Phase Characterization and Adaptive Pulse Compression Using MIIPS In Air

Within the minimum mean-square error (MMSE) context the adaptive pulse compression (APC) algorithm has been shown to effectively suppress the range sidelobes resulting from large targets to the level of the noise thereby unmasking nearby small targets and yielding substantial sensitivity improvement over deterministic matched/mismatched filtering techniques. However, this sensitivity improvement is limited by filter mismatch whenever the radar returns from large target(s) undergo a substantial Doppler phase shift caused by significant radial motion relative to the radar platform. To correct for the Doppler-induced filter mismatch, the Doppler compensated APC (DC-APC) algorithm is presented whereby the respective Doppler shifts for large target returns are jointly estimated along with the illuminated range profile and subsequently incorporated into the APC adaptive receive filter formulation thus suppressing the Doppler-mismatch-induced range sidelobes and regaining a significant portion of the sensitivity improvement of APC.

We analyzed the third harmonic generation (THG) of 1064 nm, 28 ps radiation in the candle and ethanol flames. The formation of soot nanoparticles in the candle flame led to the increase of third harmonic yield compared to the ethanol flame. 12- and 24-fold enhancement of the third harmonic yield in candle and ethanol flames compared to the THG in air was achieved. The influence of the nonlinear refraction in air and combustion flames on the phase-matching conditions of THG was found responsible for deviation from the cubic I3ω (Iω) dependence. THG conversion efficiencies of Nd:YAG laser radiation were found to be 1 × 10−5, 1.2 × 10−4 and 2.4 × 10−4 in the cases of air, carbon flame and ethanol flame media, respectively. Furthermore, the super-hydrophobic properties of the deposited carbon species from the candle flame were demonstrated.

The recently proposed Multistatic Adaptive Pulse Compression (MAPC) algorithm has been shown to successfully suppress both range sidelobes and interference from multiple radars operating in the same spectrum, thus enabling shared-spectrum multistatic radar. In this paper, we present a method to increase the overall information capacity of the MAPC algorithm by performing joint adaptive pulse compression in conjunction with adaptive beamforming. The addition of an adaptive beamforming stage to the MAPC algorithm enables further mutual interference suppression and thus better estimation performance such that the number of multistatic radars simultaneously operating in the same spectrum may be increased for the same mean-square estimation error. Analysis of the performance of the adaptive beamforming MAPC algorithm in a variety of scenarios is presented. In addition, Monte Carlo analyses on the number of shared-spectrum radars are also presented

In random frequency-hopping pulse–Doppler radar, the transmitted frequencies are randomly distributed in the given frequency bandwidth. As a result, this kind of radar has the advantage of anti-jamming. However, because of the sidelobe level fluctuating randomly, the sidelobe of a large scatterer can mask the presence of nearby smaller scatterers. The adaptive pulse compression (APC) algorithm has been shown capable of effectively suppressing the range sidelobes, but the performance gain is diminished whenever the return signals have a low signal-to-noise ratio (SNR). Furthermore, moving targets with high speed bring about the range migration problem. To solve these problems, the coherent integration APC (CIAPC) algorithm is proposed. The simulation results indicate that in a low SNR scenario, the CIAPC algorithm can suppress the range sidelobes of large scatterers and can improve detection performance.

This study introduces a practical approach to implement real-time signal processing algorithms for general surveillance radar based on NVIDIA graphical processing units (GPUs). The pulse compression algorithms are implemented using compute unified device architecture (CUDA) libraries such as CUDA basic linear algebra subroutines and CUDA fast Fourier transform library, which are adopted from open source libraries and optimized for the NVIDIA GPUs. For more advanced, adaptive processing algorithms such as adaptive pulse compression, customized kernel optimization is needed and investigated. A statistical optimization approach is developed for this purpose without needing much knowledge of the physical configurations of the kernels. It was found that the kernel optimization approach can significantly improve the performance. Benchmark performance is compared with the CPU performance in terms of processing accelerations. The proposed implementation framework can be used in various radar systems including ground-based phased array radar, airborne sense and avoid radar, and aerospace surveillance radar.

