Manoeuvring target tracking algorithm for a radar system

Sun, H. M.; Chiang, Shu-Min

doi:10.1080/00207728908910266

Cited by 4 publications

(1 citation statement)

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“…All of these approaches increase the effective filter bandwidth so as to respond more rapidly to the measurement data during a manoeuvre; but they are risky in the presence of clutter since they increase the size of the validation gate. Some of the more recent approaches to manoeuvring target tracking include: adaptive Kalman filters [61,39], filters using correlated and semi-Markov process noise [120,119,60] usually implemented using multiple model (partitioning) filters and filter banks [131,58,95,114,125,141]; filters based on Poisson and renewal process models of acceleration [86,128]; input estimation [35,36] and input and onset-time estimation [33,38,116]; variable dimension filters [13]; track splitting filters with a finite memory constraint [147]; the generalised pseudo-Bayesian (GPB) algorithm [68,1]; and the interacting multiple model (IMM) algorithm [31,28,96]. A second-order extension of the IMM algorithm was developed in [26].…”

Section: Introductionmentioning

confidence: 99%

A Survey of Manoeuvring Target Tracking Methods

Pulford

2015

Preprint

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A comprehensive review of the literature on manoeuvring target tracking for both uncluttered and cluttered measurements is presented. Various discrete-time dynamical models including nonrandom input, random-input and switching or hybrid system manoeuvre models are presented. The problem of manoeuvre detection is covered. Classical and current filtering methods for manoeuvre tracking are presented including multi-level process noise, input estimation, variable dimension filtering, two-stage Kalman filter, the interacting multiple model algorithm, and generalised pseudo-Bayesian algorithms. Various extensions of these algorithms to the case of cluttered measurements are also described and these include: joint manoeuvre and measurement association, probabilistic data association and multi-hypothesis tracking. Smoothing schemes, including IMM smoothing and batch expectation-maximisation using the Viterbi algorithm, are also described. The use of amplitude information for target measurement discrimination is discussed. It is noted that although many manoeuvre tacking techniques exist, the literature contains surprisingly few performance comparisons to guide the design engineer although a performance benchmark has recently been introduced.

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Section: Introductionmentioning

confidence: 99%

A Survey of Manoeuvring Target Tracking Methods

Pulford

2015

Preprint

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Tracking multitarget in cluttered environment

Sun¹,

Chiang²

1992

IEEE Trans. Aerosp. Electron. Syst.

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A method for tracking multitarget and initiating tracks procedure in a cluttered environment is proposed. The algorithm is perfonned by taking a sliding w i d o w of length UT (T is the sampling time) to keep the measurement sequence at time k. When a set of measurements is received, we form a new set of data-association hypotheses for all the measurements lying in the validation gates with each cluster from time K -U + 1 to K. Thc probability of each track history is computed, and then by chowing the largest of these histories, we perform the target masurement updated with the adaptive state estimator.Instead of soking a large problem, the entire set of targets and measurements is divided into several clusteres that a number of smaller problem have solved independently The tecMque of grouping targets by age is adopted and the IYW target is detected by sekcting a group of targets satisfying the criterion that the age of the target should be greater than a given age A,. Meanwhile, the covariance-matching technique is adopted such that the accuracy of the adaptive state estimator is improved In several exanples of targets tracking, the algorithm successfully tracks targets Over a wide range of conditions.

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