Moving Point Target Research Articles

Moving target detection based on multi-satellite joint passive microwave imaging

Using satellite formations to form a space-based passive interferometric imaging system can achieve high spatial resolution, but the sparse detection baseline will cause aliasing in the inversion image, affecting the detection of single-pixel point targets. Considering the slowly varying characteristics of the observation area, a target detection method based on image sequence is proposed. The background is estimated by multi-frame images, and the background of the inversion image is eliminated; energy is gathered based on sidelobe characteristics, the noise in the target area is estimated, and candidate targets are selected; the motion trajectory of the target is obtained by combining the temporal motion characteristics, and the detection of moving point targets is realized. Taking the formation system composed of 10 geostationary orbit satellites as an example, 300 simulation experiments are carried out for the detection of 50 ship point targets. The results show that the proposed method can realize point target detection in a large field of view, with an average false alarm rate 14.5%and an average missed alarm rate 19.5%.

Method for designing a grid-slit spectrometer with low spectral-line bending

The classic single-slit spectrometer exhibits low field utilisation in the direction of the slit width and requires a push-broom observation method to visualise moving point targets. Therefore, we designed a spectrometer with a grid slit to overcome these limitations. First, a grid slit composed of two sets of vertically arranged slit arrays, each containing five slits, was established. Then, the spectral-line bending of the off-axis slits was analysed, and the parameters of the dispersive elements that minimised the spectral-line bending of the slit arrays were selected as the initial values. Next, a merit function was established to consider the systematic distortion, and finally, the system was optimised. The proposed spectrometer design enabled a grid slit with 10 slits to be implemented, which significantly enhanced the field utilisation of the spectrometer and enabled starting-observation-mode operation. The spectral-line bending of each slit was less than 0.21 μm (less than one-third of a pixel size) and the modulation transfer function was higher than 0.7 (@45.45 lp/mm). The design and analysis results confirmed the feasibility of the proposed grid-slit design method, which can potentially be applied to other spectrometers to improve their field utilisation or to enable starting-observation-mode operations.

Time-Domain Measurement Data Accumulation for Slow Moving Point Target Detection in Heavily Cluttered Environments Using CNN

In modern radars, the target detection probability is increased by lowering the detection threshold via signal processing to detect a point target with a small radar cross-section value. However, a lower threshold increases the number of false targets. In the conventional tracking method, which uses a general tracking filter, the measurement data between scans should be compared. Therefore, for a large amount of acquired measurement data, the computational complexity can be reduced by accumulating the acquired measurement data over time, recognizing the target movement as a pattern, and training a convolutional neural network (CNN) model. Here, we propose a method to create a desired target scenario by transfer learning and estimate the target position using the activation map of a binary detector CNN model. The model can detect a target using the actual acquired radar data, and the processing time remains constant, regardless of the number of false alarms.

Moving Point Target Detection Based on Temporal Transient Disturbance Learning in Low SNR

Moving target detection in optical remote sensing is important for satellite surveillance and space target monitoring. Here, a new moving point target detection framework under a low signal-to-noise ratio (SNR) that uses an end-to-end network (1D-ResNet) to learn the distribution features of transient disturbances in the temporal profile (TP) formed by a target passing through a pixel is proposed. First, we converted the detection of the point target in the image into the detection of transient disturbance in the TP and established mathematical models of different TP types. Then, according to the established mathematical models of TP, we generated the simulation TP dataset to train the 1D-ResNet. In 1D-ResNet, the structure of CBR-1D (Conv1D, BatchNormalization, ReLU) was designed to extract the features of transient disturbance. As the transient disturbance is very weak, we used several skip connections to prevent the loss of features in the deep layers. After the backbone, two LBR (Linear, BatchNormalization, ReLU) modules were used for further feature extraction to classify TP and identify the locations of transient disturbances. A multitask weighted loss function to ensure training convergence was proposed. Sufficient experiments showed that this method effectively detects moving point targets with a low SNR and has the highest detection rate and the lowest false alarm rate compared to other benchmark methods. Our method also has the best detection efficiency.

Dim Moving Point Target Detection in Cloud Clutter Scenes Based on Temporal Profile Learning

This letter presents an approach for detecting dim moving point targets in cloud clutter scenes based on temporal profile learning. The main idea is that a weak transient disturbance will appear in the temporal profiles of target-present pixels, changing the temporal profile’s characteristics. We propose a novel signal-to-signal network to learn the temporal characteristics of the background and the clutter, in which the transient disturbance is extracted by the residual between the input signal and the reconstructed background and clutter signal. The structure of the ConvBlock-1D is designed to enhance the flow and propagation of features in layers. A loss function is proposed to solve the imbalance problem. We provided a comparison to other widely used methods by using simulated datasets and real-world image sequences. The experimental results demonstrate that our method has the best performance in terms of the qualitative and quantitative assessments.

