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"Surveillance radar"
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An Improved Detecting Algorithm of Moving Targets for Airborne Maritime Surveillance Radar
The traditional method is capable of detecting and tracking stationary and slow-moving targets in a sea surface environment. However, the signal focusing capability of such a method could be greatly reduced especially for those variable-speed targets. To solve this problem, a novel tracking algorithm combining range envelope alignment and azimuth phase filtering is proposed. In this method, the motion of the airborne maritime surveillance radar platform is firstly compensated for target echoes. Secondly, range envelope alignment is performed to correct the unpredictable range migration of the target after pulse compression. The higher-order phase difference between the adjacent pulses is estimated and compensated. Ultimately, such pulse series are accumulated through azimuth Fourier transform. Traditional methods compensate only for platform motion, limiting their ability to handle variable-speed targets. The proposed algorithm addresses this limitation by compensating for both platform and target motion, significantly improving signal focusing and tracking accuracy. A detailed analysis shows that our algorithm can significantly increase the signal accumulating gain and improve the focusing effect. The simulation results are provided to demonstrate the effectiveness of the proposed algorithm.
Journal Article
Piranha
In 1902, the volcano Mt. Pele erupts on the island of Martinique, wiping out an entire city and sinking a ship carrying a German scientist on the verge of an astonishing breakthrough. More than a century later, Juan Cabrillo will have to deal with that scientist's legacy. During a covert operation, Cabrillo and the crew meticulously fake the sinking of the Oregon - but when an unknown adversary tracks them down despite their planning and attempts to assassinate them, Cabrillo and his team struggle to fight back against an enemy who seems to be able to anticipate their every move.
Book
Opportunities and challenges for big data ornithology
Recent advancements in information technology and data acquisition have created both new research opportunities and new challenges for using big data in ornithology. We provide an overview of the past, present, and future of big data in ornithology, and explore the rewards and risks associated with their application. Structured data resources (e.g., North American Breeding Bird Survey) continue to play an important role in advancing our understanding of bird population ecology, and the recent advent of semistructured (e.g., eBird) and unstructured (e.g., weather surveillance radar) big data resources has promoted the development of new empirical perspectives that are generating novel insights. For example, big data have been used to study and model bird diversity and distributions across space and time, explore the patterns and determinants of broad-scale migration strategies, and examine the dynamics and mechanisms associated with geographic and phenological responses to global change. The application of big data also holds a number of challenges wherein high data volume and dimensionality can result in noise accumulation, spurious correlations, and incidental endogeneity. In total, big data resources continue to add empirical breadth and detail to ornithology, often at very broad spatial extents, but how the challenges underlying this approach can best be mitigated to maximize inferential quality and rigor needs to be carefully considered.
Journal Article
KuRALS: Ku-Band Radar Datasets for Multi-Scene Long-Range Surveillance with Baselines and Loss Design
Compared to cameras and LiDAR, radar provides superior robustness under adverse conditions, as well as extended sensing range and inherent velocity measurement, making it critical for surveillance applications. To advance research in deep learning-based radar perception technology, several radar datasets have been publicly released. However, most of these datasets are designed for autonomous driving applications, and existing radar surveillance datasets suffer from limited scene and target diversity. To address this gap, we introduce KuRALS, a range–Doppler (RD)-level radar surveillance dataset designed for learning-based long-range detection of moving targets. The dataset covers aerial (unmanned aerial vehicles), land (pedestrians and cars) and maritime (boats) scenarios. KuRALS is real-measured by two Kurz-under (Ku) band radars and contains two subsets (KuRALS-CW and KuRALS-PD). It consists of RD spectrograms with pixel-wise annotations of categories, velocity and range coordinates, and the azimuth and elevation angles are also provided. To benchmark performance, we develop a lightweight radar semantic segmentation (RSS) baseline model and further investigate various perception modules within this framework. In addition, we propose a novel interference-suppression loss function to enhance robustness against background interference. Extensive experimental results demonstrate that our proposed solution significantly outperforms existing approaches, with improvements of 10.0% in mIoU on the KuRALS-CW dataset and 9.4% on the KuRALS-PD dataset.
Journal Article
A CFAR Algorithm Based on Monte Carlo Method for Millimeter-Wave Radar Road Traffic Target Detection
The development of Intelligent Transportation Systems (ITS) puts forward higher requirements for millimeter-wave radar surveillance in the traffic environment, such as lower time delay, higher sensitivity, and better multi-target detection capability. The Constant False Alarm Rate (CFAR) detector plays a vital role in the adaptive target detection of the radar. Still, traditional CFAR detection algorithms use a sliding window to find the target limit radar detection speed and efficiency. In such cases, we propose and discuss a CFAR detection method, which transforms the Monte Carlo simulation principle into randomly sampling instantaneous Range–Doppler Matrix (RDM) data, to improve the detection ability of radar for moving targets such as pedestrians and vehicles in the traffic environment. Compared with conventional methods, simulation and real experiments show that the method breaks through the reference window limitation and has higher detection sensitivity, higher detection accuracy, and lower detection delay. We hope to promote the detection application of millimeter-wave radar in road traffic scenes.
