Degradation In Detection Performance Research Articles

Frequency-Adaptive Learning for SAR Ship Detection in Clutter Scenes

Convolutional neural networks (CNNs) have been widely applied in the context of ship detection in synthetic aperture radar (SAR) images, but the detection performance is still not ideal in scenarios with clutter interference. Mining frequency-domain information to suppress the sea clutter in SAR ship detection has attracted wide attention. However, existing frequency-domain ship detection methods do not process frequency-domain information adaptively, which results in the degradation of ship detection performance. To overcome this problem, this article proposes a novel deep learning network called YOLO-FA. YOLO-FA contains the proposed Frequency Attention Module (FAM), which can process frequency-domain information of SAR images adaptively. The proposed method can suppress the sea clutter in the SAR images with the help of frequency-domain information. We evaluate the proposed method YOLO-FA on two datasets, i.e., the High-Resolution SAR images dataset (HRSID) and SAR Ship Detection Dataset (SSDD). Compared with the baseline method YOLOv5 and existing commonly used methods, YOLO-FA achieves state-of-the-art detection performance on both datasets.

Air Moving Target Indication in Nadir Region for Spaceborne Surveillance Radar Systems

For air moving target indication in nadir region, due to the fact that a spaceborne radar beam can illuminate the top of fuselage, the target radar cross section is usually high, which is beneficial for the detection of a low-observable target. However, due to the short slant range, specular reflection effect, and relatively low radar ground resolution, the power of clutter component from nadir region is comparatively high, leading to the insufficient clutter suppression and the degradation of target detection performance. Fortunately, when an air moving target is adequately high, the target echo is able to be separated from the main clutter echoes due to a shorter time delay, making it possible to be only mixed with low-power ambiguous clutter echoes. Based on these considerations, this paper analyzes the performance of air moving target indication in nadir region for a spaceborne surveillance radar system. It analyzes the target minimum detectable velocities with different target heights and beam center elevation angles. Also, an effective sample selection method based on adaptive range segmentation is proposed to solve the power heterogeneity issue between the main clutter area and the range ambiguous clutter area. As a conclusion, the larger the elevation angle of an air moving target is, the higher the minimum target detectable height is.

A Carrier Synchronization Lock Detector Based on Weighted Detection Statistics for APSK Signals

To solve the application limitations of conventional detectors caused by discrete phase distribution of high-order APSK signals, and the problem that the detection performance will degrade when the automatic control gain is unideal, a carrier synchronization lock detector based on weighted detection statistics is proposed for APSK signals. Based on the detection statistics of the Linn detector, the proposed detector calculates a weighted factor according to the amplitude difference of the signal on the APSK constellation to adjust the weight of detection statistics for different rings. The proposed detector solves the detection performance degradation problem of the Linn detector caused by uneven phase distribution. In order to further improve detection performance, the detection threshold and statistical signal length are reasonably designed. The expectation and variance properties are derived, and the lock detection probability is analyzed. The performance of the proposed detector is verified through simulations. Simulation results show that the proposed carrier synchronization lock detector has better performance than the Linn detector.

Ship Detection in Spaceborne SAR Images under Radio Interference Environment Based on CFAR

Spaceborne synthetic aperture radar (SAR) can be easily interfered with by narrowband radio frequency interference (RFI) from ground radiation sources, causing significant degradation of image quality. In the application of SAR ship detection, the radio interference will raise the detection threshold of a constant false alarm rate (CFAR) detector, and consequently results in the degradation of detection performance. In order to solve this problem, we propose a ship-detection method for SAR under a narrowband RFI environment. The proposed method is mainly divided into five steps: (1) transform the input SAR image with narrowband RFI into 2-D frequency domain by fast Fourier transform (FFT); (2) use CFAR detector to detect RFI in 2-D frequency domain; (3) suppress RFI data points using adaptively weighting in the 2-D frequency domain; (4) transform the RFI suppressed 2-D spectrum into the image domain via inverse FFT; (5) apply CFAR detector for ship detection. Simulation and real data experiments show that the proposed method can effectively detect ships from SAR images with ocean background even if there exists serious RFI.

