Long-range Detection Research Articles

Analysis of rotational Doppler shift with multi-ring vortex beams.

Vortex beams (VBs) with orbital angular momentum have shown great potential in the detection of transverse rotational motion of spatial targets which is undetectable in the classical radar scheme. However, most of the reported rotational Doppler measurements based on VBs can only be realized under ideal experimental conditions. The long-range detection is still a challenge. The detection distance based on rotational Doppler effect (RDE) is mainly limited by the scattered signal's signal-to-noise ratio (SNR). In this work, we investigated the influence of multi-ring vortex beams (MVBs) on the rotational Doppler frequency spectrum of scattered light from an object based on RDE and proposed a method of SNR enhancement of RDE signal. Firstly, different types of MVBs composed of a set of single-ring VBs with the same topological charge and different radii are designed, including multi-ring Laguerre Gaussian beam (MLGB), multi-ring perfect vortex beams (MPVB), and high-order Laguerre Gaussian beam (HLGB). Then, the influence of the number of rings and radial radius interval on the intensity profiles of MVBs and rotational Doppler frequency spectra under aligned and misaligned conditions is studied in detail. And the reasons why different types of MVBs lead to different SNR enhancement effectiveness with the increase of rings are also analyzed theoretically. Finally, proof-of-concept experiments were conducted to verify the effectiveness of the SNR enhancement method for RDE signals. The results showed that the amplitudes of the Doppler spectra generated by the MLGB and MPVB are improved substantially with the increase of rings, but the enhancement effect caused by the former is superior to the latter. The gain of HLGB on the RDE signal is the lowest. This study provides a useful reference for the optimization of rotational Doppler detection systems and may be of great application value in telemetry, long-range communication and optical imaging.

Enhanced range-doppler algorithm for SAR image formation

A Synthetic Aperture Radar (SAR) system is a powerful source of information due to its capability to operate day and night and in all weather conditions. It is essential for military and civilian purposes. Pulse-focusing is employed by the SAR system for achieving both long-range detection and fine-range resolution. The Range-Doppler Algorithm (RDA) is the most popular pulse-focusing technique for creating images from coherent SAR data. Pulse compression techniques for SAR signal processing targets seek to improve map resolution, lower peak power, and increase the Signal-to-Noise Ratio (SNR) of the detected object. This paper demonstrates an enhanced RDA algorithm that uses a modified pulse compression technique based on mismatched filter optimization where Quadratic Constrained Quadratic Program (QCQP) is used. The performance evaluation of the proposed method is performed using visual assessment and the standard image quality metrics peak-side-lobe-ratio (PSLR), impulse-response-width (IRW), and integrated-side-lobe-ratio (ISLR). Simulated and real SAR raw data are used for performance evaluation. The experimental results demonstrate the proposed method’s robustness, efficiency, and reliability in providing a higher-quality SAR image than the traditional RDA algorithm.

Super Sparse 3D Object Detection.

As the perception range of LiDAR expands, LiDAR-based 3D object detection contributes ever-increasingly to the long-range perception in autonomous driving. Mainstream 3D object detectors often build dense feature maps, where the cost is quadratic to the perception range, making them hardly scale up to the long-range settings. To enable efficient long-range detection, we first propose a fully sparse object detector termed FSD. FSD is built upon the general sparse voxel encoder and a novel sparse instance recognition (SIR) module. SIR groups the points into instances and applies highly-efficient instance-wise feature extraction. The instance-wise grouping sidesteps the issue of the center feature missing, which hinders the design of the fully sparse architecture. To further enjoy the benefit of fully sparse characteristic, we leverage temporal information to remove data redundancy and propose a super sparse detector named FSD++. FSD++ first generates residual points, which indicate the point changes between consecutive frames. The residual points, along with a few previous foreground points, form the super sparse input data, greatly reducing data redundancy and computational overhead. We comprehensively analyze our method on the large-scale Waymo Open Dataset, and state-of-the-art performance is reported. To showcase the superiority of our method in long-range detection, we also conduct experiments on Argoverse 2 Dataset, where the perception range ( 200m) is much larger than Waymo Open Dataset ( 75m). Code is open-sourced at https://github.com/tusen-ai/SST.

