Target parameter estimation using measurements acquired with a small number of sensors

Arnold, J.; Bar‐Shalom, Yaakov; Estrada, R.; Mucci, Ronald A.

doi:10.1109/joe.1983.1145566

Cited by 34 publications

(13 citation statements)

References 21 publications

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“…AES-23, NO. 1 other characterized by the highest frequency score (see Table III). The result, shown in Fig.…”

Section: Results Of a Computer Simulationmentioning

confidence: 99%

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Target State Estimation in a Multitarget Environment Using Multiple Sensors

Gish¹,

Mucci²

1987

IEEE Trans. Aerosp. Electron. Syst.

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An approach for target detection/state estimation that views the problem as one of a finite state search over the target parameter space is presented. This approach allows for a natural way to associate different types of measurements, such as frequency and coherence from multiple sensors, and also for dealing with multiple targets, dropouts, and clutter. We describe our model and present a computationally efficient search algorithm for target detection and target state estimation in a multitarget environment based on this model. The results of a two-sensor, multitarget computer simulation are discussed.

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“…AES-23, NO. 1 other characterized by the highest frequency score (see Table III). The result, shown in Fig.…”

Section: Results Of a Computer Simulationmentioning

confidence: 99%

“…Next consider the measurement space, which for demonstration purposes is constrained to measurements acquired with two sensors consisting of coherence C(v) and frequency Fj(f), i.e., Cj(T) = i= 1, ..., K |I rl (t)r (t + T)dtf2 (I) |rl(t) 12 dt |i |r2(t)|2 dt (1) and…”

mentioning

confidence: 99%

Target State Estimation in a Multitarget Environment Using Multiple Sensors

Gish¹,

Mucci²

1987

IEEE Trans. Aerosp. Electron. Syst.

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“…The problem of passively tracking the position of a target in two dimensions has been extensively discussed in the literature (11- [7).…”

Section: *mentioning

confidence: 99%

Target parameter estimation in the near field with two sensors

Shertukde

Bar‐Shalom

1988

IEEE Trans. Acoust., Speech, Signal Processing

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“…A large amount of TMA methods dealing with this specific case is known as bearings-only tracking (BOT) in the Naval community [1]. Additional measurements (spectral lines [2], bearings collected by another platform [3], time-delays between sensors [4,5]) or parameter constraints [6] lead gradually to several other classes of TMA methods, whose use is guided by more operational considerations. At sea experiments have demonstrated the existence of strong correlation between bottom reflected paths up to 30 km.…”

Section: Introductionmentioning

confidence: 99%

TMA from bearings and multipath time delays

Blanc‐Benon¹,

Jauffret²

1997

IEEE Trans. Aerosp. Electron. Syst.

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A novel approach for target motion analysis (TMA), which uses conventional passive bearing together with multipath time-delay measurements is examined. This so-called "Multipath TMA" offers two tactical advantages over the classical bearings-only TMA: no requirement for any ownship maneuver, and a good performance in terms of estimation error achieved in a shorter time. Both known and unknown multipath cases are addressed. Finally, Monte-Carlo simulations and at-sea trials demonstrate the practical efficiency of such a multipath TMA. Manuscript

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Target parameter estimation using measurements acquired with a small number of sensors

Cited by 34 publications

References 21 publications

Target State Estimation in a Multitarget Environment Using Multiple Sensors

Target State Estimation in a Multitarget Environment Using Multiple Sensors

Target parameter estimation in the near field with two sensors

TMA from bearings and multipath time delays

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