Undergrad Multiple hypothesis testing for radar tracking in clutter

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The discussion focuses on developing a multiple hypothesis test for radar tracking in the presence of false alarms, utilizing a particle filter for state estimation. The formulation involves calculating the next state based on current data, differentiating between hypotheses of no measurements and those with measurements from the target. Key equations are presented to express likelihoods based on a Poisson clutter model, probability of detection, and false alarm rates. The conversation seeks clarification on the relationships between various steps in the formulation and the definitions of specific variables involved in the hypotheses. The aim is to derive a comprehensive expression for likelihoods to facilitate the application of the particle filter in this context.
RichardJ
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Hello All,

The goal is to formulate a multiple hypothesis test for a radar tracking problem when false alarms are occurring and to apply a particle filter on this update step, however I first need to come to/understand the multiple hypothesis formulation in this problem.
  1. Say we are interested in the next state (i.e. position and velocity) based on the current data. $$p(s_{k+1}|Z_k)$$
  2. We can write this down something like:$$ p(s_k|z_k,H_0) p(H_0|z_k) $$$$ p (s_k|z_k, H_i) p(H_i|z_k)$$
for i = 1,..., M
In this case ##H_0## is the hypothesis that there were no measurements from the target and ##H_i## are the M-1 hypothesis were there was a measurement from the target.
3. So apparently we can write this down as $$p(z_k^1,...,z_k^M|s_k) = \sum_{i=0}^M p(z_k^1,...,z_k^M|s_k,H_i) p(H_i)$$​
The final goal is to derive some equations from step 3. to obtain an expression for the likelihoods based on a Poission clutter model, probability of detection, false alarm rate and validated measurements. The only term left should then be something like: ##p(z_k^i|s_k^j )## The likelihood function (where j stands for the particle, which I already have an expression for) to be able to run the recursion with a particle filter.
Furthermore $$ p(H_0) = (1-P_D) P_{FA} (m=M) $$
where ## P_{FA}## is a poission distribution for 'FA' (or M in this case) false alarms. and $$ p(z_k^1,...,z_k^M|s_k,H_0)=(1/V)^M $$ because under H_0 we have no targets and one assumes false alarms are distributed uniformly.
4. ## p(H_i) = P_D P_{FA}(M-1) ##
5. For the other terms i = 1,...,M something like $$ p(z_k^1,...,z_k^M|s_k,H_i) -> p(z_k^i|s_k)(1/V)^{M-1} $$​
should hold. but not sure how to write down all the terms.

So my questions are (for now):
  1. How to obtain step 2, the multiple hypothesis formulation
  2. How is step 3 actually related to 1 and 2.
  3. Why does relation 4 hold?
  4. How to write down/solve 5 for all hypothesis.
 
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Could you define some of your variables please? What are the ##s##'s? What are the ##z_k##? What are the ##M-1## hypotheses? You said they are "hypotheses where there was a measurement from the target" but why does "there is a measurement" have more than one variable? It's true or it isn't.
 
The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...

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