Sensor alignment with Earth-centered Earth-fixed (ECEF) coordinatesystem

In this work, we formulate the multiple sensor alignment problem in an Earth-centered Earth-fixed (ECEF) coordinate system. The alignment algorithm maps the sensor measurements to the ECEF coordinates using a geodetic transformation, and attributes the discrepancies in the Earth-referenced system reported by each sensor to the sensor biases. Sensor biases are then estimated using the least squares (LS) technique. Simulated and real-life radar data are used to evaluate the performance of the proposed algorithm. Comparisons are made to those algorithms based on the standard stereographic projection     

Structures for radar detection in compound Gaussian clutter

The problem of coherent radar target detection in a background of non-Gaussian clutter modeled by a compound Gaussian distribution is studied here. We show how the likelihood ratio may be recast into an estimator-correlator form that shows that an essential feature of the optimal detector is to compute an optimum estimate of the reciprocal of the unknown random local power level. We then proceed to show that the optimal detector may be recast into yet another form, namely a matched filter compared with a data-dependent threshold. With these reformulations of the optimal detector, the problem of obtaining suboptimal detectors may be systematically studied by either approximating the likelihood ratio directly, utilizing a suboptimal estimate in the estimator-correlator structure or utilizing a suboptimal function to model the data-dependent threshold in the matched filter interpretation. Each of these approaches is studied to obtain suboptimal detectors. The results indicate that for processing small numbers of pulses, a suboptimal detector that utilizes information about the nature of the non-Gaussian clutter can be implemented to obtain quasi-optimal performance. As the number of pulses to be processed increases, a suboptimal detector that does not require information about the specific nature of the non-Gaussian clutter may be implemented to obtain quasi-optimal performance     

Optical guidance for autonomous landing of spacecraft

An autonomous rendezvous guidance scheme for spacecraft to descend to small celestial bodies by using optical information is presented. First, a new guidance, navigation, and control (GNC) method based on fixation-point (FP) inheritance is proposed. A spacecraft can safely descend toward the target point on the celestial body by tracking and autonomously renewing the FPs on the surface. Next, we deal with the method of extracting the FPs. A spatial band-pass filter (BPF) is applied to pictures taken to enhance features having comparable size with the tracking window. Local variance of the filtered image is used as a criterion of the extraction. Then, the relative information between the spacecraft and the celestial body (position, velocity, attitude, etc.) is calculated from the image coordinates and the range measurements of the FPs from the spacecraft. To suppress observation noise and improve navigation accuracy, an application of the extended Kalman filter is also presented. Finally, simulations are conducted to verify the guidance precision and the fuel consumption of the proposed guidance scheme     

An interior point linear programming approach to two-scan dataassociation

We present an efficient two-scan data association method (TSDA) based on an interior point linear programming (LP) approach. In this approach, the TSDA problem is first formulated as a 3-dimensional assignment problem, and then relaxed to a linear program; the latter is subsequently solved by the highly efficient homogeneous, self-dual interior point LP algorithm. When the LP algorithm generates a fractional optimal solution, we use a technique similar to the joint probabilistic data association method (JPDA) to compute a weighted average of the resulting fractional assignments, and use it to update the states of the existing tracks generated by Kalman filters. Unlike the traditional single scan JPDA method, our TSDA method provides an explicit mechanism for track initiation. Extensive computer simulations have demonstrated that the new TSDA method is not only far more efficient in terms of low computational complexity, but also considerably more accurate than the existing single-scan JPDA method     

Estimating matched filter amplitude probability functions byfailure rate analysis

Performance analysis of echolocation systems requires knowledge of the probability density function (pdf) or cumulative distribution function (cdf) of a matched filter output. A method is presented to estimate these and other probability functions from data by estimating the failure rate function, a function employed in reliability theory. The method can also be used to derive approximations to closed-form probability functions. The method is demonstrated using experimental sonar and radar clutter data and a closed-form radar clutter model     

Analysis and results of the 1995 Yuma ground penetration SARmeasurements

In July and October 1995, a large-scale airborne SAR experiment was conducted in the Yuma Proving Ground, Yuma, Arizona, to investigate ground penetration radar phenomenology and buried target detection. This paper describes the Yuma experiment and measurement results for many tactical, utility, and environmental targets deployed in the test     

Bridging the gap between ATLAS and C: an open-systems approach toTPS re-host

As the DoD focuses on commercialization and open-architecture standards, the need to migrate existing TPSs from proprietary ATLAS-based platforms to an Open Systems platform becomes more critical. This paper will focus on a solution which leverages COTS software tools and emerging industry standards and technologies to implement object-oriented database tools which enable the conversion of an ATLAS TPS to an ANSI C environment     

On the convergence of iterative methods for bearings-only tracking

This paper deals with the analysis of the convergence of iterative methods for bearings only tracking (BOT). A geometric and unified framework is developed. Explicit sufficient conditions ensuring convergence of iterative methods for maximization of the likelihood functional are then derived     

UWB radar detection of targets in foliage using alpha-stableclutter models

We address the problem of detection of targets obscured by a forest canopy using an ultrawideband (UWB) radar. The forest clutter observed in the radar imagery is a highly impulsive random process that is more accurately modeled with the recently proposed class of alpha-stable processes as compared with Gaussian, Weibull, and K-distribution models. With this more accurate model, segmentation is performed on the imagery into forest and clear regions. Further, a region-adaptive symmetric alpha stable (SαS) constant false-alarm rate (CFAR) detector is introduced and its performance is compared with the Weibull and Gaussian CFAR detectors. The results on real data show that the SαS CFAR performs better than the Weibull and Gaussian CFAR detectors in detecting obscured targets     

Improvement of strapdown inertial navigation using PDAF

A new application of PDAF (probabilistic data association filter) for improving the accuracy of autonomous strapdown inertial navigation systems (SINS) is presented. The proposed method is a terrain-aided navigation (TAN) algorithm based on landmark detection combined with a classical SINS. It is shown via a set of simulations that the method can improve significantly the precision of autonomous navigation if the landmark spatial density and quality of landmark detectors are good enough. This new concept of navigation called PDANF (probabilistic data association navigation filter) can be integrated with a relatively low cost in existing operational TAN systems