Long-range underwater acoustic detection of aircraft surface impacts—The influence of acoustic propagation conditions and impact parameters

The 2019 crash of an F35 fighter aircraft off the east coast of Japan was detected at a range of approximately 3000 km by a hydroacoustic station of the Comprehensive Nuclear Test Ban Treaty Organisation’s International Monitoring System. In this paper we compare the acoustic propagation conditions relating to that detection to those relating to the crash of Malaysian Airlines flight MH370 in the Indian Ocean in 2014 with a view to reassessing the likelihood that acoustic signals detected on a similar hydroacoustic station could have been related to the loss of MH370. Possible differences in source levels due to different assumptions about impact parameters are also considered. Despite several extensive searches, the location of the wreckage of MH370 remains unknown.

Label-free long-range detection of prostate cancer biomarkers based on surface-enhanced Raman scattering

In the field of prostate cancer based on surface-enhanced Raman scattering (SERS), the repeatability of detection is crucial for its practical application in clinical medicine. We propose to use a new SERS substrate based on a polymer planar waveguide in combination with microfluidic technology and utilized in the label-free detection of prostate-specific antigen (PSA) in serum. The experimental results show that the detection limit of the substrate for PSA sample solution is as low as 10 pg ml−1. Additionally, the optimal transmission length for PSA is determined to be 9 mm. Further examinations encompass repetitive testing of serum samples spiked with PSA at a concentration of 4 ng ml−1, revealing a relative standard deviation (RSD) of 5.01%. This outcome is compared with single-point detection, manifesting a notable enhancement of 9.81% in terms of RSD, signifying a marked improvement in repeatability. Notably, our method not only capitalizes on the long-range averaging effect to enhance the repeatability of Raman signals, but also circumvents sample damage resulting from direct laser irradiation.

Long-Range Thermal Target Detection in Data-Limited Settings Using Restricted Receptive Fields.

Long-range target detection in thermal infrared imagery is a challenging research problem due to the low resolution and limited detail captured by thermal sensors. The limited size and variability in thermal image datasets for small target detection is also a major constraint for the development of accurate and robust detection algorithms. To address both the sensor and data constraints, we propose a novel convolutional neural network (CNN) feature extraction architecture designed for small object detection in data-limited settings. More specifically, we focus on long-range ground-based thermal vehicle detection, but also show the effectiveness of the proposed algorithm on drone and satellite aerial imagery. The design of the proposed architecture is inspired by an analysis of popular object detectors as well as custom-designed networks. We find that restricted receptive fields (rather than more globalized features, as is the trend), along with less downsampling of feature maps and attenuated processing of fine-grained features, lead to greatly improved detection rates while mitigating the model's capacity to overfit on small or poorly varied datasets. Our approach achieves state-of-the-art results on the Defense Systems Information Analysis Center (DSIAC) automated target recognition (ATR) and the Tiny Object Detection in Aerial Images (AI-TOD) datasets.

An Efficient Forest Fire Target Detection Model Based on Improved YOLOv5

To tackle the problem of missed detections in long-range detection scenarios caused by the small size of forest fire targets, initiatives have been undertaken to enhance the feature extraction and detection precision of models designed for forest fire imagery. In this study, two algorithms, DenseM-YOLOv5 and SimAM-YOLOv5, were proposed by modifying the backbone network of You Only Look Once version 5 (YOLOv5). From the perspective of lightweight models, compared to YOLOv5, SimAM-YOLOv5 reduced the parameter size by 28.57%. Additionally, although SimAM-YOLOv5 showed a slight decrease in recall rate, it achieved improvements in precision and average precision (AP) to varying degrees. The DenseM-YOLOv5 algorithm achieved a 2.24% increase in precision, as well as improvements of 1.2% in recall rate and 1.52% in AP compared to the YOLOv5 algorithm. Despite having a higher parameter size, the DenseM-YOLOv5 algorithm outperformed the SimAM-YOLOv5 algorithm in terms of precision and AP for forest fire detection.

