Optimal Detection Performance Research Articles

Detection performance of airborne FDA-MIMO radar under non-IID RCS scenario

ABSTRACT This paper studies the incoherent square-law detector in the Neyman–Pearson sense for airborne frequency diverse array multiple-input multiple-output (FDA-MIMO) radar system under a non-independent and identically distributed radar cross section model. The presented incoherent square-law detector is suitable for airborne FDA-MIMO radar with arbitrary frequency increments and correlation coefficients and achieves the optimal detection performance. The superiority of FDA-MIMO radar over traditional phased array and MIMO radars in target detection is validated by both theoretical analysis and numerical results.

SeeCucumbers: Using Deep Learning and Drone Imagery to Detect Sea Cucumbers on Coral Reef Flats

Sea cucumbers (Holothuroidea or holothurians) are a valuable fishery and are also crucial nutrient recyclers, bioturbation agents, and hosts for many biotic associates. Their ecological impacts could be substantial given their high abundance in some reef locations and thus monitoring their populations and spatial distribution is of research interest. Traditional in situ surveys are laborious and only cover small areas but drones offer an opportunity to scale observations more broadly, especially if the holothurians can be automatically detected in drone imagery using deep learning algorithms. We adapted the object detection algorithm YOLOv3 to detect holothurians from drone imagery at Hideaway Bay, Queensland, Australia. We successfully detected 11,462 of 12,956 individuals over 2.7ha with an average density of 0.5 individual/m2. We tested a range of hyperparameters to determine the optimal detector performance and achieved 0.855 mAP, 0.82 precision, 0.83 recall, and 0.82 F1 score. We found as few as ten labelled drone images was sufficient to train an acceptable detection model (0.799 mAP). Our results illustrate the potential of using small, affordable drones with direct implementation of open-source object detection models to survey holothurians and other shallow water sessile species.

Dimension-Reduced Rx Beamforming Optimized for Simultaneous Detection and Estimation

Phased array radar systems are indispensable in many applications requiring robust sensing of the environment. To achieve sensitive target detection and accurate direction-of-arrival (DOA) estimation, a large number of receiving antenna elements are needed. The high dimension of the element-level data inevitably leads to a large computational burden for digital signal processing. This problem can be overcome by transforming the element-level data into a lower dimensional beamspace. In this article, we present a novel parameter-controlled design method to construct this transformation. If the dimension reduction is not too drastic, it jointly achieves optimal detection and DOA estimation performance. Otherwise, it meets predefined performance criteria by exploiting an acceptable tradeoff between detection and DOA estimation performance. We propose a general design tool, which is not limited to a specific array configuration. It comprises a precalculated set of plots, providing the radar designer an overview of possible performance for a given scenario. We describe a straightforward method to construct the corresponding transformation. Numerical studies highlight the superiority of the proposed design method.

YOLOv3-Litchi Detection Method of Densely Distributed Litchi in Large Vision Scenes

Accurate and reliable fruit detection in the orchard environment is an important step for yield estimation and robotic harvesting. However, the existing detection methods often target large and relatively sparse fruits, but they cannot provide a good solution for small and densely distributed fruits. This paper proposes a YOLOv3-Litchi model based on YOLOv3 to detect densely distributed litchi fruits in large visual scenes. We adjusted the prediction scale and reduced the network layer to improve the detection ability of small and dense litchi fruits and ensure the detection speed. From flowering to 50 days after maturity, we collected a total of 266 images, including 16,000 fruits, and then used them to construct the litchi dataset. Then, the k-means++ algorithm is used to cluster the bounding boxes in the labeled data to determine the priori box size suitable for litchi detection. We trained an improved YOLOv3-Litchi model, tested its litchi detection performance, and compared YOLOv3-Litchi with YOLOv2, YOLOv3, and Faster R-CNN on the actual detection effect of litchi and used the F1 value and the average detection time as the assessed value. The test results show that the F1 of YOLOv3-Litchi is higher than that of YOLOv2 algorithm 0.1, higher than that of YOLOv3 algorithm 0.08, and higher than that of Faster R-CNN algorithm 0.05; the average detection time of YOLOv3-Litchi is 29.44 ms faster than that of YOLOv2 algorithm, 19.56 ms faster than that of YOLOv3 algorithm ms, and 607.06 ms faster than that of Faster R-CNN algorithm. And the detection speed of the improved model is faster. The proposed model remits optimal detection performance for small and dense fruits. The work presented here may provide a reference for further study on fruit-detection methods in natural environments.

