Interacting multiple model methods in target tracking: a survey

Mazor, E.; Averbuch, Amir; Bar‐Shalom, Yaakov; Dayan, J.

doi:10.1109/7.640267

Cited by 957 publications

(473 citation statements)

References 75 publications

Supporting

Mentioning

471

Contrasting

Unclassified

Order By: Relevance

“…Therefore, an estimation algorithm should be able to recognise both off-road and on-road movement depending on the situation, rather than constraining the estimates onto the road at all times. To address this, the IMM filter [14] is applied combining an off-road mode using the conventional filter and on-road mode using a road-constrained filter as explained above.…”

Section: Imm Filtermentioning

confidence: 99%

“…Since some vehicles could move on off-road terrain to avoid a police checkpoint or to closely monitor a particular place, temporal probability of on/off-road modes is another important source for abnormal behaviours detection. In order to obtain this mode probability whilst enabling monitoring of the moving ground target, an interactive multiple model (IMM) filter [14] is applied. The proposed IMM filter comprises an on-road moving mode using a roadconstrained filter and an off-road moving mode using a conventional filter so that both on and off mode probabilities (which are complementary to each other) can be obtained.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Airborne behaviour monitoring using Gaussian processes with map information

Shin

Kim

et al. 2013

IET Radar, Sonar & Navigation

View full text Add to dashboard Cite

Section: Imm Filtermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Airborne behaviour monitoring using Gaussian processes with map information

Shin

Kim

et al. 2013

IET Radar, Sonar & Navigation

View full text Add to dashboard Cite

“…This avoids notable computational and storage cost. The variation of the rbpmf described in this chapter is similar to the interacting multiple model (imm) method (Blom and Bar-Shalom, 1988;Mazor et al, 1998); in fact, this can thus be seen as an alternate derivation of the imm approach. The probability p (j,i) k|k−1 then corresponds to the unnormalized transition probability from mode i to mode j in the imm filter.…”

mentioning

confidence: 99%

Frequency Tracking for Speed Estimation

Lindfors¹

2018

View full text Add to dashboard Cite

Estimating the frequency of a periodic signal, or tracking the time-varying frequency of an almost periodic signal, is an important problem that is well studied in literature. This thesis focuses on two subproblems where contributions can be made to the existing theory: frequency tracking methods and measurements containing outliers.Maximum-likelihood-based frequency estimation methods are studied, focusing on methods which can handle outliers in the measurements. Katkovnik's frequency estimation method is generalized to real and harmonic signals, and a new method based on expectation-maximization is proposed. The methods are compared in a simulation study in which the measurements contain outliers. The proposed methods are compared with the standard periodogram method.Recursive Bayesian methods for frequency tracking are studied, focusing on the Rao-Blackwellized point mass filter (rbpmf). Two reformulations of the rbpmf aiming to reduce computational costs are proposed. Furthermore, the technique of variational approximate Rao-Blackwellization is proposed, which allows usage of a Student's t distributed measurement noise model. This enables recursive frequency tracking methods to handle outliers using heavy-tailed noise models in Rao-Blackwellized filters such as the rbpmf. A simulation study illustrates the performance of the methods when outliers occur in the measurement noise.The framework above is applied to and studied in detail in two applications. The first application is on frequency tracking of engine sound. Microphone measurements are used to track the frequency of Doppler-shifted variants of the engine sound of a vehicle moving through an area. These estimates can be used to compute the speed of the vehicle. Periodogram-based methods and the rbpmf are evaluated on simulated and experimental data. The results indicate that the rbpmf has lower rmse than periodogram-based methods when tracking fast changes in the frequency.The second application relates to frequency tracking of wheel vibrations, where a car has been equipped with an accelerometer. The accelerometer measurements are used to track the frequency of the wheel axle vibrations, which relates to the wheel rotational speed. The velocity of the vehicle can then be estimated without any other sensors and without requiring integration of the accelerometer measurements. In situations with high signal-to-noise ratio (snr), the methods perform well. To remedy situations when the methods perform poorly, an accelerometer input is introduced to the formulation. This input is used to predict changes in the frequency for short time intervals.v Populärvetenskaplig sammanfattningPeriodiska signaler förekommer ofta i praktiken. I många tillämpningar är det intressant att försöka skatta frekvensen av dessa periodiska signaler, eller vibrationer, genom mätningar av dem. Detta kallas för frekvensskattning eller frekvensföljning beroende på om frekvensen är konstant eller varierar över tid. Två tillämpningar studeras i denna licentiatavhandling. Målet i...

show abstract

“…In this case, the optimal filter density can be computed using a weighted mode-matched sequence of Kalman filters, one for each trajectory of modes. Since the optimal filtering density cannot be directly computed due the computational complexity which grows exponentially with time, a number of approximation techniques have been developed to deal with these systems, such as the IMM estimator [3], [10], Gaussian mixture reduction techniques [13] and particle filtering methods [8]. An excellent survey of all these techniques can be found in [11].…”

Section: Introductionmentioning

confidence: 99%

Stochastic filtering in jump systems with state dependent mode transitions

Capponi

Pilotto

2009

2009 American Control Conference

View full text Add to dashboard Cite

Abstract-We introduce a new methodology to construct a Gaussian mixture approximation to the true filter density in hybrid Markovian switching systems. We relax the assumption that the mode transition process is a Markov chain and allow it to depend on the actual and unobservable state of the system. The main feature of the method is that the Gaussian densities used in the approximation are selected as the solution of a convex programming problem which trades off sparsity of the solution with goodness of fit. A meaningful example shows that the proposed method can outperform the widely used interacting multiple model (IMM) filter in terms of accuracy at the expenses of an increase in computational time.

show abstract

Interacting multiple model methods in target tracking: a survey

Cited by 957 publications

References 75 publications

Airborne behaviour monitoring using Gaussian processes with map information

Airborne behaviour monitoring using Gaussian processes with map information

Frequency Tracking for Speed Estimation

Stochastic filtering in jump systems with state dependent mode transitions

Contact Info

Product

Resources

About