Underwater power compensated white light source based on synthetic white laser

The semiconductor white laser light source is used as a light source for underwater illumination. The required standard color temperature of white light is obtained at the underwater target surface. We studied the power compensation of a synthetic white laser source and its application to underwater illumination. First, the power ratios of the red (638 nm), green (520 nm), and blue (450 nm) lasers at a color temperature of 6500 K were obtained by using chromaticity theory. Next, the three-color and synthetic white laser parameters were obtained with transmission distance, according to the exponential attenuation characteristics of different light in clear water and seawater medium. The three-color laser power at the output was compensated, and the underwater target illumination surface reached the standard 6500 K color temperature of the white laser, improving the illumination. Finally, an experimental system for underwater white laser illumination based on power compensation was established. The errors between experimental and theoretical results of color temperature and illuminance are no more than 0.43% and 22.15%. This power-compensated synthetic white laser light source has both the advantages of long-range underwater detection and the spectral advantages of LED white light sources. The white laser light source meets specific requirements by compensating for power and optimizing white light characteristics for underwater lighting applications.

Yoga Posture Recognition by Learning Spatial-Temporal Feature with Deep Learning Techniques

Yoga posture recognition remains a difficult issue because of crowded backgrounds, varied settings, occlusions, viewpoint alterations, and camera motions, despite recent promising advances in deep learning. In this paper, the method for accurately detecting various yoga poses using DL (Deep Learning) algorithms is provided. Using a standard RGB camera, six yoga poses — Sukhasana, Kakasana, Naukasana, Dhanurasana, Tadasana, and Vrikshasana — were captured on ten people, five men and five women. In this study, a brand-new DL model is presented for representing the spatio-temporal (ST) variation of skeleton-based yoga poses in movies. It is advised to use a variety of representation learners to pry video-level temporal recordings, which combine spatio-temporal sampling with long-range time mastering to produce a successful and effective training approach. A novel feature extraction method using Open Pose is described, together with a DenceBi-directional LSTM network to represent spatial-temporal links in both the forward and backward directions. This will increase the efficacy and consistency of modeling long-range action detection. To improve temporal pattern modeling capability, they are stacked and combined with dense skip connections. To improve performance, two modalities from look and motion are fused with a fusion module and compared to other deep learning models are LSTMs including LSTM, Bi-LSTM, Res-LSTM, and Res-BiLSTM. Studies on real-time datasets of yoga poses show that the suggested DenseBi-LSTM model performs better and yields better results than state-of-the-art techniques for yoga pose detection.

Long-Range Perception System for Road Boundaries and Objects Detection in Trains

This article introduces a long-range sensing system based on millimeter-wave radar, which is used to detect the roadside boundaries and track trains for trains. Due to the high speed and long braking distance of trains, existing commercial vehicle sensing solutions cannot meet their needs for long-range target detection. To address this challenge, this study proposes a long-range perception system for detecting road boundaries and trains based on millimeter-wave radar. The system uses high-resolution, long-range millimeter-wave radar customized for the strong scattering environment of rail transit. First, we established a multipath scattering theory in complex scenes such as track tunnels and fences and used the azimuth scattering characteristics to eliminate false detections. A set of accurate calculation methods of the train’s ego-velocity is proposed, which divides the radar detection point clouds into static target point clouds and dynamic target point clouds based on the ego-velocity of the train. We then used the road boundary curvature, global geometric parallel information, and multi-frame information fusion to extract and fit the boundary in the static target point stably. Finally, we performed clustering and shape estimation on the radar track information to identify the train and judge the collision risk based on the position and speed of the detected train and the extracted boundary information. The paper makes a significant contribution by establishing a multipath scattering theory for complex scenes of rail transit to eliminate radar false detection and proposing a train speed estimation strategy and a road boundary feature point extraction method that adapt to the rail environment. As well as building a perception system and installing it on the train for verification, the main line test results showed that the system can reliably detect the road boundary more than 400 m ahead of the train and can stably detect and track the train.

