Target Identification Performance Research Articles

Clutter Effect on a Combination of Microwave Imaging and Target Identification Using Ground-Penetrating Radar

This paper presents an experimental investigation of the effect of clutter on the performance of microwave imaging and target identification using ground-penetrating radar (GPR) for buried objects. Signals collected by the GPR were formed to be B-scan and 3D images to carry out the microwave imaging of buried objects. Poles extracted using the short-time matrix pencil method from a late-time response of the received signal collected at a specific position were exploited to identify buried objects. The experiment was set up by constructing a large box filled with sand where an L-shaped metallic sheet was buried. The GPR was installed along with a 2-dimensional scanner above the box to image and identify the buried metallic sheet. In order to investigate the effect of the clutter on the performance of the GPR-based microwave imaging and target identification, small rocks were arranged on the sand surface, and their density was then varied. The experimental results have shown that clutter density significantly affects the sharpness of the B-scan and 3D images. The rocks result in an additional surface layer in the images. Moreover, the results have also shown that the clutter does not affect the performance of the identification of the buried object. From the results, the poles obtained from the GPR scanning inside and outside the buried object region differed. This confirms that the poles of the buried object should be different from those without the object. The poles of the buried object remain constant, although rocks on the sand surface exist since the clutter affects only the early-time response (not the late-time response). With varying clutter densities, the underlying poles were significantly changed, but this does not affect the identification of the buried object.

A Sorting Method for Radar Echo Extraction Based on Observation Submatrix

AbstractWith the advancement of active jamming technology, a variety of new types of coherent jamming based on digital radio frequency memory have been proposed and implemented in practice, posing serious threats to modern radar systems due to their flexibility, suppression, and deception characteristics. Hence the research on radar echo extraction is necessary and challenging. In this paper, we focus on removing interference from received time‐frequency overlapping signals,and the anti‐jamming problem is transformed into one linear underdetermined blind source separation model in a dual‐channel receiving scenario. The feasible solution is found by utilizing the observation submatrices to convert the observed signal features to a new representation, which originates from sparse component analysis. Simulations demonstrate that the developed technique outperforms standard interference countermeasures in terms of signal‐to‐interference ratio and target identification performance, which are critical for anti‐jamming.

Knowledge‐based multiple hypothesis tracking and identification of manoeuvring reentry targets

AbstractThis paper addresses the integrated tracking and identification problem of a manoeuvring reentry target that performs intentional lateral manoeuvres to disrupt ground radars. Unlike previous approaches, prior knowledge of the lift‐induced drag is incorporated into a new manoeuvring model to describe the reentry target dynamics more explicitly. This model can account for the constraint between lift and drag, which is beneficial in ensuring the reliability of target state estimation. Noticing that the lift‐induced drag is an inherent characteristics of a reentry target that distinguishes the target's identity from others belonging to the same class, the integrated target tracking and identification problem is formulated within the framework of the multiple hypothesis testing about a set of manoeuvring models constructed by different prior knowledge. The proposed approach enables the authors to derive the optimal solution to the given problem in a mathematically rigorous manner. To cope with the real‐time implementation issue, a hypothesis merging strategy is also devised in view of maintaining the target identification performance. Simulation results demonstrate that the proposed scheme provides superior performance and reliability both in target tracking and identification compared to the existing method, despite imperfectness of prior knowledge.

The Introduction of Detergents in Thermal Proteome Profiling Requires Lowering the Applied Temperatures for Efficient Target Protein Identification.

Although the use of detergents in thermal proteome profiling (TPP) has become a common practice to identify membrane protein targets in complex biological samples, surprisingly, there is no proteome-wide investigation into the impacts of detergent introduction on the target identification performance of TPP. In this study, we assessed the target identification performance of TPP in the presence of a commonly used non-ionic detergent or a zwitterionic detergent using a pan-kinase inhibitor staurosporine, our results showed that the addition of either of these detergents significantly impaired the identification performance of TPP at the optimal temperature for soluble target protein identification. Further investigation showed that detergents destabilized the proteome and increased protein precipitation. By lowering the applied temperature point, the target identification performance of TPP with detergents is significantly improved and is comparable to that in the absence of detergents. Our findings provide valuable insight into how to select the appropriate temperature range when detergents are used in TPP. In addition, our results also suggest that the combination of detergent and heat may serve as a novel precipitation-inducing force that can be applied for target protein identification.

A Novel Countermeasure Method for Radar Coherent Jamming Based on Signal Feature Transformation

With the advancement of active jamming technology, a variety of new types of coherent jamming based on digital radio frequency memory (DFRM) have been proposed and implemented in practice, posing serious threats to modern radar systems due to their flexibility, suppression, and deception characteristics. Hence, the research on relevant countermeasures is necessary and challenging. In this paper, we take a fresh look at the problem and present a method for removing interference from received time-frequency overlapping signals based on signal feature modification (SFT). The antijamming problem is transformed into one linear underdetermined blind source separation (UBSS) model in a dual-channel receiving scenario using sparse component analysis, and the feasible solution is found by transforming the observed signal features to a new time-frequency domain representation. Simulations demonstrate that the developed technique outperforms standard interference countermeasures in terms of signal-to-interference ratio (SIR) and target identification performance, which are critical for antijamming.

Online Boosting-Based Target Identification among Similar Appearance for Person-Following Robots.

It is challenging for a mobile robot to follow a specific target person in a dynamic environment, comprising people wearing similar-colored clothes and having the same or similar height. This study describes a novel framework for a person identification model that identifies a target person by merging multiple features into a single joint feature online. The proposed framework exploits the deep learning output to extract four features for tracking the target person without prior knowledge making it generalizable and more robust. A modified intersection over union between the current frame and the last frame is proposed as a feature to distinguish people, in addition to color, height, and location. To improve the performance of target identification in a dynamic environment, an online boosting method was adapted by continuously updating the features in every frame. Through extensive real-life experiments, the effectiveness of the proposed method was demonstrated by showing experimental results that it outperformed the previous methods.

Incorporating Neighboring Stimuli Data for Enhanced SSVEP-Based BCIs

Various spatial filters have been proposed to enhance the target identification performance of steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). The current methods only extract the target-related information from the corresponding stimulus to learn the spatial filter parameter. However, the SSVEP data from neighboring stimuli also contain frequency information of the target stimulus, which could be utilized to further improve the target identification performance. In this paper, we propose a new method incorporating SSVEPs from the neighboring stimuli to strengthen the target-related frequency information. First, The spatial filter is obtained by maximizing the summation of covariances of SSVEP data corresponding to the target and its neighboring stimuli. Then the correlation features between spatially filtered templates and test data are calculated for target detection. For the performance evaluation, we implemented the offline experiment using the 40-class benchmark dataset from 35 subjects and the 12-target self-collected dataset from 11 subjects. Compared with the state-of-art spatial filtering methods, the proposed method showed superiority in classification accuracy and information transfer rate (ITR). The comparison results demonstrate the effectiveness of the proposed spatial filter for target identification in SSVEP-based BCIs.

Multi-Objective Optimization-Based High-Pass Spatial Filtering for SSVEP-Based Brain–Computer Interfaces

Many spatial filtering methods have been proposed to enhance the target identification performance for the steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI). The existing approaches tend to learn spatial filter parameters of a certain target using only the training data from the same stimulus, and they rarely consider the information from other stimuli or the volume conduction problem during the training process. In this article, we propose a novel multi-objective optimization-based high-pass spatial filtering method to improve the SSVEP detection accuracy and robustness. The filters are derived via maximizing the correlation between the training signal and the individual template from the same target whilst minimizing the correlation between the signal from other targets and the template. The optimization will also be subject to the constraint that the sum of filter elements is zero. The evaluation study on two self-collected SSVEP datasets (including 12 and four frequencies, respectively) shows that the proposed method outperformed the compared methods such as canonical correlation analysis (CCA), multiset CCA (MsetCCA), sum of squared correlations (SSCOR), and task-related component analysis (TRCA). The proposed method was also verified on a public 40-class SSVEP benchmark dataset recorded from 35 subjects. The experimental results have demonstrated the effectiveness of the proposed approach for enhancing the SSVEP detection performance.

Latency Aligning Task-Related Component Analysis Using Wave Propagation for Enhancing SSVEP-Based BCIs.

