Infrared Search And Track Research Articles

Accuracy test and analysis for infrared search and track system

Infrared search and track (IRST) attracts many attentions nowadays. It is important to evaluate the performance of the detection and tracking performance for the development of IRST. This paper presents a tracking accuracy indoor test method for IRST. The proposal approach compares the simulated target trajectories with the tracks given by the IRST and calculates the dynamic tracking accuracy of the tested system. An infrared point target track simulator was built to simulate the actual target and missile trajectories. The movement of the simulator was driven by a high-precision stepper motor. A blackbody was used to simulate the infrared radiation of targets. The simulator was controlled by computer via RS-232 serial port. The simulator moving speed, direction and the operation angle value was managed in a software interface written in Visual Basic. The moving point target's coordinates, azimuth and elevation angle trajectories were reconstructed through the algorithm of coordinate translation. A comparison between the simulated target track and IRST recorded trajectory was performed to get the tracking accuracy of tested system. The paper describes the tracking accuracy test system design and implementation in detail. The experimental test and result analysis are provided to verify the effectiveness.

형태학적 연산에 기반한 해수면 온도 분포 추정 알고리즘

원거리의 표적을 탐지하기 위한 IRST시스템에서 표적은 배경영역에 포함된 많은 클러터로 인하여 검출이 매우 어렵다. 특히 해상 환경에서 획득된 영상의 경우, 불균일한 해수면 온도 분포는 원거리에서 접근하는 소형 표적의 검출을 어렵게 하는 요인으로 작용한다. 이에 본 논문에서는 형태학적 연산을 기반으로 하는 불균일한 해수면 온도 분포를 추정하는 알고리즘을 제안한다. 정확한 해수면 온도 분포 추정을 위하여 상한 분포와 하한 분포로 나누어 추정하고, 추정된 결과의 평균값으로 최종 해수면 온도 분포를 도출한다. 또한 형태학적 연산을 적용함으로써 발생하는 서브샘플링 효과 문제를 해결하기 위하여 다양한 크기의 구조요소를 이용하는 다중 가중치(multi-weight) 기법을 적용한다. 제안 방법의 타당성을 검증하기 위하여 다양한 환경에서 획득된 해수면 영상에 대한 해수면 온도 분포 추정 결과를 기존 알고리즘과 비교 검증하였다. Target detection is very difficult with complex clutters in IRST(Infrared Search and Track) system for a long distance target. Especially sea-clutter and ocean-surface with non-uniform temperature distribution make it difficult to detect incoming targets in images obtained in sea environment. In this paper, we propose a novel method based on morphological method for estimation of ocean surface with non-uniform temperature flow. In order to estimate the exact ocean surface temperature flow, we divided it into upper and lower bound flow. And after estimating it, the final ocean surface temperature flow is derived by a mean value of the estimated results. Also, we apply the multi-weighted technique with a variety of sizes of structure elements to overcome sub-sampling effect by using morphology method. Experimental results for ocean surface images acquired from many different environments are compared with results of existing method to verify the performance of the proposed methods.

Scale invariant small target detection by optimizing signal-to-clutter ratio in heterogeneous background for infrared search and track

This paper presents a novel mathematical method for incoming target detection in a cluttered background motivated by the robust properties of the human visual system (HVS). The robust detection of small targets is very important in IRST (Infrared Search and Track) applications for self-defense or attacks. HVS shows the best efficiency and robustness for the task of object detection in cluttered backgrounds. The robust properties of HVS include the contrast mechanism of figure-ground, multi-resolution representation of an object, size adaptation of object boundary, and pop-out phenomena in a complex environment. Based on these facts, a plausible computational model integrating these facts is proposed using Laplacian scale-space theory and an optimization method. Simultaneous target signal enhancement and background clutter suppression are achieved by tuning and maximizing the signal-to-clutter ratio (TM-SCR) in Laplacian scale-space. At the first stage, Tune-Max of the signal to background contrast produces candidate targets with estimated target scale. At the second stage, Tune-Max of the signal-to-clutter ratio (SCR) produces maximal SCR that is used to sort the detection results. Especially, the row-directional-local background removal filter (RD-LBRF) is preprocessed in the horizontal region to enhance the TM-SCR method. The evaluation results of incoming target sequence validate the detection capability of the proposed method from dim, small targets to strong, large targets in comparison with the Top-hat method at the same rate of false alarms. The experimental results of various cluttered background images show that the proposed TM-SCR produces less false alarms (4.3 times reduction) compared to that of the Top-hat at the same detection rate. Finally, TM-SCR after RD-LBRF can maximize the detection rate in horizontal regions.

