A new inhibition side peak acquisition (ISPA) algorithm is proposed for binary offset carrier (BOC) modulated signals, which will be utilized in global navigation satellite systems (GNSS). We eliminate all side peaks of the BOC correlation function (CF) by structuring special sequences composed of PRN code and cycle rectangular sequences. The new algorithm can be applied to both generic sine- and cosine-phased BOC signals, as well as to all modulation orders. Theoretical and simulation results demonstrate that the new algorithm can completely eliminate the ambiguity threat in the acquisition process, and it can adapt to lower SNR. In addition, this algorithm is better than the traditional algorithms in acquisition performance and inhibition side peak ability.

With the development and application of global navigation satellite systems (GNSS) [

BOC modulation signal acquisition techniques focus on recovering the main correlation peak or eliminating ambiguities in the form of side peaks. At present, various techniques are proposed for side peak cancellation and are built on the basis of the correlation function (CF) of the BOC signals. Thus, the side band processing method originated from BPSK-like method [

In this paper, considering filter restriction and generic deficiency problems in traditional algorithms, we propose an inhibition side peak acquisition algorithm, which is applicable to all orders and to both generic sine- and cosine-phased BOC signals.

BOC modulation signal is obtained by the product of PRN code and the square wave. The complex form of the BOC signal is expressed as

The BOC signal is usually expressed as

From the perspective of algorithm generality, the acquisition algorithm for BOC modulation signal is usually divided into three categories, namely, the full band acquisition (FBA) algorithm [

The full band acquisition algorithm principle.

The peak optimization acquisition algorithm principle.

The single peak recovery acquisition algorithm principle.

FBA is a class of traditional algorithms, in which the correlation arithmetic is executed between the received signal and the original PRN code modulated by a square wave. POA is a class of improved algorithms, in which multiple correlations are executed to improve the main peak. SPRA is a class of new methods, in which a partial signal is separated from the received signal by the corresponding operations to inhibit the square wave.

Let

Considering the represented model of the BOC base-band signal, the local rectangular sequence model is structured to inhibit the acquisition of side peaks. The structured process is shown in Figure

The structured process of the rectangular sequence model.

The original PRN code is, respectively, multiplied by the two-channel cycle rectangular sequences to structure the two new local channel sequences, which are expressed as

The beforehand processing received signal is executed by the correlation circumferential arithmetic with

When

The correlation result of BOC.

In view of these characteristics and combining (

Thus, the new correlation function is structured to eliminate side peaks, and the processing is expressed as

When the impacts of the frequency error and noise function are likely to be relatively weak, the relationship of the main peak value

To improve the peak, the result of

The

At the same time, the structured square function can be expressed as

Hence,

Where

Thus, the

The false alarm probability of the ISPA algorithm is expressed as

The acquisition detection probability of the ISPA algorithm is expressed as

Equations (

The ISPA result for

The ISPA result for

The ISPA result for

The ISPA result for

The ISPA result for

The new algorithm result is influenced by the frequency error and the mixed noise, according to (

Furthermore, the ISPA algorithm’s adaptability is simulated with the following parameters: 15.345 MHz square wave frequency and 122.76 MHz sampling frequency, modulation mode is sine mode, and modulation orders are 15, 10, 6, and 3, respectively.

The relationship between the relative main peak and frequency error is shown in Figure

The relationship between the relative main peak and frequency error.

The relationship between the relative main peak and SNR.

To verify the superiority of the ISPA algorithm, this ISPA algorithm is compared with other algorithms, namely, the FBA algorithm, POA algorithm, and SPRA algorithm. The simulation parameters are as follows: 2.046 MHz PRN code frequency, and the modulation mode is sine mode.

With changing modulation order, the main peak width changes and the main peak relative changes are shown in Figures

The relationship between the main peak width and the modulation order.

The relationship between the relative main peak and the modulation order.

The side peak relative changes and the main/side peak ratio changes with changing modulation order are shown in Figures

The relationship between the relative side peak and the modulation order.

The relationship between the main/side peak ratio and the modulation order.

The main peak relative changes with changing SNR are shown in Figure

The relationship between the relative main peak changes and SNR.

In this paper, the principle and characteristics of BOC modulation signals have been studied. To implement the BOC modulated signal acquisition, effective algorithms have been studied, including the full band acquisition (FBA) algorithm, the peak optimization acquisition (POA) algorithm, and the single peak recovery acquisition (SPRA) algorithm. Considering the filter restriction and generic deficiency problems in traditional algorithms, we propose the ISPA algorithm. We eliminate all side peaks of the BOC correlation function (CF) by structuring special sequences composed of PRN code and cycle rectangular sequences. The ISPA algorithm can be applied to both generic sine- and cosine-phased BOC signals and to all modulation orders. In addition, it outperforms the traditional algorithms in acquisition, inhibition side peak ability, and adaptability to lower SNR.

The authors declare that there is no conflict of interests regarding the publication of this paper.

This work was supported by the Program for Liaoning Innovative Research Team in University (no. LT2011005), New Century Program for Excellent Talents of Ministry of Education of China (no. NCET-11-1013), Project of Science and Technology Department of Liaoning Province (no. 20121038), Project of Education Department of Liaoning Province (no. L2013085), and the Open Foundation of Key Laboratory of Shenyang Ligong University.