In this paper, we propose to apply a novel chaos-based ultra-wide band (UWB) radar for through-the-wall imaging. The proposed chaos modulation offers superior resolution compared to conventional UWB radars when applied for through-the-wall imaging. A noncoherent receiver is designed based on expectation maximization (EM) algorithm. The theoretical detection performance is derived for through-the-wall detection in the presence and absence of room reverberations as a function of dielectric properties of walls, targets, and their geometry illustrating the robustness of the proposed modulation against room reverberations. The resolution of the proposed modulation is analyzed theoretically and verified through simulations for different wall materials. Numerical electromagnetic simulations using finite difference time domain (FDTD) method are performed to confirm the obtained theoretical results. From the theoretical and simulation analysis, we find that the proposed chaos-based pulse amplitude modulated ultra-wide band (CPAM-UWB) radar has better detection performance, penetrating ability and imaging performance compared to other conventional through-the-wall imaging radars.

In this letter, we propose an interacting multiple-model (IMM)-based abrupt change detector for ground-penetrating radar (GPR) applications. Ground clutter varies with surface roughness, soil nature, as well as depth of the soil layer, necessitating a multiple-model approach. The IMM is first trained for a chosen number of models and then used to characterize the GPR data. The IMM predictor segments the entire GPR data into regions of identical models and then identifies targets by detecting abrupt changes in model parameters. The number of models is determined using the minimum prediction error criterion. The prediction performance of the IMM predictor is theoretically analyzed, and its detection performance is also evaluated through an receiver operating characteristics analysis to illustrate the improved performance of the proposed detector.

In this paper, we address the problem of parameter estimation of systems driven by chaotic signal We propose an expectation maximization (EM) based unscented Kalman smoother (UKS) to simultaneously estimate parameters of system along with the equalized chaotic signal. The proposed method can be applied to both linear and nonlinear systems driven by chaotic signals. The performance of the proposed estimator is evaluated for identification of systems that occur frequently in communication systems. The estimation performance of the proposed algorithm is evaluated using computer simulations and shown to be better than conventional nonlinear system identification algorithms.

In this letter, we present a novel chaos-based imaging technique. The proposed technique has good range-Doppler resolution and excellent side lobe suppression characteristics that promise immense potential for high-resolution imaging applications. We derive the range and Doppler resolution functions of the proposed technique and compare it with that of conventional time modulation-based imaging. Additionally, the noise performance of the proposed scheme is derived to show the improvement compared to the conventional amplitude and time modulation schemes. The proposed imaging technique is applied to through-the-wall radar imaging. Numerical electromagnetic simulations are performed to illustrate the effectiveness of the proposed technique.