Polyphase Sequences with Good Correlation Properties and Merit Factor Based on Cyclic Algorithm Approach

Polyphase Sequences with good autocorrelation properties, such as Pn {n=1,2,3,4,x}, Frank, Golomb, and the Chu, find many applications in RADAR, SONAR, and communication. Merit Factor (MF), ISL (Integrated Sidelobe Level) are performance measures used to evaluate the goodness of any sequence. This work uses a cyclic algorithm approach to generate Polyphase sequences with lengths ranging from 10^2 to 10^3 . The merit factor and correlation features of these cyclic algorithm techniques outperform the standard scenario. The average merit factor for lengths of 100 and 1000 was found to be 40.39 and 92.02, respectively. The correlation graphs of polyphase sequences using the cyclic technique are compared to the normal case.P2 sequences with a higher merit factor for odd integer square length were made achievable using this method. The merit factor values and correlation plots of four successive even and odd integer squared length sequences were compared.For these Polyphase sequences, a cyclic algorithmic approach for collecting design metrics has been implemented in MATLAB.