Modified Extended Kalman Filtering for Tracking with Insufficient and Intermittent Observations

Chen, Pengpeng; Ma, Honglu; Gao, Shengli; Huang, Yan

doi:10.1155/2015/981727

Cited by 14 publications

(13 citation statements)

References 15 publications

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“…The iterative procedure first arranges the SSD values of each hop along the shortest path in sequence. Second, equation (4) calculates the first two SSD values, and its result together with the next SSD value is the initial input of the following iteration. Third, repeating the second step until the final SSD has been involved in calculation.…”

Section: Ssd Integral Calculationmentioning

confidence: 99%

Localization with signal-based signature distance

Chen¹,

Yin²,

Gao³

et al. 2017

International Journal of Distributed Sensor Networks

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Node localization plays a key and basic role for wireless sensor network. Existing range-free localization approaches suffer from precision limit of positioning, while range-based solutions may obtain good accuracy but pay high costs for ranging hardware. Instead of directly mapping received signal strength values into physical distances, a novel localization scheme with signal-based signature distance estimation is proposed for wireless sensor network. More specifically, we first quantify the near-far relationship between neighbor nodes through received signal strength comparison, and then the distance between non-neighbor nodes is calculated by an improved shortest path algorithm. Based on the obtained node distance, a relative map can be constructed by multidimensional scaling technology. Eventually, the locations of nodes can be acquired by virtue of Procrustes analysis method. For purpose of verifying the effectiveness of our design, several simulations are made in a sensor network with 200 randomly deployed nodes, and two experiments are implemented in real outdoor environments: a linear network and a two-dimensional grid network with 25 nodes. It is remarkable from results that the proposed scheme performs better in positioning and reduces localization errors by as much as 37% compared with state-of-the-art methods.

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Section: Ssd Integral Calculationmentioning

confidence: 99%

Localization with signal-based signature distance

Chen¹,

Yin²,

Gao³

et al. 2017

International Journal of Distributed Sensor Networks

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“…Range-free localization schemes are able to locate unknown nodes without actual measurement of the absolute distance between nodes [24,25]. They can obtain the relative position of nodes by other information (e.g., geometric relationship and hop), to estimate the localization of unknown nodes [26,27,28,29,30].…”

Section: Related Workmentioning

confidence: 99%

Achieving Passive Localization with Traffic Light Schedules in Urban Road Sensor Networks

Niu

Yang

Gao

et al. 2016

Sensors

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Localization is crucial for the monitoring applications of cities, such as road monitoring, environment surveillance, vehicle tracking, etc. In urban road sensor networks, sensors are often sparely deployed due to the hardware cost. Under this sparse deployment, sensors cannot communicate with each other via ranging hardware or one-hop connectivity, rendering the existing localization solutions ineffective. To address this issue, this paper proposes a novel Traffic Lights Schedule-based localization algorithm (TLS), which is built on the fact that vehicles move through the intersection with a known traffic light schedule. We can first obtain the law by binary vehicle detection time stamps and describe the law as a matrix, called a detection matrix. At the same time, we can also use the known traffic light information to construct the matrices, which can be formed as a collection called a known matrix collection. The detection matrix is then matched in the known matrix collection for identifying where sensors are located on urban roads. We evaluate our algorithm by extensive simulation. The results show that the localization accuracy of intersection sensors can reach more than 90%. In addition, we compare it with a state-of-the-art algorithm and prove that it has a wider operational region.

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“…In those systems, only a small number of anchors is needed, which significantly reduces the system cost. In addition, a modified extended Kalman filtering technique [32] is proposed for tracking applications with insufficient and intermittent observations. By deeply analyzing existing protocols, we find that connectivity-based localization schemes do not make full use of the information available from neighborhood sensing.…”

Section: Related Workmentioning

confidence: 99%

SSL: Signal Similarity-Based Localization for Ocean Sensor Networks

Chen

Gao

et al. 2015

Sensors

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Nowadays, wireless sensor networks are often deployed on the sea surface for ocean scientific monitoring. One of the important challenges is to localize the nodes’ positions. Existing localization schemes can be roughly divided into two types: range-based and range-free. The range-based localization approaches heavily depend on extra hardware capabilities, while range-free ones often suffer from poor accuracy and low scalability, far from the practical ocean monitoring applications. In response to the above limitations, this paper proposes a novel signal similarity-based localization (SSL) technology, which localizes the nodes’ positions by fully utilizing the similarity of received signal strength and the open-air characteristics of the sea surface. In the localization process, we first estimate the relative distance between neighboring nodes through comparing the similarity of received signal strength and then calculate the relative distance for non-neighboring nodes with the shortest path algorithm. After that, the nodes’ relative relation map of the whole network can be obtained. Given at least three anchors, the physical locations of nodes can be finally determined based on the multi-dimensional scaling (MDS) technology. The design is evaluated by two types of ocean experiments: a zonal network and a non-regular network using 28 nodes. Results show that the proposed design improves the localization accuracy compared to typical connectivity-based approaches and also confirm its effectiveness for large-scale ocean sensor networks.

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Modified Extended Kalman Filtering for Tracking with Insufficient and Intermittent Observations

Cited by 14 publications

References 15 publications

Localization with signal-based signature distance

Localization with signal-based signature distance

Achieving Passive Localization with Traffic Light Schedules in Urban Road Sensor Networks

SSL: Signal Similarity-Based Localization for Ocean Sensor Networks

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