Sensor scheduling in electronic support using Markov chains

Clarkson, I. Vaughan L.; El-Mahassni, Edwin; Howard, Stephen D.

doi:10.1049/ip-rsn:20050055

Cited by 9 publications

(6 citation statements)

References 11 publications

(14 reference statements)

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“…Clarkson et al . [10], continued the work on stochastic schedules. Like Kelly et al , the objective is to suppress synchronisation universally.…”

Section: Strategies For Es Receiver Sensor Schedulingmentioning

confidence: 99%

“…Since the CTMC strategy is important in subsequent discussion, we extend the analysis of [10]. We begin by reparameterising.…”

Section: Further Analysis Of the Ctmc‐induced Schedulementioning

confidence: 99%

“…If it was out of band on the first illumination, the probability of illumination on the second illumination is found from the lower left element of

bold-italicP false(T false)

, namely

μ_{in} false[1 - e^{- γ normalT} false] / false(μ_{in} + μ_{out} false)

. The probability of (first) intercept on the k th illumination – or k th look – of the radar is, in general

\Pr false(K = k false) = ({, \begin{matrix} 1em4pt \\ π_{i} & k = 1, \\ false(1 - π_{i} false) false(1 - ρ false)^{k - 2} ρ & k > 1 \end{matrix})

where

ρ = \frac{μ_{in} false(1 - e^{- γ normalT} false)}{μ_{in} + μ_{out}} = π_{i} (1 - {normale}^{- γ T}) .

If follows that the expected intercept time is:

E false[K false] = \frac{1 - π_{i}}{ρ} + 1 .

If we were to number radar looks from 0 instead of 1, as was the case in [10], we can now confirm from (1), (4), (6) and (7) that

f_{i} false(T false) = T false(E false[K false] - 1 false) .

From (5), we can infer that

\Pr false(K > k false) = ({, \begin{matrix} 1em4pt \\ 1 - π_{i} & k =... \end{matrix})

…”

Section: Further Analysis Of the Ctmc‐induced Schedulementioning

confidence: 99%

See 2 more Smart Citations

Sensor scheduling for electronic support to intercept beam‐agile radar

Clarkson¹

2019

IET Radar, Sonar & Navigation

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“…Clarkson et al . [10], continued the work on stochastic schedules. Like Kelly et al , the objective is to suppress synchronisation universally.…”

Section: Strategies For Es Receiver Sensor Schedulingmentioning

confidence: 99%

“…Since the CTMC strategy is important in subsequent discussion, we extend the analysis of [10]. We begin by reparameterising.…”

Section: Further Analysis Of the Ctmc‐induced Schedulementioning

confidence: 99%

“…If it was out of band on the first illumination, the probability of illumination on the second illumination is found from the lower left element of

bold-italicP false(T false)

, namely

μ_{in} false[1 - e^{- γ normalT} false] / false(μ_{in} + μ_{out} false)

. The probability of (first) intercept on the k th illumination – or k th look – of the radar is, in general

\Pr false(K = k false) = ({, \begin{matrix} 1em4pt \\ π_{i} & k = 1, \\ false(1 - π_{i} false) false(1 - ρ false)^{k - 2} ρ & k > 1 \end{matrix})

where

ρ = \frac{μ_{in} false(1 - e^{- γ normalT} false)}{μ_{in} + μ_{out}} = π_{i} (1 - {normale}^{- γ T}) .

If follows that the expected intercept time is:

E false[K false] = \frac{1 - π_{i}}{ρ} + 1 .

If we were to number radar looks from 0 instead of 1, as was the case in [10], we can now confirm from (1), (4), (6) and (7) that

f_{i} false(T false) = T false(E false[K false] - 1 false) .

From (5), we can infer that

\Pr false(K > k false) = ({, \begin{matrix} 1em4pt \\ 1 - π_{i} & k =... \end{matrix})

…”

Section: Further Analysis Of the Ctmc‐induced Schedulementioning

confidence: 99%

See 1 more Smart Citation

Sensor scheduling for electronic support to intercept beam‐agile radar

Clarkson¹

2019

IET Radar, Sonar & Navigation

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“…A common approach to design scanning strategies is to use prior knowledge gathered by electronic intelligence about radars likely to be encountered during the mission. The scheduling algorithm is then tuned to achieve high performances measured by probabilities of interception [1,2,3] (PoI) or mean intercept times [4,5,6]. These strategies have two drawbacks.…”

Section: Introductionmentioning

confidence: 99%

Learning of scanning strategies for electronic support using predictive state representations

Claude

Enderli

Grandin

et al. 2015

2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP)

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In Electronic Support, a receiver must monitor a wide frequency spectrum in which threatening emitters operate. A common approach is to use sensors with high sensitivity but a narrow bandwidth. To maintain surveillance over the whole spectrum, the sensor has to sweep between frequency bands but requires a scanning strategy. Search strategies are usually designed prior to the mission using an approximate knowledge of illumination patterns. This often results in open-loop policies that cannot take advantage of previous observations. As pointed out in past researches, these strategies lack of robustness to the prior. We propose a new closed loop search strategy that learns a stochastic model of each radar using predictive state representations. The learning algorithm benefits from the recent advances in spectral learning and rank minimization using nuclear norm penalization.

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“…Sensor scheduling plays a critical role for energy efficiency of WSNs, see, for example, [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. Sensor scheduling involves derivation of results to disclose the relationship among node density, scheduling parameters, coverage quality, detection probability, and detection delay.…”

Section: Introductionmentioning

confidence: 99%