Detection algorithm of targets for infrared search system based on area infrared focal plane array under complicated background

Infrared Search and Track (IRST) system is a wide field of view surveillance system, meant for autonomous search, detection, acquisition, and cue of potential targets. The first and second generation IRSTs utilized detectors with multiple elements followed by discrete preamplifiers for signal read-out. They have many performance limitations. With the advent of infrared focal plane array (IRFPA) sensors, the present trend is to build IRSTs based on line FPA sensors to achieve higher sensitivity and resolution. This article describes a new IRST system, different from other systems with area infrared focal plane array (IRFPA), using area infrared focal plane array (AFPA). This system has the simple and reliable structure. First this paper described the surveillance system based on AFPA. And the Point Scattering Function (PSF) of degradation model images blurred by AFPAs’ rotations and the imaging feature of the targets is presented. And the target detection algorithm is described and analyzed, the maximum filter and center filter is advanced. Finally the experiment results have proved that the IRST system using AFPA has high detection ratio and low false alarm ratio.

Min-local-LoG filter for detecting small targets in cluttered background

A novel spatial filtering method is presented to detect small targets in a cluttered background for infrared search and track (IRST). The filtering process of localised directional Laplacian-of-Gaussian (LoG) filtering, and the minimum selection can then remove false detections around cloud edges maintaining a small target detection capability. Experimental results validate the feasibility of the proposed method (called min-local-LoG).

Double Layered-Background Removal Filter for Detecting Small Infrared Targets in Heterogenous Backgrounds

Detecting small targets is essential for mitigating the sea-based Infrared search and track (IRST) problem. It is easy to detect small targets in homogeneous backgrounds such as the sky. When targets are on the border line of heterogeneous backgrounds such as the horizon in the sky and sea surface, solving the problem of detection becomes difficult. This paper presents a novel spatial filtering method, called Double Layered-Background Removal Filter (DL-BRF), for achieving high detection rates and low false alarm rates. DL-BRF consists of a Modified-Mean Subtraction Filter (M-MSF) and a consecutive Local-Directional Background Removal Filter (L-DBRF). M-MSF enhances the target signal and reduces background noise. L-DBRF removes horizontal structures, which upgrade the signal-to-clutter ratio and background suppression factor. L-DBRF used after M-MSF enhances the synergistic performance of horizontal target detection. Additionally, the adaptive Hysteresis threshold-based scheme is a suitable detection method. We validate the superior performance of the proposed method via three types of evaluation tests, including a real test scenario.

Robust scale invariant target detection using the scale-space theory and optimization for IRST

This paper presents a new small target detection method using scale invariant feature. Detecting small targets whose sizes are varying is very important to automatic target detection in infrared search and track (IRST). The conventional spatial filtering methods with fixed sized kernel show limited target detection performance for incoming targets. The scale invariant target detection can be defined as searching for maxima in the 3D (x, y, and scale) representation of an image with the Laplacian function. The scale invariant feature can detect different sizes of targets robustly. Experimental results with real FLIR images show higher detection rate and lower false alarm rate than conventional methods. Furthermore, the proposed method shows very low false alarms in scan-based IR images than conventional filters.

Small Target Detection Utilizing Robust Methods of the Human Visual System for IRST

Robust detection of small targets is very important in IRST (Infrared Search and Track). This paper presents a novel mathematical method for the incoming target detection problem in cluttered background motivated from the robust properties of human visual system (HVS). The HVS shows the best efficiency and robustness for an object detection task. The robust properties of the HVS are contrast mechanism, multi-resolution representation, size adaptation, and pop-out phenomena. Based on these facts, a plausible computational model integrating these facts is proposed using Laplacian scale-space theory and Tune-Max based optimization method. Simultaneous target signal enhancement and background clutter suppression is achieved by tuning and maximizing the signal-to-clutter ratio (TMSCR) in Laplacian scale-space. At the first stage, the Tune-Max of the signal to background contrast produces candidate targets with adapted scale. At the second stage, the Tune-Max of the signal-to-clutter ratio (SCR) produces maximal SCR which is used to pop-out detections. Experimental evaluation results for the incoming target sequence validate the upgraded detection capability of the proposed method compared with the Top-hat method at the same false alarm rate. Experimental results for the six kinds of cluttered background images show that the proposed TMSCR produces less false alarms (4.3 times reduction) compared to the Top-hat at the same detection rate.

