Range Estimation Accuracy Research Articles

Tracking filter for the mitigation of the ionospheric range bias

An extended Kalman filter (EKF) to estimate the ionospheric total electron content (TEC) along the radar line-of-sight has been developed. By varying the carrier frequency of consecutive radar pulses, it is shown that the TEC along the line-of-sight becomes observable and can be estimated in real-time to a high degree of accuracy. It is shown that the filtering formulation presented here optimally reduces the randomly varying range bias caused by the irregular electron density structure in the upper atmosphere. The performance of the tracking filter is examined with a Monte Carlo simulation analysis. It is shown that for a radar located in Thule, Greenland and operating at a nominal frequency of 435 MHz (UHF), an improvement of approximately 50% in the range estimation accuracy is achieved with a frequency separation of 18 MHz between two consecutive pulses. If the frequency separation is increased to 60 MHz, the increase in accuracy is 75%. It is also shown that in the absence of frequency separation, the performance of the tracking filter presented here reduces to that of a conventional six-state EKF that does not account for the ionospheric bias.

Multiuser Interference Mitigation in Noncoherent UWB Ranging via Nonlinear Filtering

Ranging with energy detectors enables low-cost implementation. However, any interference can be quite detrimental to range accuracy. We develop a method that performs nonlinear filtering on the received signal energy to mitigate multiuser interference (MUI), and we test it over time hopping and direct sequence impulse radio ultra-wideband signals. Simulations conducted over IEEE 802.15.4a residential line of sight ultrawideband multipath channels indicate that nonlinear filtering helps sustain range estimation accuracy in the presence of strong MUI.

Target range estimation based on video sensor tracking

This paper specifies cinematic models of target range estimation based on analyzing video frames during the tracking. The influences of process noise on range estimation accuracy are analyzed, and adaptive estimation method by noise level adjustment is nominated. .

Sensor network localisation based on sorted RSSI quantisation

Range estimation is essential in many sensor network localisation algorithms. Although wireless sensor systems usually have available received signal strength indication (RSSI) readings, this information has not been effectively used in the existing localisation algorithms. In this paper, we present a novel approach to localisation of sensors in an ad hoc sensor network based on a sorted RSSI quantisation algorithm. This algorithm can improve the range estimation accuracy when distance information is not available or too erroneous. The range level used in the quantisation process can be determined by each node, using an adaptive quantisation scheme. The new algorithm can be implemented in a distributed way and achieves significant improvement over existing range-free algorithms. The performance advantage for various sensor networks is shown, with experimental results from our extensive simulation with a realistic radio model.

1P1-N-082 屋外環境下における自律移動システムに関する研究 : 第36報 ODV/GPS/INS複合モーションステレオ視のベースライン最適選択による距離推定精度向上(3次元計測/センサフュージョン2,生活を支援するロボメカ技術のメガインテグレーション)

1P1-N-082 屋外環境下における自律移動システムに関する研究 : 第36報 ODV/GPS/INS複合モーションステレオ視のベースライン最適選択による距離推定精度向上(3次元計測/センサフュージョン2,生活を支援するロボメカ技術のメガインテグレーション)

High-Precision Range Estimation from an Omnidirectional Stereo System.

This paper presents the estimation of depth based on the cylindrical re-projection of images captured by a stereo setup of ODV (OmniDirectional Vision). In general, images obtained by such a device have relatively low spatial resolution and have some aberration in comparison to standard camera images and thus, considerable range estimation errors are caused. We propose a stereo setup of ODV, which features two mirrors with the original curvature to minimize blurring influence. We also propose an edge-based intensity interpolation method to improve the spatial resolution of the developed image. The bidirectional subpixel matching procedure and the discontinuity compensation algorithm are also presented to improve the quality of the range estimation. The experimental result reveals that the proposed ODSV (OmniDirectional Stereo Vision system) can successfully contribute to reduce disparity error and has subpixel range estimation accuracy even in the cluttered outdoor environment.

Negative correlation and optimal tracking with Doppler measurements

In target tracking systems that utilize Doppler radar (or sonar), the measurements consist of range, range rate, and one or two angles, When multiple sensors are used in the proper geometry, the final accuracy is determined primarily by each sensor's range estimation accuracy. In this case it is particularly important to extract the most accurate state estimate in the range direction by processing optimally the range and range rate measurements. The usual assumption in the design of tracking filters is that the measurement errors in range and range rate are uncorrelated. However, in the case of the commonly used upsweep chirp (linear FM) waveforms, there can be a significant negative correlation between the range and range rate measurement noises. The purpose of this note is to quantify the performance improvement one can obtain when this correlation is present, as it is in certain waveforms, and accounted for.