Stochastic Resonance Effect in Optimal Decision Solution Under Neyman–Pearson Criterion

In this paper, stochastic resonance effect on the optimal detection under Neyman–Pearson (NP) criterion is investigated for a general nonlinear system. To this end, a noise enhanced detection optimization problem for maximizing the probability of detection under a constant constraint on the probability of false-alarm is formulated, where an additive noise is added to the nonlinear system input and the final decision is made based on the system output according to the NP criterion. Firstly, the noise modified NP decision rule is derived. Since one additive noise corresponds to one noise modified NP decision rule, the noise-modified NP decision rule can be viewed as a function of the additive noise. Then, the improvability of the optimal detection performance under NP criterion via the proposed noise-modified decision solution is simply discussed. The optimal additive noise is deduced as a random signal of no more than two constant vectors and the corresponding noise-modified NP decision rule is also determined. Finally, the performance comparisons between the original and the noise-modified optimal NP decision solutions for the sine transform system and the Amplitude limit system are made to illustrate the theoretical results.

Deep neural network-aided coherent integration method for maneuvering target detection

Generalized Radon-Fourier transform (GRFT) is a classical long-time coherent integration method for radar maneuvering target detection. GRFT, whose core is to achieve motion parameter estimation via searching, can almost reach the optimal detection performance but heavily suffers from the high computational cost. Motivated by the fact that motion parameter estimation is essentially a non-linear mapping from the radar echo to the target’s motion parameters, one can use a deep neural network (DNN), a kind of modeling tool that can learn complex mappings from training data, to directly realize this mapping, thus alleviating the heavy computational burden brought by the searching efforts. Based on this idea, a DNN-aided long-time coherent integration algorithm, which can be viewed as a fast implementation of GRFT, is proposed in this paper. More specifically, we first use a pre-trained DNN to roughly estimate the motion parameters of the target to be detected from the radar echo, and then accomplish the coherent integration of the target for detection via a fine grid search in the neighborhood of the obtained rough estimation results. Finally, simulation results demonstrate that the proposed algorithm can achieve the detection performance close to that of GRFT but with a much lower computational cost.

Receiver Design With Reduced DOF in Frequency Domain for Target Detection Under Gaussian Clutter

This paper addresses the problem of target detection against a background of Gaussian clutter by using frequency snapshots with reduced degrees of freedom (DOF). We derive the optimal detector and detection performance under the Neyman-Pearson criterion for general frequency snapshot selection with arbitrary DOF. When the clutter statistics are unknown, we use a uniformly random frequency snapshot selection method and show how the DOF employed affects the detection performance. When the clutter return follows a stationary Gaussian distribution with slowly varying power spectral density, the optimal selection is derived. When the clutter is composed of reflected versions of the transmitted waveforms, a greedy-based method for selecting the frequency snapshots is presented. Numerical experiments show that a receiver with reduced DOF can lead to detection performance which is very close to that of the receiver with full DOF.

Ordering for Communication-Efficient Quickest Change Detection in a Decomposable Graphical Model

A quickest change detection problem is considered in a sensor network with observations whose statistical dependency structure across the sensors before and after the change is described by a decomposable graphical model (DGM). Distributed computation methods for this problem are proposed that are capable of producing the optimum centralized test statistic. The DGM leads to the proper way to collect nodes into local groups equivalent to cliques in the graph, such that a clique statistic which summarizes all the clique sensor data can be computed within each clique. The clique statistics are transmitted to a decision maker to produce the optimum centralized test statistic. In order to further improve communication efficiency, an ordered transmission approach is proposed where transmissions of the clique statistics to the fusion center are ordered and then adaptively halted when sufficient information is accumulated. This procedure is always guaranteed to provide the optimal change detection performance, despite not transmitting all the statistics from all the cliques. A lower bound on the average number of transmissions saved by ordered transmissions is provided and for the case where the change seldom occurs the lower bound approaches approximately half the number of cliques provided a well behaved distance measure between the distributions of the sensor observations before and after the change is sufficiently large. We also extend the approach to the case when the graph structure is different under each hypothesis. Numerical results show significant savings using the ordered transmission approach and validate the theoretical findings.

