Yes. Simply sum all the polyphase outputs and the sum result will have higher resolution.

Consider that each polyphase output is a delayed version of the same signal, so that if you commutated through all the outputs, you would get a higher sampled version of your same signal and the quantization noise of this signal would be approximately white across this higher digital frequency band. What you would do at this point is pass the signal through a decimation filter prior to taking every nth sample and indeed benefit by an increased dynamic range to the extent the quantization noise components are independent. (they should be given the delayed replicas at each output would have independent quantization noise). So the decimation filter could be a simple moving average in which case it is easy to see that your best solution is to sum average the outputs of your polyphase filters and use that average as the final result rather than just selecting any one of them. The moving average introduces passband droop- so with more complication you could do more elaborate filtering. I would be inclined to use a CIC filtering approach as the moving average followed by a simple 3 tap inverse sinc shaper if I was concerned about the resulting passband droop.

Yes. Simply sum all the polyphase outputs and the sum result will have higher resolution.

Consider that each polyphase output is a delayed version of the same signal, so that if you commutated through all the outputs, you would get a higher sampled version of your same signal and the quantization noise of this signal would be approximately white across this higher digital frequency band. If you passed this signal through a low pass filter prior to taking every nth sample in a decimation process you would indeed benefit by an increased dynamic range to the extent the quantization noise components in each sample are independent. (they should be; the delayed replicas at each output would have independent quantization noise and coherent signal components). So the decimation filter could be a simple moving average in which case it is easy to see that your best solution is to sum average the outputs of your polyphase filters and use that average as the final result rather than just selecting any one of them. The moving average introduces passband droop- so with more complication you could do more elaborate filtering. I would be inclined to use a CIC filtering approach as the moving average followed by a simple 3 tap inverse sinc shaper if I was concerned about the resulting passband droop.

Consider that each polyphase output is a delayed version of the same signal, so that if you commutated through all the outputs, you would get a higher sampled version of your same signal and the quantization noise of this signal would be approximately white across this higher digital frequency band. What you would do at this point is pass the signal through a decimation filter prior to taking every nth sample and indeed benefit by an increased dynamic range to the extent the quantization noise components are independent. (they should be given the delayed replicas at each output would have independent quantization noise). So the decimation filter could be a simple moving average in which case it is easy to see that your best solution is to sum average the outputs of your polyphase filters and use that average as the final result rather than just selecting any one of them. The moving average introduces passband droop- so with more complication you could do more elaborate filtering. I would be inclined to use a CIC filtering approach as the moving average followed by a simple 3 tap inverse sinc shaper if I was concerned about the resulting passband droop.

Yes. Simply sum all the polyphase outputs and the sum result will have higher resolution.

Consider that each polyphase output is a delayed version of the same signal, so that if you commutated through all the outputs, you would get a higher sampled version of your same signal and the quantization noise of this signal would be approximately white across this higher digital frequency band. What you would do at this point is pass the signal through a decimation filter prior to taking every nth sample and indeed benefit by an increased dynamic range to the extent the quantization noise components are independent. (they should be given the delayed replicas at each output would have independent quantization noise). So the decimation filter could be a simple moving average in which case it is easy to see that your best solution is to sum average the outputs of your polyphase filters and use that average as the final result rather than just selecting any one of them. The moving average introduces passband droop- so with more complication you could do more elaborate filtering. I would be inclined to use a CIC filtering approach as the moving average followed by a simple 3 tap inverse sinc shaper if I was concerned about the resulting passband droop.