- Research
- Open Access
Accurate three-dimensional tracking method in bistatic forward scatter radar
- Cheng Hu^{1}Email author,
- Tao Zeng^{1} and
- Chao Zhou^{1}
https://doi.org/10.1186/1687-6180-2013-66
© Hu et al.; licensee Springer. 2013
- Received: 3 July 2012
- Accepted: 20 February 2013
- Published: 2 April 2013
Abstract
Accurate three-dimensional (3D) tracking in bistatic forward scatter radar (BFSR) isa challenging problem because of absent range resolution and poor measurements. Inthis article, an accurate 3D tracking method of BFSR is proposed. Aiming to overcomethe filter divergence caused by large initial state estimation error, firstly, anaccurate initial state estimation approach is presented based on analytic derivationand Levenberg–Marquardt algorithm, which has the potential to improve theaccuracy of initial state estimation. Furthermore, in order to reduce the computationcost of filtering process and speed up the filtering convergence rate, the accurateinitial state estimation and extended Kalman filter algorithm in BFSR are combined toachieve a precise target 3D tracking. Finally, the proposed accurate tracking methodis verified through comparative analysis of the simulation results.
Keywords
- Forward scatter radar
- 3D tracking
- Initial state estimation
- Filtering algorithm
Introduction
Because of the gain in bistatic RCS (up to 20–40 dB relative to monostatic RCS) inforward scatter region, bistatic forward scatter radar (BFSR) can effectively detect andtrack the target with low-speed or small RCS (including the stealth target) [1, 2]. Its target tracking technology attracted more and more attention in recentyears. An existing challenge of BFSR is parameter estimation of aerial [2]–[9] and ground [10]–[17] targets. This article focuses on parameter estimation of aerial targettrajectory.
Systems using continuous quasi-harmonic probe signal have been proved to be mostpromising amongst various structures of BFSR [3]–[5]. In this case, measurements of echo Doppler shift f _{d}, angle of arrival θ, and elevation angle β areusually used to obtain object parameter estimations (i.e., target position and velocitydenoted as x _{ k } , y _{ k } , h, V _{ x } , V _{ y }). Due to the nonlinear relations between f _{d}, θ, β, and trajectory parameters,targetparameter estimation in BFSR is a nonlinear optimization problem. To obtain five unknownvariables (i.e.,x _{ k } , y _{ k } , h, V _{ x } , and V _{ y }) using three known variables (i.e.,f _{d}, θ, β), equations composed by groups ofobservations are needed. Hence, object parameter estimation in BFSR is actually solvingover-determined nonlinear equations. The optimization is achieved using nonlinear leastsquares algorithms, of which Gauss–Newton method has widely been used.
In previous research [3]–[7], Gauss–Newton method is adopted to solve the nonlinear equations. Onthis basis, the classical methods of target tracking using two-dimensional (2D) andthree-dimensional (3D) BFSR are proposed, respectively, in [4, 6]. The major problem is that Gauss–Newton method is not a“real-time” algorithm, since we have to collect n measurements toimplement it. To achieve higher data update rate and reduce computation in parameterestimation, extended Kalman filter (EKF) algorithm [18] is used. However, initial filtering value of EKF can only be obtained bysolving over-determined nonlinear equations, which can never be avoided. In classicalmethod, due to the special geometry of FSR system, the initial value estimation byGauss–Newton method requires high-precision Doppler shift measurement and anglemeasurement, which are unavailable due to the limitation of antenna size andaccumulation time. Therefore, Gauss–Newton method is easy to cause a large errorof initial value estimation and filtering divergence.
In this article, based on analytical derivation and Levenberg–Marquardt (LM) [19, 20] algorithm, a new approach of initial state estimation is presented, which canimprove the accuracy of initial state estimation without high-precise target parametersmeasurement. Hereafter, the accurate initial state estimation and EKF algorithm arecombined to achieve fast convergence and high accuracy. Simulation results prove thataccurate target trajectory parameters estimation can be obtained by this method beforethe target crossing the baseline.
The remainder of this article is organized as follows: Section 1 briefly describes thedevelopment of forward scatter radar, pointing out the existing problems of air targetparameter estimation and focus of this article; Section 2 defines the geometry of 3Dforward scatter radar, the target motion model and system observation model; Section 3introduces the proposed tracking method for forward scatter radar in details; Section 4verifies the validity of accurate tracking method through comparative analysis of thesimulation results; Conclusions are drawn in Section 5.