Due to the high robustness to artifacts, steady-state visual evoked potential (SSVEP) has been widely applied to construct high-speed brain-computer interfaces (BCIs). Thus far, many spatial filtering methods have been proposed to enhance the target identification performance for SSVEP-based BCIs, and task-related component analysis (TRCA) is among the most effective ones. In this paper, we further extend TRCA and propose a new method called Latency Aligning TRCA (LA-TRCA), which aligns visual latencies on channels to obtain accurate phase information from task-related signals. Based on the SSVEP wave propagation theory, SSVEP spreads from posterior occipital areas over the cortex with a fixed phase velocity. Via estimation of the phase velocity using phase shifts of channels, the visual latencies on different channels can be determined for inter-channel alignment. TRCA is then applied to aligned data epochs for target recognition. For the validation purpose, the classification performance comparison between the proposed LA-TRCA and TRCA-based expansions were performed on two different SSVEP datasets. The experimental results illustrated that the proposed LA-TRCA method outperformed the other TRCA-based expansions, which thus demonstrated the effectiveness of the proposed approach for enhancing the SSVEP detection performance.

재진입 표적추적을 위한 복수 레이더 2단계 정보융합 기법

This paper proposes a two-stage track-to-track fusion algorithm for re-entry target tracking using multiple radars. The existing track-to-track fusion strategy relying only on the kinematic information shows unreliable target tracking performance when the target of interest is adjacent with other objects. To prevent tracking performance degradation due to false track fusion, the track paring hypothesis is evaluated using both the feature data and the kinematic data provided by radars. A recursive Bernoulli filter is desinged to discriminate the target identity by fusing binary decision data which correspond to the most probable track pairing hypothesis. Since our approach exploits the statistical property of the available decision data, it can enhance the target tracking and identification performance. Through the computer simulations for a typical re-entry target tracking scenario, the effectiveness of the suggested data fusion scheme is demonstrated.

On the detection-to-track association for online multi-object tracking

Driven by recent advances in object detection with deep neural networks, the tracking-by-detection paradigm has gained increasing prevalence in the research community of multi-object tracking (MOT). It has long been known that appearance information plays an essential role in the detection-to-track association, which lies at the core of the tracking-by-detection paradigm. While most existing works consider the appearance distances between the detections and the tracks, they ignore the statistical information implied by the historical appearance distance records in the tracks, which can be particularly useful when a detection has similar distances with two or more tracks. In this work, we propose a hybrid track association (HTA) algorithm that models the historical appearance distances of a track with an incremental Gaussian mixture model (IGMM) and incorporates the derived statistical information into the calculation of the detection-to-track association cost. Experimental results on three MOT benchmarks confirm that HTA effectively improves the target identification performance with a small compromise to the tracking speed. Additionally, compared to many state-of-the-art trackers, the DeepSORT tracker equipped with HTA achieves better or comparable performance in terms of the balance of tracking quality and speed.

Category-based attentional capture can be influenced by color- and shape-dimensions independently in the conjunction search task.

Attention can be involuntarily attracted by a distractor that matches the current attentional control settings (ACSs). However, it remains unclear whether two category-specific ACSs can operate independently. By defining a target as a combination of two prototype-based categories, the present event-related potential (ERP) study investigated how color-category and shape-category ACSs operate within a search task paradigm and the effects of temporal task demands on these ACSs. The matching level between target and distractor was manipulated to separate the effects of each ACS. The relative position between target and distractor was employed to isolate the attentional processing of the distractor from the target. Furthermore, two display durations were used to manipulate the temporal task demands, including a short fixed window (800ms) and a dynamic window extended until the user responded. Our results support a two-stage selection scenario. In early stage, the color- and shape-ACS independently guided attention to task-relevant property (N2pc components) and suppressed attention toward task-irrelevant properties (PD components). In late stage, these two independent ACSs were integrated into a holistic ACS to interfere with the consolidation (contralateral delay activity components) and behavioral performance (accuracy and RTs) of target identification. Moreover, an early N1/P1 component might reflect a preattentive enhancement of relevant information or a preattentive suppression of irrelevant objects. These two category-specific ACSs weights differently in varied temporal task demands. These findings support the idea that independent early processing is followed by integrated late processing, which can be applied to category-based attentional capture with different temporal task demands.

Performance benefits of small-pitch, high-dynamic-range, digitally restored infrared imaging systems using triangle orientation discrimination and NVIPM.

