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A New Algorithm of Data Process in Infrared Search and Track (IRST) System

Leili Hu', Yuqing Chen

State Key Laboratory of Modem Optical Instrumentation Zhejiang University, Hangzhou, Zhejiang 3 10027, China

*Email:huleili@263.net; Telephone:0086 379 393 8591; Fax:0086 379 391 2424 ABSTRACT

Infrared Search and Track (IRST) system is one of the most typical airborne sensors for the remote target measurement, detection and tracking in the fighter planes. In recent years the problem of target recognition and tracking has been studied more and more extensively. However, as the important difficulties that target recognition and tracking involves, the problems synthesizing target motion analysis and data process have been ignored to a certain extent. In this paper, the infrared model and kinematic model of targets are descri6ed. On basis of the models, a novel and more practical algorithm of data process for bearings-only tracking applications is formed and the major parameters are defined. Finally data simulation is implemented, and the results demonstrate the new algorithm has a better performance in target recognition and tracking. Keywords: Infrared Search and Track (IRST), data process, target motion analysis (TMA), infrared model.

1 INTRODUCTION IRST systems are electro-optical sensors which, either acting alone or as part of an overall sensor suite, provide measurement, detection and tracking of potential targets by receiving infrared radiation. Their high resolving ability and fine anti-interference make them especially suitable for remote target detection and tracking. In usual, at the first stage, the infrared signal process is implemented, where the IRST system defines the threshold values by analyzing the infrared characteristic of targets and background; at the second stage, the points whose infrared radiation intensities exceed threshold are regarded as potential targets, then the state equations and measurement equations of the potential targets are formed and filtering and estimation are applied. Based on target motion analysis, every potential target is decided and tracked. At this stage, the received infrared radiation intensities are not considered. Under some circumstances, this method is helpful to simplify the processing procedure. However as the case always is: a single moving observer plane monitors an IRST system to search radiating infrared sources which are assumed to be constant speed or maneuvering targets, then it processes measurement data to obtain estimates of source positions and velocities and so on. Unfortunately the estimation problem is not amenable to simple solution. Since range measurement is not available, the data process is unobservable. With regard to this problem, much research has been If the infrared characteristic and kinematic characteristic of targets can be taken together, the received infrared radiation intensities are treated as a dimension measurement information, the recognition and tracking of targets will be easier and more effective.

2 MATHEMATICAL MODEL Assume that the ownship platform has a IRST system capable of measuring the azimuth and elevation angles U,@), and the dynamic state of the ownship is known. The dynamic state of the target is unknown, as shown in Fig.1. The received infrared radiation intensity I is got and considered: I oc r 2 . Choose another

referenced information a as a dimension measurement: a = - . For arbitrary

kinematic model of targets, the equation can be formed. Here for simplicity, take the constant velocity model for example. For the discrete system, the recursion formula can be derived:

1 I

Fig. 1 Problem illustration

By selection and comparison, def te X=[a(k) B(k) p(k)]' , use the following equation for a filter:

0-7803-6513-5/00$10.00 @ 2000 IEEE. ' 443

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Prediction error Algo- rithm Mean Meansquare

deviation (A) 0.944‘ 26.0466‘

(B) 4.161‘ 34.9669‘

(c) 1.632’ 27.1858’

*(k/k) = g ( k / k - 1) + K[X(k) - X ( k / k - l)] 3 RIPPLE INDEX

The components BandPofXare amenable to normal distribution, while a satisfy a = kat-:. Here define

lime (6) l ime

ripple index 17 = ka - for analyzing the E{k, I

variation of k,. Define E is the angle between the target’s heading and velocity vector. Then the angle E

can be divided into 2 components: 6’‘ and PE , regarded as approximately uniform distribution random variables. For a typical target plane that is traveling without thrust augmentation, its inhred radiant intensity in different angles is shown in Fig.2. Moreover by a great deal of Monte Carlo trials, ripple index q can be simulated, shown in Fig.3.

no Fig.2 IR radiant intensity of a

typical target in different angles

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Vincent J. Aidala and Sherry E. Hammel. Utilization of Modified Polar Coordinates for Bearings-Only Tracking. IEEE

Song T.L. and Speyer J.L., “A Stochastic Analysis of a Modified Gain Extended Kalman Filter with Applications to Estimation with Bearings-Only Measurements”, IEEE Vol. AC-30, No. 10, 1985: 940-949 Walter Grossman. Bearings-Only Tracking: A Hybrid Coordinate system Approach. Journal of Guidance, Control, and Dynamics, Vol. 17, No. 3, May-June 1994: 451-457 Leili Hu, Yuqing Chen, Shenghuai Zou, et al. Design Of Tracking Gate in I n h e d Search and Track Systems, Chinese Journal ofInpared andMillimeter Waves, Vol 19, No.6,2000

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