Probabilistic data association applications to data fusion

We describe a new approach to fusion techniques in a multiple target tracking system for an infrared search and track (IRST) system operating in the mid- and long-wave infrared (IR) bands. The use of the two IR bands allows better performances in terms of detection probability, lower number of false tracks, and shorter time for track declaration. To properly merge data from the two sensors, an enhancement of the probabilistic data association (PDA) technique is introduced in the process. A simplification of the approach used brings back the algorithm to the well-known PDA, allowing its use in conjunction with the interacting multiple model (IMM) with increased tracking ability of the system.

Detection performance loss due to jitter in naval IRST systems

Infrared search and track (IRST) systems based on 3-D velocity filters are quite sensitive to the random motion that remains after egomotion compensation has occurred (jitter). These systems are designed to detect small targets at long range and they often employ track-before-detect (TBD) strategies to integrate the target signal along consecutive frames. Jitter causes frame misalignment and leads to a reduction of the detection performance. We analyze the effects of jitter on the false alarm and detection probabilities and on the detection range. The analytical expressions here derived are a valuable tool both in the analysis and synthesis of IRST systems based on 3-D velocity filters.

Method to estimate infrared and radio-frequency synergy

A method is developed to study the synergism that can exist between horizon search radar and IR search and track (IRST) systems onboard ship under various meteorological conditions within the marine surface layer (<50 m). The method shows that four operational regions can be defined through the effect of the air-sea temperature difference and the air-sea water vapor pressure difference to produce sub- or superrefractive IR and rf propagation. It is also shown that no conditions can exist such that both IR and rf have subrefractive propagation. Applying the method to many meteorological observations shows that the method works quite well; however, it also indicates that there is not necessarily a detection range advantage to having both an optical and a radar system. However, the advantages to having an optical system are not solely dependent on its range performance. The precision with which an optical system can provide target track parameters, its ability to maintain track when radar systems cannot, and its ability to identify targets are synergisms that are quite significant.

English

Infrared search and track (IRST) systems are required for fighter aircraft to enable them to passively search, detect, track, classify, and prioritise multiple airborne targets under all aspects, look-up, look-down, and co-altitude conditions and engage them at as long ranges as possible. While the IRST systems have been proven in performance for ground-based and naval-based platforms, it is still facing some technical problems for airborne applications. These problems arise from uncertainty in target signature, atmospheric effects, background clutter (especially dense and varying clouds), signal and data processing algorithms to detect potential targets at long ranges and some hardware limitations such as large memory requirement to store and process wide field of view data. In this paper, an overview of airborne IRST as a system has been presented with detailed comparative simulation results of different detectionitracking algorithms and the present status of airborne IRSTs

Passive ranging using an infrared search and track sensor

We present new techniques for passive ranging with a dual-band IR search and track (IRST) sensor aboard a ship. Three distance estimation methods are described: the atmospheric propagation model, the apparent surface of the target, and target motion analysis (TMA). These methods are tested on the sensor output of real data during cold water trials (CWTs). They are evaluated by comparing with simultaneously obtained radar reference data at the test site. Results of these three passive ranging and three fusion processes, combining the preceding methods, are presented. This demonstrates the effectiveness of IR passive ranging techniques in the anti-air-warfare scenario. Majority voting fusion shows that improvement of the distance estimation is achieved in the CWT scenario when combining these three different methods. A range-error reduction of 41% is obtained, and a typical uncertainty of 5% is at a 8-km distance. During warm water trials (WWTs) the TMA algorithm was adapted to deal with a dynamic environment of the antisurface warfare scenario (ASuW). These WWTs prove that TMA in combination with an IRST system can extend the basic IRST functionality significantly for a dynamic ASuW scenario.

Synergy decision in the multi-target tracking based on IRST and intermittent-working radar

Abstract It is known that one of the most effective methods to prevent radar from being locked-on by the adverse ESM is to use the electromagnetic covertness. In this paper, a general structure and a new algorithm are proposed to solve such problems as making synergy decision between infrared search and track (IRST) and an intermittent-working radar to reduce the opportunity for the radar being locked-on by the adverse ESM, retrieving target range information after the radar is turned off, and detecting target maneuver. In doing so, we have, firstly, suggested an “angular distance” to confirm the initial tracks of IRST and the radar. Then, we utilize the polynomial system based on the sufficient observations done before the radar is turned off to predict the possible target position values. Next, the predicted values can be combined with the data of the observation from IRST to form a set of discriminants for detecting the target maneuver. And finally, a simulation has been done based on the given scenario and simulation results prove the effectiveness of the algorithm.

