False-Alarm Reduction for Low-Frequency Active Sonar With BPSK Pulses: Experimental Results

Colin, M.E.G.D.; Beerens, S.P.

doi:10.1109/joe.2010.2094770

Cited by 10 publications

(5 citation statements)

References 22 publications

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“…Thus, we choose LFM and BPSK of the same B , T and frequency as compared to analyse the methods proposed in this paper. LFM and BPSK waveform could be expressed as

\begin{matrix} 1em4pt \\ S_{lfm} false(t false) = \sin false(2 π f_{c} t + π k t^{2} false), & t \in false[0, T false], \end{matrix}

and

S_{bpsk} false(t false) = \sin false[2 π f_{c} t false(2 b false(t false) - 1 false) false], t \in false[0, T false] .

where

f_{c}

is the carrier frequency,

k = false(B / T false)

is the frequency modulation factor of LFM, and b ( t ) is a binary sequence of N bits [2]. It is proved in [20] that m ‐sequence coded BPSK has good cross‐correlation properties and minimal inter‐sequence interference.…”

Section: Modellingmentioning

confidence: 99%

“…The sidelobe level (SLL) of BPSK [2] is

SLL = - 10 lo g_{10} false(N false),

SLL < - 3 dB

as N > 2. So the sidelobe has no influence on the Doppler tolerance as BT is large enough in this paper.…”

Section: Modellingmentioning

confidence: 99%

“…The active sonar is regarded as an appropriate choice, but the echoes of low‐Doppler targets are always blended with high‐level discrete clutters when we use linearly frequency modulated (LFM) waveform in shallow water, which heavily increases the probability of false alarm

false(P_{fa} false)

. The experiment results indicate that Doppler sensitive waveform performs better in reducing clutters and decreasing

P_{fa}

than insensitive one in low‐speed target detection, which is termed as ‘clutter filter’ effect [1, 2]. By comparison, it seems that sensitive waveforms are more suitable to be used on high‐frequency sonar for short range detection, because the distortion of a long pulse may result in energy loss of matched filtering.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Clutter‐suppressing performance estimation methods of active sonar waveform based on reverberation statistical models in littoral environment

Zhang

et al. 2017

IET Radar, Sonar & Navigation

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“…Thus, we choose LFM and BPSK of the same B , T and frequency as compared to analyse the methods proposed in this paper. LFM and BPSK waveform could be expressed as

\begin{matrix} 1em4pt \\ S_{lfm} false(t false) = \sin false(2 π f_{c} t + π k t^{2} false), & t \in false[0, T false], \end{matrix}

and

S_{bpsk} false(t false) = \sin false[2 π f_{c} t false(2 b false(t false) - 1 false) false], t \in false[0, T false] .

where

f_{c}

is the carrier frequency,

k = false(B / T false)

Section: Modellingmentioning

confidence: 99%

“…The sidelobe level (SLL) of BPSK [2] is

SLL = - 10 lo g_{10} false(N false),

SLL < - 3 dB

as N > 2. So the sidelobe has no influence on the Doppler tolerance as BT is large enough in this paper.…”

Section: Modellingmentioning

confidence: 99%

false(P_{fa} false)

. The experiment results indicate that Doppler sensitive waveform performs better in reducing clutters and decreasing

P_{fa}

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Clutter‐suppressing performance estimation methods of active sonar waveform based on reverberation statistical models in littoral environment

Zhang

et al. 2017

IET Radar, Sonar & Navigation

View full text Add to dashboard Cite

“…These harsh conditions make underwater multi-target tracking a difficult problem for MSNs. Especially in lowfrequency active sonar systems, clutter distributions indicate coherent characteristics [5].…”

Section: Introductionmentioning

confidence: 99%

Robust Model-Dependent Poisson Multi Bernoulli Mixture Trackers for Multistatic Sonar Networks

Onzer¹,

Hocaoglu

2021

IEEE Access

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This work proposes a robust tracker based on the Poisson Multi Bernoulli Mixture (PMBM) filter for multistatic sonar networks (MSNs) systems. The PMBM based trackers estimate the number of targets and provide the target information via Bernoulli and Poisson Point Processes. The PMBM based trackers handle existing tracks, undetected targets, and new births separately at each computation step by using these two processes together. In practice, the PMBM tracker aims to initiate the track as soon as possible and maintain the track continuity. Initiating track and maintaining track continuity is hard in challenging underwater environments without adapting the algorithm to changing environmental conditions. This paper uses the adaptive measurement-driven birth process and multistatic acoustic modeldependent probability of detection specifications. The adaptive measurement-driven birth process improves the robustness of the track initiation, and the multistatic acoustic model-dependent probability of detection advances the track continuity through the transition regions. These contributions to the PMBM tracker make it robust in terms of tracker performance in challenging underwater environments and acoustic transition regions where it is hard to get an accurate measurement.

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“…The non‐stealthy scenario is very relevant because passive sensors that cannot be detected by an intruder are increasingly found to be ineffective in a number of application areas (see e.g., for a case study in submarine detection), so intruder detection is carried out by active sensors that, by emitting one kind of signal or the other, open themselves up to detection by the very intruders they are supposed to be detecting (see e.g., for a case study of obstacle avoidance for helicopters.) In this paper, we focus on this scenario, differentiating between presence sensors that can detect an intruder whenever it is present in their sensing range and motion sensors that detect an intruder only if it moves while in their sensing range.…”

Section: Introductionmentioning

confidence: 99%

Surveillance using non‐stealthy sensors: A new intruder model

Bagchi

Kalayappan

Sankhla³

2013

Security Comm Networks

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We study the problem of intruder tracking with non‐stealthy sensors, i.e., sensors whose ON/OFF state can be detected by an intruder, sometimes in advance. The sensor field is assumed to be operating with a sleep schedule to conserve energy. Both motion sensors and presence sensors are considered. We provide a rigorous basis for the study of this scenario by defining a new intruder model, the Ideal Intruder, that knows the entire sleep schedule of all the sensors in the field. More realistic intruders that have spatially and temporally limited knowledge of the sensor states are also defined. We study the well‐known Random Independent Sleep (RIS) scheduling scheme with a single parameter p, giving mathematical bounds for the ideal intruder's crossing time in the motion sensor case and showing that the crossing probability in the presence sensor case undergoes a sharp transition as p increases. Further, we show that non‐ideal intruders perform almost as well as the ideal intruder against RIS. Motivated by this finding and by the comparison between Barrier Coverage and RIS, we define a new sleep scheduling scheme, Spotlight, that is more robust to faults than Barrier Coverage and more effective than RIS. But more than that, Spotlight is shown to be specifically suited to the non‐stealthy case because a non‐ideal intruder, that is, one with limited information performs significantly worse against it than the ideal intruder. Spotlight, therefore, apart from being a novel and interesting sleep scheduling scheme in its own right, also illustrates the power of the analytical framework we introduce in this paper. Copyright © 2013 John Wiley & Sons, Ltd.

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False-Alarm Reduction for Low-Frequency Active Sonar With BPSK Pulses: Experimental Results

Cited by 10 publications

References 22 publications

Clutter‐suppressing performance estimation methods of active sonar waveform based on reverberation statistical models in littoral environment

Clutter‐suppressing performance estimation methods of active sonar waveform based on reverberation statistical models in littoral environment

Robust Model-Dependent Poisson Multi Bernoulli Mixture Trackers for Multistatic Sonar Networks

Surveillance using non‐stealthy sensors: A new intruder model

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