Slow-Time MIMO Waveform Design Using Pulse-Agile-Phase-Coding for Range Ambiguity Mitigation

This paper proposed a Pulse-Agile-Phase-Coding slow-time MIMO (PAPC-st-MIMO) waveform, where the phase-coded signal is utilized as the intra-pulse modulation of the slow-time MIMO waveform. Firstly, the signal model of the proposed waveform is derived. To improve the orthogonality of the phase-coded waveform sets, a novel hybrid evolutionary algorithm based on Cyclic Algorithm New (CAN) is proposed. After the optimization process of the phase-coded waveform sets, the signal processing method of the PAPC-st-MIMO waveform is derived. Finally, the effectiveness of the proposed method is verified with a simulation experiment. The mitigation ratio of the near-range detection waveform can achieve −30 dB, while the long-range detection waveform can achieve −35 dB. This approach ensures waveform orthogonality while enabling the slow-time MIMO waveform to achieve distance selectivity. By conducting joint pulse-Doppler processing across multiple range segments, range ambiguity can be suppressed, increasing the system’s Pulse Repetition Frequency (PRF) without introducing ambiguity.

Surface-enhanced Raman spectroscopy long-range detection investigation based on the alternating coupling of a surface plasmonic array and dielectric waveguide.

Surface-enhanced Raman spectroscopy (SERS) is widely used to detect low-concentration samples in biology, medicine, etc. We design and theoretically investigate a SERS sensor with a surface plasmonic array coupled alternately with a dielectric waveguide. The effect of the incident angle on the coupling efficiency of an evanescent field is systematically studied. The results show that the maximum evanescent field coupling efficiency can be obtained at an incident angle of 66°. The proposed SERS sensor has a transmission length of 1.027cm and a high enhancement performance with an enhancement factor of 1.574×104 at a wavelength of 633nm. The integration of this SERS sensor with a metal array and a dielectric waveguide prevents the direct illumination of the sample molecules by the excited light. Furthermore, the long-range nondestructive detection of the SERS signals of the low-concentration sample molecules can be achieved.

Atmospheric effects on the laser-driven avalanche-based remote detection of radiation

The effect of realistic atmospheric conditions on mid-IR (λ = 3.9 µm) and long-wave-IR (λ = 10 µm) laser-induced avalanche breakdown for the remote detection of radioactive material is examined experimentally and with propagation simulations. Our short-range in-lab mid-IR laser experiments show a correlation between increasing turbulence level and a reduced number of breakdown sites associated with a reduction in the portion of the focal volume above the breakdown threshold. Simulations of propagation through turbulence are in excellent agreement with these measurements and provide code validation. We then simulate propagation through realistic atmospheric turbulence over a long range (0.1-1 km) in the long-wave-IR regime (λ = 10 µm). The avalanche threshold focal volume is found to be robust even in the presence of strong turbulence, only dropping by ∼50% over a propagation length of ∼0.6 km. We also experimentally assess the impact of aerosols on avalanche-based detection, finding that, while background counts increase, a useful signal is extractable even at aerosol concentrations 105 times greater than what is typically observed in atmospheric conditions. Our results show promise for the long-range detection of radioactive sources under realistic atmospheric conditions.