Adaptive Radar Detection in the Clutter and Noise Cover Pulse Jamming Environment

This paper deals with the adaptive detection of the point-like target under clutter and noise cover pulse (NCP) jamming. In the design stage, we assume that the jamming is orthogonal to the signal subspace. Simultaneously, two training datasets are considered available. According to generalized likelihood ratio test (GLRT), Wald, and Rao criteria, NCP-GLRT, NCP-Wald, and NCP-Rao detectors are designed respectively. Then, considering the degraded detection performance of the NCP-Rao, a modified detector called NCP-MRao is proposed. Moreover, the performance of the newly presented detectors is analyzed, and the probability of false alarm and the detection probability for the NCP-GLRT are derived. Finally, numerical simulation results show that compared with the analogous counterparts, the proposed detector can improve detection performance.

Adaptive Robust Radar Target Detector Based on Gradient Test

The exact knowledge of the signal steering vector is not always known, which may result in detection performance degradation when a signal mismatch occurs. In this paper, we discuss the problem of designing a robust radar target detector in the background of Gaussian noise whose covariance matrix is unknown. To improve robustness to mismatched signals, a random perturbation that follows the complex normal distribution is added under the alternative hypothesis. Since traditional detectors that divide complex parameters into real parts and imaginary parts are sometimes difficult to obtain, a new robust, complex parameter gradient test is derived directly from the complex data. Moreover, the CFAR property of the new detector is proven. The performance assessment indicates that the gradient detector exhibits suitable robustness to the mismatched signals.

Underwater Tone Detection with Robust Coherently-Averaged Power Processor

The detection of tonal signals with unknown frequencies is an important area of study in underwater signal processing. A common approach to address this issue is to use the Discrete Fourier Transform (DFT) for observations. When a tone does not lie precisely at the discrete DFT frequency point, its energy will leak to adjacent frequency point. This phenomenon is known as scalloping loss or Picket Fence Effect (PFE). PFE leads to the degradation of detection performance based on DFT. This paper studies the problem of robust detection in the case of PFE. A coherently-averaged power processor utilizing the information of adjacent frequency bins is designed. The results of simulations and experiments show that the proposed method is robust against PFE, and is highly suitable for tone detection in practical circumstances.

Theoretical and experimental study on interface stiffness measurement of rough surface using improved acoustic model

Structural health monitoring (SHM) of interface stiffness is of great significance in many fields, such as accurate rotor dynamics modeling and interface friction performance analysis. Acoustic wave is an important interface detection method. However, the interface mechanics and elastic wave theory are relatively independent, and the interaction mechanism between the contact asperity parameters and the energy transfer of elastic waves has not been thoroughly explored. The existing interface stiffness detection indexes do not consider the uncertainty of boundary parameters, which easily leads to the degradation of detection performance and limits the promotion of interface stiffness detection. In this paper, we proposed an acoustic model considering contact interface and boundary effects integrating microwave transmission line theory. A new interface stiffness detection index is constructed to ensure the robustness of the detection results. The relationship between contact parameters and elastic wave energy transfer is analyzed, and the robustness of different detection indexes and the uncertainty of different detection methods are compared. The experimental results of the pressure-bearing connection structure are obtained to verify the correctness of the acoustic model and the feasibility of the detection method. The proposed method is further applied to multi-bolted structures, and the robustness of the proposed method is discussed. The results show that the proposed method has the ability of in-situ interface stiffness detection and potential engineering utility.

Low Complexity, Pairwise Layered Tabu Search for Large Scale MIMO Detection

This paper presents a low complexity pairwise layered tabu search based detection algorithm for a large-scale multiple-input multiple-output system. The proposed algorithm can compute two layers simultaneously and reduce the effective number of tabu searches. An efficient Gram matrix and matched filtered output update strategy is developed to reuse the computations from past visited layers. Also, a precomputation technique is adapted to reduce the redundancy in computation within tabu search iterations. Complexity analysis shows that the upper bound of initialization complexity in the proposed algorithm reduces from \(O(N_t^4)\) to \(O(N_t^3)\). The detection performance of the proposed detector is almost the same as the conventional complex version of LTS for 64QAM and 16QAM modulations. However, the proposed detector outperforms the conventional system for 4QAM modulation, especially in \(16 \times 16\) and \(8 \times 8\) MIMO. Simulation results show that the percent of complexity reduction in the proposed method is approximately 75% for \(64 \times 64\), 64QAM and 85% for \(64 \times 64\) 16QAM systems to achieve a BER of \(10^{-3}\). Moreover, we have proposed three layer-wise iteration allocation strategies that can further reduce the upper bound of complexity with minor degradation in detection performance.