System modeling
Geometry and system implementation of 3D BFSR
- 1)
In the transmitter position, there is a wide beam antenna illuminating the whole forward scatter region with a quasi-harmonic signal.
- 2)
In the receiver position, there is a multi-beam antenna to illuminate the forward scatter region.
- 3)
There is no block in line of sight between the transmitting and receiving antenna.
Target model
Observation model
where f _{ dk }, θ, β _{ k } are observations of the Doppler frequency shift, echo azimuth angle, andelevation angle.
Accurate 3D tracking in forward scatter radar
Because of the special geometry of forward scatter radar, parameters (i.e., Dopplershift, azimuth angle, elevation angle) cannot directly be measured when the targetcrosses the baseline, resulting in a large estimation error and seriously decreasing thetracking precision. In addition, the detection area of forward scatter radar is a verynarrow region near the baseline where bistatic angle ranges from 135° to 180°,indicating that target tracking is effectively implemented during a very short period oftime. Therefore, a stable tracking trajectory should be obtained before the targetcrosses the baseline, which requires fast convergence of filtering algorithm. However,the large error of initial state estimation will decrease the convergence speed, evenlead to filtering divergence. In summary, the initial state estimation is significantfor target tracking in forward scatter radar, which extremely affects the filteringaccuracy and convergence speed.
The classical method uses Gauss–Newton iteration to estimate the initial state oftarget. Gauss–Newton iteration is prone to singular matrix causing inaccuratecomputation results. And if the first value of iteration deviates far away from its truevalue, the results of iteration easily fall into local minimum and then cause largeerror of initial state estimation. To overcome these problems, based on analyticalderivation and LM algorithm, a novel method is presented to obtain high-precise initialstate estimation.
The basic principles of this new initial state estimation method are as follows.
Based on the observation vectors (f _{ d 1}, θ _{1}, β _{1}), (f _{ d 2}, θ _{2}, β _{2}),…(f _{ dn }, θ _{ n }, β _{ n }) of previous time instants, the target state vector (x _{ n }, y _{ n }, V _{ x }, V _{ y }, h) in n th time instant can be obtained. The process is asfollows:
Solving Equation (22) by the LM algorithm can achieve the optimal solution of V _{ y }, and x _{ n }, V _{ x }, y _{ n }, h, which can also be obtained by Equation (16), (17), (18), and (20).Thus, the numerical solution of the target state (x _{ n } , y _{ n } , h, V _{ x } , V _{ y }) in n th instant is acquired.
Because measurement noise is considerably large relative to the measurement data ofazimuth angle, it is necessary to smooth primary measurements through polynomial fittingbefore the initial state estimation. Polynomial coefficients can be obtained by leastsquare method, which can reduce the initial state estimation error by an order ofmagnitude.
where h(k, X(k)) denotes the measurement vector,measurement noise denoted by W(k) is Gaussian white noise process withzero mean. Assuming the process noise covariance and measurement noise covariance attime k are, respectively, expressed as Q(k) andR(k), then the steps of filtering are as follows:
Simulations and analysis
In order to verify the proposed accurate tracking algorithm, the parameters of FSRexperiment system listed in references [3]–[6] are taken as the simulation parameters.
