Target Detection Techniques Research Articles

Through-Wall Single and Multiple Target Imaging Using MIMO Radar

The ability to perform target detection through walls and barriers is important for law enforcement, homeland security, and search and rescue teams. Multiple-input-multiple-output (MIMO) radar provides an improvement over traditional phased array radars for through-wall imaging. By transmitting independent waveforms from a transmit array to a receive array, an effective virtual array is created. This array has improved degrees of freedom over phased arrays and mono-static MIMO systems. This virtual array allows us to achieve the same effective aperture length as a phased array with a lower number of elements because the virtual array can be described as the convolution of transmit and receive array positions. In addition, data from multiple walls of the same room can be used to collect target information. If two walls are perpendicular to each other and the geometry of transmit and receive arrays is known, then data can be processed independently of each other. Since the geometry of the arrays is known, a target scene can be created where the two data sets overlap. The overlapped scene can then be processed so that image artifacts that do not correlate between the data sets can be excised. The result gives improved target detection, reduction in false alarms, robustness to noise, and robustness against errors such as improperly aligned antennas. This paper explores MIMO radar techniques for target detection and localization behind building walls and addresses different mitigation techniques, such as a singular value decomposition of wavelet transform method to improve localization and detection of targets. Together, these techniques demonstrate methods that show a reduction in size and complexity of traditional through-wall radar systems while still providing accurate detection and localization. The use of the range migration algorithm in single and multi-target scenarios is shown to provide adequate imaging of through the wall targets in near and far field. Also, a multi-view algorithm is used to provide improved target detection and localization by fusing together multiple wall views.

Hyperspectral anomaly detection based on stacked denoising autoencoders

Hyperspectral anomaly detection (AD) is an important technique of unsupervised target detection and has significance in real situations. Due to the high dimensionality of hyperspectral data, AD will be influenced by noise, nonlinear correlation of band, or other factors that lead to the decline of detection accuracy. To overcome this problem, a method of hyperspectral AD based on stacked denoising autoencoders (AE) (HADSDA) is proposed. Simultaneously, two different feature detection models, spectral feature (SF) and fused feature by clustering (FFC), are constructed to verify the effectiveness of the proposed algorithm. The SF detection model uses the SF of each pixel. The FFC detection model uses a similar set of pixels constructed by clustering and then fuses the set of pixels by the stacked denoising autoencoders algorithm (SDA). The SDA is an algorithm that can automatically learn nonlinear deep features of the image. Compared with other linear or nonlinear feature extraction methods, the detection result of the proposed algorithm is greatly improved. Experiment results show that the proposed algorithm is an excellent feature learning method and can achieve higher detection performance.

OAM-Based Multitarget Detection: From Theory to Experiment

The vortex electromagnetic (EM) wave, which carries orbital angular momentum (OAM), is exploited to multitarget detection applications. The detection model in the squint mode is derived first. The elevation and azimuth angles of the targets are estimated by the fast Fourier transform method and validated by the simulation. Subsequently, proof-of-concept experiments are performed to validate the principle of the proposed OAM-based target detection technique. The signal processing method for the real-world data obtained from the experiments is proposed to identify the positions of the targets. Experimental results demonstrate the effectiveness of the proposed detection method and indicate that the vortex EM wave can readily be applied to detect multiple targets simultaneously. The work and results show the new applications of the vortex EM wave as well as the development of the novel radar detection technology.

Monitoring the Changes of Vegetal Cover of Karblaa Province (Iraq) using Target Detection and Classification Techniques

Monitoring the Changes of Vegetal Cover of Karblaa Province (Iraq) using Target Detection and Classification Techniques

Multi‐frame fractional Fourier transform technique for moving target detection with space‐based passive radar

This study puts forward a moving target detection technique for space-based passive radar. The main drawback of satellite transmitters for radar applications is the very low power density on the ground level, which requires long integration times to adequately increase the signal to background ratio. To this aim, in this study a new technique is proposed relying on a hybrid coherent/non-coherent integration of the received signal in the fractional Fourier transform (FrFT) domain. After the compensation of the range walk by Keystone reformatting, a coherent integration is performed over consecutive short time intervals (frames) such that the coherence of the scattering mechanism within each frame can be assumed. Then, a proper combination of the multiple frames is performed based on the kernel of the FrFT, allowing the reliable detection of low observable targets. The effectiveness of the proposed technique has been proved by simulated analysis considering very low EIRP satellites as opportunity transmitters.