A variational Bayesian approach to multiantenna spectrum sensing under correlated noise

In this paper, we address the problem of spectrum sensing when the secondary user (SU) is equipped with a multiantenna receiver and noise across antenna elements are correlated. Covariance matrix of correlated noise is non-diagonal and its standard-condition-number (SCN) is much greater than one, which results in significantly degraded detection performance. Many of the existing techniques either whiten the covariance matrix or utilize geometrical properties of noise covariance matrix for the design of decision statistic. However, these techniques have low detection performance in uncorrelated noise or when the correlation is low. To address this problem and further improve the detection performance, a novel spectrum sensing algorithm based on variational Bayesian Gaussian mixture model (VBGMM) is proposed. Specifically, a new decision vector is proposed which is amenable to VBGMM and incorporates signal features suitable for spectrum sensing in both correlated and uncorrelated noise. Decision vectors extracted from the received signal are clustered using variational expectation-maximization (VEM) algorithm to detect the presence of primary user (PU). Compared to existing techniques, the proposed method is shown by simulation to achieve higher detection rates for the entire range of correlation.

Joint Design Method of Transmit-Receive for Airborne MIMO Radar Based on Feasible Point Pursuit

Consider the moving target detection performance degradation of airborne multiple-input multiple-output (MIMO) radar in the presence of inaccurate target prior information. This paper proposes a joint design method of transmit waveform and receive filter bank of airborne MIMO radar based on feasible point pursuit successive convex approximation (FPP-SCA). Firstly, a set of receive filter banks is designed in the region where the target may appear on the angle-Doppler plane, and the worst-case output signal-to-clutter-plus-noise ratio (SCNR) is maximized as the optimization criterion. Secondly, considering the energy constraint and similarity on the transmit waveform, the maximin joint design problem is formulated to improve the robustness of the MIMO space-time adaptive processing (STAP) radar against the uncertainty of target parameters. Finally, an FPP-SCA algorithm is employed to solve the maximin nonconvex joint design problem. Simulation results demonstrate the effectiveness of the proposed method in terms of better output SCNR, lower computational load, and more robustness against the errors of target parameters.

A Study of LiDAR’s Detection Performance Degradation in Fog and Rain Climate

A Study of LiDAR’s Detection Performance Degradation in Fog and Rain Climate

Event-Based Motion Capture System for Online Multi-Quadrotor Localization and Tracking.

Motion capture systems are crucial in developing multi-quadrotor systems due to their ability to provide fast and accurate ground truth measurements for tracking and control. This paper presents the implementation details and experimental validation of a relatively low-cost motion-capture system for multi-quadrotor motion planning using an event camera. The real-time, multi-quadrotor detection and tracking tasks are performed using a deep learning network You-Only-Look-Once (YOLOv5) and a k-dimensional (k-d) tree, respectively. An optimization-based decentralized motion planning algorithm is implemented to demonstrate the effectiveness of this motion capture system. Extensive experimental evaluations were performed to (1) compare the performance of four deep-learning algorithms for high-speed multi-quadrotor detection on event-based data, (2) study precision, recall, and F1 scores as functions of lighting conditions and camera motion, and (3) investigate the scalability of this system as a function of the number of quadrotors flying in the arena. Comparative analysis of the deep learning algorithms on a consumer-grade GPU demonstrates a 4.8× to 12× sampling/inference rate advantage that YOLOv5 provides over representative one- and two-stage detectors and a 1.14× advantage over YOLOv4. In terms of precision and recall, YOLOv5 performed 15% to 18% and 27% to 41% better than representative state-of-the-art deep learning networks. Graceful detection and tracking performance degradation was observed in the face of progressively darker ambient light conditions. Despite severe camera motion, YOLOv5 precision and recall values of 94% and 98% were achieved, respectively. Finally, experiments involving up to six indoor quadrotors demonstrated the scalability of this approach. This paper also presents the first open-source event camera dataset in the literature, featuring over 10,000 fully annotated images of multiple quadrotors operating in indoor and outdoor environments.

Assessing the Image Concept Drift at the OBSEA Coastal Underwater Cabled Observatory

The marine science community is engaged in the exploration and monitoring of biodiversity dynamics, with a special interest for understanding the ecosystem functioning and for tracking the growing anthropogenic impacts. The accurate monitoring of marine ecosystems requires the development of innovative and effective technological solutions to allow a remote and continuous collection of data. Cabled fixed observatories, equipped with camera systems and multiparametric sensors, allow for a non-invasive acquisition of valuable datasets, at a high-frequency rate and for periods extended in time. When large collections of visual data are acquired, the implementation of automated intelligent services is mandatory to automatically extract the relevant biological information from the gathered data. Nevertheless, the automated detection and classification of streamed visual data suffer from the “concept drift” phenomenon, consisting of a drop of performance over the time, mainly caused by the dynamic variation of the acquisition conditions. This work quantifies the degradation of the fish detection and classification performance on an image dataset acquired at the OBSEA cabled video-observatory over a one-year period and finally discusses the methodological solutions needed to implement an effective automated classification service operating in real time.

End-to-End Deep Learning-Based Cells Detection in Microscopic Leucorrhea Images

Vaginitis is a prevalent gynecologic disease that threatens millions of women’s health. Although microscopic examination of vaginal discharge is an effective method to identify vaginal infections, manual analysis of microscopic leucorrhea images is extremely time-consuming and labor-intensive. To automate the detection and identification of visible components in microscopic leucorrhea images for early-stage diagnosis of vaginitis, we propose a novel end-to-end deep learning-based cells detection framework using attention-based detection with transformers (DETR) architecture. The transfer learning was applied to speed up the network convergence while maintaining the lowest annotation cost. To address the issue of detection performance degradation caused by class imbalance, the weighted sampler with on-the-fly data augmentation module was integrated into the detection pipeline. Additionally, the multi-head attention mechanism and the bipartite matching loss system of the DETR model perform well in identifying partially overlapping cells in real-time. With our proposed method, the pipeline achieved a mean average precision (mAP) of 86.00% and the average precision (AP) of epithelium, leukocyte, pyocyte, mildew, and erythrocyte was 96.76, 83.50, 74.20, 89.66, and 88.80%, respectively. The average test time for a microscopic leucorrhea image is approximately 72.3 ms. Currently, this cell detection method represents state-of-the-art performance.

Candidate-plots-based dynamic programming algorithm for track-before-detect

Compared with the classical detect-before-track (DBT) technique, the track-before-detect (TBD) is an effective method to improve the performance of detecting weak targets. Operating directly on the plot-lists data obtained by the detector and plot-extractor, the classical dynamic programming for the TBD algorithm (DP-TBD) confronts the loss of merit function (MF), which will lead to extra degradation of detection performance. Thus, we analyze the influence of relevant factors on the loss of MF, such as target detection probability and clutter. We present results from the analysis that clutter plots near the positions of target-missed detections contribute to ease the loss of MF. The information of target-missed detections is beneficial to prevent the loss of MF. Then, we propose a candidate-plots-based DP-TBD (CP-DP-TBD) method providing candidate plots carrying the information of target-missed detections by the modified MF transfer procedure. Finally, the computational efficiency and the detection performance of CP-DP-TBD are demonstrated by comparing it with other methods in both simulated data and experimental data.

The Impact of Channel Type on Spectrum Sensing

In this letter, we investigate the impact of channel correlation and line-of-sight (LOS) on the performance of spectrum sensing, focusing on energy detection (ED) with estimated noise power and the sphericity test (ST). We derive the expected value and variance of the test statistic for ED with estimated noise power, showing that detection probability decreases with channel correlation but increases with LOS propagation. For the ST, we show that detection improves with increased LOS, but the effects of correlation are complex. Depending on the system parameters correlation can improve or degrade detection performance. The analysis is corroborated by means of simulations for correlated Rayleigh, Ricean and ray-based channel models. Finally, we leverage these results to investigate the performance of the recently proposed mixture detector (MD).

