Track-before-detect processing for an airborne type radar

Krämer, Jan; Reid, W. S.

doi:10.1109/radar.1990.201202

Cited by 37 publications

(7 citation statements)

References 2 publications

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“…This paper adopts the same quantization method and the same definition of possible transition states as in [9].The target state after quantization is given by (6) When the number of possible transition states q is set as 9, 16 or 25 respectively, the possible state of X k+1 is q ",9 X k+IE{[Xk+i>0"Yk+Yk�6;l;6i,6 J "'�1,0,1} (7) q '" 16 X k+1 E {[xk + i"k � o"Yk + Yk � 6 J ];£5,A '" �2,� 1, 0, I}(S) q '" 25 X k+1 E {[xk + i"k � o"Yk + Yk � 6j];£5,,6) '" �2,� 1,0,1, 2} (9) ( i"" Yk) is transition step between frame k and k + I.…”

Section: A Dynamic Programming Algorithmmentioning

confidence: 99%

A Kalman filtering-based dynamic programming track-before-detect algorithm for turn target

Yue

Kong

Yang

et al. 2010

2010 International Conference on Communications, Circuits and Systems (ICCCAS)

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Dynamic programming-based track-before-detect algorithm is an effective approach to track target with uniform rectilinear or slowly maneuvering motion. But when target performs a turn motion it has a poor performance for the transition step is fixed. In this paper a new algorithm which combines dynamic programming with Kalman filtering has been proposed. This algorithm makes use of state prediction operation in Kalman filtering to change the transition step in DP algorithm adaptively. Numerical results show that this new method has a much better tracking ability than the traditional procedure when target turns.

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Section: A Dynamic Programming Algorithmmentioning

confidence: 99%

A Kalman filtering-based dynamic programming track-before-detect algorithm for turn target

Yue

Kong

Yang

et al. 2010

2010 International Conference on Communications, Circuits and Systems (ICCCAS)

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“…The TBD concept was originally proposed for a staring satellite‐based imaging sensor [4] with its first application to a scanning pulse‐Doppler airborne radar discussed in Ref. [5]. These early approaches used dynamic programming to solve the TBD.…”

Section: Introductionmentioning

confidence: 99%

Bernoulli track‐before‐detect smoothing for maritime radar

Ristić

Guan

Kim

et al. 2022

IET Radar Sonar & Navi

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Detection and tracking of small targets in sea clutter using a high-resolution radar is a challenging problem as the sea surface constantly moves in a complex manner, with rough seas creating strong target-like returns. Recently, a Bernoulli track-before-detect (TBD) filter has been developed for an airborne scanning radar in the maritime domain. Its purpose is to detect and track short-exposure targets by using the radar in a fast scanning mode. The current paper investigates the potential improvement in detection and tracking performance of the Bernoulli TBD filter by incorporating a backward smoothing step. The developed algorithm, referred to as the Bernoulli TBD forward-backward smoother (FBS) for maritime radar, is making a delayed decision by taking into account multiple future scans of radar data. A numerical analysis of a joint detection and tracking error with a fixed-lag smoothing is presented, as a function of the number of delay lags, signal-to-interference ratio and the sea clutter spikiness.

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“…An alternative approach, referred to as Track-Before-Detect (TBD), consists in supplying the processor with either thresholded or unthresholded data from several consecutive scans [5]. Unlike conventional techniques that declare the presence of a target at each scan of the radar, TBD-based procedures jointly process more consecutive scans and, exploiting the physically admissible target transitions, jointly declare the presence of a target and its track.…”

Section: Introductionmentioning

confidence: 99%

A Track-Before-Detect Strategy Based on Sparse Data Processing for Air Surveillance Radar Applications

et al. 2021

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In this paper we consider the tracking problem of a moving target competing against noise and clutter in a surveillance radar scenario. For a single array-antenna multiple-target tracking system and according to the Track-Before-Detect paradigm, we present a novel approach based on a three-stage processing chain that involves the Sparse Learning via Iterative Minimization algorithm, the k-means clustering method and the ad hoc detector by exploiting the sparse nature of the operating scenario. Under the latter assumption, the detection strategy declares the presence of targets subsequently to the retrieval of their corresponding tracks performed by jointly processing the received echoes of multiple consecutive radar scans. Simulation results show that the proposed approach is able to provide good tracking and detection capabilities for different multiple target trajectories with low Signal-to-Interference-plus-Noise ratio and results in providing advantages when compared to a number of other reference Track-Before-Detect strategies based on sparse data processing techniques.

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Track-before-detect processing for an airborne type radar

Cited by 37 publications

References 2 publications

A Kalman filtering-based dynamic programming track-before-detect algorithm for turn target

A Kalman filtering-based dynamic programming track-before-detect algorithm for turn target

Bernoulli track‐before‐detect smoothing for maritime radar

A Track-Before-Detect Strategy Based on Sparse Data Processing for Air Surveillance Radar Applications

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