Novel background removal algorithm for Navy infrared search and track systems

Navy infrared search and track (IRST) systems aim at detecting long range airborne targets closing the naval unit at low altitude above the sea surface.1 The target signal is a 2-D pulse of small spatial extent and is well approximated by the sensor point spread function (PSF) further, the signal to clutter ratio (SCR) is usually very low and detection cannot be performed on a single frame. Multiple frame detection algorithms are therefore used to improve the detection probability.

Bearings-Only Target Motion Analysis Using a Dual-Waveband Infrared System

Infrared systems are widely used for target detection, designation and tracking. For example, an Infrared Search and Track (IRST) system, as a typical airborne or shipboard detecting device, is widely equipped for the remote target detection and tracking. In recent years, the problem of target motion analysis (TMA) and tracking has been studied increasingly extensively. In an airborne infrared system, the problem becomes more difficult due to absence of range information. In this paper, the infrared model and motion model of typical aerial targets are described. An airborne dual-waveband IRST system, which is quite familiar nowadays, is choosed for implementation of target motion analysis. Based on the above, a novel and more practical algorithm of target tracking via bearings-only measurements is formed and the major parameters are defined according to a typical airborne dual-waveband IRST system. Finally, data simulation is implemented, and the results demonstrate the new algorithm has a better performance than before for bearings-only target tracking.

Improved tracking capability and efficient radar allocation through the fusion of radar and infrared search-and-track observations

The benchmark problem addresses the efficient allocation of an agile-beam radar in the presence of highly maneuverable targets and radar ECM. The multisensor benchmark tracking solution is aided by the presence of a scanning infrared search-and-track (IRST) system. This paper presents methods for applying a tracker with interacting multiple- model (IMM) filtering and multiple-hypothesis tracking (MHT) data asso- ciation to this multiple-sensor tracking and resource allocation problem. It presents a hybrid multisensor tracking architecture in which an IMM- MHT tracker operating on IRST data provides the global IMM-MHT tracker with selected observations. Simulation results quantify the poten- tial improvement from the use of advanced tracking methods and IRST data to enhance agile-beam radar tracker capability. © 2000 Society of Photo-Optical Instrumentation Engineers. (S0091-3286(00)03205-0)

Tracks real-time classification based on fuzzy rules

A real-time track classifier based on fuzzy logic is presented here. The application deals with false alarm rejection in an airborne Infra-Red Search and Track (IRST) system. The approach is based on the use of a set of rules representing heterogeneous knowledge on behavior and appearance of physical objects. Uncertainty rules are considered to model knowledge on classes, while gradual rules are used to compute some auxiliary variables. Examples of rules are given, as well as results on simulated data. © 1997 John Wiley & Sons, Inc.

Target identification with Bayesian networks in a multiple hypothesis tracking system

A multisensor fusion algorithm that integrates model-based target identification (ID) and multiple-hypothesis tracking (MHT) is de- scribed. The algorithm augments the target state representation to in- clude ID information and to manipulate the new target state. The major innovation of the algorithm is using Bayesian networks to modify the hypothesis-evaluation process by taking into account target ID. This re- search was conducted as part of a larger effort to design a decision- theoretic sensor management system. In the system, two types of sen- sors, electronically scanned radar (ESA) and IR search and track (IRST) are assumed to be available. The ESA radar is modeled to have search and update capabilities as well as three radar identification modes: ultra- high-resolution radar (UHRR), radar signal modulation (RSM), and radar electronic support mode (RESM). A Bayesian network is used to model the detection and observation processes for these ID techniques and compute the association likelihoods between measurements and tracks. © 1997 Society of Photo-Optical Instrumentation Engineers.

RASSP Methodology Evaluation and Lessons Learned Developing IRST Signal Processor

The development of a real-time infrared search and track (IRST) processing system served as a real-world test of progress toward the fourfold reduction in cycle time expected at the end of the four-year Rapid Prototyping of Application Specific Signal Processors (RASSP) program. This signal processor development tested the effectiveness of the RASSP methodology and processes and provided critical metrics to evaluate the 4X roadmap. Virtual Prototyping, a VHDL technique for validating hardware and software design before building hardware, makes the RASSP top-down methodology unique. This paper describes the 4X roadmap for cycle time improvement, our experiences with virtual prototyping and important lessons learned for improving the methodology.