Cooperative Fusion Architecture-based Distributed Spectrum Sensing Under Rayleigh Fading Channel

Cognitive radio is one of the most promising technologies due to the spectrum scarcity, especially in the microwave band. Spectrum sensing forms an essential functionality for CR systems. However, such detection performance is usually compromised by shadowing and fading channel conditions. Cooperative sensing is one of the crucial solutions to overcome degraded detection performance. A distributed architecture for the processing and fusion of sensing information is proposed to improve sensing performance and reduce reporting error. The decision fusion for cooperated users could be complex in dense network scenarios and reported sensing traffic may require significant bandwidth. The paper presents a new cooperative distributed PUs detection and proposes dynamic threshold based on controlled probability of false alarm to enhance sensing efficiency and reliability in a Rayleigh fading environments. A cooperative distributed PUs detection is realized by fusion node (FNs) that dynamically selected from the group members. The adaptive detection threshold is computed dynamically using the link quality indicators (LQIs) of the sensing channels. Moreover, the proposed scheme can significantly minimize the typically transmitted bits in the reporting channels. This work also deliberated the design parameters of the CR on the performance of fusion values. The simulation evaluation presents that the adapted threshold considerably improves the performance of the distributed cooperative sensing (DCS) process. The simulation results verified that the error was minimized remarkably. The ROC graph is notably enhanced for the sensing process in term of probability of detection and probability of false alarm. Finally, it was presented that with the proposed scheme the sensitivity requirements could be significantly enhanced up to 0.95.

Epilepsy Detection Based on Variational Mode Decomposition and Improved Sample Entropy.

Epilepsy detection based on electroencephalogram (EEG) signal is of great significance to diagnosis and treatment of epilepsy. The denoised EEG signal is adopted by most traditional epilepsy detection methods. But due to nonideal denoising ability, the loss of local information and residual noise will occur, resulting in detection performance degradation. To solve the problem, the paper proposed an epilepsy detection method in noisy environment. Although epileptic signals and nonepileptic signals have some discrimination, they need to overcome the interference of noise. Hence, the improved sample entropy and phase synchronization indexes of corresponding 2 intrinsic mode functions (IMFs) caused by variational mode decomposition (VMD) are proposed as features, which can reduce the impact of noise on detection performance. The experimental results show that the accuracy, sensitivity, and specificity are 91.78%, 91.27%, and 93.61%, respectively. It can be used as an auxiliary method for clinical treatment of epilepsy.

DNN-Based Peak Sequence Classification CFAR Detection Algorithm for High-Resolution FMCW Radar

Multitarget detection is very challenging, especially when targets are densely distributed. In conventional constant false alarm rate (CFAR) detection algorithms, the detection threshold is determined based on a pre-estimated background level (BL). However, interfering targets inevitably lead to inaccurate BL estimation, resulting in degraded detection performance. In our previous work, a new solution was developed and verified to be effective; in that solution, compressed sensing (CS)-based detector performs target detection without relying on BL estimation, and a subsequent CFAR regulation processor achieves the desired false alarm rate based on the statistical information of the reduced samples obtained by removing the detected targets and adjacent guard cells from the original samples. However, for scenes with a high target density, the CS-based detector suffers from performance degradation while acquiring the correlation between linear measurements of the signal and the sensing matrix due to the high local signal sparsity. To address this shortcoming, this article proposes a deep neural network (DNN)-based detector that further improves the detection performance by converting the target detection into a problem of peak sequence classification of frequency intensity (FI) measurements from radar. A DNN detector trained on augmented simulated data shows excellent generalization ability for deployment in real scenes. In addition, to achieve better computational efficiency, a Taylor-series-based approximate maximum likelihood estimator with an explicit expression is applied in the CFAR regulation process for the first time. Both simulation and field tests are performed to verify the superiority of the proposed algorithm over conventional algorithms.