Track-while-scan Research Articles

항공기 AESA 레이다에서 TWS를 이용한 지상 이동 표적 추적

A target-tracking method using the ground moving target indicator (GMTI) mode on active electronically scanned array (AESA) radar is proposed. The GMTI mode can operate in wide ground areas. However, the 3-dB beamwidth of the X-band radar is narrow, and a target is detected on multiple adjacent beams. To delete overlapping target plots, beam-to-beam processing considering a real-time system is proposed. In addition, a track while scan (TWS) multiple-target-tracking algorithm was constructed for the system. Through a flight test with true car mounted GPS, it was confirmed that the real-time processing ability and target-tracking performance of the GMTI algorithm were satisfactory.

Using a New Circular Prediction Algorithm to Design an IMM Filter for Low Update Rate Radar System.

For radar systems with low update rates; such as track-while-scan (TWS) systems using rotating phased array antennas; reducing the prediction error is a very important issue. A good interacting multiple models (IMM) hybrid filter combined with circular and linear filters that are defined in relation to three measurements has been proposed in the literature. However; the algorithm requires three previous measurements; and too much prior information will result in a reduced ability to predict the future position of a highly maneuvering target. A new circular prediction algorithm for maneuvering target tracking is proposed as a non-linear prediction filter in this paper. Based on this new predictor; we also proposed a new type of IMM filter that has good estimation performance for high maneuvering targets. The proposed hybrid filter is entirely defined in relation to two measurements in a three-dimensional space to obtain a better maneuver following capability than the three measurements hybrid filter. Two target profiles are included for a comparison of the performance of our proposed scheme with that of the conventional circular; linear and IMM filters. The simulation results show that under low update rates; the proposed filter has a faster and more stable estimation response than other filters

레이더에서의 Markov Chain 분석을 이용한 TWS 방식과 Adaptive Tracking 방식의 추적 형성 거리 비교

Compared with the TWS(Track While Scan) tracking that uses scan-to-scan correlation at search illuminations for targets track, a phased array radar can use adaptive tracking which assigns additional track illuminations and the track formation range can be improved as a result. In this paper, an adaptive tracking, the search and track illuminations of a target are synchronized such that the extra illuminations are evenly distributed between the search illuminations, is proposed. Markov chain and track formation range for the proposed adaptive tracking are shown with them for the conventional TWS. The simulation result shows that the proposed adaptive tracking has improved track formation range by 27.6 % compared with the conventional TWS tracking under same track confirmation criterion.

Airport Surveillance Processing Chain for High Resolution Radar

The paper focuses on the design of the digital processing chain of a surface movement radar (SMR) for airport traffic in the frame of A-SMGCS (advanced-surface movements guidance and control system); in order to reach, or exceed, the demanding requirements for the A-SMGCS surveillance function, special attention has to be paid to SMR processing, whose main functional blocks are the CFAR (constant false alarm rate) processor, the plot extractor, and the track-while-scan (TWS). A description of these processing functions, tailored to a noncoherent, high-resolution radar, and of their implementation is provided. The performance evaluation is accompanied by trials on recorded data, as obtained from a national research and development project called fast prototyping (FP) project (2001-2003) and its subsequent activities. The aim of the work presented here is to give a system view of the radar processing chain for high-resolution SMR, being rather different than standard surveillance radar.

UNSCENTED TRANSFORM AND ITS APPLICATION IN ATC TRACKING

Nonlinear filtering is certainly very important in estimation since most real-world problems are nonlinear. In this paper, we devote the effort to use the unscented transform (UT) to improve Air Traffic Control (ATC) tracking. The simulation results show that the UT improved the tracking performance compared to the traditional methods for the track-while-scan (TWS) ATC application.

Airport surface surveillance with a network of miniradars

A distributed system based on a network of short-range (1.5-3 km) radars, operating in the W-band (93-95 GHz), is proposed for the ground surveillance of airports. Both the architecture and the main features of each subsystem are described. The system can boast very high resolution, the elimination of shadowing, and the inclusion of image processing techniques in the track-while-scan (TWS) function.

An alpha-beta tracking filter combined with ellipsoidal prediction using the generalized Hough transform

In track-while-scan (TWS) systems using phased array antennas, an α-β filter has been employed. An α-β filter can estimate reliably the target position and velocity. However, when the target maneuvers, the quality of the position and velocity estimates provided by an α-β filter can degrade significantly. The target may be lost, since the filter uses only linear prediction. As the nonlinear tracking filter for a maneuvering target, the α-β tracking filter combined with maneuver-driven circular prediction was proposed. On the other hand, the generalized Hough transform has been used for detecting curves in image processing. In this paper, we take account of the fact that a maneuvering target trajectory can be regarded as all ellipsoidal arc and propose the use of an α-β tracking filter combined with ellipsoidal prediction using the generalized Hough transform to improve tracking quality over the α-β filter combined with circular prediction. Simulation results for two target profiles are included for comparison of the performance of our proposed scheme with that of a conventional α-β tracking filter, the Kalman filter, the two-stage Kalman filter, and the α-β filter combined with circular prediction. The effectiveness of our proposed scheme is examined by means of a computational simulation. © 1998 Scripta Technica. Electron Comm Jpn Pt 1, 81(8): 9–18, 1998

New approach to improved detection and tracking performance in track-while-scan radars. Part 2: Detection, track initiation and association

The paper presents, in three parts, a new approach to improve the detection and tracking performance of a track-while-scan (TWS) radar. Part 1 presents a review of current status. In this part, Part 2, it is shown how the detection can be improved by utilising information from tracker. A new multitarget tracking algorithm, capable of tracking manoeuvring targets in clutter, is then presented. The algorithm is specifically tailored so that the solution to the combinatorial problem presented in a companion paper can be applied. The implementation aspects are discussed and a multiprocessor architecture identified to realise the full potential of the algorithm. Part 3 presents analytical derivations for quantitative assessment of the performance of the TWS radar system. It also shows how the performance can be optimised.