Target Localization Using Microwave Time-Reversal Mirror in Reflection Mode

Time-reversal (TR) focusing is based on the fact that when a wave solution is reversed in time and back propagated, it focuses back at the source. TR techniques using microwave measurements have proved to be promising for radar detection and defect imaging in nondestructive evaluation of dielectric samples. This paper presents a TR technique for target detection and localization applications using far-field microwave measurements in reflection mode. Simulation results investigate the feasibility and robustness of this approach and determine the limits of this technique. Experimental results based on reflection mode measurements validate the approach for the detection of source and closely spaced multiple dielectric targets.

Hyperspectral Sensors as a Management Tool to Prevent the Invasion of the Exotic Cordgrass Spartina densiflora in the Doñana Wetlands

We test the use of hyperspectral sensors for the early detection of the invasive dense-flowered cordgrass (Spartina densiflora Brongn.) in the Guadalquivir River marshes, Southwestern Spain. We flew in tandem a CASI-1500 (368–1052 nm) and an AHS (430–13,000 nm) airborne sensors in an area with presence of S. densiflora. We simplified the processing of hyperspectral data (no atmospheric correction and no data-reduction techniques) to test if these treatments were necessary for accurate S. densiflora detection in the area. We tested several statistical signal detection algorithms implemented in ENVI software as spectral target detection techniques (matched filtering, constrained energy minimization, orthogonal subspace projection, target-constrained interference minimized filter, and adaptive coherence estimator) and compared them to the well-known spectral angle mapper, using spectra extracted from ground-truth locations in the images. The target S. densiflora was easy to detect in the marshes by all algorithms in images of both sensors. The best methods (adaptive coherence estimator and target-constrained interference minimized filter) on the best sensor (AHS) produced 100% discrimination (Kappa = 1, AUC = 1) at the study site and only some decline in performance when extrapolated to a new nearby area. AHS outperformed CASI in spite of having a coarser spatial resolution (4-m vs. 1-m) and lower spectral resolution in the visible and near-infrared range, but had a better signal to noise ratio. The larger spectral range of AHS in the short-wave and thermal infrared was of no particular advantage. Our conclusions are that it is possible to use hyperspectral sensors to map the early spread S. densiflora in the Guadalquivir River marshes. AHS is the most suitable airborne hyperspectral sensor for this task and the signal processing techniques target-constrained interference minimized filter (TCIMF) and adaptive coherence estimator (ACE) are the best performing target detection techniques that can be employed operationally with a simplified processing of hyperspectral images.

Multi-Frequency Target Detection Techniques for DVB-T Based Passive Radar Sensors.

This paper investigates the possibility to improve target detection capability in a DVB-T- based passive radar sensor by jointly exploiting multiple digital television channels broadcast by the same transmitter of opportunity. Based on the remarkable results obtained by such a multi-frequency approach using other signals of opportunity (i.e., FM radio broadcast transmissions), we propose appropriate modifications to the previously devised signal processing techniques for them to be effective in the newly considered scenarios. The resulting processing schemes are extensively applied against experimental DVB-T-based passive radar data pertaining to different surveillance applications. The obtained results clearly show the effectiveness of the proposed multi-frequency approaches and demonstrate their suitability for application in the considered scenarios.