For the past year, the authors have been studying a long-wave infrared (LWIR) sensor design concept that combines high detector well capacity, small-pitch detectors, and digital image processing to optimize target acquisition. Theoretical performance modeling [via the Night Vision Integrated Performance Model (NVIPM)] suggests that our approach offers a large increase in target identification range, but multiple field trials using triangle orientation discrimination (TOD) have yielded results that are inconsistent with the model's predictions. For this reason, we have performed human perception experiments on simulated TOD targets, with and without image processing, to assess the utility of our approach and the value of TOD as an evaluation for digital image enhancement. The results do not agree with the NVIPM modeling of range improvement. We present our results, along with our modeling and experiment methodologies, to guide future investigations into the effects of boost or restoration filtering on target identification performance.

Latent common source extraction via a generalized canonical correlation framework for frequency recognition in SSVEP based brain–computer interfaces

Objective. This study introduces and evaluates a novel target identification method, latent common source extraction (LCSE), that uses subject-specific training data for the enhancement of detection of steady-state visual evoked potential (SSVEP). Approach. LCSE seeks to construct a common latent representation of the SSVEP signal subspace that is stable across multiple trials of electroencephalographic (EEG) data. The spatial filter thus obtained improves the signal-to-noise ratio (SNR) of the SSVEP components by removing nuisance signals that are irrelevant to the generalized signal representation learnt from the given data. In this study a comparison of SSVEP identification performance between the proposed method, extended canonical correlation analysis (ExtCCA) and multiset canonical correlation analysis (MsetCCA) was conducted using SSVEP benchmark data of 40 targets recorded from 35 subjects to validate the effectiveness of the LCSE framework. Main results. The results indicate that the LCSE framework significantly outperforms the other two methods in terms of both classification accuracy and information transfer rates (ITRs). Significance. The significant improvement in the target identification performance demonstrates that the proposed LCSE method can be seen as a promising potential candidate for efficient SSVEP detection in brain–computer interface (BCI) systems.

Effects of visual complexity and ambiguity of verbal instructions on target identification

ABSTRACTResearch has shown that visual complexity and the ambiguity of verbal information affect the speed and accuracy of locating targets during visual search. The higher the visual complexity and description ambiguity, the slower and poorer the target identification performance. Because these factors are seldom studied in combination (even though they regularly co-occur), it is unclear whether they would interact. Therefore, in two experiments, participants viewed images that displayed cartoon-like characters and had to correctly identify a character from a verbal description under conditions of low/high visual complexity and low/high description ambiguity (manipulated within-subjects). Results revealed that high ambiguity descriptions resulted in lower accuracy and slower response times. However, our manipulation of visual complexity did not affect performance or response times either in itself or in interaction with verbal ambiguity. Findings are discussed in terms of theoretical and practical implications, for instance, for multimedia learning.

Using Rapid Serial Visual Presentation to Measure Set-Specific Capture, a Consequence of Distraction While Multitasking.

This method uses a rapid serial visual presentation (RSVP) paradigm to measure the cost of distraction when participants maintain multiple search goals. The protocol identifies two types of distraction within a single task - contingent attentional capture and set-specific capture - that represent different types of limitations of cognitive processing. Participants search for letters in two or more "target" ink colors (e.g., green and orange) within a continuous RSVP stream of heterogeneously colored letters, while ignoring two peripheral RSVPs of letters. Upon detecting a target, participants are to identify the letter. On some trials, target-colored distractors appear in the periphery just prior to the presentation of a target, causing a drop in target identification performance. Contingent attentional capture is observed by examining performance on trials in which the peripheral distractor is the same color as the target on that trial (e.g., both orange). Set-specific capture is represented by performance on trials in which the peripheral distractor is target-colored (e.g., orange), but not the same color as the target on that trial (e.g., green.) By varying the amount of time (i.e., the number of stimuli appearing) between the presentation of the distractor and the target, researchers can observe how participants recover from these distraction costs over time. As compared to static displays that are often used to measure contingent attentional capture, the dynamic display produces much larger effects, allowing the researcher to identify subtle effects of smaller manipulations. An unusual aspect of our design is that it employs a continuous display; "filler" stimuli connect one trial to the next seamlessly, and participants respond during this interval whenever they detect a target. The continuous display reduces chance performance to near-zero levels (rather than 50%) and provides researchers with a more sensitive measure of performance differences across trial types.

