Bearing only Target Tracking using Single and Multisensor: A Review

Abstract

The brief review of methods used for estimating the target state in single and multi-sensor bearing only tracking (BOT) is presented in this paper. It deals with the target state estimation using bearing only measurements. BOT is difficult because of its poor observability in target state and nonlinearity in measurements. The complete survey is done on existing techniques, involved to overcome the difficulties caused by BOT. Here, the target tracking scenarios are divided into three different categories based on the nature of target motion and the number of target and sensors involved. The existing techniques involved are reviewed in detail. Finally the future trends for BOT are also discussed.

Coordinated CRLB-based Control for Tracking Multiple First Responders in 3D Environments

“…To compute this likelihood function, first observe that the measurement set Φ j t+1 contains a random number of random measurements i.e., multiple false-alarm measurements φj t+1,i ∈ Φ j t+1 coming with a Poisson rate Λ, which are distributed according to p φ( φj t+1,i ), and up to one target measurement φj t+1 ∈ Φ j t+1 which is received with probability p D , and which is distributed according to c( φj t+1 ) = N ( φj t+1 ; ℓ(x j t+1 , s t+1 ), σ 2 ϕ ) as discussed in Sec. II-C. That said, the measurement likelihood function is derived as: (10) where n j = |Φ j t+1 | is the total number of received measurements, and Ψ(n j ; Λ) is probability mass function of the Poisson distribution with rate parameter Λ, and input argument n j . Therefore, the first term in Eq.…”

“…In this work we will focus mainly on the task of passive target monitoring with a single UAV agent which utilizes angle measurements (i.e., bearings), for estimating the target's state. A recent survey paper on this topic can be found in [10]. Regarding the bearingsonly passive target monitoring, the authors in [11] provide a thorough analysis of 3 different state-estimation approaches for tracking a single target with a single sensor.…”

Tracking an Underwater Target in a Large Surveillance Region With Sensor Location Uncertainty

“…However, for a target following a straight line motion single sensor system suffers from observability issues [1], [5] and therefore maneuvering of the observer is indispensable [6], [7]. To overcome the limitation of the single sensor system and extend the tracking performance, a multi-sensor tracking system is advocated [8]- [10].…”

Emitter Tracking via Direct Target Motion Analysis

“…Up to date, the TMA methods can be briefly classified into two basic types, i.e., batched and recursive processing manners [4]. In the batch processing technique, a set of measurements are simultaneously considered for state estima-tion, mostly based on pseudolinear estimation (PLE) [3], [5], [6], the maximum likelihood estimation (MLE) [7]- [9], and modified instrumental variable estimation (MIV) [5], [7], [10]- [13], etc.…”

“…Similar to TMA, the proposed DTMA approach is also affected by intrinsic observability properties, i.e., the target state is observable only when the observer outmaneuvers the target [4], [36], [37]. Therefore, in DTMA, we have to deal with two main problems: system's nonlinearity and inscrutability of the range of target.…”