New approach to improved detection and tracking performance in track-while-scan radars. Part 1: Introduction and review

The paper presents a new approach to improve the detection and tracking performance of a track-while-scan (TWS) radar. The contribution consists of three parts. In Part 1 the scope of various papers in this field is reviewed. In Part 2, a new approach for integrating the detection and tracking functions is presented. It shows how a priori information from the TWS computer can be used to improve detection. A new multitarget tracking algorithm has also been developed. It is specifically oriented towards solving the combinatorial problems in multitarget tracking. In Part 3, analytical derivations are presented for quantitatively assessing, a priori, the performance of a track-while-scan radar system (true track initiation, false track initiation, true track continuation and false track deletion characteristics). Simulation results are also shown.

Retention Probability in a Track-While-Scan Radar

The probability of maintaining the track file, after acquisition in a track-while-scan (TWS) radar, has been formulated and computed under various conditions of initial range, velocity, and track drop time. All calculations are based on a predetermined update probability versus range characteristic. Results, obtainable with a digital computer in just seconds of computing time, readily demonstrate the relative superiority of retention characteristics achievable for TWS radar with an electronically steerable array antenna.

An Adaptive Two-Dimensional Kalman Tracking Filter

A continuously adaptive two-dimensional Kalman tracking filter for a low data rate track-while-scan (TWS) operation is introduced which enhances the tracking of maneuvering targets. The track residuals in each coordinate, which are a measure of track quality, are sensed, normalized to unity variance, and then filtered in a single-pole filter. The magnitude Z of the output of this single-pole filter, when it exceeds a threshold Z1 is used to vary the maneuver noise spectral density q in the Kalman filter model in a continuous manner. This has the effect of increasing the tracking filter gains and containing the bias developed by the tracker due to the maneuvering target. The probability of maintaining track, with reasonably sized target gates, is thus increased, The operational characteristic of q versus Z assures that the tracker gains do not change unless there is high confidence that a maneuver is in progress.

Heading and Speed Errors for x, y Tracking Filters

Heading and speed errors are analytically determined for noneumavering targets at the output of an x, y tracking filter which processes range and bearing measurements from a radar sensor in a track-while-scan (TWS) operation. These errors are shown to depend upon target range and speed, the angle between the radius and velocity vectors, sensor accuracies, and tracking filter parameters. eters. Depending upon the tracking filter implementation, these errors may also be a function of target bearing.

Track-While-Scan Algorithm in a Clutter Environment

A track-while scan (TWS) algorithm is developed for targets in a clutter environment. The problem has been studied using only the position measurements [1, 5-8], but the simulation results have not been satisfactory. Modern processing techniques (FFT processor) ) in air traffic control and surveillance radar receivers provide both position and radial velocity. The radial velocity measurement may be conveniently used in the target-track correlation process, which will reduce the association ambiguity in the clutter environment. t. In the clear environment the algorithm using the position and radial velocity measurements has been treated in [3, 4]. A TWS algorithm, using both position and radial velocity measurements for targets in a clutter environment, is presented here. The algorithm obtained is nonlinear and adaptive. In order to evaluate the improvement due to radial velocity measurement a simulation has been performed on a digital computer. The algorithm was run with and without radial velocity measurements to compare its performances. An improvement was noted especially when the target path included an accelerated portion.

Analytical Results for the x, y Kalman Tracking Filter

A two-dimensional x, y Kalman tracking filter is analyzed for a track-while-scan (TWS) operation when the radar sensor measures range and bearing (r, ?) at uniform sampling intervals T seconds apart. This development explicitly considers the coupling between the quantities measured by the sensor (r, ?) and the Cartesian x, y coordinate system selected for the tracking operation. The steadystate components of the gain and error covariance matrixes are analytically determined under the assumption of a white noise maneuver acceleration model in two dimensions. These results are verified by computer calculation of the Kalman filter matrix equations.

Synthesis of an optimal set of radar track-while-scan smoothing equations

Performance measures are described which realistically reflect both noise-reduction and maneuver-following capability of a radar track-while-scan (TWS) system. Illustrations are given which compare various smoothing equations on the basis of this performance measure. Finally, a set of position-and-velocity tracking equations is synthesized by a calculus-of-variations technique. The synthesized set is optimum for both position and velocity tracking within the given performance sense, in the class of all fixed parameter, linear tracking equations. The resulting optimally synthesized set characterizes the commonly termed " <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">\alpha-\beta</tex> " tracker, with the important proviso that <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">\beta=\alpha^{2}/(2 - \alpha)</tex> .