Efficient Transceiving Search Scheme and Implementation Method for Collocated Distributed Coherent Aperture Radar via Grating Lobes Exploitation

Distributed coherent aperture radar (DCAR) can realize high-precision long-range detection because of its excellent angular resolution. However, it is accompanied by a low search efficiency problem caused by the narrow main lobe and the angular ambiguity problem stemming from the high grating lobes. By simultaneously utilizing multiple grating lobes, we propose an effective search method and pertinent unambiguous signal processing algorithms to address the issue of using collocated DCAR for unambiguous target search. We investigate the beam migration of a grating lobe-irradiated target at various frequencies in order to carry out the multi-frequency coherent integration of the suggested search method in DCAR. In order to address the issue of using multiple beam lobes to simultaneously detect multiple targets, we also take into account the scenario of multiple targets and introduce the multiple hypothesis method to solve the problem. The DCAR search efficiency can be improved by tens of times through grating lobes exploitation, which makes use of DCAR to make the searches feasible. We also improved the performance of multi-frequency DOA ambiguity solving compared to existing algorithms with the sliding window clustering scheme. The viability of using grating lobes for the DCAR target searching and detection is shown in simulations. The experiments confirm the performance enhancement of multi-frequency coherent integration for parameter estimation.

Infrared Gas Detection and Concentration Inversion Based on Dual-Temperature Background Points

Gas detection based on infrared thermal imaging is applied in many areas, but it is generally applied as a qualitative detection method to observe the target area; on the other hand, quantitative research on gas concentration is less common, the measurement accuracy is poor, and the calculation method of concentration in the commonly adopted transmission model is also complicated. In this paper, based on the radiance transfer model of gas infrared imaging technology, the influence of gas concentration, gas temperature, and background temperature on gas imaging detection is investigated, a gas detection and concentration inversion method based on dual-temperature background points is proposed, and the effects of the choice of reference band on background temperature correction are analyzed in relation to the changing trend of dual-band radiance difference. To verify the effectiveness of this method, a gas detection system with dual-temperature background spots was constructed in this paper utilizing a cooled mid-wave infrared focal plane detector plus a reference filter and a measurement filter, which achieved a promising concentration accuracy of less than 10% for carbon dioxide at a detectable range. Meanwhile, an infrared imaging system with a noise equivalent temperature difference (NETD) of 40 mK was employed to simulate the detection of methane, which enables the detection and concentration inversion of methane gas at a minimum concentration of 500 ppm·m at a distance of 1 km, which proves the capability of long-range detection.

Исследование характеристик алгоритма корреляционно-экстремальной навигации для летательного аппарата

Correlation-extremal navigation and guidance systems are widely used in controlling the moving objects. The paper considers the aircraft correlation-extreme navigation algorithm designed for the aviation complexes. This algorithm provides radio identification and guidance for long-range detection of the enemy objects, as well as guidance of the destruction and interception weaponry against it. Classification of methods of the correlation-extreme navigation is presented, and each of the methods is being considered separately. Using the example of an airfield identified from the air, the error between the current and the reference images is determined as the distance between two points in the original image pixel coordinate system. When moving, the object image could be distorted by geometric, luminance and noise transformations. Using the found image, error dependencies on alterations in brightness, rotation angle and noise level of the image fragment, as well as the error dependencies on the selected navigation element, are investigated. Results are presented in the form of graphs. It is shown that the phase correlation method is more resistant to brightness and noise distortions of the image fragment, and the gradient correlation method is more resistant to the geometric transformations. It was found that navigation of heterogeneous and diverse navigation elements (cities) is better than the monotonous and homogeneous ones (roads and forests).

A low-power DTOF image sensor with double-frame-multiplexing mode for long-range detection

A novel time-to-digital converter (TDC) operation mode, called double-frame-multiplexing (DFM), is proposed for time-of-flight (TOF) measurements. It is based on the separate detection of the minimum distance and depth of the measured targets, which reduces the power consumption of in-pixel TDCs and expands the maximum detection distance under high resolution. A 64 × 64 direct TOF image sensor chip is fabricated in a 0.18 μm CMOS process. Driven by a 125 MHz multi-phase clock and a 50 MHz data readout clock, the chip achieves a maximum frame rate of 20 kframe/s, a maximum detection distance of 750 m (5 μs), and a maximum depth of field of 3 m (20 ns) with a temporal resolution of 1 ns. The DFM mode reduces the average power consumption to 60 mW without consideration of SPADs. The worst-case measured differential nonlinearity is −0.61/+0.69 least significant bit (LSB) and the maximum integral nonlinearity is −1.06/+0.66 LSB.