An interpolation algorithm to improve range estimation for the linear frequency modulated radar

Radar with the linear frequency-modulated signal may range the target. Its range resolution is decided by the signal frequency bandwidth. This paper proposes an interpolation algorithm based on fast Fourier transform to improve the accuracy of range estimation without increasing the frequency bandwidth. The algorithm application in practice is described. The simulation result of estimation accuracy is also given.

Eliminating autocorrelation reduces biological relevance of home range estimates

1. Destructive subsampling or restrictive sampling are often standard procedures to obtain independence of spatial observations in home range analyses. We examined whether home range estimators based upon kernel densities require serial independence of observations, by using a Monte Carlo simulation, antler flies and snapping turtles as models. 2. Home range size, time partitioning and total straight line distances travelled were tested to determine if subsampling improved kernel performance and estimation of home range parameters. 3. The accuracy and precision of home range estimates from the simulated data set improved at shorter time intervals despite the increase in autocorrelation among the observations. 4. Subsampling did not reduce autocorrelation among locational observations of snapping turtles or antler flies, and home range size, time partitioning and total distance travelled were better represented by autocorrelated observations. 5. We found that kernel densities do not require serial independence of observations when estimating home range, and we recommend that researchers maximize the number of observations using constant time intervals to increase the accuracy and precision of their estimates.

Effects of Sample Size on Kernel Home Range Estimates

Kernel methods for estimating home range are being used increasingly in wildlife research, but the effect of sample size on their accuracy is not known. We used computer simulations of 10-200 points/ home range and compared accuracy of home range estimates produced by fixed and adaptive kernels with the reference (REF) and least-squares cross-validation (LSCV) methods for determining the amount of smoothing. Simulated home ranges varied from simple to complex shapes created by mixing bivariate normal distributions. We used the size of the 95% home range area and the relative mean squared error of the surface fit to assess the accuracy of the kernel home range estimates. For both measures, the bias and variance approached an asymptote at about 50 observations/home range. The fixed kernel with smoothing selected by LSCV provided the least-biased estimates of the 95% home range area. All kernel methods produced similar surface fit for most simulations, but the fixed kernel with LSCV had the lowest frequency and magnitude of very poor estimates. We reviewed 101 papers published in The Journal of Wildlife Management (JWM) between 1980 and 1997 that estimated animal home ranges. A minority of these papers used nonparametric utilization distribution (UD) estimators, and most did not adequately report sample sizes. We recommend that home range studies using kernel estimates use LSCV to determine the amount of smoothing, obtain a minimum of 30 observations per animal (but preferably >50), and report sample sizes in published results.

Fundamental limits on acoustic source range estimation performance in uncertain ocean channels

Matched-field methods which exploit complex multipath propagation models to localize underwater acoustic sources are particularly sensitive to errors in the assumed environmental parameters. In this paper, the Cramer–Rao lower bound (CRLB) is used to determine fundamental limits on the accuracy of range estimates obtained in the presence of uncertain environmental parameters. Theoretical results obtained using an adiabatic normal mode propagation model and a realistic model of sound-speed profile uncertainty indicate that without prior statistical knowledge of the environmental parameters, the CRLB may diverge even when the number of unknowns is less than the number of acoustic modes. When the prior probability distribution of the environmental parameters is available, however, the CRLB indicates a significant improvement in range estimation performance is possible. Numerical evaluation of the bound on range estimation in the presence of sound-speed profile uncertainty is performed both for an ideal waveguide and a realistic shallow-water channel using environmental data collected off the New England coast.

Accuracy of source localization using multipath delays

The depth and range of underwater source can be estimated from measurements of propagation delay differences along multiple propagation paths. The accuracy of depth and range estimation using the Cramer-Rao lower bound is studied. The formulas derived can be used in conjunction with a propagation medium (nonconstant velocity profile). Explicit formulas for the bounds are derived for the case of homogeneous medium (constant velocity profile). Numerical examples are presented to illustrate the behavior of these bounds.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>