One of the most successful natural language compression methods is word-based Huffman. However, such a two-pass semi-static compressor is not well suited to many interesting real-time transmission scenarios. A one-pass adaptive variant of Huffman exists, but it is character-oriented and rather complex. In this paper we implement word-based adaptive Huffman compression, showing that it obtains very competitive compression ratios. Then, we show how End-Tagged Dense Code, an alternative to word-based Huffman, can be turned into a faster and much simpler adaptive compression method which obtains almost the same compression ratios.

This article discusses a post-processing method to achieve highly accurate radar cross section (RCS) measurements in challenging test environments. In the ideal RCS measurement setup, the only received signal is the reflection from the object under test (OUT). However, this scenario is often not realizable in practice as outside measurements suffer from a multitude of scattering objects beyond the OUT, and even anechoic chambers have nonnegligible multipath clutter. Time-gating has been shown to be a powerful post-processing clutter suppression tool by temporally filtering any undesired return signals. However, this technique is only sufficient when there is ample spacing between the OUT and undesired return signals or when the sidelobes of one scatterer do not mask the return of a smaller nearby scatterer in the time domain. In the latter scenario, traditional time-gating techniques cannot separate the sidelobes from a nearby dominant scatterer overlapping with the main lobe of the OUT, and the RCS of the OUT cannot be extracted accurately. By applying the proposed stepped frequency continuous wave (SFCW) adaptive pulse compression (APC) algorithm, a range-dependent filter is created to iteratively estimate the range profile and suppress sidelobes allowing for temporal separation of the scatterers and accurate RCS estimation. To validate the concept, the RCS of a 15.2 cm sphere is extracted in two measurement configurations (with and without a large nearby dominating scatterer). The traditional matched filter data processing accuracy is compared to the proposed APC technique. Implementation of APC in post-processing yields a 71.2% improvement in average RCS error compared to traditional processing in the sphere-only measurement and an 88.2% improvement in the presence of a metal plate.

Subband-domain algorithms provide an attractive technique for wideband radar array processing. The subband-domain approach decomposes a received wideband signal into a set of narrowband signals. While the number of processing threads in the system increases, the narrowband signals within each subband can be sampled at a correspondingly slower rate. Therefore, the data rate at the input is similar to that at the output of the subband processor. There are several advantages to the subbanding method. It can simplify typical radar algorithms such as adaptive beamforming and equalization by the virtue of reducing subband signal bandwidth, thereby potentially reducing the computational complexity over an equivalent tapped-delay line approach. It also allows for a greater parallelization of the processing task, hence enabling the use of slower and less power consuming hardware. In order to evaluate the validity of the subbanding approach, it is compared with conventional processing methods. This paper focuses on adaptive beamforming and pulse compression performance for a wideband radar system. The performance of an adaptive beamformer is given for a polyphase filter based subband approach and is measured against narrowband processing. SINR loss curves and beampatterns for a subband system are presented. Design criteria for subband polyphase filter processing that minimizes signal distortion are provided and the distortion is characterized. Finally subband- domain pulse compression is demonstrated and compared with the conventional approach.

A description of STAP algorithms used in the May 1996 Real Time Multichannel Airborne Radar Measurement (RT-MCARM) program is presented. The system collected and processed IF sampled data from 16 phased array radar receiver channels, performing IF to baseband conversion, Doppler processing, adaptive clutter cancellation, pulse compression, and formation of multiple receive beams within an intentionally broadened transmit beam. Selective PRI stagger was implemented in Doppler regions of strong clutter to provide increased cancellation. An innovative method of adaptation with variable beam constraints was developed, which allows preservation of target phase across boundaries between range intervals with differing clutter statistics. In addition to improved angle estimation compared to unconstrained adaptive processing, the constraint makes it possible to perform pulse compression after beamforming thereby reducing the computational requirements.