Cramér–Rao Lower Bounds for Estimating Time Delay and Doppler Stretch for Monostatic and Bistatic Impulse Radar Systems Using Ultrawideband-Throb Signal

In this article, the wideband ambiguity function (WBAF) of monostatic radar and that of bistatic radar, both operating with ultrawideband (UWB)-throb signals, are presented and used to calculate Cramér–Rao lower bounds (CRLBs) on the estimation accuracy of time delay and Doppler stretch in the case of monostatic radar and CRLBs on the estimation accuracy of receiver-to-target range and relative-velocity of a moving point target in the case of bistatic radar. Performance analysis is carried out to investigate the effects of the parameters that characterize the UWB-throb signal, i.e., pulse scaling factor, initial pulse repetition frequency (PRF), throb rate, and signal duration, and the parameters defining the bistatic-radar geometry, i.e., receiver-to-target range, baseline distance, and elevation angle, on the estimation accuracy of the monostatic-CRLBs and on that of the bistatic-CRLBs. Computer plots demonstrate that the estimation accuracy of the monostatic-CRLBs and the bistatic-CRLBs is influenced by the aforementioned radar parameters. It is also shown that target relative velocity has no major effects on the estimation performance of monostatic-CRLBs and bistatic-CRLBs.

Infrared moving dim point target detection based on spatial-temporal local contrast

Aiming at the detection of dim and small moving point targets in space-based infrared (IR) imaging systems, a detection method based on spatial-temporal local contrast (STLC) is presented. Firstly, use the spatial and temporal information to estimate the background gray value of each pixel, and obtain two prediction results respectively. Then, the spatial local contrast (SLC) and the temporal local contrast (TLC) are defined according to the original image and two estimated backgrounds. Next, the STLC is defined as the product of SLC and TLC. Finally, a threshold value is set in the STLC for target detection. Four groups of experiments are conducted, and the simulation results indicate that the proposed method has a great advantage in terms of background suppression factor (BSF) and gain of signal-to-noise (GSNR).

Validation of MA-ATI SAR Theory Using Numerical Simulation for Estimating the Direction of Moving Targets and Ocean Currents

A theory of multiaperture along-track interferometric (MA-ATI) synthetic aperture radar (SAR) was previously proposed to estimate the velocity vector of moving targets by utilizing conventional ATI SAR data. In the MA-ATI SAR method, multi/sublook processing is applied to both the raw data acquired by the fore-and-aft antennas, yielding two sets of forward-looking images and two sets of backward-looking images. From the interferograms of corresponding directions, two velocity components can be estimated, and hence the moving directions of a target are also obtained. The velocity vector is calculated by combining the estimated moving direction with the velocity in the range direction measured by the conventional ATI method. In order to show initial validation of this theory, a numerical SAR simulator, which has a function to produce a time series SAR raw data, was further developed to simulate MA-ATI SAR by taking into account the multilook processing. In the present validation study, this simulator (in time-domain processing) was applied to estimate the moving directions of a moving point target and ocean currents in the case of an airborne radar system. In particular, the effect on the subaperture azimuth beam angle was discussed since it is a key factor for the estimation. The preliminary results demonstrated that the estimation accuracy exhibited a direction error of 5°-10° for both the moving target and ocean currents, respectively, when half of the subaperture azimuth beam angle was larger than 1.25°.

Combined penalized weights based GM-PHD for point target tracking in starry-sky background

It is well known that the point targets observed by the space-based platform are extremely difficult to track due to the interference of a large number of stellar targets and background noise. The traditional GM-PHD, due to its probability accumulation, is easy to mistakenly identify the star as a derived new target when the real target passes closely to the star. In order to achieve precise tracking of multiple moving point targets for space-based observations in a complex background environment such as a starry sky background, this paper proposes a simple and effective combined penalized weights based GM-PHD for filtering. First, a unique label is given for each target to determine which real target the estimate came from. Next, the moving region of target is discretized into 10 irregular intervals to construct ten different motion templates, numbered one by one, for discretization of the direction of the target motion. Next, the true target at time k − 1 and all the estimated values having the same label at time k are used to calculate the direction penalty factor and the speed penalty factor to obtain the penalty strength matrix and penalized weight matrix, respectively. Finally, the penalized weight matrix is used to output the target with the highest weight in each label as the real target at time k. And a given penalty threshold is used to remove some fuzzy weights with greater penalty in the penalty strength matrix, so as to selectively filter out some less relevant targets to further avoid the stars participating in the next iteration. In order to verify the effectiveness of the proposed algorithm, we used the Tycho-2 catalog to simulate four kinds of starry-sky data sets with different complexity and conducted comparative experiments on each data set. The experimental results show that the proposed algorithm can effectively track multiple space-based infrared weak point targets in complex starry-sky scenarios.