Charge Mapping of Pseudomonas aeruginosa Using a Hopping Mode Scanning Ion Conductance Microscopy Technique.

Scanning ion conductance microscopy (SICM) is a topographic imaging technique capable of probing biological samples in electrolyte conditions. SICM enhancements have enabled surface charge detection based on voltage-dependent signals. Here, we show how the hopping mode SICM method (HP-SICM) can be used for rapid and minimally invasive surface charge mapping. We validate our method usingPseudomonas aeruginosaPA14 (PA) cells and observe a surface charge density of σPA = -2.0 ± 0.45 mC/m2 that is homogeneous within the ∼80 nm lateral scan resolution. This biological surface charge is detected from at least 1.7 μm above the membrane (395× the Debye length), and the long-range charge detection is attributed to electroosmotic amplification. We show that imaging with a nanobubble-plugged probe reduces perturbation of the underlying sample. We extend the technique to PA biofilms and observe a charge density exceeding -20 mC/m2. We use a solid-state calibration to quantify surface charge density and show that HP-SICM cannot be quantitatively described by a steady-state finite element model. This work contributes to the body of scanning probe methods that can uniquely contribute to microbiology and cellular biology.

A W-Band Self-Packaged SIW-Based Slot Antenna With Gain and Bandwidth Enhancement

This study presents the design of a self-packaged substrate integrated waveguide (SIW)-based slot antenna with a low profile at the W-band. A <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1\times $ </tex-math></inline-formula> 4 SIW slot antenna array is developed using a standard printed circuit board (PCB) process. Then, the array is embedded in a silicon wafer. The loss of the benzocyclobutene (BCB)-based coplanar waveguide with ground (CPWG) to PCB-based SIW transition is investigated and measured to interconnect and integrate the antenna array with other components. The performance of the SIW-based slot antenna is enhanced about 2 dB without extra feeding networks by using a novel method of parasitic folded patches, and the fractional impedance bandwidth is increased from 10% to 15% by changing the metallic posts besides the SIW. The antennas are fabricated using our in-house silicon-based MEMS photosensitive composite film BCB fabrication process, which is described in detail. The proposed antenna has a measured maximum gain of 11.7 dBi at 98 GHz, with a fractional impedance bandwidth of 17%. Measured results show a close relationship with the simulated values. Compared with other antennas, our proposed antenna exhibits excellent performance, such as high gain, low profile, low loss interconnection structure, and broad bandwidth. Finally, a novel heterogeneous integration transceiver application is introduced. In this application, self-packaged SIW-based slot antennas are integrated with different types of chips. This 1 transmitter and 2 receivers (1T2R) transceiver can be further used in long-range radar detection, vital sign monitoring, and synthetic aperture radar imaging.

A mammal for all seasons: Contributions from the bottlenose dolphin TRO to bioacoustics research at the US Navy Marine Mammal Program

Among the notoriously small sample sizes of marine mammals in cooperative behavioral research, there is an even smaller subset of subjects that can be considered “experts.” In essence, these are subjects that have “learned how to learn” in the contexts of novel experimental paradigms. One such subject is the bottlenose dolphin TRO at the US Navy Marine Mammal Program. The first scientific publication including TRO heralded his potential; aided by a phantom echo generator, he was likely the first marine mammal in history to echolocate while out of water. Subsequent behavioral experiments with TRO have examined numerous aspects of biosonar, including transmit beam characteristics and the use of click “packets” during long-range target detection. The patient nature of TRO has made him an ideal subject for electrophysiological studies, where he has diligently listened to exotic acoustic stimuli designed to examine auditory processing in the dolphin brainstem. The most significant scientific contributions from TRO are arguably from studies that have used auditory evoked potential methods during biosonar tasks. These data provide the temporal resolution necessary to examine hearing at the level of click production and echo reception and are a result of TRO’s dependable participation in studies combining multiple experimental techniques