In the past decades, there has been an extensive research interest in the areas of both waveform diversity/design and advanced signal processing algorithms departing from the more classical solutions based on Linear Frequency Modulated (LFM) pulses and Matched Filters (MF). In the waveform diversity community, especially within the context of spectrum sharing, MIMO and cognitive radars, several waveform optimization and design methodologies have been studied, see [1], [2] and references therein. In parallel to waveform design, several signal processing techniques have also been proposed which exploit some kind of prior knowledge and/or iterative algorithms to improve the performance of the more classical MF, such as the Adaptive Pulse Compression(APC) [3], MUSIC [4], CLEAN [5] and Sparse Signal Processing (SSP) [6], [7]. In this paper we present some results and examples to show how the combination of waveform design with SSP can lead to improved performance in radar compared to the more classical approach.

Two-dimensional (2D) materials have brought a spectacular revolution in fundamental research and industrial applications due to their unique physical properties of atomically thin thickness, strong light-matter interaction, unity valley polarization and enhanced many-body interactions. To fully explore their exotic physical properties and facilitate potential applications in electronics and optoelectronics, an effective and versatile characterization method is highly demanded. Among the many methods of characterization, optical second harmonic generation (SHG) has attracted broad attention because of its sensitivity, versatility and simplicity. The SHG technique is sufficiently sensitive at the atomic scale and therefore suitable for studies on 2D materials. More importantly, it has the capacity to acquire abundant information ranging from crystallographic, and electronic, to magnetic properties in various 2D materials due to its sensitivity to both spatial-inversion symmetry and time-reversal symmetry. These advantages accompanied by its characteristics of non-invasion and high throughput make SHG a powerful tool for 2D materials. This review summarizes recent experimental developments of SHG applications in 2D materials and also provides an outlook of potential prospects based on SHG.

Healthcare institutions face a growing demand to use machine learning to improve patient outcomes while adhering to strict privacy laws and data security measures. Although data heterogeneity, institutional privacy restrictions, and different contribution values across participating institutions pose challenges for current methodologies, cross-silo federated learning offers a viable alternative for collaborative healthcare analytics. In this paper, a novel paradigm for privacy-preserving healthcare collaboration in crosssilo environments is presented: adaptive contribution-based federated learning (ACFL). By modifying both compression rates and differential privacy measures according to each participant's contribution to the global model, resource availability, and data characteristics, ACFL overcomes the drawbacks of conventional federated learning. The framework introduces several important innovations: A feature importance-based differential privacy approach that applies different noise levels to model parameters based on their clinical significance, a comprehensive contribution tracking mechanism that uses exponential moving average smoothing to evaluate both parameter update magnitude and performance impact, and adaptive sparsification that maximizes communication efficiency while maintaining valuable updates. Through an experimental evaluation on the MIMIC-IV prescription dataset, adaptive compression and privacy methods of ACFL maintain to treat important characteristics while lowering communication overhead.

: New digital techniques for forming antenna beam patterns and matched filtering of radar waveforms are investigated and compared to the traditional analog methods. The development at NRL of an integrated digital processor for performing both adaptive beamforming and pulse compression is presented along with performance data obtained from experiments performed in the Adaptive Processing Laboratory.

We propose and experimentally demonstrate a new, aberration-free method of pulse spectral compression, based on second harmonic generation. The technique is self-reference by the use of a nonlinear-spectronic reference pulse self-shaped in a single-mode fiber without gain. As compared with the traditional spectral compression, the proposed method is free of demand to separate the reference and signal radiation by frequency or polarization.

User experience has a fundamental role in determining the effectiveness of image compression methods. This work presents the subjective evaluation of a new compression plug-in for current compression formats developed by Cogisen. The quality of image compression methods is often evaluated by objective metrics based on subjective quality datasets, rather than by using subjective quality evaluation tests. Cogisen's compression method follows an adaptive compression process that evaluates the saliency of any image and calculates the level of compression beyond which viewers shall be aware of image quality degradation. The Single Stimulus Continuous Quality Scale method was used to conduct the subjective quality evaluation of image compression. Pictures compressed by the Facebook Mobile lossy JPEG compression and by the Cogisen plug-in integrated in the Facebook Mobile compression settings were used. The results of the user quality evaluation of pictures show about a 45i¾?% compression improvement, with no loss in perceived image quality, for pictures compressed by the Cogisen plug-in compared to jpeg pictures as compressed by Facebook Mobile.