Infrared moving point target detection using a spatial-temporal filter

Infrared point target detection technology has widely applications, such as in tracking and surveillance system. In this paper, an moving point target detection method is proposed to detect targets from infrared sequence images. Firstly, a modified spatial-temporal total variation model is proposed for background prediction. Then the subtraction image is obtained by subtracting the predicted background from the corresponding sequence image. Finally, the target can be detected from the result that the product of the subtraction image and a temporal contrast filter. The effectiveness of the modified spatial-temporal filter on infrared moving point targets detection is verified by detecting targets from different infrared sequence images.

Spatial-temporal local contrast for moving point target detection in space-based infrared imaging system

Infrared (IR) moving point target detection is an important technique in many onboard applications such as remote sensing, IR searching and tracking (IRST) and early warning system. However, it has been facing great challenges due to the complicated background and the limited processing resources in the onboard system. In this paper, a novel spatial-temporal local contrast method is proposed for moving point target detection in space-based IR imaging system. Firstly, a simple but effective spatial filter based on multi-direction filtering fusion is designed to obtain the spatial local contrast map (SLCM). An enhanced time domain difference method is also proposed to obtain the temporal local contrast map (TLCM). Then, the spatial–temporal local contrast map (STLCM) is obtained by multiplying the newly defined SLCM and TLCM. Finally, an enhanced threshold segmentation method is proposed for target detection and false alarm suppression. To verify the performance of our detection algorithm, we conduct several groups of experiments on four different real IR image sequences with simulated targets. The final experimental results show that our algorithm significantly outperforms other methods in terms of background suppression and target detection.

*An approach to ship position estimation and clutter suppression based on GEO satellite/airborne bistatic sar with a multi-receive antenna

ABSTRACTGeosynchronous spaceborne-airborne bistatic synthetic aperture radar (GEO SA-Bi SAR), which employs a geosynchronous orbit (GEO) satellite as a transmitter and an aeroplane that works on the ‘receive-only’ mode as a receiver, is distinct from general bistatic synthetic aperture radar (SAR) systems. In this paper, a GEO SA-Bi SAR system with multi-receive antenna is consider for marine surveillance. First, ship position is estimated in the configuration of GEO SA-Bi SAR with multi-receive antenna, using maximum likelihood (ML) method. Then, according to the estimation result, sea clutter in the signal of a moving point target is suppressed by adaptive beamforming (ABF). Moreover, the performance of our method is analysed and studied in terms of the point target imaging. The simulation results demonstrate the correctness and effectiveness of the proposed method.

Infrared moving point target detection based on an anisotropic spatial-temporal fourth-order diffusion filter

Infrared moving point target detection is a challenging task in infrared tracking systems. In this paper, an anisotropic spatial-temporal fourth-order diffusion filter (ASTFDF) is proposed for background prediction from infrared frame images. Using it, we can detect infrared point targets by subtracting the predicted background from the original image. Experiments in detecting targets from several different infrared image frame sequences indicated that the proposed ASTFDF can predict the background effectively and that the target can be detected precisely while achieving a higher background suppression factor (BSF) and signal-to-clutter ratio gain (SCRG) compared with state-of-the-art methods.

New Methods and Algorithms of Search, Detection and Tracking of Moving Point Targets along a Complex Path on a Complex Background

The article is devoted to solving the fundamental scientific problems of developing universal methods of video sequence processing for the detection and tracking of objects of interest. During work on the subject, authors designed new methods and algorithms for universal monitoring systems for wide range of objects in the video sequences which could be implemented at miniaturized data processing systems. In this paper we describe a new method of search and recognition of point objects on a complex background, a new algorithm for the analysis of the found set of descriptors of local features to filter false positives filter local features, object detection criterion in the image, a new algorithm for the analysis of the trajectory of a point object on the basis of a recursive Kalman filter to predict the trajectory the movement of point objects.