OFDM MIMO radar waveform design for targets identification

In order to obtain better target identification performance, an efficient waveform design method with high range resolution and low sidelobe level for orthogonal frequency division multiplexing (OFDM) multiple-input multiple-output (MIMO) radar is proposed in this paper. First, the wideband CP-based OFDM signal is transmitted on each antenna to guarantee large bandwidth and high range resolution. Next, a complex orthogonal design (COD) is utilized to achieve code domain orthogonality among antennas, so that the spatial diversity can be obtained in MIMO radar, and only the range sidelobe on the first antenna needs suppressing. Furthermore, sidelobe suppression is expressed as an optimization problem. The integrated sidelobe level (ISL) is adopted to construct the objective function, which is solved using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. The numerical results demonstrate the superiority in performance (high resolution, strict orthogonality, and low sidelobe level) of the proposed method compared to existing algorithms.

Pedestrian and Bicyclist Identification Through Micro Doppler Signature With Different Approaching Aspect Angles

In realistic road scenarios, two main urban hazards, pedestrians and bicyclists, can approach the radar from any aspect angle which will affect the ability of a radar system to perform target identification. However, the identification of these two particular types of targets with different approaching directions has not been investigated thoroughly until now. In this paper, we explore the micro Doppler signature to accurately distinguish pedestrian and the bicyclist. One feature extraction method that is based on the sparse coding of a measured micro Doppler signature is proposed and validated. Further numerical analysis of the sparse matrices was performed to reduce the number of dimensions whilst keeping a sufficient number of features. As on comparison, the two feature extractors, i.e., singular value decomposition and robust principal component analysis are extensively studied and compared with the proposed method. Support vector machine (SVM) and extreme learning machine were adopted to classify the extracted features. Indoor experimental results indicate that the proposed methods can achieve higher identification accuracies than alternative approaches and the outdoor measurement results presented show good performance with realistic targets.

Effects of Resolution, Range, and Image Contrast on Target Acquisition Performance.

We sought to determine the joint influence of resolution, target range, and image contrast on the detection and identification of targets in simulated naturalistic scenes. Resolution requirements for target acquisition have been developed based on threshold values obtained using imaging systems, when target range was fixed, and image characteristics were determined by the system. Subsequent work has examined the influence of factors like target range and image contrast on target acquisition. We varied the resolution and contrast of static images in two experiments. Participants (soldiers) decided whether a human target was located in the scene (detection task) or whether a target was friendly or hostile (identification task). Target range was also varied (50-400 m). In Experiment 1, 30 participants saw color images with a single target exemplar. In Experiment 2, another 30 participants saw monochrome images containing different target exemplars. The effects of target range and image contrast were qualitatively different above and below 6 pixels per meter of target for both tasks in both experiments. Target detection and identification performance were a joint function of image resolution, range, and contrast for both color and monochrome images. The beneficial effects of increasing resolution for target acquisition performance are greater for closer (larger) targets.

Application of Computer Identification and Location Algorithm in Small far Infrared Target Recognition of Ship Under Surge Interference

Abstract The small far infrared target of ship in surge interference is easy to have visual deviation causing the low accuracy of target positioning and the bad target identification performance. In order to improve the accuracy of far small infrared targets recognition of ship under surge interference, this paper proposed far small infrared target recognition algorithm of ship based on distributed target position estimation and DOA location in computer vision model for constructing coherent distributed source array model of far small infrared target distribution of ship. This algorithm used MUSIC algorithm for the beamforming processing of far small infrared target echo model of ship. Combined with the adaptive filtering algorithm we carried out the surge interference suppression and the estimation of central direction of arrival and angle spread of far small infrared target of ship through multidimensional spectrum peak searching algorithm, realizing the joint estimation of distance of ship target, DOA and frequency parameters, so as to realize the accurate positioning and recognition of targets. Simulation results show that using this method for the far small infrared target recognition of ship under surge interference, the spectral peak sharpness of spectral peak search of target position is high, side-lobe suppression performance is good, which shows the high accuracy of target position estimation and location, the accuracy and anti-interference performance of far small infrared target recognition of ship is good, and has superior performance.