A constant gain Kalman filter approach to target tracking in wireless sensor networks

Yadav, Ashwin; Naik, Naren; Ananthasayanam, M. R.; Gaur, Anurag; Singh, Yatindra Nath

doi:10.1109/iciinfs.2012.6304803

Cited by 26 publications

(18 citation statements)

References 16 publications

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“…A detailed description covering the background of the CGKF and its foundation is covered in [11], but the same is restated here for better understanding in the current context. It is observed as illustrated in Figure 1 that the gain matrix stabilizes to a constant value and this coincides with a similar plot for the state error covariance P .…”

Section: A) Motivationmentioning

confidence: 99%

“…A simple cost function minimization based CGKF approach has been suggested by Anil Kumar et al [10] in a problem concerned with prediction of re-entry of risk objects wherein they have used a genetic algorithm (GA) based minimization of an innovation cost function to compute an optimal constant gain matrix. In previous work by the author and colleagues [11] a similar cost function approach as in [6,10] was applied to the target tracking in Wireless sensor Network (WSN) domain. What is further known as a fundamental observation is that the KF gain stabilizes to a constant value after some point of time during the filter (algorithm) operation under conditions that the covariance matrices R, Qdo not change subsequently.…”

Section: Introductionmentioning

confidence: 99%

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A constant gain Kalman filter approach to track maneuvering targets

Yadav¹,

Awasthi

Naik

et al. 2013

2013 IEEE International Conference on Control Applications (CCA)

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Tracking of maneuvering targets is an important area of research with applications in both the military and civilian domains. One of the most fundamental and widely used approaches to target tracking is the Kalman filter. In presence of unknown noise statistics there are difficulties in the Kalman filter yielding acceptable results. In the Kalman filter operation for state variable models with near constant noise and system parameters, it is well known that after the initial transient the gain tends to a steady state value. Hence working directly with Kalman gains it is possible to obtain good tracking results dispensing with the use of the usual covariances. The present work applies an innovations based cost function minimization approach to the target tracking problem of maneuvering targets, in order to obtain the constant Kalman gain. Our numerical studies show that the constant gain Kalman filter gives good performance compared to the standard Kalman filter. This is a significant finding in that the constant gain Kalman filter circumvents or in other words trades the gains with the filter statistics which are more difficult to obtain. The problems associated with using a Kalman filter for tracking a maneuvering target with unknown system and measurement noise statistics can be circumvented by using the constant gain approach which seeks to work only with the gains instead of the state and measurement noise covariances. The approach is applied to a variety of standard maneuvering target models.

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Section: A) Motivationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A constant gain Kalman filter approach to track maneuvering targets

Yadav¹,

Awasthi

Naik

et al. 2013

2013 IEEE International Conference on Control Applications (CCA)

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“…The first one employs characteristics of traffic loads, such as spatial/temporal relevance or selfsimilarity [11]. The second category employs techniques, such as exponential smoothing to study the intrinsic dimensionality [12], Kalman filtering to capture the evolution of traffic [13] or modern signal processing techniques such as compressive sensing [14]. In this paper, we investigate which of the above methods fits best the capacity broker paradigm and we provide a set of enhancements, to compensate the lack of periodicity and non-uniformities of a short-term prediction.…”

Section: State Of the Artmentioning

confidence: 99%

“…For our study, we examine the following short-term capacity forecasting methods: ARIMA [11], compressive sensing-based method [14], Kalman filter [13], and Holt-Winters [12]. To identify the most suitable method for the capacity broker, we generated data that spanned in a two-hour prior time period (T p = 120 minutes) using SLAW mobility model [17] and we obtained a T f = 20 minute forecast.…”

Section: B Forecasting Evaluationmentioning

confidence: 99%

A capacity broker architecture and framework for multi-tenant support in LTE-A networks

Tseliou

Samdanis

Adelantado

et al. 2016

2016 IEEE International Conference on Communications (ICC)

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Resource provisioning in multi-operator scenarios requires an estimate of the tenants' traffic needs. This is necessary in the scenario where a Mobile Network Operator (MNO) owns the Radio Access Network (RAN) and many Mobile Virtual Network Operators (MVNOs) act as resellers of their host network's capacity under their own brands, to their own customers. In such scenarios, the forecasted MVNO traffic is the basis for providing resources suitable with the corresponding MVNOs demand. To that end, the dynamic provision of resources among MVNOs should be performed in flexible, short-term time scales. In this paper, we effectively address this issue by integrating the capacity broker into the 3rd Generation Partnership Project (3GPP) network management network architecture using the minimum set of enhancements. In addition, to fully exploit its capabilities, we propose the Multi-tenant Slicing (MuSli) of capacity algorithm, to allocate resources towards MVNOs in coarse time scales. MuSli considers the estimated capacity and the impact of the traffic type (i.e., guaranteed QoS and Best-Effort) in each MVNO, to provide better utilization of the host network's capacity. Our results highlight the gains in the number of served requests without compromising their service quality.

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“…Authors in [15] assume that the object just sometimes move non-linear, so they use moving average estimator with a proposed correction mechanism. In [16][17][18], Kalman filter based methods such as Extended-KF and SOI-KF was proposed. So far there is no research works about prediction based WSN tracking object with variable velocity.…”

Section: Introductionmentioning

confidence: 99%

Low Power Prediction Mechanism for Wsn-based Object Tracking

Mirsadeghi

Mahani

2014

Procedia Technology

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Power source replacement of the sensor node which are once deployed in the network area, is generally difficult. So energy saving is one of the most important issues for object tracking in wireless sensor networks. To reduce the consumed energy and prolong the network lifetime the nodes surrounding the mobile object should be responsible for sensing the target. The number of participant nodes in target tracking can be reduced by an accurate prediction of the object location. In this paper we present fast energy efficient with high accuracy target tracking scheme which is based on location prediction. The missing rate of proposed predictor is very low in comparison with other predictors especially in random way point mobility model in which after pause time the three main parameters: direction, velocity and acceleration would be changed. The accuracy of predictor has direct effect on missing rate and so strongly reduces the consumed energy. Additionally a new node selection criterion is proposed in which minimum nodes surrounding the object are wakened and track the object. Simulation results show that our proposed predictor has low consumed energy and complexity in comparison with EKF and UKF predictors.

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A constant gain Kalman filter approach to target tracking in wireless sensor networks

Cited by 26 publications

References 16 publications

A constant gain Kalman filter approach to track maneuvering targets

A constant gain Kalman filter approach to track maneuvering targets

A capacity broker architecture and framework for multi-tenant support in LTE-A networks

Low Power Prediction Mechanism for Wsn-based Object Tracking

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