Moving Point Target Detection Based on Higher Order Statistics in Very Low SNR

This letter presents an approach for the detection of moving point targets on high-frame-rate image sequences with low spatial resolution and low SNR based on higher order statistical theory. We propose a novel method for analyzing the time-domain evolution of image data for distinguishing between the background and the target in situations when the spatial signal of the target is swamped by noise. Our method is formulated to detect a time-domain transient signal of unknown scale and arrival time in noisy background. We proposed a bispectrum-based model to characterize the temporal behavior of pixels, and the detection ability under different frame rates and SNRs is analyzed. The method is evaluated using both simulated and real-world data, and we provide a comparison to other widely used point target detection approaches. Our experimental results demonstrate that our algorithm can efficiently detect extremely low SNR targets that are virtually invisible to humans based on time-domain analysis of image sequences.

Infrared moving point target detection based on spatial–temporal local contrast filter

Infrared moving point target detection is a challenging task. In this paper, we define a novel spatial local contrast (SLC) and a novel temporal local contrast (TLC) to enhance the target’s contrast. Based on the defined spatial local contrast and temporal local contrast, we propose a simple but powerful spatial–temporal local contrast filter (STLCF) to detect moving point target from infrared image sequences. In order to verify the performance of spatial–temporal local contrast filter on detecting moving point target, different detection methods are used to detect the target from several infrared image sequences for comparison. The experimental results show that the proposed spatial–temporal local contrast filter has great superiority in moving point target detection.

Analgorithmic Framework for Automatic Detection and Tracking Moving Point Targets in IR Image Sequences

Imaging sensors operating in infrared (IR) region of electromagnetic spectrum are gaining importance in airborne automatic target recognition (ATR) applications due to their passive nature of operation. IR imaging sensors exploit the unintended IR radiation emitted by the targets of interest for detection. The ATR systems based on the passive IR imaging sensors employ a set of signal processing algorithms for processing the image information in real-time. The real-time execution of signal processing algorithms provides the sufficient reaction time to the platform carrying ATR system to react upon the target of interest. These set of algorithms include detection, tracking, and classification of low-contrast, small sized-targets. Paper explained a signal processing framework developed to detect and track moving point targets from the acquired IR image sequences in real-time. Defence Science Journal, Vol. 65, No. 3, May 2015, pp.208-213, DOI: http://dx.doi.org/10.14429/dsj.65.8164

Moving point target detection based on clutter suppression using spatiotemporal local increment coding

A point target detection method based on clutter suppression is proposed to detect moving point targets in infrared (IR) image sequences. First, an algorithm with spatiotemporal local increment coding is proposed to estimate the clutter background. Then the target is detected by suppressing the estimated clutter background. During the spatiotemporal coding process, both inter-frame and intra-frame properties of the IR image sequence are taken into account. Experimental results demonstrate the validity of the proposed method.

基于空时域融合处理检测超大视场红外目标

When a super wide-field infrared staring system is used to detect a target,it may show complex background,more noise jamming and little target information.Therefore,this paper proposes a spatial-temporal fusion algorithm.In spatial processing,the single image was filtered by an improved Robinson operator,and the primary detection of the target was carried out.Then,the image autopartition and spatial false-alarm suppression were presented to eliminate the suspected target from a non-target detection region.In temporal processing,the temporal characteristics of the target was considered,the neighbor judgment method was improved to confirm the real target.A detection experiment for the space target was carried out in a moon light,and this proposed algorithm was vili-fied.It shows that the background interference of original image is suppressed greatly,the local Signal to Noise Ratio(SNR)can be improved more than 1.3times,the false target number decreases by70% after spatial processing.Moreover,the infrared weak target is detected successfully with the detection probability of higher than 95%,the false-alarm probability of less than 1.5% and the minimum detection SNR of 2.86 after spatial processing.It concludes that this algorithm is suitable for the infrared weak target detection of super wide-field image and can detect the moving point targets effectively.It has a good suppression effect for background interference,isolated point sources and instantaneous noise.

Design of High-Speed Moving Point Target Measurement System

In view of the geometry, the area of movement and measurement form, put forward the measurement way and method.Laser sensor scans the same area many times in a preset time ,increased the probability of capture the target by optical imaging principle; Tear down analytical expression of the model, system layout by matlab, analysis of the experimental data by Labview. System can satisfy the demand of practical engineering by simple structure, convenient operation, and good stability.