo random sequence. The autocorrelation component of the carrier wave in the BOC signal is eliminated based on quadrature channel correlation. By detecting the autocorrelation peaks, the BOC signal can be detected.

The outline of this paper is as follows. In Section 2, we study the characteristics of the BOC signal. Section 3 describes the analysis of the characteristics of the multiple autocorrelation peaks for the BOC signal and the algorithm for detecting the BOC signal. Section 4 provides simulation results demonstrating the performance of the algorithm. Finally, Section 5 presents our conclusions and final comments.

#### 2. Characteristics of the BOC Signal

The BOC signal , modulated by BPSK, is given bywhere is the carrier amplitude, is the baseband data, is the pseudo random sequence, is the square wave, is the carrier frequency, and *φ* is the phase. The frequency of is , and the frequency of is .

Firstly, the spread spectrum sequence is obtained by XOR baseband data with the pseudo random sequence. Then, the spread spectrum sequence is XORed again with a square wave to generate the BOC sequence. Finally, the BOC signal is generated by modulating the BOC sequence to the main carrier. The BOC signal is denoted as BOC (, ), where means the ratio of to the reference frequency , and means the ratio of to the reference frequency . In GNSS systems, the reference frequency .023 MHz.

The normalized power spectral density (PSD) of the BOC signal can be expressed as [12] where

The distribution of the normalized power spectral density for the BOC signals is shown in Figure 1, where DS is the normalized power spectral density of the DSSS signals, in which the frequency of the pseudo random sequence is ten times as much as .