&lt;title&gt;Comparison of the particle filter with range-parameterized and modified polar EKFs for angle-only tracking&lt;/title&gt;

Arulampalam, Sanjeev; Ristić, Branko

doi:10.1117/12.391985

Cited by 89 publications

(75 citation statements)

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“…where , −1 is the deterministic vector which accounts for the effect of mismatch between the ownship and target dynamic model [1,28]. The nonlinear measurement model in Cartesian coordinates ( ) at time is defined as,…”

Section: Dynamic Model and Measurement Model In Cartesian Coordinatesmentioning

confidence: 99%

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Multiple Model Adaptive Nonlinear Filter for Range Estimation Using Bearing Only Tracking

Babulogiah¹,

Jayaraman²,

Sargunaraj³

2017

IJIES

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This paper discusses about the range estimation using multiple model method for single sensor bearing only tracking (BOT) in 3D. For BOT, the ownship is assumed to take a manoeuvre to gain observability in range and target state. The unknown target range was divided into uniform sub-intervals and nonlinear filters like Extended and unscented Kalman filter (EKF and UKF) with Cartesian and modified spherical coordinates (MSC) were implemented. Comparative results indicate that, UKF in MSC performs better with high computational time. This paper introduces Adaptive nonlinear filter (ANF) to get better range estimate with reduced computational time without affecting the filter performance. It is accomplished by conjoining Cartesian EKF with UKF in MSC, adaptively during the stationary and manoeuvring ownship conditions. The performance comparison was analysed using root mean square error, bias error and computational time. Simulation results reveal that ANF shows better results with reduced computational time.

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Section: Dynamic Model and Measurement Model In Cartesian Coordinatesmentioning

confidence: 99%

“…The bearing only tracking (BOT) is the most widely used target motion analysis (TMA) in radar, sonar, space surveillance and wireless sensor networks [1][2][3]. The objective of the paper is to estimate the target range using multiple model method and to estimate the target state.…”

Section: Introductionmentioning

confidence: 99%

Multiple Model Adaptive Nonlinear Filter for Range Estimation Using Bearing Only Tracking

Babulogiah¹,

Jayaraman²,

Sargunaraj³

2017

IJIES

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“…The presentation follows the development in [21]. The RPEKF method described in [4,23] consists of a bank of extended Kalman filters in Cartesian coordinates, initialized to different range assumptions for the angle-only tracking application. In [29] the filter bank is expressed in modified polar coordinates.…”

Section: B Range Parameterized Extended Kalman Filtersmentioning

confidence: 99%

“…The realizations in the Monte Carlo simulation use independent measurement noise and the initial position for each realization is perturbed by a uniform distribution around its nominal value. The RPEKFs have range interval r min = 1 and r max = 64 km, with N F =6 filters, yielding C R = 0.1925 as in [4,29]. For the particle filter method, N = 60000 particles are used.…”

Section: Passive Ranging Applications a Air-to-air Passive Rangingmentioning

confidence: 99%

Recursive Bayesian estimation: bearings-only applications

Karlsson

Gustafsson

2005

IEE Proc., Radar Sonar Navig.

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Abstract-In this paper Bayesian recursive estimation methods are applied to several bearings-only applications. Both Air-toAir passive ranging as well as terrain induced constraints for Air-to-Sea applications are discussed. The bearings-only problem is tested on experimental data from a torpedo, i.e., Sea-to-Sea with a passive sonar sensor. The Bayesian estimation problem is solved using both the particle filter method and the marginalized particle filter. As a comparison the range parameterized extended Kalman filter (RPEKF) is used. In a simulation study the particle filter outperforms the RPEKF.

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“…The single platform case has been studied in numerous publications, some more recent ones being [1], [3,Ch.5], [4]. It is well known that in the single observer case the ownship must "outmanoeuvre" the target in order to estimate the full target kinematic state [5].…”

Section: Introductionmentioning

confidence: 99%

The influence of communication bandwidth on target tracking with angle only measurements from two platforms

2001

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<title>Comparison of the particle filter with range-parameterized and modified polar EKFs for angle-only tracking</title>

Cited by 89 publications

References 0 publications

Multiple Model Adaptive Nonlinear Filter for Range Estimation Using Bearing Only Tracking

Multiple Model Adaptive Nonlinear Filter for Range Estimation Using Bearing Only Tracking

Recursive Bayesian estimation: bearings-only applications

The influence of communication bandwidth on target tracking with angle only measurements from two platforms

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