Extracting Objects of Interest based on Three Dimensional Feature

Objectives: This paper proposes a technique of segmenting a target object more robustly by combining and clustering 2-dimensional and 3-dimensional features of 3D stereoscopic images coming sequentially. Methods/Statistical analysis: The proposed object detection technique first applies a disparity computation algorithm to the left and right stereo input images that are captured to extract the depth values representing the distance between a camera and a target object by each image pixel. The technique then effectively clusters color and depth features using feature similarity. Subsequently, the method excludes the background area from the image and finally detects an object of interest. Findings: The experimental results of this paper show that the proposed 3-dimensional feature-based target detection technique extracted objects more robustly than other existing object detection methods. To evaluate the performance of the proposed algorithm of detecting an object of interest, this paper uses various types of indoor and outdoor input images without any particular constraints. To compare the performance of the suggested object segmentation method, this paper defined root mean square error measure. The measure which is the scale to deal with general and particular viewpoints of image quality is often used to measure the difference between an actual value and a measured value. It is known as a good scale tool for accuracy measurement. The two existing methods use 2D features only to segment an object of interest, and thereby include many false positive errors, whereas the proposed method effectively clusters the 3-dimensional distance feature as well as 2-dimensional feature so that it segments an object of interest more accurately than the other two methods in terms of quantitative aspect. Improvements/Applications: It is expected that the proposed technique of detecting an object of interest will be used in various types of actual application areas related to multimedia contents.

Recursive Band Processing of Automatic Target Generation Process for Finding Unsupervised Targets in Hyperspectral Imagery

Automatic target generation process (ATGP) has been widely used for unsupervised target detection. However, as designed, it detects targets using full-band information. Unfortunately, on many occasions, various targets can be detected using varying bands, and ATGP can only provide one-shot target detection with all bands being used. This paper develops a new approach which can implement ATGP bandwise in a progressive manner, called progressive band processing of ATGP (PBP-ATGP) so that ATGP can be carried out band by band. Since PBP-ATGP must repeatedly implement orthogonal projections, recursive equations are further derived for PBP-ATGP, to be called recursive band processing of ATGP (RBP-ATGP) which can implement PBP-ATGP recursively. As a result, many advantages can be benefited from RBP-ATGP. Most importantly, RBP-ATGP can generate 3-D interband progressive profiles from band to band that can be used for progressive target detection, a task for which no target detection techniques using full-band information can provide.

Target Detection Using Matched Filtering Analysis of the ASTER Data for Exploration of Uranium Mineralization in the Northern Part of El-Erediya Pluton, Central Eastern Desert, Egypt.