Simulation parameters
Transmitted signal wavelength | 0.77 m | Standard deviation of process noise | 1 |
Baseline length | 40 km | Target’s velocity | 150 m/s |
Data update rate | 1 Hz | Flight-path angle | 15° |
Standard deviation of Doppler shift measurements | 0.5 Hz | Target’s initial X-coordinate | 20 km |
Standard deviation of azimuth measurements | 0.5° | Target’s initial Y-coordinate | −6 km |
Standard deviation of elevation measurements | 0.5° | Target’s initial H-coordinate | 2 km |
Simulation results of initial state estimation
RMSE | x _{ n }/m | y _{ n }/m | h/m | V _{ x }/(m/s) | V _{ y }/(m/s) | |
---|---|---|---|---|---|---|
$\begin{array}{l}{\sigma}_{f}=0.5\\ {\sigma}_{\theta}=0.5\\ {\sigma}_{\beta}=0.5\end{array}$ | Classical method | 2028.2 | 537.1 | 264.6 | 220.1 | 20.3 |
New method | 854.3737 | 255.1232 | 96.3750 | 2.1184 | 7.4299 | |
$\begin{array}{l}{\sigma}_{f}=0.1\\ {\sigma}_{\theta}=0.1\\ {\sigma}_{\beta}=0.1\end{array}$ | Classical method | 773.0488 | 87.3466 | 83.5152 | 48.4883 | 9.8001 |
New method | 172.2670 | 50.3490 | 18.3618 | 0.9642 | 1.3896 |
Averaging run time of the three methods
Classical method | New method | UKF algorithm | |
---|---|---|---|
Average time needed for a complete filtering (s) | 8.564927 | 0.14912884 | 0.32414323 |
Table 2 shows the initial statement estimation errors ofclassical method and the proposed method under the condition of different targetmeasured parameters estimation accuracy. It is obviously that the statement estimationerrors are affected by the estimation accuracy of target measured parameters. As can beseen, with high accuracy of target parameter measurement (i.e.,σ _{ f } = 0.1 Hz, σ _{ θ } = 0.1°, σ _{ β } = 0.1°), estimation errors of the two methods are both within the errortolerance and the proposed method has higher accuracy. However, when measurement noiseincreases (i.e.,σ _{ f } = 0.5 Hz, σ _{ θ } = 0.5°, σ _{ β } = 0.5°), the initial state estimation error of classical methodsignificantly increases, while that of proposed method is much smaller than the former.In particular, the velocity estimation error along the direction of the baseline isfairly small.
Figure 4 shows that the initial state estimation error ofclassical method is relatively large in the general measurement noise level. In theprocess of Gauss–Newton iteration, the error is not significantly reduced due toits unstable performance. In the same condition, the initial error of the proposedmethod is quite small, and gradually decreases until convergence in the follow-uptracking stage. UKF algorithm has a deterioration trend in follow-up tracking.
To compare the computation of different methods, averaging is performed in terms of 100independent implementations. The time required for one complete filtering of threemethods are as follows:
- (1)
The acquisition of accurate initial state estimation by classical method required very high precision measurement. The initial estimation error significantly increases by the measurement noise.
- (2)
The proposed method demonstrated its superiority in initial state estimation. In general measurement accuracy, its estimation error is smaller than the classical method’s by an order of magnitude. Applied to EKF, the initial value obtained by the proposed method can effectively improve the convergence speed. Then a stable track is able to be formed before the target crossing baseline.
- (3)
In general measurement precision, classical method showed non-steady performance and fluctuation of estimation error. Tracking accuracy of UKF algorithm is moderate. But UKF’s estimation error has an increasing trend because of its sensitivity to initial error. EKF algorithm can achieve high tracking accuracy and fast convergence with a large tolerance for initial error. Furthermore, its computation is the smallest among three algorithms.
Conclusions
In this article, an accurate 3D tracking method in forward scatter radar is presented.To solve the problem of filter divergence results from the large initial estimationerror, an accurate initial state estimation approach based on analytic derivation and LMalgorithm is proposed, which can improve the accuracy of initial state estimationwithout requiring high-precise measurement. For the purpose of reducing the computationof filtering and speeding up the convergence rate, the accurate initial state estimationis combined with EKF algorithm. Then, an accurate 3D tracking in forward scatter radaris derived and verified by the simulation results.
Declarations
Acknowledgment
This study was supported by the National Natural Science Foundation of China (GrantNos. 61172177, 61032009, 61225005 and 61120106004).
