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 propose an expectation maximization (EM) trained interacting multiple model (IMM) abrupt change detector for land mine detection applications. The proposed EM algorithm learns the parameters of the different models in real time without requiring a priori information on either the number of models or the model parameters. Using the real ground penetrating radar (GPR) data, the learning performance of the EM-IMM technique is analyzed and commented upon. Numerical receiver operating characteristics (ROC) analysis and detected images indicate that the proposed EM-IMM based abrupt change detector has a better detection and imaging performance than the conventional Kalman filter for land mine detection applications.

In today's information age, information and network security are of primary importance to any organization. Network intrusion is a serious threat to security of computers and data networks. In internet protocol (IP) based network, intrusions originate in different kinds of packets/messages contained in the open system interconnection (OSI) layer 3 or higher layers. Network intrusion detection and prevention systems observe the layer 3 packets (or layer 4 to 7 messages) to screen for intrusions and security threats. Signature based methods use a pre-existing database that document intrusion patterns as perceived in the layer 3 to 7 protocol traffics and match the incoming traffic for potential intrusion attacks. Alternately, network traffic data can be modeled and any huge anomaly from the established traffic pattern can be detected as network intrusion. The latter method, also known as anomaly based detection is gaining popularity for its versatility in learning new patterns and discovering new attacks. It is apparent that for a reliable performance, an accurate model of the network data needs to be established. In this paper, we illustrate using collected data that network traffic is seldom stationary. We propose the use of multiple models to accurately represent the traffic data. The improvement in reliability of the proposed model is verified by measuring the detection and false alarm rates on several datasets.

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.

In this paper, we propose a novel chaos based ultra-wideband (UWB) sensor to enhance homeland security applications. The proposed chaos based modulation has a good resolution when used for wall penetrating applications. The receiver exploits the deterministic nature of chaos to cancel room reverberations avoiding complex synchronization procedure. Numerical electromagnetic (EM) simulations using finite difference time domain (FDTD) method are performed to illustrate the imaging performance of the proposed radar under real life surveillance situations with hidden and moving targets. The simulations are also employed to analyze the extent of penetrating ability of the proposed scheme for different structures. The effect of various structures and thickness on the detection performance are also commented upon.

In this paper, we propose an expectation maximization (EM) based approach for semiblind identification of linear moving average (MA) systems. The system is driven by chaotic signals and a robust EM based estimator is formulated to estimate the system parameters and the driving chaotic signal. It is shown through numerical simulations that the proposed EM semiblind estimation technique outperforms other conventional techniques such as minimum nonlinear prediction error (MNPE) method and that based on extended Kalman filter (EKF). Also the proposed estimator is applied in the equalization of chaos based communication to illustrate the performance improvement.

This work presents a robust chaos radar system for collision detection and vehicular ranging in intelligent transportation systems (ITS). The robustness of the scheme lies in its multipath mitigation characteristics. By exploiting the spread spectrum (SS) nature of chaos, a high resolution radar system is designed. A cost effective receiver architecture for multipath mitigation in vehicular channel is proposed here. The receiver adaptively equalizes the vehicular multi-path channel minimizing a non-linear prediction error (MNPE) criteria. The MNPE receiver performance is derived to analyze its multi-path mitigation performance. Numerical simulations are performed to validate the theoretical results. The performance of the proposed radar is compared with the conventional direct sequence spread spectrum (DS-SS) ranging scheme. It is shown that the proposed chaos radar outperforms the conventional DS-SS ranging scheme in the vehicular multi-path environment.

In this paper, we present a novel chaos based ultra-wideband (UWB) imaging radar for surveillance applications in a closed environment. The proposed chaos based UWB imaging radar has good range resolution and excellent range-sidelobe suppression characteristics that enhance its imaging performance. The imaging performance of the proposed radar is better than that of the conventional time modulated UWB (TM-UWB) radars in reducing false alarms and in imaging targets closely lying behind the wall. Numerical electromagnetic (EM) simulations using finite difference time domain (FDTD) method illustrate the definite advantages of the proposed radar under different scenarios.