Information Decorrelation for an Interacting Multiple Model Filter

Acar, Duygu
Orguner, Umut
In a sensor network compensation of the correlated information caused by previous communication is of utmost interest for distributed estimation. In this paper, we investigate different information decorrelation approaches that can be applied when using an interacting multiple model filter in a local sensor node. The related decorrelation and the corresponding fusion operations are discussed. The different approaches are compared on a simple distributed single maneuvering target tracking example.


Decorrelation of Previously Communicated Information for an Interacting Multiple Model Filter
Acar, Duygu; Orguner, Umut (2021-02-01)
In a sensor network compensation of the correlated information caused by previous communication is of utmost interest for distributed estimation. In this article, we investigate an information decorrelation approach that can be applied when using interacting multiple model filters in the sensor nodes for a family of jump Markov linear systems. Implementation issues that might arise while applying the decorrelation approach are addressed in detail. The investigated approach is compared with alternatives on s...
Distributed Target Tracking with Propagation Delayed Measurements
Orguner, Umut (2009-07-09)
This paper presents a framework for making distributed target tracking under significant signal propagation delays between the target and the sensors. Each sensor considered makes estimation using its own measurements compensating for the involved signal propagation delay using a deterministic sampling based algorithm proposed previously. Since the individual sensor readings might not be enough to localize the target, the sensors have to share their estimates with each other at specific time instants and co...
Multi-target tracking with PHD filter using Doppler-only measurements
Guldogan, Mehmet B.; Lindgren, David; Gustafsson, Fredrik; Habberstad, Hans; Orguner, Umut (2014-04-01)
In this paper, we address the problem of multi-target detection and tracking over a network of separately located Doppler-shift measuring sensors. For this challenging problem, we propose to use the probability hypothesis density (PHD) filter and present two implementations of the PHD filter, namely the sequential Monte Carlo PHD (SMC-PHD) and the Gaussian mixture PHD (GM-PHD) filters. Performances of both filters are carefully studied and compared for the considered challenging tracking problem. Simulation...
Reliable Transmission of Short Packets Through Queues and Noisy Channels Under Latency and Peak-Age Violation Guarantees
Devassy, Rahul; Durisi, Giuseppe; Ferrante, Guido Carlo; Simeone, Osvaldo; Uysal, Elif (Institute of Electrical and Electronics Engineers (IEEE), 2019-04-01)
This paper investigates the probability that the delay and the peak-age of information exceed a desired threshold in a point-to-point communication system with short information packets. The packets are generated according to a stationary memoryless Bernoulli process, placed in a single-server queue and then transmitted over a wireless channel. A variable-length stop-feedback coding scheme-a general strategy that encompasses simple automatic repetition request (ARQ) and more sophisticated hybrid ARQ techniq...
Chernoff Fusion of Gaussian Mixtures for Distributed Maneuvering Target Tracking
GUNAY, Melih; Orguner, Umut; Demirekler, Mübeccel (2015-07-09)
A fusion methodology for tracks represented by Gaussian mixtures is proposed for distributed maneuvering target tracking with unknown correlation information between the local agents. For this purpose, Chernoff fusion is applied to the Gaussian mixtures provided by the local interacting multiple-model (IMM) filters. Chernoff fusion of Gaussian mixtures is achieved using a recently proposed method in the literature involving a sigma-point approximation. The results show that the fusion of Gaussian mixtures i...
Citation Formats
D. Acar and U. Orguner, “Information Decorrelation for an Interacting Multiple Model Filter,” 2018, Accessed: 00, 2020. [Online]. Available: