This disclosure relates to pulsed radar systems, and more specifically to a radar system and method of target classification capable of determining life form target type and movements.
See or sense through obstruction sensors are needed to satisfy current and future operations for enhanced capability to detect, locate, identify, and classify moving and stationary humans for rescue and clearing operations. The sensors could be used by the military, police, security, and firemen. Additionally the sensors could provide standoff human biometric monitoring for medical personnel to help save lives.
Radar technology sensors can be used for standoff range sensing. Radar can measure both the range to target and the “Doppler” or velocity of the target.
Prior approaches have involved impulse radars and pulse compression radars. Impulse radar transmits an ultra short pulse for high range resolution. Less than 1 nsec pulses are required to image a human target. The short pulses result in very little energy on target. In each of these cases, the goal it to achieve a range resolution for target imaging while applying as much energy on the target as possible.
Faced with the constraints of range resolution verses energy on target, Radar Systems use a concept called pulse compression. Pulse compression refers to a family of techniques that increase the bandwidth of radar pulses without shortening the pulse width. The result is a range resolution which is higher than that associated with an uncoded pulse. Many methods exist to achieve this, including binary phase coding, polyphase coding, frequency modulation, and frequency stepping. A side-effect of these techniques is the appearance of range sidelobes of significant amplitude in the range profile. These range sidelobes can result in a small target of interest being masked by a large target that is nearby.
Radar systems presently do not have adequate capability to image life forms for classification. For example classifying humans vs. dogs or classifying human movements. The reasons for this are fivefold. First, legacy radar systems are designed with imaging techniques that partition the illuminated area into high-resolution segments or pixels. These pixels are viewed like photographs. Humans use these radar photographs to design another layer of signal processing for target classification. This process is inefficient for extracting life form biometric information out of the radar data. Second, the instantaneous bandwidth to image a human would result in very short pico-second pulse widths which results in very little energy on target. Third, classical pulse compression techniques suffer from range sidelobes that distort target information and mask small target features. Fourth, until recently most radar applications and associated signal processing techniques were developed to detect fast moving targets with a large radar cross-section. For example, airplanes, missiles, and fast moving vehicles produce a large return with a large Doppler shift from DC, not small radar cross-section targets with very small Doppler shifts like a human target. Finally, there is no known technique for effectively imaging and classifying life-form targets.
More recently efforts have been made to apply pulsed radar to urban environments or an urban battlefield. In these environments the target signatures are much weaker. Instead of fast moving aircraft or missiles the targets are humans or slow moving vehicles, which present a much smaller radar cross-section and Doppler shift. Additionally the presence of buildings and other large structures exacerbates the range side lobe problem.
There is a demonstrated and ongoing need for a radar system that can accurately detect and classify life form movements in a heavily cluttered urban or foliage environments.