Cognitive Radar Detection in Nonstationary Environments and Target Tracking

Target detection and tracking are the most fundamental and important problems in a wide variety of defense and civilian radar systems. In recent years, to cope with complex environments and stealthy targets, the concept of cognitive radars has been proposed to integrate intelligent modules into conventional radar systems. To achieve better performance, cognitive radars are designed to sense, learn from, and adapt to environments. In this dissertation, we introduce cognitive radars for target detection in nonstationary environments and cognitive radar networks for target tracking.For target detection, many algorithms in the literature assume a stationary environment (clutter). However, in practical scenarios, changes in the nonstationary environment can perturb the parameters of the clutter distribution or even alter the clutter distribution family, which can greatly deteriorate the target detection capability. To avoid such potential performance degradation, cognitive radar systems are envisioned which can rapidly recognize the nonstationarity, accurately learn the new characteristics of the environment, and adaptively update the detector. To achieve this cognition, we propose a unifying framework that integrates three functions: (i) change-point detection of clutter distributions by using a data-driven cumulative sum (CUSUM) algorithm and its extended version, (ii) learning/identification of clutter distribution by using kernel density estimation (KDE) methods and similarity measures (iii) adaptive target detection by automatically modifying the likelihood-ratio test and the corresponding detection threshold. We also conduct extensive numerical experiments to show the merits of the proposed method compared to a nonadaptive case, an adaptive matched filter (AMF) method, and the clairvoyant case.For target tracking, with remarkable advances in sensor techniques and deployable platforms, a sensing system has freedom to select a subset of available radars, plan their trajectories, and transmit designed waveforms. Accordingly, we propose a general framework for single target tracking in cognitive networks of radars, including joint consideration of waveform design, path planning, and radar selection. We formulate the tracking procedure using the theories of dynamic graphical models (DGM) and recursive Bayesian state estimation (RBSE). This procedure includes two iterative steps: (i) solving a combinatorial optimization problem to select the optimal subset of radars, waveforms, and locations for the next tracking instant, and (ii) acquiring the recursive Bayesian state estimation to accurately track the target. Further, we use an illustrative example to introduce a specific scenario in 2-D space. Simulation results based on this scenario demonstrate that the proposed framework can accurately track the target under the management of a network of radars

Two-stage Neural Network for ICASSP 2023 Speech Signal Improvement Challenge

In ICASSP 2023 speech signal improvement challenge, we developed a dual-stage neural model which improves speech signal quality induced by different distortions in a stage-wise divide-and-conquer fashion. Specifically, in the first stage, the speech improvement network focuses on recovering the missing components of the spectrum, while in the second stage, our model aims to further suppress noise, reverberation, and artifacts introduced by the first-stage model. Achieving 0.446 in the final score and 0.517 in the P.835 score, our system ranks 4th in the non-real-time track.Comment: Accepted by ICASSP 202

An Investigation of Rotary Cup Burner Assembly with Three Vehicle-Mounted Cooking Stoves by Numerical Evaluation Method

The adaptability of vehicle-mounted heating systems that include burner and stove remarkably influences the system efficiency, heat flux uniformity, and pollutants emission. In this work, the performance of a rotary cup burner assembly with three different cooking stoves was investigated using ANSYS Fluent software based on five factors of thermal efficiency, heat transfer intensity, heating uniformity, CO emissions, and flue gas outlet temperature. The Eulerian-Lagrangian method was used to perform the diesel spray, and the shear stress transfer k-ω turbulence model and the probability density function model were employed to simulate the turbulent combustion. Based on the simulation results, the performance pentagon of the above five factors was constructed to evaluate the comprehensive performance of the new rotary cup burner system. The rotary cup burner had a good performance when it is used in two staple food stoves and a subsidiary food stove. In staple food stove A, its higher furnace increased the heat exchange area of the vessel, while the higher fireboard of staple food stove B caused a higher heat transfer intensity at the bottom of the vessel. However, the higher fireboard also led to higher CO emissions. In consideration of these two factors, the thermal efficiency of stove A was about 7% higher than that of stove B. Different from the staple food stove, the furnace of subsidiary food stove C had better wrapping to the bottom of the boiler so that it had the highest heat transfer intensity. The obtained performance pentagon shows that the comprehensive adaptability performance of stove A was the best and that of stove B was the worst, which is mainly caused by the height of the fireboard and the shape of the vessel. This research guides the optimization of the heating system and promotes the application of the rotary cup burner

Construction of Discrete Element Constitutive Relationship and Simulation of Fracture Performance of Quasi-Brittle Materials

In order to solve the problem that the built-in parallel bond model in the discrete element software cannot adequately simulate the post-peak fracture behavior of quasi-brittle materials, a linear cohesive model was established. First, two particles are used to simulate the interface constitutive behavior in different modes. The results show that the new model can better simulate the behavior of Mode-I fracture, Mode-II fracture, and Mixed-mode fracture. Then, the influence of micro-parameters on the newly constructed constitutive model is analyzed, which provides a basis for the determination of micro-parameter values. Finally, the proposed softening model is applied to a three-point bending test of mortar, and the fracture behavior obtained is compared to the acoustic emission results. The simulation results also show that the constitutive model we built can be used to simulate the fracture behavior of quasi-brittle materials such as mortar and concrete