Controlling peak side-lobe level (PSL) is of great importance in high-resolution applications of multiple input multiple output (MIMO) radars. In this paper, designing sequences with good autocorrelation properties are studied. The PSL of the autocorrelation is regarded as the main merit and is optimized through newly introduced cyclic algorithms, namely; PSL Minimization Quadratic Approach (PMQA), PSL Minimization Algorithm, the smallest Rectangular (PMAR) and PSL Optimization Cyclic Algorithm (POCA). It is revealed that minimizing PSL results in better sequences in terms of autocorrelation side-lobes when compared with traditional integrated side-lobe level (ISL) minimization. In order to improve the performance of these algorithms, fast-randomized Singular Value Decomposition (SVD) is utilized. To achieve waveform design for MIMO radars, this algorithm is applied to the waveform generated from a modified Bernoulli chaotic system. The numerical experiments confirm the superiority of the newly developed algorithms compared to high-performance algorithms in mono-static and MIMO radars.
One of the critical challenges facing 3D video systems and images such as holography lies in their compression technique. High-efficiency video coding (HEVC) has emerged as one of the leading schemes to address this challenge. In this article, a novel method based on wavelet transform is presented to improve HEVC, particularly in digital holography systems (object plane). In this regard, wavelet and resizing are included in the coding process, while extra HEVC decoders and encoders are added to predict and decrease errors in the target. Simulation results reveals that the proposed algorithm reduces Bjøntegaard-Delta (BD) bitrate 17.5% (based on average BD-Rate values) compared to the original HEVC (H.265) scheme while maintaining signal fidelity and even enhancing it slightly. We observe an increased BDpeak-signal-to-noise ratio (BD-PSNR) in real and imaginary parts of digital holograms of high rate quantization values up to 1.1 dB.
Compressed sensing is recently applied to time delay estimation, resulting in higher accuracy and stability compared to traditional methods. In this paper, a time delay estimation model is designed based on adaptive iterative local searching orthogonal matching pursuit (AILSOMP) algorithm, and an improved three-stage weighted least squares localization algorithm is proposed using the time delay values. Firstly, the sensor receives acoustic waves from the target in the deep-sea multipath environment. It then obtains the rectilinear propagation time delay of the sound wave through compressed sensing. Secondly, the time synchronization between the two sensors is maintained, and the difference between the estimated delays of both sensors is multiplied by the speed of sound to obtain the measured distance value. Finally, an improved three-stage weighted least squares algorithm is applied to locate the target using the time difference of arrival (TDOA). Simulation results confirm that the proposed algorithm has better localization performance compared to other methods in a multipath interference environment. INDEX TERMS Adaptive iterative local searching, orthogonal matching pursuit, time delay estimation, time difference of arrival.
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