SPx Tracking Server

Target Extraction

The SPx Extraction process examines the processed video to search for target-like returns that form a connected target-like shape. A set of configurable parameters define the target size of interest, allowing small noise returns or larger clutter or land masses to be eliminated early in the processing.

The Extraction process begins by creating a set of spans that represent intervals of video above a threshold for each processed return. These spans are then combined across returns to form connected two-dimensional shapes. The weighted centre of gravity, bounding box and total weight of the plot shape are calculated and entered into a plot database, along with a timestamp. At this stage of the processing, no merging occurs of close plots that are likely to be derived from the same target. This allows partial plots to be reported on the network, if desired, and allows the tracking process to consider the merits of merging in the context of the local tracks.

Secondary Surveillance Radar Extraction

SPx Server supports target tracking from secondary surveillance radar (SSR) transponder video, including decoding of the interrogation mode information. If the SSR/IFF transponder video is sampled with sufficient resolution, the plot extraction process within SPx Server is able to extract codes from interleaved mode 1, 2, 3/A and C responses. This information then forms part of the plot and is passed onto the tracker to assist with tracking.

Interrogation mode tags may be embedded at the start of the IFF video stream as P1/P3 pulses or a specific mode specified, telling SPx Server which responses to expect. The processing includes de-FRUITING and de-garbling to remove typical noise from an IFF video.

SPx Server's IFF video handling capabilities provide a viable, highly cost-effective alternative to traditional hardware-based IFF decoding systems.

Track Creation

The tracker maintains an active track database. The contents of the database are updated with new plot data derived from the data extraction stage. New tracks are added to the database from either a manual request (perhaps derived from an operator or else an external process), or else automatically.

The automatic track creation occurs when plots entered into the database are seen to be uncorrelated, or ungated, with any existing known target. A new preliminary track is created and is updated with future detections until confidence is established that the track is likely to be a target of interest.

The time a track is held in the preliminary stage is a programmable option and needs to be set to balance the speed of detection with the likelihood of a false alarm. In a low clutter environment, where extracted plots are likely derived from real targets, the acquisition time may be as short as 2 detections. For noisy situations, where the plot extractor is reporting false detections, the integration time in the preliminary stage may be extended.

Multi Hypothesis Tracking (MHT)

The SPx Tracker uses multi-hypothesis association. This offers significant improvements in perfomance over simpler single-hypothesis trackers. The role of a tracker is to interpret radar observations to distinguish real targets from noise, and to construct models to describe the motion of the true targets. The tracker is provided with data, typically in the form of plots, derived from the processing of the radar video. These plots are connected regions of radar video that satisfy some rules of position, amplitude, size and signal strength. Unfortunately, measurements from the radar are imperfect. There will be noise from the measurement process, clutter from the environment and unpredicted manoeuvres of the targets of interest. This means that the tracker will be presented with noisy and possibly multiple measurements from the target of interest. The tracker's responsibility is to provide the best interpretation of the data using assumed or calculated statistics for the noise and the likelihood of change.

In the single hypothesis situation, the tracker is forced to make the best interpretation it can of the available data at each update. For some updates, where there is a clear interpretation of the measurement, the best interpretation may be obvious and the single hypothesis offers a satisfactory solution. Problems arise, however, if the interpretation of the measurements is not obvious. In this case it may be desirable to defer a decision until the next update when additional information will help to decide on the correct interpretation. The ability to simultaneously consider multiple interpretations of the system is the key to the multi hypothesis tracker.

Model Based Tracking (MBT)

The MBT extensions to SPx Server (V1.72 onwards) provide a new capability for target initiation and tracking. The new track initiation method is significant because it can handle larger number of provisional tracks than the standard MHT's initiation process. This new tracking capability allows specific target models to be created, meaning that different parameter configurations can process the same radar video. The ability to detect small targets and support multiple tracking modules may offer new opportunities for tracking.

This extension to SPx Server is described here.

Track Filter

For each hypothesis, the tracker updates the current estimated position with the new measurement. If the measurement were known to be completely accurate, the update process would believe the measurement and the new estimate would be exactly the measured value. For various reasons, the measurement is inaccurate so the update process must take a weighted combination of the expected position and the measured position. This is the track filtering. SPx offers a number of track filtering modes. The simplest mode uses fixed gains in the components of the measurement. This can be successful for tracking applications where the target is clearly identified and relatively clutter free.

The filter works by computing a dynamic filter gain, K, based on estimated system noise and measurement noise models. The system noise is used to model uncertainty in the known dynamics of the target, including its ability to manoeuvre. As system noise increases, or equivalently as measurement noise decreases, the filter places more weight on the measurement so the filter gains increase. As system noise decreases or as measurement noise increases, the filter gains decreases causing less emphasis to be placed on the new measurement. The filter gains are continually changing and provide, under certain assumptions of the noise characteristics and linearity, an optimal estimation of the true target position.

Tracking Parameters

The behavior of the tracker may be configured through a set of tracking parameters. These parameters may be set initially from a configuration file and may be adjusted during operation of the tracker using either a customer GUI or network interface. The parameters control many aspects of the tracker's performance including:

• Min/max speed of target to be tracked
• Multi or single hypothesis association modes
• Fixed gain or adaptive gain
• Expected target dynamics
• Size limitations on targets to be tracked
• Measurement noise estimates