يمثل الجرانيت الأصغر من بلوتون العريضية أحد أهم مناطق التنقيب في الصحراء الشرقية الوسطى المصرية والتي تتكون من تمعدنات سطحية وتحت سطحية على شكل حرف U محصورة في الجزء الجنوبي من البلوتون. نظرًا لارتفاع نسبة اليورانيوم المحمولة جواً في الجزء الشمالي عن الجزء الجنوبي من البلوتون ، فإن الجرانيت في الجزء الشمالي بحاجة إلى إعادة تقييم إمكاناته U وفقًا للتقنيات الحديثة التي تم تطويرها في العقد الماضي. الهدف الرئيسي من هذه الورقة البحثية هو فحص كفاءة بيانات مقياس إشعاع الارتداد والانبعاثات الحرارية المتقدمة المحمولة في الفضاء (ASTER) في رسم خرائط لمناطق التغيير التي يمكن أن تستضيف تمعدن اليورانيوم داخل الجرانيت الأصغر في بلوتون العريضية باستخدام تقنيات التحليل الطيفي. . تعد بيانات ASTER جذابة بشكل خاص للعديد من الباحثين الجيولوجيين في إفريقيا ، نظرًا لتوافرها المفتوح والتكاليف الدنيا المرتبطة بالاستخدام الأكاديمي. تمت معايرة النطاقات الثلاثة المرئية والستة للأشعة تحت الحمراء القريبة ، والتي تبلغ دقتها 15 مترًا و 30 مترًا ، على التوالي ، باستخدام تصحيح الغلاف الجوي. تم تطبيق الكشف عن الهدف باستخدام خوارزمية المطابقة الطيفية للتعرف على مكونات التغيير الأكثر شيوعًا مثل الهيماتيت ، والكاولينتي ، والإيلايت ، والوحدة ، نظرًا لأنهم يعتبرون إلى حد ما مكتشف مسار تمعدن اليورانيوم لتتبع المناطق الواعدة المتأثرة بهذه التغييرات داخل الجرانيت. العريضية بلوتون. تم اكتشاف ثلاث مناطق واعدة مشتقة من ASTER ، بما في ذلك منطقة التنقيب ، باستخدام طرق التصفية المتطابقة بثقة تتراوح من 68 إلى 95٪. كل هذه المناطق التي تم تحديدها بواسطة تقنية الكشف عن الهدف تظهر ارتباطًا جيدًا بتلك الموجودة في الميدان وعلى الخريطة الجيولوجية المرجعية. تم إثبات اثنتين من هذه المناطق المستهدفة في الميدان وتم تأسيسهما على طول السمات الهيكلية التي تقطع الأجزاء الشمالية من بلوتون العريضية التي لم يتم تحديدها من قبل. أجريت الدراسات المعدنية باستخدام مجهر الخامات والمسح الضوئي و XRD لبعض العينات المعدنية الانتقائية التي تم جمعها من إحدى المناطق المستهدفة. تؤكد هذه التحقيقات وجود معدن اليورانوفان مثل سيليكات اليورانيل بالإضافة إلى الزركون والفلوريت. تم العثور على هذه المعادن في ارتباط وثيق مع تجلط الدم ، kaolinitization ، وتغيير التوعط.

Fractional 푸리에 변환을 이용한 능동소나 표적탐지

Many studies in detection and classification of the targets in the underwater environments have been conducted for military purposes, as well as for non-military purpose. Due to the complicated characteristics of underwater acoustic signal reflecting multipath environments and spatio-temporal varying characteristics, active sonar target detection technique has been considered as a difficult technique. In this paper, we describe the basic concept of Fractional Fourier transform and optimal transform order. Then we analyze the relationship between time-frequency characteristics of an LFM signal and its spectrum using Fractional Fourier transform. Based on the analysis results, we present active sonar target detection method. To verify the performance of proposed methods, we compared the results with conventional FFT-based matched filter. The experimental results demonstrate the superiority of the proposed method compared to the conventional method in the aspect of AUC(Area Under the ROC Curve).

파형역산 기법을 이용한 수중표적 탐지 연구

중주파수 및 고주파수 대역을 이용한 근거리 수중표적 탐지와 식별 기술은 이미 성숙단계에 있으나, 수중 위협세력의 은닉화 및 고속화에 따른 저주파수 대역을 이용한 원거리 탐지 요구가 새롭게 대두되고 있다. 본 논문에서 소개할 파형역산 기술은 최근 국내외 석유탐사 관련 학계 및 업계에서 매우 각광받는 최신 기술로, 저주파수 대역을 이용하여 해저 수 킬로미터 이상의 해저 지층을 고해상도로 구축하는 수치해석 기법이다. 이러한 파형역산 기술을 응용하여 작전 해역에서의 해저지층을 영상화하는 동시에, 수중에 위치하는 인공표적의 탐지 가능성을 확인하였다. 본 제안 기술은 인공표적의 형상뿐만 아니라 음파속도 등의 물성정보를 정확하게 추정할 수 있기 때문에 오탐지 확률을 획기적으로 줄일 수 있으리라 기대된다. A short-range underwater target detection and identification techniques using mid- and high-frequency bands have been highly developed. However, nowadays the long-range detection using the low-frequency band is requested and one of the most challengeable issues. The waveform inversion technique is widely used and the hottest technology in both academia and industry of the seismic exploration. It is based on the numerical analysis tool, and could construct more than a few kilometers of the subsurface structures and model-parameters such as P-wave velocity using a low-frequency band. By applying this technique to the underwater acoustic circumstance, firstly application of underwater target detection is verified. Furthermore, subsurface structures and it's parameters of the war-field are well reconstructed. We can confirm that this technique greatly reduces the false-alarm rate for the underwater targets because it could accurately reproduce both the shape and the model-parameters at the same time.

