Authors: L. Anjaneyulu, N.V.S.N. Sarma, N.S. Murthy
Addresses: Department of ECE, National Institute of Technology, Warangal, AP, 506 021, India. ' Department of ECE, National Institute of Technology, Warangal, AP, 506 021, India. ' School of Computer and Communication Engineering, Universiti Malaysia Perlis, P.O. Box 77, Pejabat Pos Besar, 01007 Kangar, Perlis, Malaysia
Abstract: LPI radars use continuous wave, wide bandwidth low power signals of the order of a few watts making its detection difficult. The important advantage of LPI radar is to go undetected, while maintaining a strong battlefield awareness. Common spectral analysis and conventional methods fail to detect emissions of LPI radars and even normal radars in noisy environments. This leads us to use higher order spectral analysis (HOSA) techniques enabling us to extract much more information from the same intercept and hence facilitating detection. This paper reports the results of HOSA techniques (bi-spectrum, bi-coherence and tri-spectrum) and artificial neural networks (ANNs), applied to LPI radar signals. Bi-phase Barker coded signals of different lengths, P1, P2, P3 and P4 polyphase coded signals and Frank signal are analysed using HOSA techniques to produce 2-D signatures of these signals. An artificial neural network (ANN) is trained on these signatures so that it will be able to detect and identify the LPI radar signal whose type is unknown when received. The results obtained clearly indicate the promising capability of these techniques to identify the type of LPI signal even with SNRs as low as −3 dB.
Keywords: low probability of intercept radar; LPI radar; emitter identification; higher order spectral analysis; HOSA; artificial neural networks; ANNs; back-propagation; communications technology; battlefield awareness.
International Journal of Information and Communication Technology, 2009 Vol.2 No.1/2, pp.142 - 155
Published online: 11 Jun 2009 *Full-text access for editors Access for subscribers Purchase this article Comment on this article