Authors’ Affiliations
References
- Willis NY: Bistatic Radar (Technology Service Corporation. MD: Silver Spring; 1995.Google Scholar
- Blyakhman AB: Forward scatter bistatic radar, in PIERS Workshop on Advances in RadarMethods. Italy; 1998:107-113.Google Scholar
- AB Blyakhman, IA Runova, Forward scatter radiolocation bistatic RCS andtarget detection, in The Record of the IEEE: International Radar Conference. Massachusetts, USA 1999, 1999: 203-208. 10.1109/NRC.1999.767314Google Scholar
- Blyakhman AB, Ryndyk AG, Sidorov SB: Forward scatter radar moving object coordinate measurement. Alexandria Virginia: The Record of the IEEE 2000 International RadarConference; 2000:678-682.Google Scholar
- Myakinkov AV, Ryndyk AG: Space-time processing in three-dimensional forward scatter radar, in 4thInternational Conference on Antenna Theory and Techniques . Ukraine 2003, 1: 355-358. 10.1109/ICATT.2003.1239228Google Scholar
- Blyakhman AB, Myakinkov AV, Ryndyk AG: Tracking algorithm for three-dimensional bistatic forward scatter radar withweighting of primary measurements, in EURAD Radar Conference, 2005. Paris; 2005:153-156.Google Scholar
- Myakinkov AV: Optimal detection of high-velocity targets in forward scatter radar, in 5thInternational Conference on Antenna Theory and Techniques. Ukraine; 2005:345-347.Google Scholar
- Ryndyk AG, Kuzin AA, Myakinkov AV, Blyakhman AB: Target tracking in forward scatter radar with multi-beam transmitting antenna,in Radar Conference - Surveillance for a Safer World, 2009. RADAR: Bordeaux; 2009. pp. 1–4Google Scholar
- Nezlin DV, Kostylev VI, Blyakhman AB, Ryndyk AG, Myakinkov AV: Bistatic Radar: Principles and Practice. Edited by: Cherniakov M. UK: Wiley; 2007.Google Scholar
- Abdullah RSA: Forward scatter radar for vehicle classification. Phd thesis. School of Electronic, Electrical and Computer Engineering of The Universityof Birmingham; 2005.Google Scholar
- Hu C, Long T, Zeng T: Statistic characteristic analysis of forward scatter surface clutter in bistaticradar. Sci. ChinaInf. Sci. 2010, 53(12):2675-2686. 10.1007/s11432-010-4119-1View ArticleGoogle Scholar
- Chernicakov M, Salous M, Avdullah R: Forward scatter radar for ground targets detection and recognition. Edinburgh: 2nd EMRS DTC Technical Conference; 2005:A14-A19.Google Scholar
- Long T, Hu C, Zeng T: Physical modeling and spectrum spread analysis of surface clutter in forwardscatter radar. Sci. ChinaInf. Sci. 2010, 53(11):2310-2322. 10.1007/s11432-010-4085-7View ArticleGoogle Scholar
- Cherniakov M, Abdullah RSAR, Jancovic P: Automatic ground target classification using forward scatter radar. IEE Proc. Radar Sonar & Navigation. 2006, 153(5):427-437. 10.1049/ip-rsn:20050028View ArticleGoogle Scholar
- Long T, Hu C, Cherniakov M: Ground moving target signal model and power calculation in forward scatter microradar. Sci. China Ser FInf. Sci 2009, 52(9):1704-1714. 10.1007/s11432-009-0154-1View ArticleGoogle Scholar
- Zeng T, X-l L, Hu C: Investigation on accurate signal modeling and imaging of ground moving target inforward scattering radar. IET Radar Sonar Navigat 2011, 5(8):862-870. 10.1049/iet-rsn.2010.0276View ArticleGoogle Scholar
- Hu C: Theory and signal processing methods of short baseline forward scatter radarsystem, PhD Thesis. Beijing Institute of Technology; 2009:7.Google Scholar
- Song TL, Speyer JL: A stochastic analysis of a modified gain extended Kalman filter with applicationto estimation with bearing only measurements. IEEE Trans. Autom. Control 1985, 30(10):940-949. 10.1109/TAC.1985.1103821View ArticleGoogle Scholar
- Levenberg K: A method for the solution of certain nonlinear problems in least squares. Q. Appl. Math. 1944, 2(2):164-168.MathSciNetGoogle Scholar
- Marquardt D: An algorithm for the least-squares estimation of nonlinear parameters. SIAM J. Appl. Math. 1963, 11(2):431-441. 10.1137/0111030MathSciNetView ArticleGoogle Scholar
Copyright
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative CommonsAttribution License (http://creativecommons.org/licenses/by/2.0), whichpermits unrestricted use, distribution, and reproduction in any medium, provided theoriginal work is properly cited.