Detection and Tracking of Infrared Dim Small Image Sequence Moving Target

Infrared dim target detection and tracking is widely used in precision guidance, automatic control and other fields. As the small target imaging system has far distance, small area, low SNR infrared characteristics and undergoes image preprocessing, dim target detection technology is more important and more difficult. As a full play of the ad- vantages of infrared small target detection technique has been provided, this has significance in infrared imaging. Based on introducing the tracking technology development status at home and abroad by the study of small target detection, the problems of detecting and tracking small moving target in infrared image sequence of background noise and interference conditions, aiming at dynamic planning in energy diffusion problem are presented. The improved algorithm of dynamic programming is also presented including weighted dynamic programming algorithm and the realization steps of the algo- rithm. Detection structure and the experimental results are analyzed. The results show that the algorithm has a good effect. In the end, this paper studies the rejected false targets in IR image track association method to obtain real object trajecto- ries for moving small target detection, so as to improve the detection rate and reduce the false alarm rate.

FPGA Implementation of Digital Modulation Technique for HRR Target Detection

In Radar signal processing, at the receiving end signal detection is difficult when the signal is corrupted by noise. Conventional digital signal processing techniques are not capable to detect the corrupted signal in noise. In this paper, different methods for design of Binary Phase Shift Keying (BPSK) modulation are described. The design and simulation of the different blocks such as signed multipliers, Barker code generators, multiplexer etc. Consequential Digital project design is practicable because of advance hardware description languages (eg. verilog or VHDL) and implementation of Field Programmable Gate Array (FPGA). Digital communication is steady and secure than that of analog communication. BPSK is competent and important technique in digital communication. For implementation of software defined radar systems, the resolution, sensibility and extensibility requirements are driving the development of RF signal generator with phase, pulse amplitude and stable frequency carriers verilog hardware description language is used to design entire system and implemented on Sparten-6 FPGA device.

Effective small target enhancement and detection in infrared images using saliency map and image intensity

In this paper, we propose a small target enhancement and detection method for infrared (IR) images in complex backgrounds. The detection of targets obtained from infrared search and track systems is very important in the military field. However, the accurate detection of small targets in complex backgrounds with a variety of clutter and sensor noise remains difficult. Therefore, with the aim of enhancing and detecting small targets in complex backgrounds, the proposed method applies a saliency map to enhance targets using their shape and temperature information. Additionally, a target detection technique is proposed that uses image intensity and distance information. The experimental results indicate that the proposed method can efficiently enhance and detect targets in various IR images.

Autonomous detection and tracking under illumination changes, occlusions and moving camera

In this paper, an autonomous multiple target detection and tracking technique for dynamic scenes that are influenced by illumination variations, occlusions and camera instability is proposed. The framework combines a novel Dynamic Reverse Analysis (DRA) approach with an Enhanced Rao-Blackwellized Particle Filter (E-RBPF) for multiple target detection and tracking, respectively. The DRA method, in addition to providing accurate target localization, presents the E-RBPF scheme with costs associated with the differences in intensity caused by illumination variations between consecutive frame pairs in any video of a dynamic scene. The E-RBPF inherently models these costs, thus allowing the framework to (a) adapt learning parameters, (b) distinguish between camera-motion and object-motion, (c) deal with sample degeneracy, (d) provide appropriate appearance compensation during likelihood measurement and (e) handle occlusion. The proposed detect-and-track method when compared against other competing baseline techniques has demonstrated superior performance both in accuracy and robustness on challenging videos from publicly available datasets.

