Efficient Low-PAR Waveform Design Method for Extended Target Estimation Based on Information Theory in Cognitive Radar

This paper addresses the waveform design problem of cognitive radar for extended target estimation in the presence of signal-dependent clutter, subject to a peak-to-average power ratio (PAR) constraint. Owing to this kind of constraint and the convolution operation of the waveform in the time domain, the formulated optimization problem for maximizing the mutual information (MI) between the target and the received signal is a complex non-convex problem. To this end, an efficient waveform design method based on minimization–maximization (MM) technique is proposed. First, by using the MM approach, the original non-convex problem is converted to a convex problem concerning the matrix variable. Then a trick is used for replacing the matrix variable with the vector variable by utilizing the properties of the Toeplitz matrix. Based on this, the optimization problem can be solved efficiently combined with the nearest neighbor method. Finally, an acceleration scheme is used to improve the convergence speed of the proposed method. The simulation results illustrate that the proposed method is superior to the existing methods in terms of estimation performance when designing the constrained waveform

Waveform Design for Cognitive Radar Under Low PAR Constraints by Convex Optimization

To improve the detection performance of the radar transmit waveform while enabling the transmitter to perform at its maximal efficiency, a joint design method is proposed for the transmit and receive filter in the presence of signal-dependent clutter with a Peak-to-Average-power Ratio (PAR) constraint of the transmit waveform. First, an optimized model of the radar’s output Signal-to-Interference-plus-Noise Ratio (SINR) for range-extended target detection is established. Second, the analytic expression of the receiver is obtained by converting the optimization problem into the Rayleigh quotient model. The optimal matrix solution is then obtained by transforming the non-convex problem into a convex problem via the semi-definite matrix of the waveform. Finally, the optimal vector solution of the waveform is extracted from the optimal matrix solution by combining the rank-one approximation method combined with the nearest neighbor method. An optimal waveform with a maximal output SINR for a given PAR range is obtained using the proposed method. The SINR value of the waveform when PAR = 2 is the same as the SINR value of the optimized waveform under the energy constraint and is about 0.5 dB higher than the waveform when PAR = 1. Simulation results demonstrate the effectiveness of the proposed method

Improving the Performance of ZnS Photocatalyst in Degrading Organic Pollutants by Constructing Composites with Ag2O

ZnS is a promising photocatalyst in water purification, whereas its low photon efficiency and poor visible-light response restrict its application. Constructing composites may help solve these problems. In this work, Ag2O was introduced to ZnS for the first time based on their energy band characteristics to form a novel ZnS/Ag2O composite photocatalyst. In the model reaction of degrading methylene blue, the as-designed catalyst exhibited high catalytic activity among a series of ZnS-based composite photocatalysts under similar conditions. The catalytic rate constant was up to 0.138 min−1, which is 27.4- and 15.6-times higher than those of ZnS and Ag2O. This composite degraded 92.4% methylene blue in 50 min, while the ratios were 31.9% and 68.8% for ZnS and Ag2O. Catalytic mechanism study based on photoluminescence and radical-scavenging experiments revealed that the enhanced photocatalytic activity was attributed to the composite structure of ZnS/Ag2O. The structure not only facilitated the separation and transmission of photogenerated carriers but also extended the light response range of the catalyst. The as-designed ZnS/Ag2O composite is promising in degrading organic pollutants in water