Journal Article
A comparison of traffic estimates of nocturnal flying animals using radar, thermal imaging, and acoustic recording
There are several remote-sensing tools readily available for the study of nocturnally flying animals (e.g., migrating birds), each possessing unique measurement biases. We used three tools (weather surveillance radar, thermal infrared camera, and acoustic recorder) to measure temporal and spatial patterns of nocturnal traffic estimates of flying animals during the spring and fall of 2011 and 2012 in Lewes, Delaware, USA. Our objective was to compare measures among different technologies to better understand their animal detection biases. For radar and thermal imaging, the greatest observed traffic rate tended to occur at, or shortly after, evening twilight, whereas for the acoustic recorder, peak bird flight-calling activity was observed just prior to morning twilight. Comparing traffic rates during the night for all seasons, we found that mean nightly correlations between acoustics and the other two tools were weakly correlated (thermal infrared camera and acoustics, r = 0.004 ± 0.04 SE, n = 100 nights; radar and acoustics, r = 0.14 ± 0.04 SE, n = 101 nights), but highly variable on an individual nightly basis (range = –0.84 to 0.92, range = –0.73 to 0.94). The mean nightly correlations between traffic rates estimated by radar and by thermal infrared camera during the night were more strongly positively correlated (r = 0.39 ± 0.04 SE, n = 125 nights), but also were highly variable for individual nights (range = –0.76 to 0.98). Through comparison with radar data among numerous height intervals, we determined that flying animal height above the ground influenced thermal imaging positively and flight call detections negatively. Moreover, thermal imaging detections decreased with the presence of cloud cover and increased with mean ground flight speed of animals, whereas acoustic detections showed no relationship with cloud cover presence but did decrease with increased flight speed. We found sampling methods to be positively correlated when comparing mean nightly traffic rates across nights. The strength of these correlations generally increased throughout the night, peaking 2–3 hours before morning twilight. Given the convergence of measures by different tools at this time, we suggest that researchers consider sampling flight activity in the hours before morning twilight when differences due to detection biases among sampling tools appear to be minimized.
Journal Article
Effective Non-Stationary Clutter Suppression Method via Elevation Oblique Subspace Projection for Moving Targets Detection with a Space-Based Surveillance Radar
The clutter becomes non-stationary for a space-based surveillance radar (SBSR), which is harmful for the moving targets detection due to the earth’s rotation. The non-stationarity will degrade the accuracy of clutter covariance matrix (CCM) estimation and increase the clutter degree of freedom (DOF), thereby degrading the performance of clutter suppression. To solve this problem, this paper proposes a novel non-stationary clutter suppression method using an elevation oblique subspace projection method. After analyzing the range ambiguity and non-stationarity of the clutter, the proposed method utilized the oblique projection matrix to project the signal onto the subspace spanned by the near-range and far-range clutter components along the subspace spanned by the main lobe clutter component. Then, the projected signal was used to estimate the elevation covariance matrix and calculate the optimal weight vector for the elevation adaptive filter. The proposed method can suppress the non-stationary clutter effectively with a higher improvement factor (IF) and a narrower main lobe width. Finally, the simulation results were given to verify the correctness and effectiveness of the proposed method.
Journal Article
Spatial downscaling of radar-derived rainfall field by two-dimensional wavelet transform
Downscaling of rainfall fields, either as images or products of global circulation models, have been the motive of many hydrologists and hydro-meteorologists. The main concern in downscaling is to transform high-resolution properties of the rainfall field to lower resolution without introducing erroneous information. In this paper, rainfall fields obtained from Next Generation Weather Surveillance Radar (NEXRAD) Level III were examined in the wavelet domain which revealed sparsity for wavelet coefficients. The proposed methodology in this work employs a concept named Standardized Rainfall Fluctuation (SRF) to overcome the sparsity of rainfall fields in wavelet domain which also exhibited scaling behaviors in a range of scales. SRFs utilizes such scaling behaviors where upscaled versions of the rainfall fields are downscaled to their actual size, using a two-dimensional discrete wavelet transform, to examine the reproduction of the rainfall fields. Furthermore, model modifications were employed to enhance the accuracy. These modifications include removing the negative values while conserving the mean and applying a non-overlapping kernel to restore high-gradient clusters of rainfall fields. The calculated correlation coefficient, statistical moments, determination coefficient and spatial pattern display a good agreement between the outputs of the downscaling method and the observed rainfall fields.
Journal Article
Enhancing the performance of the Primary Surveillance Radar using Multilateration
One way to improve the measurements of the PSR (Primary Surveillance Radar) is to utilize the cinematic model of the aircraft (A/C) in a Kalman filter. Another newly developed method would be to implement multilateration using a large number of ground-based ADS-B (Automatic Dependent Surveillance-Broadcast) receivers. Originating in airport surveillance, multilateration grew to become the primary system for ATM (Air Traffic Management) in airspaces without PSR coverage. Given that each of the systems has its own advantages and limitations, we propose an evaluation of an alternative approach that uses data from multiple ADS-B receivers to implement a data fusion algorithm between PSR acquired position and MLAT (Multilateration) estimated position. Among the many ways to implement data fusion, have chosen to analyze two possible solutions: the direct fusion of the two available positions provided by the two systems using a traditional Kalman Filter and a linearization approach for the multilateration solution that does not require position computation. In both cases, these will improve the Kalman filter and lower the position estimation errors. The evaluation takes into consideration the possible sources of inaccuracies and provides sensibility analyses in regards to the number and positioning of ADS-B receivers involved in multilateration. This paper will conclude with a discussion of the computational power required for the two implementations.
Journal Article