Automated detection of abnormal heart sound signals using Fano-factor constrained tunable quality wavelet transform

Manual interpretation of heart sounds is insensitive and prone to subjectivity. Automated diagnosis systems incorporating artificial intelligence and advanced signal processing tools can potentially increase the sensitivity of disease detection and reduce the subjectiveness. This study proposes a novel method for the automated binary classification of heart sound signals using the Fano-factor constrained tunable quality wavelet transform (TQWT) technique. Optimal TQWT based decomposition can reveal significant information in subbands for the reconstruction of events of interest. While transforming heart sound signals using TQWT, the Fano-factor is applied as a thresholding parameter to select the subbands for the clinically relevant reconstruction of signals. TQWT parameters and threshold of the Fano-factor are tuned using a genetic algorithm (GA) to adapt to the underlying optimal detection performance. The time and frequency domain features are extracted from the reconstructed signals. Overall 15 unique features are extracted from each sub-frame resulting in a total feature set of 315 features for each epoch. The resultant features are fed to Light Gradient Boosting Machine model to perform binary classification of the heart sound recordings. The proposed framework is validated using a ten-fold cross-validation scheme and attained sensitivity of 89.30%, specificity of 91.20%, and overall score of 90.25%. Further, synthetic minority over-sampling technique (SMOTE) is applied to produce balanced data set which yielded sensitivity and specificity of 86.32% and 99.44% respectively and overall score of 92.88%. Our developed model can be used in digital stethoscopes to automatically detect abnormal heart sounds and aid the clinicians in their diagnosis.

SA-YOLOv3: An Efficient and Accurate Object Detector Using Self-Attention Mechanism for Autonomous Driving

Object detection is becoming increasingly significant for autonomous-driving system. However, poor accuracy or low inference performance limits current object detectors in applying to autonomous driving. In this work, a fast and accurate object detector termed as SA-YOLOv3, is proposed by introducing dilated convolution and self-attention module (SAM) into the architecture of YOLOv3. Furthermore, loss function based on GIoU and focal loss is reconstructed to further optimize detection performance. With an input size of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$512\times 512$ </tex-math></inline-formula> , our proposed SA-YOLOv3 improves YOLOv3 by 2.58 mAP and 2.63 mAP on KITTI and BDD100K benchmarks, with real-time inference (more than 40 FPS). When compared with other state-of-the-art detectors, it reports better trade-off in terms of detection accuracy and speed, indicating the suitability for autonomous-driving application. To our best knowledge, it is the first method that incorporates YOLOv3 with attention mechanism, and we expect this work would guide for autonomous-driving research in the future.

Maneuvering target detection in random pulse repetition interval radar via resampling-keystone transform

With good electronic counter-countermeasures capability, random pulse repetition interval (PRI) radars receive increasing attention recently. However, the problem of maneuvering target detection in random PRI radars is rarely studied yet. In this problem, the difficulty lies in not only the range migration (RM) and Doppler frequency migration (DFM) effects but also the non-uniform sampling pulses. This paper proposes a novel algorithm for this problem. In the proposed algorithm, we first combine the non-uniform resampling operation and the keystone transform to propose the resampling-keystone transform, which can eliminate the RM and resample the non-uniform sampling pulses into uniform ones in one step. Then, the dechirp process, whose implementation can benefit from the fast Fourier transform, is employed to accomplish coherent integration for target detection by compensating the DFM. The proposed algorithm is applicable for both single-target and multi-target scenarios. Besides, the available integration time and computational complexity of the proposed algorithm are analyzed. Finally, simulation results are given to show that the proposed algorithm can approach the optimal detection performance with a much lower computational cost than the well-known generalized Radon Fourier transform.

Linear Canonical Wigner Distribution of Noisy LFM Signals via Multiobjective Optimization Analysis Involving Variance-SNR

The existing output signal-to-noise ratio (SNR) optimization model seems not accurate enough to characterize the closed-form instantaneous cross-correlation function type of Wigner distribution’s optimum detection performance, resulting in an unsatisfactory optimal linear canonical transform free parameters selection strategy. A more appropriate output SNR definition, the so-called variance-SNR, is then proposed to model a new kind of optimization, the variance-SNR optimization model. By integrating these two optimization models, it follows a multiobjective optimization, whose solutions to noisy single component linear frequency-modulated signals are also deduced. The newly derived strategy on determination of the optimal parameters implies the previous one so that there is a better detection performance in terms of noise suppression.

Covert Wireless Communications Under Quasi-Static Fading With Channel Uncertainty

Covert communications enable a transmitter to send information reliably in the presence of an adversary, who looks to detect whether the transmission took place or not. We consider covert communications over quasi-static block fading channels, where users suffer from channel uncertainty. We investigate the adversary Willie’s optimal detection performance in two extreme cases, i.e., the case of perfect channel state information (CSI) and the case of channel distribution information (CDI) only. It is shown that in the large detection error regime, Willie’s detection performances of these two cases are essentially indistinguishable, which implies that the quality of CSI does not help Willie in improving his detection performance. This result enables us to study the covert transmission design without the need to factor in the exact amount of channel uncertainty at Willie. We then obtain the optimal and suboptimal closed-form solution to the covert transmission design. Our result reveals fundamental difference in the design between the case of quasi-static fading channel and the previously studied case of non-fading AWGN channel.

