Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Sequential monte carlo methods for multi-target filtering with random finite sets
2005923 citationsBa‐Ngu Vo, Sumeetpal S. Singh et al.IEEE Transactions on Aerospace and Electronic Systemsprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Sumeetpal S. Singh
Since
Specialization
Citations
This map shows the geographic impact of Sumeetpal S. Singh's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Sumeetpal S. Singh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sumeetpal S. Singh more than expected).
Fields of papers citing papers by Sumeetpal S. Singh
This network shows the impact of papers produced by Sumeetpal S. Singh. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Sumeetpal S. Singh. The network helps show where Sumeetpal S. Singh may publish in the future.
Co-authorship network of co-authors of Sumeetpal S. Singh
This figure shows the co-authorship network connecting the top 25 collaborators of Sumeetpal S. Singh.
A scholar is included among the top collaborators of Sumeetpal S. Singh based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Sumeetpal S. Singh. Sumeetpal S. Singh is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Jiang, Lan, et al.. (2014). A new particle filtering algorithm for multiple target tracking with non-linear observations. Cambridge University Engineering Department Publications Database. 1–8.1 indexed citations
Singh, Sumeetpal S., et al.. (2012). A Monte Carlo expectation maximisation algorithm for multiple target tracking. Cambridge University Engineering Department Publications Database.1 indexed citations
Singh, Sumeetpal S., Nikolas Kantas, Ba‐Ngu Vo, Randal Douc, & Robin J. Evans. (2005). On the convergence of a two timescale stochastic optimisation algorithm for optimal observer trajectory planning. Cambridge University Engineering Department Publications Database.1 indexed citations
13.
Singh, Sumeetpal S., et al.. (2005). Novel particle filter methods for recursive and batch maximum likelihood parameter estimation in general states space models. Cambridge University Engineering Department Publications Database.15 indexed citations
14.
Singh, Sumeetpal S., Ba‐Ngu Vo, Robin J. Evans, & Randal Douc. (2004). Variance Reduction for Monte Carlo Implementation of Adaptive Sensor Management. Cambridge University Engineering Department Publications Database.5 indexed citations
15.
Panta, Kusha, Ba‐Ngu Vo, Sumeetpal S. Singh, & Randal Douc. (2004). Probability hypothesis density filter versus multiple hypothesis tracking. Cambridge University Engineering Department Publications Database.50 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.