A sparse optimisation method based on cross‐correlation function for chirp signals

Abstract

This letter proposes a novel time delay estimation method based on sparse optimisation of the cross‐correlation function to improve the estimation accuracy of delay parameters of chirp signals in multipath environments. In this method, the time delay estimation model is converted into a correlation‐function based model to estimate the parameter of exponent signals with frequency information, and the covariance matrix of such correlation function is solved by a sparse iteration optimisation algorithm abbreviated as CCF‐SPICE. Experimental simulations show that the proposed delay estimation algorithm has a sharper peak with less power leakage compared with the existing SPICE algorithm. Its performance is better than the SPICE algorithm and conventional MUSIC algorithm, especially under low SNR in multipath environments. The MSE performance of the proposed CCF‐SPICE algorithm is closer to Cramer–Rao bounds.

A DOA and TOA joint estimation algorithm based on deep transfer learning

“…In the simulations, chirp signal is used as transmission signal $$s(t)$$ [12] and signal parameters are set as shown in Table 1. There are $$K=10$$ array antennas and $$P=3$$ paths.…”

“…After transfer learning procedure, the shared module stands still and the new private modules are more suitable for testing data in target scenario which can output the DOA and TOA representations with high accuracy. (10) Simulation results: In the simulations, chirp signal is used as transmission signal s(t ) [12] and signal parameters are set as shown in Table 1. As shown in Figure 4 and Table 2, the proposed network performs better than traditional methods TST-MUSIC, DFT-ESPRIT and the DL algorithm with a single-task network under different SNR scenarios, especially for high SNR scenarios.…”