Adaptive subspace detection based on two-step dimension reduction in the underwater waveguide

In the underwater waveguide, the conventional adaptive subspace detector (ASD), derived by using the generalized likelihood ratio test (GLRT) theory, suffers from a significant degradation in detection performance when the samplings of training data are deficient. This paper proposes a dimension-reduced approach to alleviate this problem. The dimension reduction includes two steps: firstly, the full array is divided into several subarrays; secondly, the test data and the training data at each subarray are transformed into the modal domain from the hydrophone domain. Then the modal-domain test data and training data at each subarray are processed to formulate the subarray statistic by using the GLRT theory. The final test statistic of the dimension-reduced ASD (DR-ASD) is obtained by summing all the subarray statistics. After the dimension reduction, the unknown parameters can be estimated more accurately so the DR-ASD achieves a better detection performance than the ASD. In order to achieve the optimal detection performance, the processing gain of the DR-ASD is deduced to choose a proper number of subarrays. Simulation experiments verify the improved detection performance of the DR-ASD compared with the ASD.

A versatile fluorometric in situ hybridization method for the quantitation of hairpin conformations in DNA self-assembled monolayers.

As the performance of hairpin DNA (hpDNA)-based biosensors is highly dependent on the yield of stem-loop (hairpin) conformations, we report herein a versatile fluorometric in situ hybridization protocol for examining hpDNA self-assembled monolayers (SAMs) on popularly used biochip substrates. Specifically, the ratio of fluorescence (FL) intensities of hpDNA SAMs (in an array format) before and after hybridization was adopted as the key parameter for performing such a determination. Upon confirming the existence of mixed and tunable DNA conformations in binary deposition solutions and efficient hybridization of the hairpin strands with the target DNA via gel electrophoresis assays, we tested the fluorometric protocol for determining the coverages of hpDNA in hpDNA/ssDNA SAMs prepared on gold; its accuracy was validated by Exonuclease I (Exo I)-assisted electrochemical quantitation. To further confirm its versatility, this FL protocol was adopted for quantifying hairpin conformations formed on glass and polycarbonate (PC) substrates. The molar ratios of surface-tethered hairpin conformations on the three different substrates were all found to be proportional to but less than those in the binary deposition solutions, and were dependent on the substrate morphology. The findings reported herein are beneficial for the construction of highly efficient DNA hairpin-based sensing surfaces, which essentially facilitates the creation of hpDNA-based biosensors with optimal detection performance.

Distributed Detection of Sparse Stochastic Signals via Fusion of 1-bit Local Likelihood Ratios

In this letter, we consider the detection of sparse stochastic signals with sensor networks (SNs), where the fusion center (FC) collects 1-bit data from the local sensors and then performs global detection. For this problem, a newly developed 1-bit locally most powerful test (LMPT) detector requires 3.3Q sensors to asymptotically achieve the same detection performance as the centralized LMPT (cLMPT) detector with Q sensors. This 1-bit LMPT detector is based on 1-bit quantized observations without any additional processing at the local sensors. However, direct quantization of observations is not the most efficient processing strategy at the sensors since it incurs unnecessary information loss. In this letter, we propose an improved-1-bit LMPT (Im-1-bit LMPT) detector that fuses local 1-bit quantized likelihood ratios (LRs) instead of directly quantized local observations. In addition, we design the quantization thresholds at the local sensors to ensure asymptotically optimal detection performance of the proposed detector. It is shown theoretically and numerically that, with the designed quantization thresholds, the proposed Im-1-bit LMPT detector for the detection of sparse signals requires less number of sensor nodes to compensate for the performance loss caused by 1-bit quantization.

Sparse detection with orthogonal matching pursuit in multiuser uplink quadrature spatial modulation MIMO system

Quadrature spatial modulation (QSM) is one of the most prevalent transmission techniques for future wireless mobile network due to its high spectral efficiency (SE) and low complexity. However, it is restrictive to downlink of the cellular network deployments. Therefore, this paper proposes QSM for the multiuser uplink data transmission that increases the SE of the network by deploying multiple antennas at the mobile user with only two radio frequency (RF) chains. Maximum likelihood (ML) decoder is used to detect the received signal and attains the optimal detection performance but it is impractical because of its high computational complexity. Consequently, this paper proposes compressed sensing (CS) based orthogonal matching pursuit (OMP) detection as it has a suboptimal performance with a low computational complexity which can be a practical solution in the high-density uplink multiuser network. However, conventional OMP algorithm has a low estimation performance due to multiuser interference which corrupt channel matrix. Thus, this work design an equalizer that mitigates the multiuser interference and improve the detection performance by orthonormalizing the columns of channel matrix. Simulation analysis confirm that the proposed QSM technique based on CS detector outperforms the conventional schemes in terms of SE and bit error rate (BER).

Distinct Waking States for Strong Evoked Responses in Primary Visual Cortex and Optimal Visual Detection Performance.

Variability in cortical neuronal responses to sensory stimuli and in perceptual decision making performance is substantial. Moment-to-moment fluctuations in waking state or arousal can account for much of this variability. Yet, this variability is rarely characterized across the full spectrum of waking states, leaving the characteristics of the optimal state for sensory processing unresolved. Using pupillometry in concert with extracellular multiunit and intracellular whole-cell recordings, we found that the magnitude and reliability of visually evoked responses in primary visual cortex (V1) of awake, passively behaving male mice increase as a function of arousal and are largest during sustained locomotion periods. During these high-arousal, sustained locomotion periods, cortical neuronal membrane potential was at its most depolarized and least variable. Contrastingly, behavioral performance of mice on two distinct visual detection tasks was generally best at a range of intermediate arousal levels, but worst during high arousal with locomotion. These results suggest that large, reliable responses to visual stimuli in V1 occur at a distinct arousal level from that associated with optimal visual detection performance. Our results clarify the relation between neuronal responsiveness and the continuum of waking states, and suggest new complexities in the relation between primary sensory cortical activity and behavior.SIGNIFICANCE STATEMENT Cortical sensory processing strongly depends on arousal. In the mouse visual system, locomotion (associated with high arousal) has previously been shown to enhance the sensory responses of neurons in primary visual cortex (V1). Yet, arousal fluctuates on a moment-to-moment basis, even during quiescent periods. The characteristics of V1 sensory processing across the continuum of arousal are unclear. Furthermore, the arousal level corresponding to optimal visual detection performance is unknown. We show that the magnitude and reliability of sensory-evoked V1 responses are monotonic increasing functions of arousal, and largest during locomotion. Visual detection behavior, however, is suboptimal during high arousal with locomotion, and usually best during intermediate arousal. Our study provides a more complete picture of the dependence of V1 sensory processing on arousal.

Radar Detection of Moderately Fluctuating Target Based on Optimal Hybrid Integration Detector

The optimal detector of the moderately fluctuating model has been shown to be a weighted noncoherent integrator of sampled pulse returns. Nevertheless, the calculation of the optimal weights in the optimal detector needs dynamic inversion of a covariance matrix, which results in huge computation complexity. In this paper, a general description of the hybrid integration detector (HID) for moderately fluctuating target is given to achieve a good balance between detection performance and computation complexity. In the HID, the integration time is divided into several subapertures with coherent integration implemented within each subaperture, with noncoherent integration among subapertures. To obtain the optimal detection performance of the HID, the optimal length of subaperture is derived and theoretical detection threshold is given, based on which an optimal HID (OHID) is proposed for the detection of moderately fluctuating Rayleigh targets in noise background. Furthermore, according to the fluctuation degree which can be reflected by the relationship of detection performance between two special cases of HID, i.e., coherent integration detector and noncoherent integration detector, three kinds of target types are defined, namely, strong, ordinary, and weak fluctuating targets. At the cost of small detection performance loss of these moderately fluctuating targets, the proposed OHID can improve the computation efficiency remarkably. Numerical experiments are provided to demonstrate the effectiveness and efficiency of the proposed method.

Implementation Methodologies of Deep Learning-Based Signal Detection for Conventional MIMO Transmitters

In this paper, simple methodologies of deep learning application to conventional multiple-input multiple-output (MIMO) communication systems are presented. The deep learning technologies with deep neural network (DNN) structure, emerging technologies in various engineering areas, have been actively investigated in the field of communication engineering as well. In the physical layer of conventional communication systems, there are practical challenges of application of DNN: calculating complex number in DNN and designing proper DNN structure for a specific communication system model. This paper proposes and verifies simple solutions for the difficulty. First, we apply a basic DNN structure for signal detection of one-tap MIMO channel. Second, convolutional neural network (CNN) and recurrent neural network (RNN) structures are presented for MIMO system with multipath fading channel. Our DNN structure for one-tap MIMO channel can achieve the optimal maximum likelihood detection performance, and furthermore, our CNN and RNN structures for multipath fading channel can detect the transmitted signal properly.