The maximum likelihood method is applied to the problem of extracting the correct sequence of spatial patterns from the corresponding sequence of measurement frames corrupted by background noise Each measurement frame is modeled as the sum of a pattern matrix and a background noise matrix The model proposed for the statistics of the sequence of background matrices results in jointly Gaussian elements whose correlations are product separable in row, column, and frame indices The logarithm of the likelihood function is computed and involves a matched filtering operation on the measurement frames, which acts to suppress the background relative to the pattern Because of the product separability in the back-ground element correlations, this matched filtering operation is accomplished by pre- and post-multiplying the measurement frames by the inverses of the background row and column correlation matrices, respectively Thus, the measurement frames are operated on in their original matrix format without resorting to stacking Applications include the detection of targets in background noise© (1979) COPYRIGHT SPIE--The International Society for Optical Engineering Downloading of the abstract is permitted for personal use only

Matrix Spatial And Temporal Matched Filter For Background Suppression

: This report discusses a mosaic sensor system simulation program (MSSP) as a viable tool for the evaluation of a variety of mosaic sensor concepts. The idea of using a mosaic array to represent an image is an old one. Sensors constructed from the mosaic concept have the advantage of simplicity. Theoretically the image information may be instantaneously obtained from the outputs of all the detector cells in the mosaic array, thus eliminating the time delay problem inherent in a scanning-type sensor. A mosaic sensor system is often more reliable than a scanning sensor system because there are a minimum of mechanical components susceptible to failure. Typically, a mosaic focal plane consists of a large number of detector cells in order to produce fine image resolution. Digital simulation of a mosaic sensor system requires a considerable amount of repetitive calculations because of the large number of cells. It is very important to design a mosaic sensor simulation program with computation algorithms that are highly efficient in order to minimize the run time of the computer.

http://www.dtic.mil/cgi-bin/GetTRDoc?AD=ADA075743&Location=U2&doc=GetTRDoc.pdf

Simulation of a Mosaic Sensor System.

For staring infrared sensors in geostationary orbit, the clutter caused by the high-frequency jitter and low-frequency drift of the sensor line-of-sight (LOS) is the impact of background features, sensor parameters, LOS motion characteristics, and background suppression algorithms. In this paper, the spectra of LOS jitter caused by cryocoolers and momentum wheels are analyzed, and the time-related factors such as the jitter spectrum, the detector integration time, the frame period, and the temporal differencing background suppression algorithm are considered comprehensively; they are combined into a background-independent jitter-equivalent angle model. A jitter-caused clutter model in the form of multiplying the background radiation intensity gradient statistics by the jitter-equivalent angle is established. This model has good versatility and high efficiency and is suitable for the quantitative evaluation of clutter and the iterative optimization of sensor design. Based on satellite ground vibration experiments and on-orbit measured image sequences, the jitter-caused clutter and drift-caused clutter models are verified. The relative deviation between the model calculation and the actual measurement results is less than 20%.

/pdf/jitter-caused-clutter-and-drift-caused-clutter-of-staring-2ysniqbg.pdf

Jitter-Caused Clutter and Drift-Caused Clutter of Staring Infrared Sensor in Geostationary Orbit

Background suppression techniques and algorithms for on-board processing applications to earth-looking and deep space IR detection and tracking systems are presented. Modeling considerations of the background and sensor system noise sources are addressed. Some current research and development efforts in signal extraction and background suppression techniques, and computational aspects are briefly discussed.

Invited Paper IR Background Suppression

Based on temporal and spatial characterizations of IR background clutter, an optimal algorithm, which inessence, is equivalent to a maximum likelihood test, is derived for clutter suppression and target detec- tion. The algorithm is adaptive in that the algorithm parameters (temporal and spatial correlations of thebackground clutter) could be continually updated if desired.The algorithm also makes use of the a priori knowledge of target intensity, whenever possible.Preliminary simulation results indicate that background clutter can be sufficiently suppressed so thatthe detection of low intensity targets (signal -to -noise ratio of 1) can be achieved. Introduction A recurring problem common to most IR sensor systems for detection is the presence of interferingbackground events which degrade the sensors' target detection capability. In most cases, the backgroundevents drive the design and limit the performance of IR sensors. When the background is not as intense asthe target of interest, simple thresholding could eliminate the background interference, and only the targetsignals would be above the threshold. However, in most sensors, occasions arise when the background isas intense as the target, and the background returns far outnumber the target returns. It is in those situ-ations that an effective detection algorithm could be implemented to distinguish a target from the back -ground by taking advantage of certain statistical properties of the background.Sensors are generally designed to suppress the background signals, to the extent possible, while en-hancing target signals. However, it is often extremely difficult to design against all background situations.For example, one complicating factor is often the similarity in phenomenology between the backgroundevents and the target emissions so that background events cannot be totally suppressed from the sensoroutput by sound sensor design practice alone. A background suppression algorithm is often required tofurther enhance the sensor's target detection capability.A number of ad hoc background suppression schemes such as thresholding, frame -to -frame subtraction,have been proposed in various systems with some success. These techniques are usually adequatewhen the background events are benign in the sense that the background induced returns at the sensor out-put are few in number or weak in magnitude.One systematic approach to solving the background suppression problem is to model the background -induced outputs statistically and derive a target detection algorithm based on the "best" model of the back-

A Technique For Infrared Background Suppression

The effect of spacecraft induced line-of-sight (LOS) jitter on the performance of a spaceborne IR sensor in the presence of spatially varying earth background is analyzed. This paper presents a one-dimensional stochastic model of the spatially varying background process as seen by a staring sensor with its LOS under-going jitter which is in turn prescribed by a stochastic process. More specifically, the background and jitter processes are combined in deriving a closed-form expression for the temporal autocorrelation function of the irradiance. A method of quantitatively evaluating the effect of LOS jitter on sensor performance in terms of signal-to-noise ratio degradation is also included.

Statistical modeling of infrared (IR) background due to spacecraft-induced line-of-sight jitter

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Invited Paper IR Background Suppression

A Technique For Infrared Background Suppression

Statistical modeling of infrared (IR) background due to spacecraft-induced line-of-sight jitter