Dual-Mode FPGA Implementation of Target and Anomaly Detection Algorithms for Real-Time Hyperspectral Imaging

Target and anomaly detection are important techniques for remotely sensed hyperspectral data interpretation. Due to the high dimensionality of hyperspectral data and the large computational complexity associated to processing algorithms, developing fast techniques for target and anomaly detection has received considerable attention in recent years. Although several high-performance architectures have been evaluated for this purpose, field programmable gate arrays (FPGAs) offer the possibility of onboard hyperspectral data processing with low-power consumption, reconfigurability and radiation tolerance, which make FPGAs a relevant platform for hyperspectral processing. In this paper, we develop a novel FPGA-based technique for efficient target detection in hyperspectral images. The proposed method uses a streaming background statistics (SBS) approach for optimizing the constrained energy minimization (CEM) and Reed-Xiaoli (RX) algorithms, which are widely used techniques for target and anomaly detection, respectively. Specifically, these two algorithms are implemented in streaming fashion on FPGAs. Most importantly, we present a dual mode that implements a flexible datapath to decide in real time which one among these two algorithms should be used, thus allowing for the dynamic adaptation of the hardware to either target detection or anomaly detection scenarios. Our experiments, conducted with several well-known hyperspectral scenes, indicate the effectiveness of the proposed implementations.

DETECTION OF SHIP TARGETS IN POLARIMETRIC SAR DATA USING 2D-PCA DATA FUSION

Abstract. Data fusion has lately received a lot of attention as an effective technique for several target detection and classification applications in different remote sensing areas. In this work, a novel data fusion scheme for improving the detection accuracy of ship targets in polarimetric data is proposed, based on 2D principal components analysis (2D-PCA) technique. By constructing a fused image from different polarization channels, increased performance of ship target detection is achieved having higher true positive and lower false positive detection accuracy as compared to single channel detection performance. In addition, the use of 2D-PCA provides the ability to discriminate and classify objects and regions in the resulting image representation more effectively, with the additional advantage of being more computational efficient and requiring less time to determine the corresponding eigenvectors, compared to e.g. conventional PCA. Throughout our analysis, a constant false alarm rate (CFAR) detection model is applied to characterize the background clutter and discriminate ship targets based on the Weibull distribution and the calculation of local statistical moments for estimating the order statistics of the background clutter. Appropriate pre-processing and post-processing techniques are also introduced to the process chain, in order to boost ship discrimination and suppress false alarms caused by range focusing artifacts. Experimental results provided on a set of Envisat and RadarSat-2 images (dual and quad polarized respectively), demonstrate the advantage of the proposed data fusion scheme in terms of detection accuracy as opposed to single data ship detection and conventional PCA, in various sea conditions and resolutions. Further investigation of other data fusion techniques is currently in progress.

Location Detection and Tracking of Moving Targets by a 2D IR-UWB Radar System

In indoor environments, the Global Positioning System (GPS) and long-range tracking radar systems are not optimal, because of signal propagation limitations in the indoor environment. In recent years, the use of ultra-wide band (UWB) technology has become a possible solution for object detection, localization and tracking in indoor environments, because of its high range resolution, compact size and low cost. This paper presents improved target detection and tracking techniques for moving objects with impulse-radio UWB (IR-UWB) radar in a short-range indoor area. This is achieved through signal-processing steps, such as clutter reduction, target detection, target localization and tracking. In this paper, we introduce a new combination consisting of our proposed signal-processing procedures. In the clutter-reduction step, a filtering method that uses a Kalman filter (KF) is proposed. Then, in the target detection step, a modification of the conventional CLEAN algorithm which is used to estimate the impulse response from observation region is applied for the advanced elimination of false alarms. Then, the output is fed into the target localization and tracking step, in which the target location and trajectory are determined and tracked by using unscented KF in two-dimensional coordinates. In each step, the proposed methods are compared to conventional methods to demonstrate the differences in performance. The experiments are carried out using actual IR-UWB radar under different scenarios. The results verify that the proposed methods can improve the probability and efficiency of target detection and tracking.