Acquisition of DBOC Signals in a Multicomponent LFM Interference and Dynamic Environment

‍ ‍To address the challenge of the lack of acquisition algorithms for dual binary offset carrier （DBOC） signals in multicomponent linear frequency modulation （LFM） interference and dynamic environment， this study proposes an interference suppression algorithm using blackman window preprocessing combined with fractional Fourier transform （FRFT） and a capture algorithm using discrete polynomial phase transform （DPT）， partial matching filter （PMF） combined with fast Fourier transform （FFT）， and spectral correction. First， this algorithm performs blackman window preprocessing on the received signal to suppress the output sidelobes of interference spectral lines and improve the accuracy of interference convergence. Subsequently， a hierarchical algorithm is used to quickly search for the optimal order of the signal， reducing computational complexity while ensuring accuracy. Finally， FRFT is performed at the corresponding optimal order， combined with interference judgment and polling algorithm， to suppress multicomponent LFM interference one at a time. The dynamic order of the signal after interference suppression is first determined using a fixed-order algorithm. Thereafter， the dynamic signal is reduced in order through DPT to remove the dynamic term. Finally， the PMF-FFT and spectral correction are used to capture the DBOC signal. The simulation results showed that the proposed algorithm can accurately and completely suppress three LFM interference components one at a time， and the suppressed interference components cannot affect the suppression of other interference components. When the detection probability reaches 1， the signal-to-noise ratio of the algorithm after blackman window preprocessing increased by 4 and 6.4 dB compared with the algorithm after hamming window and Kaiser window preprocessing， respectively， and by 7.2 dB compared with the algorithm without preprocessing. In addition， the use of spectrum correction highlights the capture peak and improves capture accuracy. Compared with the algorithm without spectral correction， the signal-to-noise ratio of the algorithm after spectral correction increases by 1.6 dB and reduces a certain amount of capture time.